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Centers for Disease Control and Prevention 





Morbidity and Mortality Weekly Report 
Supplement / Vol. 69 / No. 1 August 21, 2020 


Youth Risk Behavior Surveillance — 
United States, 2019 


U.S. Department of Health and Human Services 
Centers for Disease Control and Prevention 





Supplement 


CONTENTS 


Overview and Methods for the Youth Risk Behavior 
Surveillance System — United States, 2019.........ssssssssssossseccesssssssssssssssseee 1 
Condom and Contraceptive Use Among Sexually Active High School 
Students — Youth Risk Behavior Survey, United States, 2019......... 11 
Trends in Violence Victimization and Suicide Risk by Sexual Identity 
Among High School Students — Youth Risk Behavior Survey, 
United: States, 2015-201 9 5 ssssccescvescssessvascsnnsnsasiscasstinrennistebenusistsnonanteeatigs 19 
Interpersonal Violence Victimization Among High School Students — 
Youth Risk Behavior Survey, United States, 2019........ssssssssccssccesseneeee 28 
Prescription Opioid Misuse and Use of Alcohol and Other Substances 


Among High School Students — Youth Risk Behavior Survey, 


WWiniGea/ States; DONS ssszecsice tata naanin N ei 38 
Suicidal Ideation and Behaviors Among High School Students — 

Youth Risk Behavior Survey, United States, 2019 ........ssssssssssssseceresssssss 47 
Tobacco Product Use Among High School Students — Youth Risk 

Behavior Survey, United States, 2019 .........ssssscssssssssssscsseccssssseeneeessneeeseee 56 


Dietary and Physical Activity Behaviors Among High School 
Students — Youth Risk Behavior Survey, United States, 2019......... 64 

Transportation Risk Behaviors Among High School Students — 
Youth Risk Behavior Survey, United States, 2019 sses 77 


The MMWR series of publications is published by the Center for Surveillance, Epidemiology, and Laboratory Services, Centers for Disease Control and Prevention (CDC), 
U.S. Department of Health and Human Services, Atlanta, GA 30329-4027. 


Suggested citation: [Author names; first three, then et al., if more than six.] [Title]. MMWR Suppl 2020;69(Suppl-#):[inclusive page numbers]. 


Centers for Disease Control and Prevention 
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Virginia A. Caine, MD Jewel Mullen, MD, MPH, MPA Morgan Bobb Swanson, BS 


Jeff Niederdeppe, PhD 


Supplement 


Overview and Methods for the Youth Risk Behavior 
Surveillance System — United States, 2019 


J. Michael Underwood, PhD!; Nancy Brener, PhD}; Jemekia Thornton, MPA!; William A. Harris, MM!; Leah N. Bryan, MPH!; 
Shari L. Shanklin, MPH!; Nicholas Deputy, PhD! ; Alice M. Roberts, MS3; Barbara Queen, MS4, David Chyen, MS!; Lisa Whittle, MPH}; 
Connie Lim, MPA!; Yoshimi Yamakawa, MPH!; Michelle Leon-Nguyen, MPH!; Greta Kilmer, MS!; Jennifer Smith-Grant, MSPH!; 
Zewditu Demissie, PhD!; Sherry Everett Jones, PhD, JD!; Heather Clayton, PhD}; Patricia Dittus, PhD! 


1 Division of Adolescent and School Health, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC; ? Epidemic Intelligence Service; 
3ICF International, Rockville, Maryland; 4Westat, Rockville, Maryland 


Abstract 


Health risk behaviors practiced during adolescence often persist into adulthood and contribute to the leading causes of morbidity 
and mortality in the United States. Youth health behavior data at the national, state, territorial, tribal, and local levels help monitor 
the effectiveness of public health interventions designed to promote adolescent health. The Youth Risk Behavior Surveillance 
System (YRBSS) is the largest public health surveillance system in the United States, monitoring a broad range of health-related 
behaviors among high school students. YRBSS includes a nationally representative Youth Risk Behavior Survey (YRBS) and separate 
state, local school district, territorial, and tribal school-based YRBSs. This overview report describes the surveillance system and 
the 2019 survey methodology, including sampling, data collection procedures, response rates, data processing, weighting, and 
analyses presented in this MMWR Supplement. A 2019 YRBS participation map, survey response rates, and student demographic 
characteristics are included. In 2019, a total of 78 YRBSs were administered to high school student populations across the United 
States (national and 44 states, 28 local school districts, three territories, and two tribal governments), the greatest number of 
participating sites with representative data since the surveillance system was established in 1991. The nine reports in this MMWR 
Supplement are based on national YRBS data collected during August 2018—June 2019. A full description of 2019 YRBS results 
and downloadable data are available (https://www.cdc.gov/healthyyouth/data/yrbs/index.htm). 


Efforts to improve YRBSS and related data are ongoing and include updating reliability testing for the national questionnaire, 
transitioning to electronic survey administration (e.g., pilot testing for a tablet platform), and exploring innovative analytic methods 
to stratify data by school-level socioeconomic status and geographic location. Stakeholders and public health practitioners can 
use YRBS data (comparable across national, state, tribal, territorial, and local jurisdictions) to estimate the prevalence of health- 
related behaviors among different student groups, identify student risk behaviors, monitor health behavior trends, guide public 
health interventions, and track progress toward national health objectives. 


Introduction among U.S. adolescents were attributable to motor-vehicle 
crashes, followed by suicide and homicide (3). In contrast, 
that same year, a separate study reported the leading causes of 
death among persons of all ages were heart disease, followed 
by cancer and unintentional injuries (e.g., burns, drowning, 


; f j ; falls, poisoning, and motor-vehicle crashes) (4). 
of acquiring a sexually transmitted disease (STD), including The Youth Risk Behavior Surveillance Syacin RBS 


human immunodeficiency virus (HIV) infection, and increase iors health behav diti d 

E N O E aud monitors health behaviors, conditions, and experiences among 
opportunities or su > i P ae high school students throughout the United States. The system 
interpersonal violence or self-harm. Risky health behaviors includes a Aational Youth Risk Behavior Survey (YRES) 
practiced during adolescence often persist into adulthood conducted ty CDC, and separate siste local school disiet 
(2). In 2018, CDC reported that the leading causes of death territorial, and tribal school-based YRBSs, which are referred 
to as site-level surveys. YRBSS is designed to monitor priority 
Corresponding author: J. Michael Underwood, PhD, Division of health risk behaviors that contribute to the leading causes of 
Adolescent and School Health, National Center for HIV/AIDS, Viral mortality, morbidity, and social problems among youths and 


Adolescence is typically a healthy period of life, and CDC 
reports that youths continue to make better decisions for 
their health (7). However, some high school-aged youths 
experience disparate health risks that increase the possibility 


Hepatitis, STD, and TB Prevention, CDC. Telephone: 404-718-1471; 


E-mail: jmunderwood@cdc.gov. 


adults. The following categories of behaviors are included 
in the system: 1) behaviors that contribute to unintentional 





US Department of Health and Human Services/Centers for Disease Control and Prevention MMWR / August 21, 2020 / Vol.69 / No.1 1 


Supplement 


injury and violence; 2) tobacco use; 3) alcohol and other 
drug use; 4) sexual behaviors that contribute to unintended 
pregnancy and STD/HIV infection; 5) dietary behaviors; and 
6) physical inactivity. 

This report describes the 2019 YRBS methodology, 
including sampling, data collection, processing, weighting, 
and analyses. Results include a 2019 YRBS participation 
map, survey response rates (1991-2019), and student 
demographic characteristics from the national survey. 
Furthermore, this overview report is one of nine featured 
in this MMWR Supplement. Each report uses YRBS data to 
assess a priority public health topic among adolescents. In 
addition to this overview report, this supplement includes 
national YRBS updates regarding condom and contraceptive 
use; violence victimization and suicide ideation by sexual 
identity; interpersonal violence victimization; opioid, alcohol, 
and other substance use behaviors; suicide ideation and 
behaviors; tobacco use, including vaping; dietary behaviors 
and physical activity; and transportation risk behaviors. Each 
report might not include all national YRBS data related to the 
topics that were collected in 2019, and this supplement does 
not include any data from site-level surveys; however, all the 
data are publicly available. (YRBS data and documentation 
are available at https://www.cdc.gov/healthyyouth/data/yrbs/ 
data.htm.) Stakeholders and public health practitioners can 
use YRBS data (comparable across national, state, tribal, and 
local jurisdictions) to estimate the prevalence of health-related 
behaviors among different student groups, identify student 
risk behaviors, monitor health behavior trends, guide public 
health interventions, and track progress toward national 
health objectives. 


National YRBS Methodology 


Overview 


The national YRBS is conducted biennially during the 
spring of odd-numbered years and allows CDC to assess how 
risk behaviors change temporally among the U.S. high school 
population. The national YRBS provides comparable data 
across years and allows state and local entities conducting their 
own YRBS to demonstrate how the behaviors of their youths 
compare with those at the national level. YRBS is conducted 
among students in grades 9-12 who attend U.S. public and 
private schools. A nationally representative sample of schools 
and a random sample of classes within those schools are selected 
to participate. The survey is self-administered anonymously by 
using a computer-scannable questionnaire booklet and takes 
one class period (approximately 45 minutes) to complete. 


2 MMWR / August 21,2020 / Vol.69 / No.1 


Questionnaire 


In 2019, the YRBS questionnaire consisted of 99 questions. 
Eighty-nine of those questions were included in the standard 
questionnaire* used by sites. Ten additional questions were 
added to the standard questionnaire that reflect areas of interest 
for CDC and other stakeholders, forming the 99-question 
national YRBS questionnaire. As in all cycles, both the standard 
questionnaire and additional national-only questions were 
revised to ensure that emerging and prevailing risk behaviors 
among high school students were measured. Subject matter 
experts from CDC and elsewhere proposed changes, additions, 
and deletions to the questionnaire. New and revised questions 
were reviewed for format, readability, and clarity and were 
subjected to cognitive testing. CDC made further refinements 
to the questions on the basis of those testing results. 

All questions, except those assessing height, weight, and 
race, were multiple choice, with a maximum of eight mutually 
exclusive response options and only one possible answer per 
question. The survey questions have undergone test-retest 
analysis and demonstrated good reliability (5,6). The wording 
of each question, including recall periods and response options, 
and operational definitions for each variable, are available by 
reviewing the 2019 YRBS questionnaire and data user's guide. 
(YRBSS data and documentation are available at https://www. 
cdc.gov/healthyyouth/data/yrbs/data.htm.) 


Sampling 

The 2019 YRBS sampling frame consisted of all regular 
public (including charter schools), parochial, and other 
nonpublic schools with students in at least one of grades 9-12 
in the 50 U.S. states and the District of Columbia. Alternative 
schools, special education schools, schools operated by the U.S. 
Department of Defense, the Bureau of Indian Education, and 
vocational schools serving only students who also attended 
another school were excluded. Schools with an enrollment 
of <40 students across grades 9-12 also were excluded. The 
sampling frame was based on data sets obtained from Market 
Data Retrieval, Inc., and the National Center for Education 
Statistics (NCES). NCES data sets were based on the Common 
Core of Data (https://nces.ed.gov/ccd) for public schools 
and the Private School Universe Survey (https://nces.ed.gov/ 
surveys/pss) for nonpublic schools. 

A three-stage cluster sampling design was used to produce 
a nationally representative sample of students in grades 9-12 


*The standard YRBSS questionnaire includes 89 questions. YRBS coordinators 
(located in CDC-funded states, local school districts, territories, and tribes) 
voted for or against each proposed change, addition, and deletion. Final content 
of the standard YRBS questionnaire was decided on the basis of the results of 
this voting process. 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


who attend public and private schools. The first-stage sampling 
frame comprised 1,257 primary sampling units (PSUs), 
consisting of entire counties, groups of smaller adjacent 
counties, or parts of larger counties. The 1,257 PSUs were 
categorized into 16 strata according to their metropolitan 
statistical area status (e.g., urban or rural) and the percentages 
of non-Hispanic black (black) and Hispanic students in each 
PSU. From the 1,257 PSUs, 54 were sampled with probability 
proportional to overall school enrollment size for that PSU. For 
the second-stage sampling, secondary sampling units (SSUs) 
were defined as a physical school with grades 9-12 or a school 
created by combining nearby schools to provide all four grades. 
From the 54 PSUs, 162 SSUs were sampled with probability 
proportional to school enrollment size. To provide adequate 
coverage of students in small schools, an additional 15 small 
SSUs were selected from a subsample of 15 PSUs from the 54 
PSU sample. These 177 SSUs corresponded to 184 physical 
schools. The third stage of sampling comprised random 
sampling of one or two classrooms in each of grades 9-12 
from either a required subject (e.g., English or social studies) 
or a required period (e.g., homeroom or second period). 
All students in sampled classes were eligible to participate. 
Schools, classes, and students who refused to participate were 
not replaced in the sampling design. 


Data Collection Procedures 


CDC's Institutional Review Board approved the protocol 
for the YRBS. Survey procedures were designed to protect 
students’ privacy by allowing for anonymous and voluntary 
participation. Before survey administration, local parental 
permission procedures were followed. During survey 
administration, students completed the self-administered 
questionnaire during one class period and recorded their 
responses directly on a computer-scannable booklet. 


Response Rates and Data Processing 


For the 2019 YRBS, 13,872 questionnaires were completed 
in 136 schools. The national data set was cleaned and edited 
for inconsistencies. Missing data were not statistically imputed. 
A questionnaire failed quality control when <20 responses 
remained after editing or when it contained the same answer 
to 215 consecutive questions. Among the 13,872 completed 
questionnaires, 195 failed quality control and were excluded 
from analysis, resulting in 13,677 usable questionnaires. The 
school response rate was 75.1%; the student response rate was 
80.3%; and the overall response rate (i.e., [student response 
rate] x [school response rate]) was 60.3%. 

Race/ethnicity was ascertained from two questions: 
1) “Are you Hispanic or Latino?” (with response options of 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


“yes” or “no”) and 2) “What is your race?” (with response 
options of “American Indian or Alaska Native,” “Asian,” “black 
or African American,” “Native Hawaiian or other Pacific 
Islander,” or “white”). For the second question, students 
could select more than one response option. For this report, 
students were classified as Hispanic/Latino and are referred 
to as Hispanic if they answered “yes” to the first question, 
regardless of how they answered the second question. Students 
who answered “no” to the first question and selected only black 
or African American to the second question were classified as 
black or African American and are referred to as black. Students 
who answered “no” to the first question and selected only white 
to the second question were classified and are referred to as 
white. Race/ethnicity was classified as missing for students 
who did not answer the first question and for students who 
answered “no” to the first question but did not answer the 
second question. 

To obtain a sufficient sample size for analyses of health- 
related behaviors by sexual identity and sex of sexual contacts, 
students were divided into groups (Table 1). Students who had 
no sexual contact were excluded from analyses related to sexual 
behaviors, female students who had sexual contact with only 
females were excluded from analyses on condom use and birth 
control use, and male students who had sexual contact with 
only males were excluded from analyses on birth control use. 


Weighting 

A weight based on student sex, race/ethnicity, and grade 
was applied to each record to adjust for school and student 
nonresponse and oversampling of black and Hispanic students. 
The overall weights were scaled so that the weighted count 
of students equals the total sample size, and the weighted 
proportions of students in each grade match the national 
population proportions. Therefore, weighted estimates 
are nationally representative of all students in grades 9-12 
attending U.S. public and private schools. 


Analytic Methods 


Findings presented in this MMWR Supplement and Youth 
Online (https://nccd.cdc.gov/Youthonline/App/Default.aspx), 
an interactive data analysis tool that allows access to all YRBSS 
data, follow analytic methods similar to what is described in 
this overview report. For more information regarding the 
analyses presented in this supplement (e.g., variables analyzed, 
custom measures, and data years), see the Methods section in 
each individual report. 

All statistical analyses were conducted on weighted data by 
using SAS (version 9.4; SAS Institute) and SUDAAN (version 
11.0.1; RTI International) software to account for the complex 


MMWR / August 21,2020 / Vol.69 / No.1 3 


Supplement 


TABLE 1. Processing of sexual identity and sex of sexual contacts questions — Youth Risk Behavior Survey, United States, 2019 


Question 


Sexual identity 


Which of the following best describes you? 
1) Heterosexual (straight), 2) gay or lesbian, 3) bisexual, or 


4) not sure Not sure 


Sex of sexual contacts 


During your life, with whom have you had sexual contact? 
1) | have never had sexual contact, 2) females, 3) males, or 


4) females and males Contact: 
Female 

What is your sex? Male 

1) Male or 2) female er 
Male 


Females and males 


Female 


Females and males 


* Excluded from analyses on sexual behaviors. 
t Excluded from analyses on birth control use and condom use. 


sampling designs. In all reports, prevalence estimates and 
confidence intervals were computed for variables in the YRBS 
data set. Pairwise differences between populations (e.g., sex, race/ 
ethnicity, grade, sexual identity, and sex of sexual contacts) were 
determined using ¢-tests. Prevalence estimates were considered 
statistically significant if the test p value was <0.05. 

In reports that analyzed data related to temporal trends, 
prevalence estimates for variables assessed with identically 
worded questions were examined. Logistic regression analyses 
were used to account for all available estimates; control for 
sex, grade, and racial/ethnic changes over time; and assess 
long-term linear and quadratic trends. A p value associated 
with the regression coefficient that was <0.05 was considered 
statistically significant. Linear and quadratic time variables 
were treated as continuous and were coded by using orthogonal 
coefficients calculated with PROC IML in SAS. A minimum 
of 3 survey years was required for calculating linear trends, 
and a minimum of 6 survey years was required to calculate 
quadratic trends. Separate regression models were used to 
assess linear and quadratic trends for every variable. When a 
significant quadratic trend was identified, Joinpoint software 
was used to automate identification of the year when the 
nonlinear (i.e., quadratic) trend changed. Regression models 
were used to identify linear trends occurring in each segment. 
Cubic and higher-order trends were not assessed. A quadratic 
trend indicates a statistically significant but nonlinear trend 
in prevalence over time. A long-term temporal change that 
includes a significant linear and quadratic trend demonstrates 
nonlinear variation (e.g., leveling off or change in direction) in 
addition to an overall increase or decrease over time. 


4 MMWR / August 21,2020 / Vol.69 / No.1 


Student response 


Heterosexual (straight) 
Gay or lesbian or bisexual 


| have never had sexual contact* 


Analytic description 


Heterosexual students 
Lesbian, gay, or bisexual students 
Not-sure students 


Students who had no sexual contact 


Student: Students who had sexual contact with only the 
Male opposite sex 

Female 

Student: Students who had sexual contact with only the 
Malet same sex or with both sexes 

Male 

Femalet 

Female 


In reports that analyzed 2-year changes in health-related 
behaviors, prevalence estimates from 2017 and 2019 were 
compared by using ¢tests for variables assessed with identically 
worded questions in both survey years. Prevalence estimates were 
considered statistically different if the ż-test p value was <0.05. 


Data Availability and Dissemination 


YRBS data (1991—2019) are available from the YRBSS 
data and documentation website (https://www.cdc.gov/ 
healthyyouth/data/yrbs/data.htm), as are additional resources, 
including data documentation and analysis guides. Data are 
available in both Access and ASCII formats. SAS and SPSS 
programs are provided for converting the ASCII data into 
SAS and SPSS data sets. Variables are standardized to facilitate 
trend analyses and for combining data. YRBSS data are also 
available online by using Youth Online (https://nccd.cdc.gov/ 
Youthonline/App/Default.aspx), a tool that allows point-and- 
click data analysis and creation of customized tables, graphs, 
maps, and fact sheets. Youth Online also performs statistical 
tests by health topic and filters and sorts data by race/ethnicity, 
sex, grade, and sexual orientation (sexual identity and sex of 
sexual contacts). Finally, YRBS Explorer is a new application 
featuring user-friendly options to view and compare national, 
state, and local data via tables and graphs (https://yrbs-explorer. 
services.cdc.gov). Data requests and other YRBSS-related 
questions can be sent to CDC by using the data request form 
(https://www.cdc.gov/healthyyouth/data/yrbs/contact.htm). 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


State, Local School District, 
Territorial, and Tribal YRBS 
Methodology 


Overview 


Biennial administration of site-level YRBSs allows state 
and local education and health agencies to assess how risk 
behaviors change temporally among the high school population 
in their respective jurisdiction. Site-level YRBS data provide 
comparable data across years and allow comparisons of student 
behaviors across jurisdictions (e.g., national or state). Site- 
level surveys are conducted among students in grades 9-12 
attending public schools by using samples representative of 
the state, local, territorial, or tribal jurisdiction where they are 
administered. The survey is self-administered anonymously 
and takes one class period (approximately 45 minutes) to 
complete. State and local institutional review boards approved 
the protocol for their respective YRBSs. Survey methodology 
for data collection, processing, and analytic methods were the 
same as those described for the national YRBS. 


Questionnaires 


The 2019 YRBS standard questionnaire contained 
89 questions and was used as the starting point for site-level 
YRBS questionnaires. Sites could add or delete questions but 
were required to use at least 60 of the questions on the standard 
questionnaire. This flexibility allows YRBS coordinators and 
other local stakeholders the opportunity to pursue topics of 
interest by customizing their survey. 


Sampling 

Sites used a two-stage cluster sampling design to produce 
a representative sample of students in grades 9-12 in their 
jurisdiction. In 41 states, three local school districts, and one 
territory, in the first sampling stage, public schools with any 
of grades 9-12 were sampled with probability proportional 
to school enrollment size. In two states, 25 local school 
districts, and two territories, all schools in the jurisdiction 
were selected to participate (i.e., a census of schools). In the 
second sampling stage, intact classes from either a required 
subject (e.g., English or social studies) or a required period 
(e.g., homeroom or second period) were sampled randomly. 
In three sites (Vermont, the District of Columbia, and Palau), 
a census of students was eligible to participate. 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Response Rates and Nonresponse 
Bias Analyses 


Before the 2019 YRBS cycle, CDC required a minimum 
60% overall response rate for data from a jurisdiction to be 
weighted. As response rates in federal surveys continue to 
decline (7), a better understanding of the complex association 
between nonresponse and nonresponse bias is needed. In 2019, 
CDC chose three YRBS sites with overall response rates of 
50%-60% (Nebraska; Texas; and Spartanburg County, South 
Carolina) to pilot nonresponse bias analyses to evaluate data 
representativeness. Because of data limitations, comparisons 
were limited to responding and nonresponding schools by 
school size and responding and nonresponding students by 
grade. Weighted sample and population percentages by grade, 
sex, and race/ethnicity were also compared. Overall, few 
statistically significant differences between comparison groups 
were found, which suggested that the data were generally 
representative of their respective populations. For the 2019 
cycle, CDC used nonresponse bias analysis results to help 
determine whether data were weighted for sites with overall 
response rates <60%. 


Weighting 

YRBS data were weighted if sites collected data from a 
representative sample of students (determined either by an 
overall response rate of 260% or nonresponse bias analysis 
indicating no significant bias). A weight based on student sex, 
race/ethnicity, and grade was applied to each record to adjust 
for school and student nonresponse in each jurisdiction. The 
weighted count of students equals the student population in 
each jurisdiction. Data from 44 states and 28 local school 
districts were weighted. In 26 states and 13 local school 
districts, weighted estimates are representative of all students 
in grades 9-12 attending regular public schools, and in 
13 states and eight local school districts, weighted estimates are 
representative of regular public school students plus students in 
grades 9-12 in other types of public schools (e.g., alternative 
or vocational schools). 


Data Availability and Dissemination 


A combined data set including national, state, local school 
district, territorial, and tribal YRBS data (1991—2019) is available 
from the YRBSS data and documentation website (https://nccd. 
cdc.gov/Youthonline/App/Default.aspx). Availability of site 
data depends on survey participation, data quality, and data- 
sharing policies. Information about YRBSS data is available at 
the participation maps and history website (https://www.cdc. 
gov/healthyyouth/data/yrbs/participation.htm). Data requests 


MMWR / August 21,2020 / Vol.69 / No.1 5 


Supplement 


and other YRBS-related questions can be sent to CDC by 
using the data request form. (The YRBSS question, comment, 
and data request form is available at https://www.cdc.gov/ 
healthyyouth/data/yrbs/contact.htm.) Site-level YRBS data 
(from high school and middle school surveys) collected during 
1991-2019 are available through Youth Online (https://nccd. 
cdc.gov/Youthonline/App/Default.aspx) and YRBS Explorer 
(https://yrbs-explorer.services.cdc.gov). 


YRBS Response Rates and 2019 
Demographic Characteristics 


During 1991-2019, national YRBS overall response rates 
remained at >60% (Figure 1). They reached a high of 71% 
during the 2009 and 2011 YRBS cycles, followed by steady 
decreases; response rates have remained in the low 60% range 
during the 2015-2019 cycles. Since 1991, school response 
rates have varied from 70% to the low 80% range, whereas 
student participation rates have been consistent at 80%-90%. 

Data were weighted to match national population 
proportions. Thus, 50.6% of students were male, and 26.6% 
were in 9th grade; 25.5% were in 10th grade; 24.2% were in 
11th grade; and 23.5% were in 12th grade (Table 2). In regard 
to race/ethnicity, the majority of students were no-Hispanic 


white (white) (51.2%), followed by Hispanic (26.1%), black 
(12.2%), and other (10.6%), which is defined as American 
Indian or Alaska Native, Asian, Native Hawaiian or other 
Pacific Islander, or multiracial but non-Hispanic. 

Nationwide, 84.4% of students self-identified as heterosexual, 
2.5% as gay or lesbian, and 8.7% as bisexual; 4.5% were not 
sure of their sexual identity (Table 3). In 2019, 45.4% of 
students had sexual contact with only the opposite sex, 2.2% 
with only the same sex, and 4.8% with both sexes; 47.6% had 
had no sexual contact. 


2019 Site-Level YRBS Participation and 
Student Response Rates 


In 2019, a total of 44 states, 28 local school districts, three 
territories, and two tribal governments had representative 
data (Figure 2). In 2019, the median response rate for state 
YRBSs with representative data was 65.0% (Figure 3), which 
has typically remained at 60%-70% since 1991. The median 
response rate for local school district YRBSs with representative 
data was 76.5% (Figure 3) and has typically remained at 
70%-80% since 1991. Since the inception of YRBSS in 1991, 
the number of sites with representative data has increased, 
reaching a high of 77 in 2019 (Figure 4). 


FIGURE 1. Overall, school, and student response rates for the national Youth Risk Behavior Surveys — United States, 1991-2019 


100 


90 


80 


70 


60 


50 


Percentage 


40 


=a a Overall response rate 


30 == == School response rate 
== Student response rate 


20 


1991 1993 1995 1997 1999 2001 2003 


6 MMWR / August 21,2020 / Vol.69 / No.1 





2005 2007 2009 2011 2013 2015 2017 2019 


Year 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 2. Youth Risk Behavior Survey student demographic 
characteristics — United States, 2019 


Characteristic No. (%) 
Participating schools 136 (100) 
Student sample size 13,677* (100) 
Response rates 

Schools (75.1) 
Students (80.3) 
Total (60.3) 
Sext 

Male 6,641 (50.6) 
Female 6,885 (49.4) 
Race/Ethnicity'$ 

White, non-Hispanic 6,668 (51.2) 
Black, non-Hispanic 2,040 (12.2) 
Hispanic 3,038 (26.1) 
Other 1,493 (10.6) 
Grade? 

9 3,637 (26.6) 
10 3,717 (25.5) 
11 3,322 (24.2) 
12 2,850 (23.5) 


* Among the 13,872 completed questionnaires, 195 failed quality control and 
were excluded from analysis, resulting in 13,677 usable questionnaires. 

t Does not included students who responded “ungraded” or “other grade.” 

S Percentages might not total 100% because of rounding. 


Discussion 


YRBSS is the largest public health surveillance system in the 
United States, monitoring multiple health-related behaviors 
among high school students. Since 1991, YRBSS has collected 
data from approximately 4.9 million high school students in 
approximately 2,100 separate surveys. Survey response rates 
have remained slightly above 60%, since YRBSS inception. 
Consistent and relatively high response rates allow for 
long-term trend analyses of student health behaviors and 
experiences. During the 2019 cycle, 78 separate jurisdictions 
successfully collected YRBS data from a broad diversity of 
high school students. Nationally representative data from 
adolescents of various demographic profiles (e.g., sex, race 
and ethnicity, sexual identity) provide information regarding 


disparities in health-related behaviors and highlight long-term 
trends in the prevalence of these behaviors. 

In 2019, CDC launched the Public Health Data 
Modernization Initiative to enhance the potential of using 
data for disease detection and elimination. The initiative 
envisions a future in which data drives action efficiently, 
flexibly, rapidly, and with impact. CDC leverages technology, 
knowledge, leadership, access, and collaboration to harness 
the life-saving power of data. YRBSS has both longstanding 
and newly implemented features that align with the 
modernization initiative. CDC scientists provide technical 
support to help state and local education and health agencies 
administer their YRBS. Flexibility in the questionnaire 
design process allows stakeholders to collect data of interest 
across student populations. Detailed YRBS site reports are 
rapidly returned to state and local departments of health and 
education, often within 16 weeks of survey administration. 
In 2019, YRBSS reach (measured by the number of sites with 
representative data) has increased to 78 sites including the 
national survey, the most in YRBSS history. These data will 
help identify student risk behaviors, affect decision-making, 
and guide public health interventions. 

The public release of YRBS data coincides with the 
publication of this nine-part MMWR Supplement and is an 
agencywide collaboration. Subject matter experts from selected 
CDC programs contributed to this supplement to highlight 
public health concerns among U.S. high school students. 
YRBS data dissemination is managed through online requests, 
Youth Online, and YRBS Explorer. This year, CDC updated 
Youth Online to strengthen data presentation, improve user 
experience, and ultimately expand reach for YRBS data. These 
improvements to data dissemination will improve YRBS access, 
expand usage, and maximize impact. 

CDC continually works to strengthen YRBSS, and new 
developments are under way. In 2019, CDC launched a 
project to update reliability testing for the national YRBS 
questionnaire. As other school-based surveys move toward 


TABLE 3. Number and percentage of students, by sexual identity and sex of sexual contacts — Youth Risk Behavior Survey, United States, 2019 


Total 
Characteristic No. (%) 95% Cl 
Sexual identity 
Heterosexual 10,853 (84.4) 83.4-85.3 
Gay or lesbian 380 (2.5) 2.1-3.0 
Bisexual 1,151 (8.7) 8.0-9.4 
Not sure 591 (4.5) 3.9-5.0 
Sex of sexual contacts 
Opposite sex only 4,856 (45.4) 42.8-48.1 
Same sex only 292 (2.2) 1.8-2.7 
Both sexes 526 (4.8) 4.2-5.5 
No sexual contact 4,953 (47.6) 44.8-50.4 


Abbreviation: CI = confidence interval. 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Male Female 

No. (%) 95% CI No. (%) 95% CI 
5,728 (91.2) 90.1-92.3 5,048 (77.6) 75.9-79.3 
157 (2.1) 1.6-2.7 211 (2.9) 2.3-3.6 
201 (3.4) 2.8-4.1 929 (13.9) 12.7-15.2 
223 (3.2) 2.7-3.9 350 (5.6) 4.7-6.6 
2,642 (49.5) 46.2-52.8 2,214 (41.3) 38.7-44.0 
99 (1.6) 1.2-2.0 193 (2.8) 2.2-3.6 
90 (1.8) 1.4-2.3 436 (7.8) 6.7-9.1 
2,346 (47.1) 43.9-50.4 2,607 (48.0) 45.1-50.9 


MMWR / August 21,2020 / Vol.69 / No.1 7 


Supplement 


FIGURE 2. State, local school district, territorial, and tribal government Youth Risk Behavior Surveys — selected U.S. sites, 2019 


Winnebago Tribe of Nebraska 


= New York City 
Newark 


Philadelphia 


District of Columbia 


Oakland 
San Francisco 


Los Angeles Gaston County 


San Diego Spartanburg County 


Nashville 
Shelby County 
Duval County 
Orange County 
Palm Beach County 
Broward County 


Cherokee Nation 


Pasco County 


Hill h Count 
@ Northern Mariana Islands ilisbörough County 


@ Guam @ Puerto Rico 


@ Local school district, territory, 
or tribal government: representative data 


i State: representative data 
Gi State: no representative data 
Did not participate 





electronic platforms (e.g., computer, smart phone, or tablet), 
some site-level YRBSs have also transitioned to electronic 
survey administration. CDC recently completed pilot 
testing for a tablet-based survey administration of the YRBS 
questionnaire and is considering using tablets for future 
YRBSs. Finally, CDC is exploring innovative analytic methods 
to stratify YRBS data by school-level socioeconomic status 
and geographic location. A recent study using this approach 
reported students attending schools in low socioeconomic 
areas were more likely to experience violence, poor emotional 


well-being, and suicidality (8). 


Limitations 


Reports in this supplement include a limitations section 
describing the analyses pertaining to that particular report. In 
general, YRBSS findings are subject to at least six limitations. 
First, these data apply only to youths who attend school and 
therefore are not representative of all persons in this age group. 
In 2019, approximately 5% of high school-aged youths (ages 
14-17 years) were not enrolled in school (9). Those youths 
might engage in riskier health behaviors than their peers, and 


8 MMWR / August 21, 2020 / Vol.69 / No.1 


those behaviors are not captured in the school-administered 
YRBS. Second, the extent of underreporting or overreporting 
of health-related behaviors cannot be determined, although 
the survey questions demonstrate good test-retest reliability 
(5,6). Third, not all states and local school districts administer 
YRBS, and those that did administer it might not include all 
the standard questions on their YRBS questionnaire; therefore, 
data for certain variables are not available for some sites. Fourth, 
YRBS data analyses are based on cross-sectional surveys and 
can only provide an indication of association, not causality. 
Moreover, the survey is descriptive and not designed to explain 
the reasons behind any observed trends. Fifth, limitations exist 
related to assessment of sexual and gender identity. Students 
might not be fully aware of their sexual identity at the time of 
assessment or might not have understood the sexual identity 
question. The category of students who are not sure of their 
sexual identity might encompass students who are unsure of 
their sexuality, students who were uncomfortable answering 
the question, or students who did not understand the question. 
In addition, although some sites asked questions about 
transgender students, the national YRBS does not include a 
question about gender identity; therefore, national prevalence 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


FIGURE 3. National, state, and local school district Youth Risk Behavior Survey response rates — United States and selected U.S. sites, 


1991-2019* 
100 


90 


80 


70 


60 


50 


Percentage 


40 


=== National response rate 


30 = = State median response rate 


=== Local school district median response rate 
20 


1991 1993 1995 1997 1999 2001 2003 





2005 2007 2009 2011 2013 2015 2017 2019 


Year 


* Does not include Youth Risk Behavior Survey data from U.S. territories and tribal governments. 


estimates for this population of students cannot be assessed. 
Finally, a limitation exists regarding the aggregation of race 
and ethnicity data. The national YRBS aggregates these data 
into broad categories of white, black, and Hispanic. All other 
students are classified as “other.” More detailed racial/ethnic 
information, as published elsewhere, provides valuable data 
regarding health disparities among high school students (10). 


Conclusion 


YRBSS is the best source for quality data at the national, 
state, territorial, tribal, and local school district levels for 
monitoring health-related behaviors that contribute to the 
leading causes of mortality and morbidity among U.S. high 
school students and that can lead to health problems as adults. 
A recent report from the National Academies of Sciences, 
Engineering, and Medicine used YRBS as its data source 
on the basis of the strengths of the system (//). In 2019, in 
addition to the national data, 44 states, 28 local school districts, 
three territories, and two tribal governments received data 
representative of their high school student populations. 

This overview report describes YRBSS methods for guiding 
the analyses presented in this MMWR Supplement. A full 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


description of 2019 YRBS results and downloadable data 
are available (https://www.cdc.gov/healthyyouth/data/yrbs/ 
index.htm). 


Conflicts of Interest 


All authors have completed and submitted the International 
Committee of Medical Journal Editors form for disclosure of 
potential conflicts of interest. No potential conflicts of interest 
were disclosed. 


References 


1. CDC. Youth Risk Behavior Survey: data summary and trends report, 
2007-2017. Atlanta, GA: US Department of Health and Human 
Services, CDC; 2018. https://www.cdc.gov/healthyyouth/data/yrbs/pdf/ 
trendsreport.pdf 

2. Wiium N, Breivik K, Wold B. Growth trajectories of health behaviors 
from adolescence through young adulthood. Int J Environ Res Public 
Health 2015;12:13711-29. https://doi.org/10.3390/ijerph121113711 

3. Murphy SL, XuJ, Kochanek KD, Arias E. Mortality in the United States, 
2017. NCHS Data Brief 2018; (328):1-8. 

4. Curtin SC, Heron M, Miniño AM, Warner M. Recent increases in injury 
mortality among children and adolescents aged 10-19 years in the United 
States: 1999-2016. Natl Vital Stat Rep 2018;67:1-16. 

5. Brener ND, Kann L, McManus T, Kinchen SA, Sundberg EC, Ross JG. 
Reliability of the 1999 youth risk behavior survey questionnaire. 
J Adolesc Health 2002;31:336-42. https://doi.org/10.1016/ 
$1054-139X(02)00339-7 


MMWR / August 21,2020 / Vol.69 / No.1 9 


Supplement 


FIGURE 4. Number of states, local school districts, territories, and tribal governments with representative Youth Risk Behavior Survey data, by 
year of survey — selected U.S. sites, 1991-2019 


2019 
2017 
2015 
2013 
2011 
2009 
2007 


2005 


Year 


2003 


2001 


1999 


1997 


1995 


1993 


Hi States (Districts [J Territories [] Tribal governments 


1991 





0 10 20 30 40 50 60 70 80 90 100 


Number of representative sites 


6. Brener ND, Mcmanus T, Galuska DA, Lowry R, Wechsler H. Reliability 9. US Department of Education, National Center for Education Statistics. 


and validity of self-reported height and weight among high school Fast facts: enrollment trends. Washington, DC: US Department of 
students. J Adolesc Health 2003;32:281~7. https://doi.org/10.1016/ Education; 2019. https://nces.ed.gov/fastfacts/display.asp?id=65 
$1054-139X(02)00708-5 10. Lowry R, Eaton DK, Brener ND, Kann L. Prevalence of health-risk 
7. Czajka JL, Beyler A. Declining response rates in federal surveys: trends behaviors among Asian American and Pacific Islander high school 
and implications. Final report. Vol. I. Washington, DC: Mathematica students in the U.S., 2001-2007. Public Health Rep 2011;126:39-49. 
Policy Research; 2016. https://aspe.hhs.gov/system/files/pdf/255531/ https://doi.org/10.1177/003335491112600108 
Decliningresponserates.pdf 11. National Academies of Sciences, Engineering, and Medicine. Promoting 
8. Everett Jones S, Underwood JM, Pampati S, et al. School-level poverty positive adolescent health behaviors and outcomes: thriving in the 21st 
and persistent feelings of sadness or hopelessness, suicidality, and century. Washington, DC: The National Academies Press; 2019. http:// 


experiences with violence victimization among public high school 
students. J Health Care Poor Underserved 2020;30 [In press]. 


10 MMWR / August 21,2020 / Vol.69 / No.1 


www.nap.edu/catalog/25552/promoting-positive-adolescent-health- 
behaviors-and-outcomes-thriving-in-the 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


Condom and Contraceptive Use Among Sexually Active High School 
Students — Youth Risk Behavior Survey, United States, 2019 


Leigh E. Szucs, PhD!; Richard Lowry, MD?; Amy M. Fasula, PhD3; Sanjana Pampati, MPH*; Casey E. Copen, PhD3; Khaleel S. Hussaini, PhD; 
Rachel E. Kachur, MPH3; Emilia H. Koumans, MD3; Riley J. Steiner, PhD3 


'Division of Adolescent and School Health, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, 
CDC: ? Office of the Director, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC; 
3 Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, CDC; Oak Ridge Institute for Science and Education; 
> Division of STD Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention CDC 


Abstract 


Preventing unintended pregnancy and sexually transmitted diseases (STDs), including human immunodeficiency virus (HIV) 
infection, among adolescents is a public health priority. This report presents prevalence estimates for condom and contraceptive 
use among sexually active U.S. high school students from the 2019 Youth Risk Behavior Survey. Behaviors examined included 
any condom use, primary contraceptive method use, and condom use with a more effective contraceptive method, all reported at 
last sexual intercourse. Analyses were limited to sexually active students (i.e., those who had sexual intercourse with one or more 
persons during the 3 months before the survey). Except for any condom use, students reporting only same-sex sexual contact were 
excluded from analyses. Weighted prevalence estimates were calculated, and bivariate differences in prevalence were examined 
by demographic characteristics (sex, race/ethnicity, and grade) and other sexual risk behaviors (age of sexual initiation, previous 
3-month and lifetime number of sex partners, and substance use before last sexual intercourse). Nationwide, 27.4% of high school 
students reported being sexually active (n = 3,226). Among sexually active students who reported having had sexual contact with 
someone of the opposite sex (n = 2,698), most students (89.7%) had used a condom or a primary contraceptive method at last 
sexual intercourse. Prevalence of any condom use at last sexual intercourse was 54.3%, and condoms were the most prevalent 
primary contraceptive method (43.9% versus 23.3% for birth control pills; 4.8% for intrauterine device [IUD] or implant; and 
3.3% for shot, patch, or ring). Approximately 9% had used condoms with an IUD, implant, shot, patch, ring, or birth control pills. 
Using no pregnancy prevention method was more common among non-Hispanic black (23.2%) and Hispanic (12.8%) students 
compared with non-Hispanic white students (6.8%); compared with Hispanic students, using no pregnancy prevention method 
was more common among non-Hispanic black students. Prevalence of condom use was consistently lower among students with 
other sexual risk behaviors. Results underscore the need for public health professionals to provide quality sexual and reproductive 
health education and clinical services for preventing unintended pregnancy and STDs/HIV and decreasing disparities among 
sexually active youths. 


and 18-19 years were 7.2 and 32.3 births per 1,000 females, 
respectively (4). Moreover, racial/ethnic, geographic, and 
socioeconomic disparities persist (4). For example, in 2018, 
birth rates among non-Hispanic black (black) (26.3) and 
Hispanic (26.7) persons aged 15—19 years were almost two 
times the rate for non-Hispanic white (white) (12.1) persons (4). 

Contraceptive methods vary in effectiveness and highly and 
moderately effective methods do not prevent STDs, which 
disproportionately affect adolescents (5). Highly effective 
reversible contraceptive methods (IUDs and implants) are 
associated with a <1% failure rate during the first year of typical 
use; moderately effective contraceptive methods (injectables, 
patches, rings, and birth control pills) are associated with a 
4%-7% failure rate during the first year of typical use; and 
less effective methods (condoms, diaphragm, and spermicides) 


Introduction 


Preventing unintended pregnancy and sexually transmitted 
diseases (STDs), including human immunodeficiency virus 
(HIV) infection, is a U.S. public health priority, particularly 
among adolescents (/). U.S. birth rates among youths aged 
15-19 years have decreased to record lows; evidence suggests 
that increasing use of a range of contraceptive options, 
including intrauterine devices (IUDs) and implants, also 
known as long-acting reversible contraception, is a contributing 
factor (2). However, U.S. birth rates among adolescents 
remain higher than rates in comparable Western industrialized 
nations (3). In 2018, U.S. birth rates for persons aged 15-17 


Corresponding author: Leigh E. Szucs, PhD, Division of Adolescent 
and School Health, National Center for HIV/AIDS, Viral Hepatitis, 


STD, and TB Prevention, CDC. Telephone: 404-718-6785; E-mail: 


Iszucs@cdc. gov. 





US Department of Health and Human Services/Centers for Disease Control and Prevention 


are associated with a >10% failure rate during the first year 
of typical use (6). Condoms, although categorized as a less 


MMWR / August 21, 2020 / Vol.69 / No.1 11 


Supplement 


effective method of pregnancy prevention (6), remain vital 
for STD/HIV prevention and promoting condom use is 
particularly important given increasing STD rates in the 
United States (5). Professional medical organizations (7,8) and 
federal agencies, including CDC, recommend using condoms 
for STD/HIV prevention with a more effective method of 
contraception for optimal protection against unintended 
pregnancy (9). However, recent decreases in condom use 
have been documented, and the proportion of adolescents 
using condoms with more effective methods of contraception 
has been consistently low, with recent national estimates of 
approximately 9% of sexually active high school students (10). 

Because of these challenges to pregnancy- and STD/HIV- 
prevention goals, monitoring condom and contraceptive use 
behaviors among sexually active youths is essential. This study 
reports prevalence estimates from the 2019 Youth Risk Behavior 
Survey (YRBS) for any condom use at last sexual intercourse 
among sexually active U.S. high school students. In addition, 
prevalence estimates of primary contraceptive method use and 
condom use with more effective methods of contraception 
at last sexual intercourse among sexually active students who 
had sexual contact with the opposite sex during their lifetime 
are reported. Variations in these behaviors by demographic 
characteristics and sexual risk behaviors were examined to 
support public health professionals in implementing quality 
sexual and reproductive health education and clinical services 
that prevent STDs/HIV and unintended pregnancy. 


Methods 


Data Source 


This report includes data from the 2019 YRBS, a cross- 
sectional, school-based survey conducted biennially since 
1991. Each survey year, CDC collects data from a nationally 
representative sample of public and private school students in 
grades 9-12 in the 50 U.S. states and the District of Columbia. 
Additional information about YRBS sampling, data collection, 
response rates, and processing is available in the overview report 
of this supplement (11). The prevalence estimates for all sexual 
behavior questions for the overall study population and by 
sex, race/ethnicity, grade, and sexual orientation are available 
at https://nccd.cdc.gov/youthonline/App/Default.aspx. The 
full YRBS questionnaire is available at https://www.cdc.gov/ 
healthyyouth/data/yrbs/pdf/2019/2019_YRBS-National-HS- 
Questionnaire.pdf. 


12 MMWR / August 21,2020 / Vol.69 / No.1 


Measures 


Behaviors analyzed included any condom use, primary 
contraceptive method, and condom use with more effective 
methods of contraception, all reported at last sexual intercourse. 
Any condom use was assessed by the question, “The last time 
you had sexual intercourse, did you or your partner use a 
condom?” Response options included the following: I have 
never had sexual intercourse, yes, or no. Primary contraceptive 
method was assessed through a separate question, “The last 
time you had sexual intercourse, what one method did you or 
your partner use to prevent pregnancy?” Respondents could 
select only one response from the following list of options: 
I have never had sexual intercourse; no method was used to 
prevent pregnancy; birth control pills; condoms; an IUD 
(such as Mirena or ParaGard) or implant (such as Implanon 
or Nexplanon); a shot (such as Depo-Provera), patch (such 
as Ortho Evra), or birth control ring (such as NuvaRing); 
withdrawal or some other method; or not sure. Dichotomous 
(yes versus no) variables for each response option were created, 
except for “not sure”; although participants selecting this 
response (n = 93; 3.9%) were included in the analytic sample, 
prevalence estimates for this category are not reported. 

A dichotomous (yes versus no) variable for any condom 
use with an IUD, implant, shot, patch, ring, or birth control 
pills was constructed by using the separate items for any 
condom use and primary contraceptive method at last sexual 
intercourse. These two items were also used to create the 
following dichotomous (yes versus no) indicators: condom 
use only (yes to any condom use and condoms or no method 
for pregnancy prevention); highly or moderately effective 
contraceptive use only (no to any condom use and an IUD, 
implant, shot, patch, ring, or birth control pills for pregnancy 
prevention); withdrawal or some other contraceptive method 
use only (no to any condom use and withdrawal or some other 
method for pregnancy prevention); and use of no condom and 
no primary contraceptive method (no to any condom use and 
no method for pregnancy prevention). 

Condom and contraceptive use were examined by 
demographic characteristics and sexual risk behaviors. 
Demographic characteristics included sex (female or male), 
race/ethnicity (non-Hispanic white [white], non-Hispanic 
black [black], or Hispanic; other/multiple responses are not 
reported), and grade (9, 10, 11, or 12). Four dichotomous 
sexual risk behaviors were created: age of sexual initiation 
(<13 years versus 213 years); lifetime number of sex partners 
(1-3 versus 24); number of sex partners during the previous 
3 months (1 versus 22); and alcohol or drug use before last 
sexual intercourse (yes versus no). 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


Analysis 


The analytic sample was restricted to sexually active students 
(i.e., those who reported having had sexual intercourse with 
one or more persons during the 3 months before the survey). 
Analyses involving pregnancy prevention methods excluded 
students who only had same-sex sexual contacts during their 
lifetime, on the basis of an item about respondents’ sex (“What 
is your sex?” with response options including female or male) 
and another item assessing the sex of sexual contacts (“During 
your life, with whom have you had sexual contact?” with 
response options including I have never had sexual contact, 
females, males, and females and males). 

All analyses were conducted using SUDAAN (version 11.0.0; 
RTI International) to account for the complex sampling design. 
Weighted prevalence estimates and 95% confidence intervals 
were calculated for each outcome. Chi-square statistics 
were used to examine bivariate differences by demographic 
characteristics and sexual risk behaviors. For significant overall 
differences by race/ethnicity and grade, t-tests were used to 
identify pairwise differences. Differences were considered 
significant if p<0.05. 


Results 


Among the 27.4% of sexually active students (n = 3,226), 
approximately half were female (52.2%) and white (52.3%); 
approximately one third were in grade 12 (36.9%) (Table 1). 
Regarding sexual risk behaviors among those sexually active 
students, 7.0% had sexual intercourse for the first time before 
age 13 years (3.0% of all YRBS respondents reported having 
had sexual intercourse for the first time before age 13 years); 
26.9% had sexual intercourse with >4 persons during their 
lifetime (8.6% of all YRBS respondents reported having had 
sexual intercourse with 24 persons during their lifetime); 
20.5% had sexual intercourse with >2 persons during the 
previous 3 months; and 21.2% had drunk alcohol or used 
drugs before last sexual intercourse. 

Among sexually active students, prevalence of any condom 
use at last sexual intercourse was 54.3% (Table 2). Among 
sexually active students who reported having had sexual 
contact with someone of the opposite sex (i.e., excluding 
those who reported only same-sex sexual contact) (n = 2,698), 
condoms (43.9%) were the most prevalent primary pregnancy 
prevention method, based on responses to the distinct item 
assessing pregnancy prevention method, followed by birth 
control pills (23.3%); withdrawal or other method (10.1%); 
IUD or implant (4.8%); and shot, patch, or ring (3.3%). (Of 
participants excluded from the analytic sample for primary 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


method of pregnancy prevention, 95 students reported having 
had only same-sex sexual contact and 433 students did not 
answer the questions, “What is your sex?” or “During your life, 
with whom have you had sexual contact?”) Approximately one 
tenth (10.7%) had not used a pregnancy prevention method 
at last sexual intercourse; 9.1% had used a condom with an 
IUD, implant, shot, patch, ring, or birth control pills at last 
sexual intercourse. Prevalence of condom and IUD or implant 
use (<1.0%) and condom and shot, patch, or ring use (<1.0%) 
was lower than condom and birth control pills use (7.5%). 

Prevalence estimates for mutually exclusive categories that 
reflect both pregnancy and STD/HIV prevention effectiveness 
and account for any condom use in addition to a primary 
pregnancy prevention method indicate that condom use 
only was most common (44.3%), followed by highly or 
moderately effective contraceptive method use only (22.2%) 
(Figure). Prevalence of condom use with an IUD, implant, 
shot, patch, ring, or birth control pills (9.1%) was similar to 
prevalence of using withdrawal or other method only (9.5%) 
and using no condom and no primary pregnancy prevention 
method (10.3%). 

Analyses revealed significant differences in any condom 
use and primary pregnancy prevention method at last sexual 
intercourse by demographic characteristics (Table 2). By 
sex, no differences occurred in not using any method of 
pregnancy prevention (i.e., no method); however, differences 
were identified in type of method used. Compared with male 
students’ report of contraceptive use by their female partner, 
prevalence as reported by female students was higher for shot, 
patch, or ring (4.5% versus 2.1%) and birth control pills 
(26.1% versus 20.2%). In contrast, prevalence of condom use 
as the primary method for pregnancy prevention reported by 
male students (49.4%) was higher than female students’ report 
of condom use by their male partner (38.8%), as was any 
condom use at last sexual intercourse (60.0% versus 49.6%). 

A similar pattern emerged when examining prevalence of 
any condom and primary contraceptive method use by grade. 
The prevalence of using no method was the same across grades; 
however, differences occurred in method type. Any condom 
use and condom use as the primary pregnancy prevention 
method was more prevalent in lower versus higher grades. In 
contrast, use of an IUD or implant; birth control pills; and 
condom with an IUD or implant, shot, patch, ring, or birth 
control pills was typically more prevalent in higher versus lower 
grades. For example, condom use as the primary pregnancy 
prevention method was more common among students in 
grades 9 (55.3%), 10 (47.7%), and 11 (45.3%) versus students 
in grade 12 (37.4%) (and grade 9 versus grade 11), whereas 
IUD or implant use was less common among 9th-grade 


MMWR / August 21, 2020 / Vol.69 / No.1 13 


Supplement 


TABLE 1. Prevalence of demographic characteristics and sexual risk behaviors among sexually active* high school students — Youth Risk 


Behavior Survey, United States, 2019 


Characteristic 


Sex 

Female 

Male 
Race/Ethnicity! 
Black, non-Hispanic 
Hispanic 

White, non-Hispanic 
Grade 


Sexual risk behavior 

Had sexual intercourse before age 13 years 

Had sexual intercourse with 24 persons during their lifetime 

Had sexual intercourse with =2 persons during the previous 3 months 
Had drunk alcohol or used drugs before last sexual intercourse 


Abbreviation: Cl = confidence interval. 


No.t (%5) 95% CI 
1,679 (52.2) 49.4-55.0 
1,510 (47.8) 45.0-50.6 

474 (11.2) 8.9-14.0 

771 (28.4) 22.3-35.5 
1,602 (52.3) 46.4-58.1 

389 (11.3) 9.8-13.0 

741 (21.4) 19.3-23.6 

967 (30.4) 27.8-33.2 
1,089 (36.9) 33.4-40.4 

242 (7.0) 5.7-8.5 

854 (26.9) 24.3-29.7 

658 (20.5) 18.5-22.7 

652 (21.2) 18.8-23.9 


* Defined as having had sexual intercourse with at least one person during the 3 months before the survey (n = 3,226). 


t Unweighted. 
S Weighted estimates. 


4 Race/ethnicity values do not total 100% because “other/multiple” responses are not reported (i.e., American Indian/Alaska Native, Asian, Native Hawaiian/Other 


Pacific Islander, and multiple race). 


students (<1.0%), compared with students in grades 10 (3.3%), 
11 (3.2%), and 12 (8.2%). Prevalence of IUD or implant use 
among 10th- and 11th-grade students was also lower than 
among 12th-grade students. 

In contrast with sex and grade, not using a pregnancy 
prevention method differed by race/ethnicity, with higher 
prevalence of no method among black (23.2%) and Hispanic 
(12.8%) students, compared with white students (6.8%); 
compared with Hispanic students, using no pregnancy 
prevention method was more common among black students. 
Additional racial/ethnic differences in type of method were 
identified, with the general pattern that prevalence of using 
a more effective method of contraception was lower among 
black and Hispanic students compared with white students. 
Specifically, prevalence among black and Hispanic students 
was lower than among white students for use of an IUD or 
implant (2.0% and 1.6% versus 6.7%, respectively); birth 
control pills (12.1% and 15.5% versus 29.7%, respectively); 
and condom use with an IUD, implant, shot, patch, ring, or 
birth control pills (7.5% and 4.8% versus 12.4%, respectively). 
In contrast, prevalence of withdrawal or other method use was 
higher among black (13.9%) and Hispanic (13.1%) students 
than among white students (7.7%). Condom use as the 
primary method for pregnancy prevention was higher among 
Hispanic students (49.6%), compared with black (37.2%) and 
white (42.3%) students, and any condom use at last sexual 
intercourse was higher among Hispanic (56.2%) and white 
(55.8%) students compared with black students (48.2%). 


14 MMWR / August 21, 2020 / Vol.69 / No.1 


Differences by sexual risk behaviors in the prevalence of using no 
contraceptive method and in the type of method used, including 
any condom use, also were observed (Table 3). Comparing 
students who had initiated sex before age 13 years with students 
who had not, differences in no method use were not significant; 
however, prevalence was lower for any condom use at last sexual 
intercourse (40.9% versus 55.4%), condom use as the primary 
method of pregnancy prevention (30.4% versus 44.8%), and 
withdrawal or other method use (5.5% versus 10.4%). Students 
who had >4 lifetime partners had higher prevalence of no method 
use (14.7% versus 9.2%) and lower prevalence of any condom 
use (46.6% versus 57.1%); condom use as the primary pregnancy 
prevention method (36.2% versus 46.6%); and condom use with 
an IUD, implant, shot, patch, ring, or birth control pills (6.5% 
versus 10.1%) compared with students who had <é lifetime sex 
partners. A similar pattern was observed for students who reported 
having had 22 recent partners, although no significant differences 
in no method use were observed. Comparing students who had 
drunk alcohol or used drugs before last sexual intercourse with 
students who had not, use of no method was higher (14.7% 
versus 9.6%), whereas any condom use (47.4% versus 56.0%) 
and condom use as the primary pregnancy prevention method 
(39.3% versus 45.1%) were lower. 


Discussion 


This report provides the most recent nationally representative 
estimates of condom and contraceptive use among sexually 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 2. Prevalence of condom and primary contraceptive use at last sexual intercourse among sexually active* high school students, by 
demographic characteristics — Youth Risk Behavior Survey, United States, 2019 


Primary contraceptive method Condoms and IUD, 


implant, shot, 








Any IUD or Shot, patch, Birth control Withdrawal or No patch, ring, or 
condom uset implant or ring pills Condom other method method birth control pills 

Demographic %5 p %5 p %5 p %5 p %5 p %S p %5 p %5 

characteristic (95%CI) valuef (95%CI) value’ (95%CI) value! (95%CI) value’ (95%CI) value’ (95%CI) value’ (95%CI) value’ (95%CI) valuef 

Total 54.3 NA 4.8 NA 3.3 NA 23.3 NA 43.9 NA 10.1 NA 10.7 NA 9.1 NA 
(52.0-56.6) (3.3-7.0) (2.3-4.7) (19.8-27.2) (40.6-47.3) (8.5-12.0) (8.8-12.8) (7.4-11.2) 

Sex NA <0.01 NA 0.20 NA <0.05 NA <0.01 NA <0.01 NA 0.34 NA 0.21 NA 0.10 

Female 49.6 NA 5.6 NA 4.5 NA 26.1 NA 38.8 NA 10.8 NA 11.9 NA 10.3 NA 
(45.6-53.6 4.0-7.6) (2.9-6.8) (22.1-30.5) 34.0-44.0) 8.9-13.0) (9.1-15.3) (8.3-12.7) 

Male 60.0 NA 4.0 NA 2.1 NA 20.2 NA 49.4 NA 93 NA 9.3 NA 7.9 NA 
(57.0-63.0 2.1-7.4) (1.2-3.6) (16.4-24.7) 45.8-53.1) 7.1-12.2) (7.1-12.1) (5.8-10.7) 

Grade NA <0.05 NA <0.01 NA 0.29 NA <0.01 NA <0.01 NA 0.20 NA 0.63 NA <0.01 

9 61.3** NA 0.1%*tHSS NA 24 NA 10.9*%*tt NA 55.3%%tt NA 10.6 NA 14.1 NA 47** NA 
(54.6-67.5 0.0-0.7) (0.9-6.2) (6.0-19.1) ‘47 462.9) 6.9-15.9) (9.1-21.2) (2.7-8.2) 

10 55.4 NA 3.3** NA 2.1 NA  18.2**łłt NA 47.7** NA 12.5 NA 10.5 NA 7.0** NA 
(50.2-60.4 2.0-5.6) (1.0-4.4) (13.4-24.3) 41.2-54.3) 9.5-16.4) (7.2-15.1) (4.7-10.3) 

11 56.3 NA 3.2** NA 42 NA 25.8 NA 45.3** NA 79 NA 10.1 NA 8.9 NA 
(51.9-60.6 1.8-5.8) (2.6-6.7) (21.0-31.3) 39.4-51.4) 5.9-10.5) (7.9-12.8) (6.1-12.9) 

12 50.3 NA 8.2 NA 3.6 NA 27.7 NA 37.4 NA 10.3 NA 10.2 NA 11.6 NA 
(46.9-53.8 (5.5-12.2) (2.0-6.3) (23.3-32.5) 33.4-41.6) 7.9-13.3) (6.9-14.9) (8.9-15.0) 

Race/Ethnicity NA <0.05 NA <0.01 NA 0.07 NA <0.01 NA <0.01 NA <0.01 NA <0.01 NA <0.01 

Black, non-Hispanic 48.2"*** NA 2.011 NA 54 NA 12.119 NA 37.2*** NA 13.951 NA 23.2%%** NA 7.51 NA 
(43.2-53.3 (1.0-4.0) (2.9-9.9) (8.7-16.5) 31.2-43.6) 8.4-22.2) (19.2-27.7) (5.1-10.8) 

Hispanic 56.2 NA 1.6" NA 14 NA 15.519 NA 49.611 NA 13.1% NA 12.811 NA 4.811 NA 
(52.0-60.3 (0.7-3.4) (0.6-3.2) (11.5-20.5) 44.7-54.4) (10.0-17.0) (9.1-17.8) (3.1-7.4) 

White, non-Hispanic 55.8 NA 6.7 NA 4.0 NA 29.7 NA 42.3 NA 77 NA 6.8 NA 12.4 NA 
(52.9-58.6 (5.0-9.0) (2.5-6.4) (25.7-34.0) 38.2-46.5) (6.1-9.8) (5.3-8.6) (10.1-15.2) 








Abbreviations: Cl = confidence interval; |UD = intrauterine device; NA = not applicable. 

* Defined as having had sexual intercourse with at least one person during the 3 months before the survey (n = 3,226). Except for any condom use at last sexual intercourse, students 
reporting only same-sex sexual contact use were excluded; therefore, the analytic sample was restricted to sexually active students who reported having had sexual contact with someone 
of the opposite sex (n = 2,698). Among sexually active students, excluding those who only had same-sex sexual contact, a total of 93 (3.9%) students answered the pregnancy prevention 
question “not sure”; findings are not presented for this group. 

t Any condom use at last sexual intercourse was measured by a separate item from condoms as the primary method used for preventing pregnancy. 

$ Weighted estimates. 

1 Significance is defined as p<0.05, by chi-square test. 

** Significantly different than grade 12, by linear contrast t-test 

tt Significantly different than grade 11, by linear contrast t-test. 

55 Significantly different than grade 10, by linear contrast t-test. 

11 Significantly different than white, non-Hispanic race/ethnicity, by linear contrast t-test. 
*** Significantly different than Hispanic race/ethnicity, by linear contrast t-test. 


active U.S. high school students. In addition, notable differences 
in these behaviors by demographic characteristics and sexual 
risk behaviors are identified that can support implementation 
of interventions to improve condom and contraceptive use 
among adolescents most in need. Doing so will help to achieve 
unintended pregnancy and STD/HIV prevention goals, 
including reducing disparities by race/ethnicity. 

Overall, most (89.7%) sexually active students (excluding 
those who only reported same-sex sexual contact) used a 
condom or a primary contraceptive method at last sexual 
intercourse, yet approximately one fifth (19.8%) reported 
using withdrawal or some other method only or no condom 
and no primary contraceptive method. Moreover, prevalence 
estimates by method type, as well as differences by demographic 
characteristics and sexual risk behaviors, underscore the 
importance of meeting the unintended pregnancy and STD/ 
HIV prevention needs of all sexually active high school students. 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Only 9.1% of sexually active students (excluding those who 
only reported same-sex sexual contact) reported having used a 
condom with a more effective contraceptive method, which is 
the recommended approach for preventing both unintended 
pregnancy and STDs/HIV because the most effective forms of 
contraception confer no STD/HIV protection (7-9). Although 
use of condoms alone can prevent both adverse outcomes and 
was the most prevalent method used, only approximately half 
of sexually active students reported any condom use at last 
sexual intercourse, which is concerning given the high risk 
for STDs among this population (5). Moreover, condoms are 
categorized as a less effective pregnancy prevention method, 
given that they are associated with a 13.0% pregnancy risk 
during the first year of typical use (6), and prevalence of 
any highly or moderately effective method use at last sexual 
intercourse was only 31.4%. 


MMWR / August 21, 2020 / Vol.69 / No.1 15 


Supplement 


FIGURE. Prevalence of condom and primary contraceptive use* at last sexual intercourse among sexually activet high school students — 
Youth Risk Behavior Survey, United States, 2019 


100 


I 95% Confidence interval 





50 
S 
w 40 
v 
= 
0] 
© 30 
> 
L 
œ~ 20 
10 
0 
Condom with highly or Condom Highly or Withdrawal or other No condom and 
moderately effective only moderately effective contraceptive no primary 
contraceptive contraceptive method only contraceptive 
method method only method 
Method used 


* Condom with highly or moderately effective contraceptive method = students who responded “yes” to any condom use at last sexual intercourse and intrauterine 
device or implant; shot, patch, or ring; or birth control pills (i.e., highly or moderately effective methods) as primary pregnancy prevention method. Condom only = 
students who responded “yes” to any condom use at last sexual intercourse and condom or no method as primary pregnancy prevention method. Highly or 
moderately effective contraceptive method only = students who responded “no” to any condom use at last sexual intercourse and intrauterine device or implant; 
shot, patch, or ring; or birth control pills (i.e., highly or moderately effective methods) as primary pregnancy prevention method. Withdrawal or other contraceptive 
method only = students who responded “no” to any condom use at last sexual intercourse and withdrawal or some other method as primary pregnancy prevention 
method. No condom and no primary contraceptive method = students who responded “no” to any condom use at last sexual intercourse and no method of 
pregnancy prevention. 

t Defined as having had sexual intercourse with at least one person during the 3 months before the survey (n = 2,698). Students reporting only same-sex sexual 


contact were excluded from the analytic sample. 


Notable demographic differences in condom and contraceptive 
use warrant particular attention. Compared with white students, 
black and Hispanic students had higher prevalence of no 
pregnancy prevention method use and lower prevalence of 
highly and moderately effective contraceptive method use. Black 
students also had lower prevalence of any condom use at last sexual 
intercourse than white and Hispanic students. On the basis of 
these findings and the documented racial/ethnic disparities in birth 
and STD rates among adolescents (4,5), meeting the unintended 
pregnancy and STD/HIV prevention needs of black and Hispanic 
youths is vital. Understanding and addressing structural barriers 
that might contribute to the observed differences are important 
next steps. As for grade, differences indicate that younger students 
are more likely to use condoms, whereas older students are more 
likely to use an IUD or implant, birth control pills, and condoms 
with a more effective contraceptive method. Therefore, improving 
younger adolescents’ knowledge of, comfort with, and access to the 
most effective methods of pregnancy and STD/HIV prevention is 
needed. Whereas findings related to race/ethnicity and grade have 
clear practice implications, patterns by sex might largely reflect 
reporting differences on the basis of who uses a given method. As 
compared with female students, the proportion of male students 
reporting condom use was higher, and the proportion reporting 
their partners’ use of shot, patch, or ring, and birth control pill use 


16 MMWR / August 21, 2020 / Vol.69 / No.1 


was lower. For the latter female-controlled methods, self-report 
by females is considered more accurate (12). 

Finally, differences in condom and contraceptive use by 
sexual risk behaviors reveal that use of preventive strategies 
is suboptimal among high school students who engage in 
those behaviors. The general pattern was that students with a 
given risk indicator, compared with those without, had lower 
prevalence of condom use and higher prevalence of using 
no method of contraception, although not all differences 
were significant. Such findings might reflect potential 
disempowerment in sexual interactions (13) and the challenge 
of using condoms correctly and consistently while under the 
influence of alcohol or drugs (14). Because number of partners 
is an indicator of STD/HIV risk, findings that students 
with >2 recent or 24 lifetime partners had lower prevalence 
of condom use, alone or with a highly or moderately effective 
contraceptive method, are particularly concerning. 

Collectively, these findings from the 2019 YRBS highlight the 
importance of programmatic efforts that can improve condom 
and contraceptive use among adolescents. The effectiveness of 
sexual risk reduction education is well documented (15); because 
of given decreasing attention to condom-related topics in school- 
based instruction (16), efforts to strengthen implementation 
are warranted. Such education should ensure that highly and 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 3. Prevalence of condom and primary contraceptive use at last sexual intercourse among sexually active* high school students, by 
sexual risk behaviors — Youth Risk Behavior Survey, United States, 2019 


Condom and IUD, 
implant, shot, 


Primary contraceptive method 


Any IUD or Shot, patch, Birth control Withdrawal or No patch, ring, or 
condom uset implant or ring pills Condom other method method birth control pills 
%8 p %8 p %5 p %8 p %5 p %5 p %8 p %5 p 
Sexual risk behavior (95%CI) value’ (95%CI) valuef (95%CI) value’ (95%CI) value" (95%CI) value" (95%CI) valuef (95%CI) value’ (95%CI) value’ 
Had sexual NA <0.05 NA 0.05 NA 0.83 NA 0.81 NA <0.05 NA <0.05 NA 0.05 NA 0.12 
intercourse before 
age 13 years 
Yes 40.9 NA 2.6 NA 3.0 NA 22.1 NA 30.4 NA 5.5 NA 22.8 NA 48 NA 
(30.4-52.4) (1.1-6.0) (1.0-8.2) (13.4-34.1) (19.3-44.2) (2.9-10.3) (12.6-37.7) (1.7-13.3) 
No 55.4 NA 5.0 NA 3.4 NA 23.4 NA 44.8 NA 10.4 NA 9.8 NA 9.4 NA 
(52.9-57.9) (3.4-7.2) (2.3-4.9) (19.7-27.5) (41.4-48.3) (8.6-12.5) (8.0-12.0) (7.6-11.6) 
Had sexual NA <0.01 NA <0.01 NA 0.60 NA 0.43 NA <0.01 NA 0.32 NA <0.05 NA <0.05 
intercourse with 
24 persons during 
their lifetime 
Yes 46.6 NA 7.4 NA 2.9 NA 21.5 NA 36.2 NA 11.5 NA 14.7 NA 6.5 NA 
(42.9-50.2) (5.0-10.8) (1.6-5.2) (17.0-26.9) (31.0-41.7) (8.7-15.0) (10.9-19.6) (4.3-9.8) 
No TA NA 3.9 NA 35: NA 23.9 NA 46.6 NA 9.6 NA 9.2 NA 10.1 NA 
(54.3-59.8) (2.6-5.8) (2.3-5.4) (19.7-28.6) (43.1-50.1) (7.7-11.9) (7.3-11.7) (8.1-12.4) 
Had sexual NA <0.01 NA 0.69 NA <0.05 NA 0.13 NA <0.05 NA 0.14 NA 0.20 NA <0.01 
intercourse with 
22 persons during 
the previous 
3 months 
Yes 47.1 NA 5.0 NA 1.7 NA 19.9 NA 39.3 NA 12.7 NA 14.0 NA 5.2 NA 
(43.1-51.1) (3.1-8.0) (0.6-4.5) (15.1-25.8) (35.1-43.7) (9.2-17.3) (9.5-20.0) (3.2-8.4) 
No 56.2 NA 4.7 NA 37 NA 24.1 NA 45.1 NA 9.4 NA 9.8 NA 10.1 NA 
(53.4-58.9) (3.3-6.8) (2.6-5.3) (20.4-28.3) (41.3-48.9) (7.7-11.4) (8.2-11.7) (8.3-12.4) 
Had drunk alcohol or NA <0.05 NA 0.35 NA 0.35 NA 0.10 NA <0.05 NA 0.68 NA <0.05 NA 0.10 
used drugs before 
last sexual 
intercourse 
Yes 47.4 NA 57 NA 2.3 NA 20.5 NA 39.3 NA 10.6 NA 14.7 NA 6.4 NA 
(42.0-52.9) (3.6-8.9) (1.1-5.1) (16.2-25.7) (33.4-45.5) (7.4-15.0) (11.0-19.3) (3.9-10.5) 
No 56.0 NA 47 NA 3:5: NA 24.0 NA 45.1 NA 9.7 NA 9.6 NA 9.6 NA 
(53.1-58.8) (3.1-6.9) (2.3-5.4) (20.4-28.1) (41.7-48.6) (7.9-11.9) (7.6-12.2) (7.6-12.1) 


Abbreviations: Cl = confidence interval; |UD = intrauterine device; NA = not applicable. 

* Defined as having had sexual intercourse with at least one person during the 3 months before the survey (n = 3,226). Except for any condom use at last sexual intercourse, students reporting 
only same-sex sexual contact use were excluded; therefore, the analytic sample was restricted to sexually active students who reported having had sexual contact with someone of the 
opposite sex (n = 2,698). Among sexually active students, excluding those who only had same-sex sexual contact, a total of 93 (3.9%) students answered the pregnancy prevention question 
“not sure”; findings are not presented for this group. 

t Any condom use at last sexual intercourse was measured by a separate item from condoms as the primary method used for preventing pregnancy. 

S Weighted estimates. 

1 Statistical significance is defined as p<0.05, by chi-square test. 


moderately effective contraceptive methods are clearly addressed, 
including in earlier grades (e.g., middle school). Doing so in the 
context of broader education about health services might be a 
developmentally appropriate approach. 

Engaging directly with communities most affected by 
unintended pregnancy and STD/HIV can be one strategy to help 
identify and address social determinants of health that contribute 
to disparities in condom and contraceptive use. Furthermore, 
education and clinical services can be delivered through 
community- and school-based programs tailored to serve young 
persons most in need. Fostering community-—clinic partnerships 
through youth-serving organizations is one strategy for reaching 
the most vulnerable adolescents. Such partnerships can help 
address barriers and improve access to sexual and reproductive 
health care, either through referral or service integration (17). 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


In addition to access, delivery of comprehensive, client- 
centered, and adolescent-friendly care by well-trained 
providers is essential. For example, same-day initiation of 
long-acting reversible contraception methods (i.e., providing 
the method during the initial appointment) is a best practice 
that can facilitate adolescents’ access to these methods (17). 
Another example is provider counseling about condom use 
with more effective contraceptive methods, which has been 
associated with adolescents’ use of this prevention strategy (18). 
Integrating unintended pregnancy and STD/HIV prevention 
in school-, clinic-, and community-based health promotion 
likely requires explicit attention to individual prevention goals as 
well as preferences related to the various prevention strategies (19). 


MMWR / August 21, 2020 / Vol.69 / No.1 17 


Supplement 


Limitations 


General limitations for the YRBS are available in the overview 
report of this supplement (71). The findings in this report are 
subject to at least five additional limitations. First, male students’ 
report of their female partners’ contraceptive use might not be 
accurate (171). Second, distinguishing the intended purpose of 
condom use in relation to pregnancy and STD/HIV prevention 
is not feasible. Although YRBS assesses condom use as a primary 
method for pregnancy prevention, condom use for STD/HIV 
prevention is not explicitly measured. Third, condom use with 
a more effective contraceptive method might be underestimated 
because respondents could only select one method of pregnancy 
prevention at last sexual intercourse. Fourth, the estimates 
for highly and moderately effective contraception could be 
underestimated if respondents viewed a less effective option (i.e., 
condoms or withdrawal or some other method) as their primary 
contraceptive method used at last sexual intercourse. Finally, 
because the sex of last sex partner is not measured, the analytic 
sample might include students with same-sex partners at last 
sexual intercourse for whom pregnancy prevention is not needed. 


Conclusion 


Ongoing national surveillance will remain important to 
understanding the population-level effects of public health and 
clinical approaches to preventing unintended pregnancy and 
STDs/HIV among young persons. To complement these efforts, 
implementation science and observational research should 
address unresolved questions (e.g., young men’s role in condom 
and contraceptive use, barriers and facilitators to integration of 
pregnancy and STD/HIV prevention, and effective strategies 
for addressing disparities, including racial/ethnic differences). 
Taken together, these data can be used to improve condom and 
contraceptive use for all sexually active adolescents. 


Conflicts of Interest 


All authors have completed and submitted the International 
Committee of Medical Journal Editors form for disclosure of 
potential conflicts of interest. No potential conflicts of interest 
were disclosed. 


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US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


Trends in Violence Victimization and Suicide Risk by Sexual Identity 
Among High School Students — Youth Risk Behavior Survey, 
United States, 2015-2019 


Michelle M. Johns, PhD!; Richard Lowry, MD2; Laura T. Haderxhanaj, PhD; Catherine N. Rasberry, PhD!; Leah Robin, PhD!; Lamont Scales, MA4; 
Deborah Stone, ScD°; Nicolas A. Suarez, MPH! 


! Division of Adolescent and School Health, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC; Office of the Director, National 
Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC; 3Division of STD Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, 
and TB Prevention, CDC; 4Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC; 
Division of Injury Prevention, National Center for Injury Prevention and Control, CDC 


Abstract 


Lesbian, gay, and bisexual (LGB) youths continue to experience more violence victimization and suicide risk than heterosexual 
youths; however, few studies have examined whether the proportion of LGB youths affected by these outcomes has varied over 
time, and no studies have assessed such trends in a nationally representative sample. This report analyzes national trends in violence 
victimization and suicide risk among high school students by self-reported sexual identity (LGB or heterosexual) and evaluates 
differences in these trends among LGB students by sex (male or female) and race/ethnicity (non-Hispanic black, non-Hispanic 
white, or Hispanic). Data for this analysis were derived from the 2015, 2017, and 2019 cycles of CDC’s Youth Risk Behavior Survey 
(YRBS), a cross-sectional, school-based survey conducted biennially since 1991. Logistic regression models assessed linear trends 
in prevalence of violence victimization and indicators of suicide risk among LGB and heterosexual students during 2015-2019; 
in subsequent models, sex-stratified (controlling for race/ethnicity and grade) and race/ethnicity-stratified (controlling for sex 
and grade) linear trends were examined for students self-identifying as LGB during 2015-2019. Results demonstrated that LGB 
students experienced more violence victimization and reported more suicide risk behaviors than heterosexual youths. Among LGB 
youths, differences in the proportion reporting violence victimization and suicide risk by sex and race/ethnicity were found. Across 
analyses, very few linear trends in these outcomes were observed among LGB students. Results highlight the continued need for 
comprehensive intervention strategies within schools and communities with the express goal of reducing violence victimization 
and preventing suicide risk behaviors among LGB students. 


Introduction (YRBS) revealed that LGB high school students experienced 
more bullying at school (33% among LGB students and 17% 
among heterosexual students), more sexual dating violence 
by dating partners (LGB, 16%; heterosexual, 6%), and more 
suicide attempts (LGB, 23%; heterosexual, 5%) (3) than their 
heterosexual peers. 

Notably, the 2019 YRBS data collection cycle presented 
the first opportunity for examining linear trends in violence 
victimization and suicide risk trends for LGB students across 
time in a nationally representative sample. Few studies have 
examined whether prevalence of violence victimization and 
suicide risk varies among LGB youths over time (4). School 
environments in the United States might be improving in their 
ability to meet the needs of LGB youths (5); for example, recent 
surveillance data from CDC’s 2018 School Health Profiles, 
which include representative data from 43 states, provide 


Lesbian, gay, and bisexual (LGB) youths experience more 
violence victimization and suicide risk than heterosexual youths 
(1-3). In 2015, CDC’s Youth Risk Behavior Surveillance 
System (YRBSS) added two new questions to the national 
questionnaire regarding sexual identity and sexual behavior. 
These questions facilitated the first nationally representative 
estimates of the health behaviors and experiences of sexual 
minority youths (students who identify as LGB or those who 
have sexual contact with persons of the same or both sexes) 
and affirmed the presence of substantial health disparities (i.e., 
differences in health outcomes between social groups driven 
by unequal social or environmental circumstances) in violence 
victimization and suicide risk between LGB and heterosexual 
youths . Findings from the 2017 Youth Risk Behavior Survey 


evidence that many schools are implementing supportive 
Corresponding author: Michelle M. Johns, PhD, National Center practices. In the 2018 School Health Profiles, an average of 
for HIV/AIDS, Vital. H patitis, STD, and TB Prevention CDC, 78.5% of schools across U.S. states included in the sample 
reported identifying safe spaces for LGB and transgender 


Telephone: 404-718-8858. E-mail: mjohns1 @cdc.gov. 





US Department of Health and Human Services/Centers for Disease Control and Prevention MMWR / August 21,2020 / Vol.69 / No.1 19 


Supplement 


and questioning youths, and an average of 96.1% of schools 
across these states prohibited harassment based on a student's 
perceived or actual sexual orientation or gender identity 
(6). A recent study (4) that pooled local YRBS data during 
2009-2017 to examine trends in suicide risk found that reports 
among LGB youths of suicide risk might be decreasing but 
that LGB students still are as much as three times more likely 
to have attempted suicide than heterosexual students. National 
U.S. trends have not been estimated. 

LGB youths are a heterogenous population with intersecting 
social identities (e.g., sex, race/ethnicity, or gender identity), and 
important differences might exist anong LGB youths regarding 
risk for violence and suicide. For example, LGB females appear 
to be at higher risk for dating and sexual violence than LGB 
males (7,8). In examinations of racial/ethnic differences among 
LGB youths, clear patterns of differences in experiences of 
violence victimization and suicide risk are less consistent (9,10). 
For example, one study of interpersonal violence among sexual 
minorities reported that physical victimization from an intimate 
partner was 1—4 times higher among non-white youths than 
among white youths (9). Another study reported that non- 
Hispanic white and Hispanic LGB youths were more likely to 
be bullied than non-Hispanic white heterosexual youths but 
that non-Hispanic black LGB youths were not more likely to 
be bullied than non-Hispanic white heterosexual youths (70). 
This same study reported that all LGB youths, regardless of 
race/ethnicity, were at increased risk for suicidal ideation (170). 
More systematic evaluations of the within-group differences in 
violence victimization and suicide risk behaviors among sexual 
minority youths are warranted. 

This analysis contributes to the evidence base regarding LGB 
students, violence victimization, and suicide risk. YRBS data 
were used to examine national trends in violence victimization 
and suicide risk among high school students by self-reported 
sexual identity and evaluated differences among LGB students 
by sex and race/ethnicity. The analysis was guided by the 
following four questions: 

1. How did the prevalence of violence victimization 
and suicide risk among LGB students vary during 
2015-2019? 

2. To what extent did violence victimization and suicide 
risk trends differ from these trends among heterosexual 
students during the same period? 

3. Among LGB students, to what extent did violence 
victimization and suicide risk trends vary by sex (male 
or female)? 

4. Among LGB students, to what extent did violence 
victimization and suicide risk trends vary by race/ethnicity 
(non-Hispanic black, non-Hispanic white, or Hispanic)? 


20 MMWR / August 21,2020 / Vol.69 / No.1 


Methods 


Data Source 


This report includes data from the 2015 (n = 15,624), 2017 
(n = 14,765), and 2019 (n = 13,677) cycles of the national 
YRBS (pooled n = 44,066), a cross-sectional, school-based 
survey conducted biennially since 1991. Each survey year, 
CDC collects data from a nationally representative sample 
of public and private school students in grades 9-12 in the 
50 U.S. states and the District of Columbia. Additional 
information about YRBS sampling, data collection, response 
rates, and processing is available in the overview report of this 
supplement (11). The prevalence estimates for all questions 
on violence victimization and suicide risk for the overall 
study population and by sex, race/ethnicity, grade, and sexual 
orientation are available at https://nccd.cdc.gov/youthonline/ 
App/Default.aspx. The full YRBS questionnaire is available at 
https://www.cdc.gov/healthyyouth/data/yrbs/pdf/2019/2019_ 
YRBS-National-HS-Questionnaire.pdf. 


Measures 


All measures analyzed for this report are provided 
(Table 1). Students responded to seven questions about 
violence victimization, including ever experiencing forced 
sexual intercourse; experiencing sexual dating violence, physical 
dating violence, bullying at school, electronic bullying, and 
being threatened or injured with a weapon at school during 
the previous 12 months; and missing school because of feeling 
unsafe at or on the way to or from school during the previous 
30 days. Students responded to five questions about suicide 
risk during the previous 12 months, including having felt 
persistently sad or hopeless; having seriously considered suicide; 
and having made a suicide plan, having attempted suicide, 
or having made a suicide attempt that had to be treated by 
a doctor or nurse. Students responded to five demographic 
questions relating to sex, sexual identity, grade, race, and 
ethnicity, which were used as covariates and to create relevant 
strata in all trend analyses. 


Analysis 


Data from the 2015, 2017, and 2019 national YRBS were 
examined for trends in the prevalence among LGB students in 
experiences of violence victimization and indicators of suicide 
risk. Data were analyzed by using SAS (version 9.4; SAS 
Institute) and SUDAAN (version 11.0.0; RTI International) to 
account for the complex sampling designs. Data were assessed 
using complete case analysis; missing data were not imputed. 
All outcomes were dichotomized as either yes or no, 21 time 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 1. Measures for demographic characteristics, violence victimization, and suicide risk behaviors among high school students — Youth 
Risk Behavior Survey, United States, 2019 


Construct 


Demographic characteristics 
Sexual identity 


Sex at birth 


Race 


Ethnicity 


Grade 


Violence victimization* 
Forced sex 
Sexual dating violence 


Physical dating violence 


Bullying at school 
Electronic bullying 


Felt unsafe at, to, or from school 


Threatened or injured with a weapon 
at school 


Suicide risk behaviors* 

Persistent feelings of 
sadness/hopelessness 

Seriously considered suicide 

Made a suicide plan 

Attempted suicide 

Suicide attempt requiring 
medical treatment 


Measure 


Which of the following best describes you? 
A. Heterosexual (straight) 

B. Gay or lesbian 

C. Bisexual 

D. Not sure 


What is your sex? 
A. Female 
B. Male 


What is your race? (Select one or more responses.) 
A. American Indian or Alaska Native 

B. Asian 

C. Black or African American 

D. Native Hawaiian or Other Pacific Islander 

E. White 


Are you Hispanic or Latino? 
A. Yes 
B. No 


In what grade are you? 

A. 9th grade 

B. 10th grade 

C. 11th grade 

D. 12th grade 

E. Ungraded or other grade 


Have you ever been physically forced to have sexual intercourse when you did not want to? 

During the past 12 months, how many times did someone you were dating or going out with force you to do sexual things 
that you did not want to do? (Count such things as kissing, touching, or being physically forced to have sexual intercourse.) 

During the past 12 months, how many times did someone you were dating or going out with physically hurt you on 
purpose? (Count such things as being hit, slammed into something, or injured with an object or weapon.) 

During the past 12 months, have you ever been bullied on school property? 

During the past 12 months, have you ever been electronically bullied? (Count being bullied through texting, 
Instagram, Facebook, or other social media.) 

During the past 30 days, on how many days did you not go to school because you felt you would be unsafe at school 
or on your way to or from school? 

During the past 12 months, how many times has someone threatened or injured you with a weapon, such as a gun, 
knife, or club, on school property? 


During the past 12 months, did you ever feel so sad or hopeless almost every day for 2 weeks or more in a row that you 
stopped doing some usual activities? 

During the past 12 months, did you ever seriously consider attempting suicide? 

During the past 12 months, did you make a plan about how you would attempt suicide? 

During the past 12 months, how many times did you actually attempt suicide? 

If you attempted suicide during the past 12 months, did any attempt result in an injury, poisoning, or overdose that 
had to be treated by a doctor or nurse? 


* All violence victimization and suicide risk measures were dichotomized as either “yes” (i.e., 21 time, >1 day) or “no” (i.e., 0 days, 0 times). 


or 0 times, or 21 day or 0 days. Weighted prevalence estimates 
with 95% confidence intervals (CIs) were calculated by using 
Taylor series linearization to produce nationally representative 
prevalence estimates for each survey year. 

Logistic regression models were used to assess linear trends 
in the prevalence of violence victimization and indicators 
of suicide risk among LGB and heterosexual students for 
2015-2019, controlling for sex, race/ethnicity, and grade. 
Main effects odds ratios (ORs) comparing LGB students with 
heterosexual students also were calculated for the 2015-2019 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


period. In subsequent models, sex-stratified (controlling 
for race/ethnicity and grade) and race/ethnicity-stratified 
(controlling for sex and grade) linear trends, were examined 
for students self-identifying as LGB on the survey. Main 
effects ORs comparing sex and race/ethnicity groups also were 
calculated for these subsequent regression models. Linear trends 
were considered statistically significant if p<0.05. Main effects 
ORs were considered statistically significant if 95% CIs did 
not include 1.0. 


MMWR / August 21, 2020 / Vol.69 / No.1 21 


Supplement 


Results 


Violence Victimization 


Among all students (Table 2), LGB students had greater 
odds of violence victimization than heterosexual students 
across all seven indicators, as evidenced by statistically 
significant main effects of sexual identity on each indicator 
(Table 2). Among LGB students, the percentage who reported 
experiencing physical dating violence during 2015-2019 
significantly decreased from 17.5% to 13.1%. No other 
violence victimization outcomes varied significantly among 
LGB students in this period. 

Among LGB students stratified by sex (Table 3), male 
students reported greater odds of feeling unsafe at or on the way 
to or from school (aOR: 1.61) and being threatened or injured 
with a weapon (aOR: 1.54) than female students. Conversely, 
male LGB students reported reduced odds of electronic 
bullying (aOR: 0.71), sexual dating violence (aOR: 0.66), and 
forced sex (aOR: 0.51) than female LGB students. Among male 
LGB students, the percentage reporting being threatened or 
injured with a weapon at school significantly increased from 
2015 (11.6%) to 2019 (15.9%), as did the percentage reporting 
forced sex (2015: 8.0%; 2019: 15.6%). Among female LGB 
students, the percentage reporting physical dating violence 
significantly decreased from 2015 (16.9%) to 2019 (12.1%). 

Among LGB students stratified by race (Table 4), non- 
Hispanic black (black) and Hispanic students reported higher 
odds of feeling unsafe at or on the way to or from school 
than non-Hispanic white (white) students (aOR: 1.63 and 
aOR: 1.46, respectively), and black students also reported 
greater odds of being threatened or injured with a weapon than 
white students (aOR: 1.60). With regard to bullying, black 
and Hispanic LGB students reported reduced odds of both 
bullying at school (black, aOR: 0.31; Hispanic, aOR: 0.56) and 
electronic bullying (black, aOR: 0.41; Hispanic, aOR: 0.55), 
compared with white LGB students. Black LGB students also 
reported reduced odds of sexual dating violence, compared 
with white LGB students (aOR: 0.44). The only significant 
trend among violence models stratified by race/ethnicity was 
among Hispanic LGB students, who had reduced percentage 
of reporting experiencing physical dating violence in 2019 
(9.8%), compared with 2015 (22.6%). 


Suicide Risk 


Among all students (Table 2), LGB students had greater 
odds of suicide risk than heterosexual students across all five 
indicators, as evidenced by significant main effects for each 
variable. The percentage of LGB students reporting these 
outcomes did not vary significantly during 2015-2019. 


22 MMWR / August 21, 2020 / Vol.69 / No.1 


Among LGB students stratified by sex (Table 3), male 
students had lower odds of all five suicide risk indicators than 
female students. Among female LGB students, the percentage 
reporting suicide attempts decreased significantly from 2015 
(32.8%) to 2019 (23.6%). All other trends in suicide risk in 
these sex-stratified models remained stable. 

Among LGB students stratified by race (Table 4), black 
and Hispanic students had lower odds than white students 
of reporting persistent feelings of sadness or hopelessness 
(black, aOR: 0.42; Hispanic, aOR: 0.69) and seriously 
considering attempting suicide (black, aOR: 0.43; Hispanic, 
aOR: 0.65). Black LGB students also had lower odds than 
white LGB students of making a suicide plan (aOR: 0.61). 
The percentage of LGB students reporting these outcomes in 
the race/ethnicity-stratified models did not vary significantly 
during 2015-2019. 


Discussion 


Overall, these results underscore that LGB students continue 
to have a greater prevalence of violence victimization and 
suicidal behavior than their heterosexual peers. The higher 
prevalence of violence and suicide among LGB students is 
consistent with results from other studies regarding sexual 
minorities and minority stress (72,13). Minority stress is the 
preeminent framework for understanding disparities among 
sexual minorities and refers to the process by which social 
stigma directed toward LGB and other nonheterosexual 
persons is enacted through external stressors (e.g., violence, 
discrimination, or harassment) and internal stressors (e.g., 
identity concealment or expectations of rejection) (12). Both 
types of stress shape mental and physical health (12,14, and 
the impact of violence victimization on LGB youths (15) and 
its connection to elevated suicide risk is well-documented 
(16). LGB students’ disproportionate experience of violence 
victimization and suicide risk, compared with their heterosexual 
peers in this study, underscores the continued relevance of 
minority stress among LGB youths and the continued public 
health need for action that addresses these sizeable disparities. 

Notably, the proportion of LGB students experiencing 
violence victimization or suicide risk remained fairly stable 
during 2015-2019. One exception is reports of physical dating 
violence; fewer LGB students reported experiencing physical 
dating violence in 2019 than in 2015. This downward trajectory 
of physical dating violence appears to be a continuation of 
an already documented population trend of a decrease in 
experiences of dating violence among adolescents (17), and its 
detection among LGB youths is promising. Regarding suicide 
risk, a recent study examined local trends since 2009 and 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


TABLE 2. Trends in the prevalence of violence victimization and suicide risk behaviors among high school students, by self-identified sexual 


Supplement 


identity — Youth Risk Behavior Survey, United States, 2015-2019* 





Main effect 

Health risk behavior aOR (95% Cl) 
Violence victimization 
Feeling unsafe at school (past 30 days) 

Lesbian, gay, or bisexual 1.98 (1.70-2.30) 

Heterosexual 1.0 (Ref.) 
Threatened or injured with a weapon at school (past 12 months) 

Lesbian, gay, or bisexual 2.09 (1.80-2.43) 

Heterosexual 1.0 (Ref.) 
Bullied at school (past 12 months) 

Lesbian, gay, or bisexual 2.10 (1.87-2.37) 

Heterosexual 1.0 (Ref.) 
Electronically bullied (past 12 months) 

Lesbian, gay, or bisexual 1.94 (1.72-2.20) 

Heterosexual 1.0 (Ref.) 


Physical dating violence (past 12 months) 
Lesbian, gay, or bisexual 
Heterosexual 
Sexual dating violence (past 12 months) 
Lesbian, gay, or bisexual 
Heterosexual 
Forced sexual intercourse (lifetime) 
Lesbian, gay, or bisexual 
Heterosexual 


2.06 (1.77-2.40) 
1.0 (Ref.) 


2.08 (1.69-2.57) 
1.0 (Ref.) 


3.31 (2.90-3.77) 
1.0 (Ref.) 


% (95% Cl) 


12.5 (10.2-15.3) 
4.6 (3.9-5.4) 


10.0 (7.9-12.7) 
5.1 (4.5-5.9) 


34.2 (29.6-39.0) 
18.8 (17.3-20.3) 


28.0 (24.0-32.3) 
14.2 (13.1-15.3) 


17.5 (14.4-21.2) 
8.3 (7.5-9.3) 


22.7 (18.0-28.2) 
9.1 (8.2-10.0) 


17.8 (14.4-21.8) 
5.4 (4.6-6.4) 


Suicide risk behaviors 
Persistent feelings of sadness or hopelessness (past 12 months) 
Lesbian, gay, or bisexual 3.60 (3.22-4.03) 
Heterosexual 1.0 (Ref.) 

Seriously considered attempting suicide (past 12 months) 
Lesbian, gay, or bisexual 4.51 (4.07-4.99) 
Heterosexual 1.0 (Ref.) 

Made a suicide plan (past 12 months) 
Lesbian, gay, or bisexual 
Heterosexual 

Attempted suicide (past 12 months) 
Lesbian, gay, or bisexual 


4.28 (3.84-4.77) 
1.0 (Ref.) 


4.54 (3.89-5.28) 


Heterosexual 1.0 (Ref.) 
Suicide attempt requiring medical treatment (past 12 months) 
Lesbian, gay, or bisexual 3.78 (3.02-4.73) 
Heterosexual 1.0 (Ref.) 


60.4 (55.1-65.4) 
26.4 (24.6-28.4) 


42.8 (38.4-47.3) 
14.8 (13.7-15.9) 


38.2 (34.0-42.6) 
11.9 (10.8-13.1) 


29.4 (25.7-33.3) 
6.4 (5.6-7.3) 


9.4 (7.3-12.1) 
2.0 (1.5-2.7) 


2017 2019 Linear trend 
% (95% Cl) % (95% Cl) Beta p valuet 

10.0 (8.1-12.3) 13.5 (11.0-16.5) 0.0619 0.65 
6.1 (5.1-7.3) 7.5 (6.3-8.9) 0.3749 0.00 
9.4 (7.4-11.8) 11.9 (9.3-15.2) 0.2463 0.12 
5.4 (4.8-6.0) 6.3 (5.5-7.3) 0.1629 0.02 
33.0 (27.4-39.0) 32.0 (29.5-34.6) -0.0847 0.28 
17.1 (16.1-18.2) 17.1 (15.7-18.7) -0.0800 0.10 
27.1 (23.1-31.4) 26.6 (23.3-30.2) -0.0775 0.43 
13.3 (12.4-14.4) 14.1 (12.9-15.4) -0.0047 0.92 
17.2 (14.3-20.5) 13.1 (10.5-16.1) -0.2264 0.04 

6.4 (5.8-7.1) 7.2 (6.2-8.3) -0.1448 0.047 
15.8 (12.3-20.1) 16.4 (12.7-20.9) -0.2420 0.15 

5.5 (4.8-6.3) 6.7 (5.9-7.5) -0.2785 <0.001 
21.9 (19.0-25.0) 19.4 (16.2-23.1) 0.0650 0.59 
5.4 (4.7-6.2) 5.5 (4.9-6.2) 0.0200 0.82 
63.0 (59.5-66.5) 66.3 (62.2-70.2) 0.1566 0.13 
27.5 (25.9-29.2) 32.2 (30.8-33.7) 0.1949 0.00 
47.7 (43.7-51.8) 46.8 (43.1-50.6) 0.0936 0.26 
13.3 (12.5-14.3) 14.5 (13.4-15.7) -0.0242 0.62 
38.0 (34.5-41.7) 40.2 (36.6-44.0) 0.0646 0.45 
10.4 (9.3-11.7) 12.1 (11.1-13.1) 0.0031 0.96 
23.0 (18.6-28.0) 23.4 (20.0-27.1) -0.1901 0.06 
5.4 (4.6-6.4) 6.4 (5.6-7.4) -0.0148 0.85 
7.5 (5.7-9.8) 6.3 (4.8-8.3) -0.2852 0.07 
1.7 (1.4-2.1) 1.7 (1.4-2.2) -0.1197 0.40 


Abbreviations: aOR = adjusted odds ratio; CI = confidence interval; Ref. = referent group. 
* Logistic regression models were used to assess linear trends in the prevalence of violence victimization, and indicators of suicide risk among lesbian, gay, or bisexual 
students and heterosexual students for 2015-2019, controlling for sex, race/ethnicity, and grade. 


t Statistical significance is defined as p<0.05 or a 95% CI that does not include 1.0. 


reported a decrease in reported suicide risk behaviors among 
LGB students (4). The national trends reported in this analysis 
warrant continued monitoring over time to assess whether the 
downward trajectory in local contexts (4) reflects the general 
trajectory of suicide risk and LGB youths nationally. 

Results from sex-stratified models highlight important 
differences between male and female experiences of violence 
victimization among LGB students. In this sample, male LGB 
students were more likely to report feeling unsafe at school 
and being threatened with a weapon; conversely, female LGB 
students were more likely to report bullying both at school 
and electronically. This finding is consistent with observational 
studies of bullying during adolescence; males tend to report 
more physical forms of bullying and harassment, whereas 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


females tend to report experiencing more verbal and social 
bullying (78). In addition, female LGB students had a greater 
prevalence of sexual dating violence and forced sex than male 
LGB students. This sex difference is also consistent with what 
is known about dating and sexual violence among LGB youths 
(7) and mirrors national trends in dating and sexual violence, 
in which females are consistently disproportionately affected 
by these types of victimization (19). 

Of concern, the percentage of male LGB students who 
reported being threatened or injured with a weapon at school 
and who reported forced sexual intercourse significantly 
increased over time. Although both male and female LGB 
students are negatively affected by violence, these percentages 
highlight an increasing trend in violence among male LGB 


MMWR / August 21, 2020 / Vol.69 / No.1 23 


Supplement 


TABLE 3. Trends in violence victimization and suicide risk behaviors among lesbian, gay, and bisexual high school students, by sex and sexual 


identity — Youth Risk Behavior Survey, United States, 2015-2019* 


Main effect 2015 
Health risk behavior aOR (95% Cl) % (95% Cl) 
Violence victimization 
Feeling unsafe at school (past 30 days) 
Gay or bisexual male 1.61 (1.14-2.28) 15.5 (9.5-24.4) 


Lesbian or bisexual female 


Threatened or injured with a weapon at school (past 12 months) 


Gay or bisexual male 
Lesbian or bisexual female 
Bullied at school (past 12 months) 
Gay or bisexual male 
Lesbian or bisexual female 
Electronically bullied (past 12 months) 
Gay or bisexual male 
Lesbian or bisexual female 
Physical dating violence (past 12 months) 
Gay or bisexual male 
Lesbian or bisexual female 
Sexual dating violence (past 12 months) 
Gay or bisexual male 
Lesbian or bisexual female 
Forced sexual intercourse (lifetime) 
Gay or bisexual male 
Lesbian or bisexual female 


Suicide risk behaviors 


Persistent feelings of sadness or hopelessness (past 12 months) 


Gay or bisexual male 
Lesbian or bisexual female 


1.0 (Ref.) 


1.54 (1.14-2.08) 
1.0 (Ref.) 


0.86 (0.69-1.08) 
1.0 (Ref.) 





0.71 (0.57-0.89) 
1.0 (Ref.) 


1.06 (0.72-1.58) 
1.0 (Ref.) 


0.66 (0.44-0.98) 
1.0 (Ref.) 


0.51 (0.38-0.68) 
1.0 (Ref.) 


0.39 (0.33-0.47) 
1.0 (Ref.) 


Seriously considered attempting suicide (past 12 months) 


Gay or bisexual male 
Lesbian or bisexual female 
Made a suicide plan (past 12 months) 
Gay or bisexual male 
Lesbian or bisexual female 
Attempted suicide (past 12 months) 
Gay or bisexual male 
Lesbian or bisexual female 


Suicide attempt requiring medical treatment (past 12 months) 


Gay or bisexual male 
Lesbian or bisexual female 


0.59 (0.47-0.73) 
1.0 (Ref.) 


0.57 (0.46-0.71) 
1.0 (Ref.) 


0.73 (0.55-0.96) 
1.0 (Ref.) 


0.63 (0.42-0.97) 
1.0 (Ref.) 


10.8 (8.6-13.5) 


11.6 (7.5-17.5) 
9.1 (6.6-12.4) 


26.3 (19.4-34.7) 
37.2 (32.7-42.0) 


22.4 (16.3-30.1) 
30.5 (26.0-35.4) 


19.9 (12.9-29.4) 
16.9 (13.9-20.4) 


20.9 (12.7-32.6) 
22.6 (18.0-27.9) 


8.0 (4.8-13.1) 
21.1 (17.0-25.9) 


43.9 (35.9-52.3) 
66.5 (61.4-71.2) 





32.7 (23.6-43.3) 
46.6 (42.1-51.1) 


27.0 (20.3-34.9) 
42.0 (37.1-47.2) 


19.4 (13.6-27.0) 
32.8 (28.1-37.9) 


7.0 (3.6-13.1) 
10.3 (7.8-13.4) 


2017 2019 Linear trend 

% (95% Cl) % (95% Cl) Beta p valuet 
12.3 (7.4-19.6) 18.3 (12.4-26.1) 0.1623 0.55 
9.1 (6.9-11.9) 11.5 (9.5-14.0) 0.0435 0.72 
14.6 (9.8-21.2) 15.9 (11.4-21.8) 0.3973 0.04 
7.4 (5.6-9.7) 10.6 (8.1-13.9) 0.1944 0.29 
35.0 (25.4-45.9) 31.7 (25.7-38.4) 0.0942 0.58 
32.2 (26.9-38.1) 32.0 (28.6-35.7) -0.1550 0.09 
22.3 (16.5-29.4) 25.5 (18.7-33.8) 0.0786 0.71 
28.5 (24.4-33.1) 27.1 (23.7-30.7) -0.1076 0.28 
16.8 (10.0-27.0) 15.9 (9.4-25.6) -0.0798 0.78 
16.9 (13.5-21.0) 12.1 (9.3-15.6) -0.2638 0.04 
13.5 (7.5-23.0) 10.3 (5.6-18.3) -0.4638 0.20 
16.3 (12.8-20.6) 18.2 (13.6-23.8) -0.2166 0.18 

15.6 (10.3-22.9) 15.6 (10.7-22.0) 0.4388 0.047 

23.7 (20.6-27.2) 21.0 (17.3-25.4) -0.0203 0.87 
45.5 (38.9-52.2) 53.5 (46.3-60.4) 0.2667 0.12 
68.8 (65.1-72.2) 70.5 (66.6-74.2) 0.1167 0.25 
37.0 (31.5-42.8) 40.4 (33.9-47.2) 0.1960 0.30 
51.0 (46.1-55.9) 49.0 (44.8-53.3) 0.0553 0.55 
28.7 (22.8-35.5) 33.0 (26.4-40.3) 0.2350 0.21 
40.8 (36.8-45.0) 42.4 (38.4-46.4) 0.0130 0.89 
18.3 (11.5-27.9) 23.8 (17.8-31.1) 0.1626 0.45 
23.7 (19.4-28.5) 23.6 (20.0-27.6) -0.2929 0.01 
3.8 (1.9-7.3) 5.9 (3.2-10.6) -0.2535 0.53 
8.2 (6.2-10.7) 6.6 (5.0-8.7) -0.2977 0.05 


Abbreviations: aOR = adjusted odds ratio; Cl = confidence interval; Ref. = referent group. 
* Logistic regression models were used to assess linear trends in the prevalence of violence victimization and indicators of suicide risk among lesbian, gay, and bisexual 
high school students, by sex and self-identified sexual identity for 2015-2019, controlling for race/ethnicity and grade. 


t Statistical significance is defined as p<0.05 or a 95% CI that does not include 1.0. 


students. Among adults, gay men are at greater risk for physical 
violence than lesbians (20), and the increasing prevalence 
in these types of violence among male LGB students might 
suggest an increasing disparity between sexual minority men 
and women in violence victimization. Continued monitoring of 
this trend is needed, in both adolescent and adult populations. 
Female LGB students reported fewer experiences of physical 
dating violence over time, whereas male LGB students reports 
of experiencing physical dating violence remained stable. This 
pattern might indicate that the overall reduction in physical 
dating violence in the population is not occurring among 
sexual minority males, which might be supported by the data 
regarding being threatened or injured with a weapon and 
experiencing forced sex. An assessment of the ways that violence 


24 MMWR / August 21,2020 / Vol.69 / No.1 


in schools and in dating relationships affects sexual minority 
males is warranted, both through research to understand 
underlying mechanisms and in practice to ensure violence 
prevention programming is directly addressing the needs of 
sexual minority males. 

Despite a trend of decreasing suicide attempts among 
LGB females during 2015-2019, LGB females consistently 
reported more suicide risk behaviors than LGB males. This 
pattern echoes larger population trends in which both adult 
and youth females report more suicidal ideation than adult and 
youth males (2/). Notably, this same literature finds that males 
experience more deaths by suicide (i.e., completed suicide 
attempts) than females (2/); thus, an important remaining 
question for LGB youths is whether these sex-specific patterns 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 4. Trends in violence victimization and suicide risk behaviors among lesbian, gay, and bisexual high school students, by 
race/ethnicity — Youth Risk Behavior Survey, United States, 2015-2019* 


Main effect 2015 2017 2019 Linear trend 

Health risk behavior aOR (95% Cl) % (95% Cl) % (95% Cl) % (95% Cl) Beta p valuet 
Violence victimization 
Feeling unsafe at school (past 30 days) 

Black, non-Hispanic 1.63 (1.13-2.35) 17.8 (11.4-26.6) 12.5 (7.4-20.4) 15.2 (8.3-26.2) -0.0269 0.93 

Hispanic 1.46 (1.07-1.99) 15.6 (11.5-21.0) 12.4 (8.4-18.0) 13.7 (9.5-19.5) -0.0688 0.75 

White, non-Hispanic 1.0 (Ref.) 9.0 (6.7-12.0) 8.6 (6.4-11.6) 11.1 (8.0-15.1) 0.1309 0.41 
Threatened or injured with a weapon at school (past 12 months) 

Black, non-Hispanic 1.60 (1.07-2.41) 15.6 (8.0-28.1) 15.7 (11.7-20.7) 12.9 (7.2-22.0) -0.0494 0.89 

Hispanic 0.89 (0.63-1.27) 9.0 (5.5-14.3) 9.4 (6.7-13.0) 7.7 (4.9-11.8) -0.1270 0.61 

White, non-Hispanic 1.0 (Ref.) 8.2 (5.5-12.0) 7.1 (4.9-10.1) 12.9 (8.6-18.9) 0.4292 0.09 
Bullied at school (past 12 months) 

Black, non-Hispanic 0.31 (0.22-0.44) 21.4 (12.2-34.7) 17.2 (10.6-26.9) 18.2 (12.2-26.2) -0.0651 0.82 

Hispanic 0.56 (0.45-0.70) 31.2 (25.1-38.0) 26.6 (21.0-33.1) 27.6 (23.2-32.5) -0.1077 0.44 

White, non-Hispanic 1.0 (Ref.) 42.2 (34.8-50.0) 40.8 (32.8-49.3) 37.6 (33.6-41.7) -0.1265 0.31 
Electronically bullied (past 12 months) 

Black, non-Hispanic 0.41 (0.31-0.54) 17.0 (12.6-22.6) 16.3 (12.1-21.5) 20.5 (12.5-31.6) 0.1336 0.61 

Hispanic 0.55 (0.42-0.71) 24.8 (17.8-33.4) 17.7 (13.3-23.2) 25.4 (20.1-31.5) 0.0619 0.75 

White, non-Hispanic 1.0 (Ref.) 36.0 (29.1-43.5) 36.0 (30.1-42.4) 28.2 (22.8-34.1) -0.2484 0.09 
Physical dating violence (past 12 months) 

Black, non-Hispanic 1.19 (0.81-1.75) 14.2 (8.8-22.2) 23.8 (15.5-34.7) 11.6 (7.0-18.6) -0.1425 0.53 

Hispanic 1.13 (0.83-1.54) 22.6 (16.3-30.4) 19.1 (14.2-25.2) 9.8 (5.8-16.2) -0.7080 0.003 

White, non-Hispanic 1.0 (Ref.) 15.3 (11.9-19.5) 14.1 (10.4-18.7) 13.8 (10.6-17.6) -0.0883 0.55 
Sexual dating violence (past 12 months) 

Black, non-Hispanic 0.44 (0.27-0.72) 20.4 (11.5-33.7) 6.4 (3.3-12.0) 10.3 (5.5-18.7) -0.7987 0.11 

Hispanic 0.97 (0.68-1.39) 23.0 (14.8-34.1) 18.6 (11.4-29.0) 18.3 (11.3-28.2) -0.2334 0.41 

White, non-Hispanic 1.0 (Ref.) 22.3 (16.8-29.1) 18.2 (13.7-23.8) 16.7 (11.8-23.1) -0.2426 0.21 
Forced sexual intercourse (lifetime) 

Black, non-Hispanic 0.92 (0.66-1.29) 16.0 (8.3-28.7) 23.9 (17.7-31.6) 15.4 (9.8-23.4) 0.0759 0.77 

Hispanic 1.10 (0.85-1.43) 24.0 (18.7-30.3) 21.8 (17.6-26.8) 19.1 (13.3-26.7) -0.2740 0.17 

White, non-Hispanic 1.0 (Ref.) 15.5 (11.5-20.6) 21.0 (16.8-26.1) 21.3 (16.6-26.9) 0.2531 0.12 
Suicide risk behaviors 
Persistent feelings of sadness or hopelessness (past 12 months) 

Black, non-Hispanic 0.42 (0.33-0.55) 44.8 (35.2-54.7) 52.1 (42.6-61.4) 51.1 (44.6-57.5) 0.1107 0.56 

Hispanic 0.69 (0.54-0.89) 58.2 (50.6-65.5) 61.2 (52.9-69.0) 64.1 (56.0-71.4) 0.1766 0.30 

White, non-Hispanic 1.0 (Ref.) 67.4 (60.3-73.8) 66.3 (60.9-71.3) 71.6 (65.7-76.8) 0.1679 0.24 
Seriously considered attempting suicide (past 12 months) 

Black, non-Hispanic 0.43 (0.34-0.55) 34.4 (25.7-44.3) 28.4 (21.3-36.7) 35.1 (29.2-41.4) -0.0389 0.85 

Hispanic 0.65 (0.54-0.79) 40.7 (34.7-46.9) 45.3 (38.7-52.0) 39.2 (33.2-45.6) —-0.0625 0.63 

White, non-Hispanic 1.0 (Ref.) 48.9 (42.2-55.7) 54.1 (50.4-57.6) 52.4 (47.1-57.7) 0.0959 0.44 
Made a suicide plan (past 12 months) 

Black, non-Hispanic 0.61 (0.46-0.82) 32.9 (23.6-43.7) 24.1 (16.8-33.2) 36.0 (28.8-43.9) 0.0564 0.81 

Hispanic 0.86 (0.69-1.06) 37.9 (31.3-45.0) 35.5 (29.5-42.0) 40.0 (32.8-47.7) 0.0765 0.62 

White, non-Hispanic 1.0 (Ref.) 40.1 (34.5-46.0) 42.8 (37.7-48.0) 40.3 (35.8-45.0) 0.0044 0.97 
Attempted suicide (past 12 months) 

Black, non-Hispanic 0.97 (0.70-1.34) 29.2 (23.1-36.1) 20.7 (12.5-32.3) 27.2 (18.0-38.8) 0.0293 0.91 

Hispanic 1.06 (0.83-1.37) 31.4 (26.4-36.9) 24.6 (18.3-32.2) 23.2 (17.3-30.4) -0.2610 0.12 

White, non-Hispanic 1.0 (Ref.) 28.6 (23.1-34.7) 21.8 (16.4-28.4) 22.3 (18.1-27.3) -0.2078 0.13 
Suicide attempt requiring medical treatment (past 12 months) 

Black, non-Hispanic 0.83 (0.50-1.40) 5.9 (3.2-10.7) 6.4 (3.1-12.9) 7.3 (3.0-16.6) 0.2889 0.50 

Hispanic 1.20 (0.83-1.75) 13.3 (9.2-19.0) 8.7 (5.2-14.3) 6.8 (4.3-10.5) -0.4683 0.05 

White, non-Hispanic 1.0 (Ref.) 9.3 (6.4-13.2) 7.5 (5.2-10.8) 5.6 (3.5-8.7) -0.3545 0.11 


Abbreviations: aOR = adjusted odds ratio; Cl = confidence interval; Ref. = referent group. 


* Logistic regression models were used to assess linear trends in the prevalence of violence victimization and indicators of suicide risk among lesbian, gay, and bisexual 
high school students, by race/ethnicity for 2015-2019, controlling for sex and grade. 
t Statistical significance is defined as p<0.05 or a 95% CI that does not include 1. 0. 


in deaths by suicide hold in this group; however, reliable data 
regarding sexual orientation and rates of death by suicide are 
unavailable. Such data could aid in further illuminating how 
LGB youths are affected by suicide risk behaviors and guide 
interventions for addressing this public health concern. 


In models stratified by race/ethnicity, black and Hispanic 
LGB students were more likely to feel unsafe and were more 
likely to be threatened or injured with a weapon than white 
LGB students. This finding might highlight black and Hispanic 
LGB students being at greater risk for the forms of victimization 


US Department of Health and Human Services/Centers for Disease Control and Prevention MMWR / August 21, 2020 / Vol.69 / No.1 25 


Supplement 


that directly compromise physical safety (18). Conversely, 
white LGB students were more likely to report school and 
electronic bullying, indicating they might be at greater risk 
for verbal and social victimization. Although the types of 
racial/ethnic disparities in violence victimization presented in 
this report do not mirror those reported in previous studies 
(9,10), these findings underscore that differences by race/ 
ethnicity among sexual minority youths exist. Schools seeking 
to address victimization through policies and practices designed 
to address safety concerns for LGB students can benefit from 
acknowledging differences in the experiences of LGB youths 
across races/ethnicities and ensuring all youths are served 
through these intervention strategies. 

Regarding suicide risk, although a significantly lower 
percentage of black and Hispanic LGB youths reported feeling 
sad and hopeless or considering suicide than white LGB 
youths, no differences existed among races/ethnicities in suicide 
attempts or medically serious suicide attempts. These findings 
are similar to those from other studies highlighting that all 
LGB youths are at increased risk for suicide, regardless of race/ 
ethnicity (10) and might again highlight the mental health 


impact of minority stress among all racial/ethnic groups (12). 


Limitations 


General limitations for the YRBS are available in the 
overview report of this supplement (11). The findings in this 
report are subject to at least five additional limitations. First, 
although three cycles of national data to examine trends among 
LGB youths are available, the brief 2015-2019 period might 
be inadequate to assess trends. Continued monitoring of these 
indicators over time to detect progress regarding disparities 
experienced by LGB high school students is needed. Second, 
the overall proportion of students identifying as LGB was 
small: 2015, 8.3% (n = 1,246); 2017, 10.9% (n = 1,494); 
and 2019, 11.7% (n = 1,531). Therefore, these analyses 
might be underpowered for detecting statistical differences 
in trends in models stratified by sex and race/ethnicity. As 
more data are collected from LGB youths in future cycles 
of the national YRBS, pooling data across cycles to improve 
statistical power will be essential for increasing the likelihood 
of detecting trends in stratified models. Third, this report does 
not include differences in violence victimization and suicide 
risk for students who identified their sexual identity as “not 
sure” or across sexual behavior categories; future studies might 
benefit from assessing these youths to further understand the 
experiences of sexual minority students, violence victimization, 
and suicide risk. Fourth, by pooling 2015-2019 data, the aOR 
for the difference between groups on all outcomes might mask 


26 MMWR / August 21,2020 / Vol.69 / No.1 


heterogeneity over time within each subpopulation (e.g., the 
size of the difference between LGB and heterosexual students 
might vary between years); however, a disparity between LGB 
and heterosexual students on these outcomes has been observed 
since sexual identity data began to be collected on the national 
YRBS in 2015. Finally, three survey measures had relatively large 
amounts of missing data in 2019: forced sex (approximately 
2,400 observations), sexual dating violence (approximately 3,400 
observations), and attempted suicide with injury (approximately 
4,900 observations). Most of these missing data can be attributed 
to some selected schools administering YRBS questionnaire 
versions that did not include these questions. Consequently, not 
all students in the national sample were given the opportunity 
to answer these questions and were counted as missing. 


Conclusion 
These findings highlight the continued need for policies and 


practices within school environments that reduce victimization 
and bolster the mental health of LGB students. Substantial 
evidence exists for the role of antiharassment policies, gay- 
straight alliances (or other student-led clubs designed to 
support sexual minority students), and programs aimed at 
improving staff support of LGB students in improving school 
environments for these students (22). In addition to in-school 
programs and policies, schools might consider engagement 
with community organizations and stakeholders to collaborate 
on implementation of comprehensive violence and suicide 
prevention strategies that address a range of risk and protective 
factors at the individual, relationship, community, and societal 
levels. Comprehensive packages designed to inform these 
prevention efforts are available from CDC (https://www. 
cdc.gov/violenceprevention/pub/technical-packages.html). 
For example, comprehensive approaches to suicide reduction 
help to prevent suicide risk, support persons at increased risk, 
prevent reattempts, and help survivors of suicide loss. When 
refining such practices to meet the needs of LGB students, 
special consideration of the impact of physical violence on LGB 
males, suicide risk among LGB females, and the interactions 
between race/ethnicity and these outcomes is warranted. 
Furthermore, continued monitoring of these disparities 
between LGB and heterosexual students over time is needed 
until these disparities can be eradicated. 


Conflicts of Interest 


All authors have completed and submitted the International 
Committee of Medical Journal Editors form for disclosure of 
potential conflicts of interest. No potential conflicts of interest 
were disclosed. 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


= 


10. 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


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MMWR / August 21, 2020 / Vol.69 / No.1 27 


Supplement 


Interpersonal Violence Victimization Among High School Students — 
Youth Risk Behavior Survey, United States, 2019 


Kathleen C. Basile, PhD!; Heather B. Clayton, PhD?; Sarah DeGue, PhD!; John W. Gilford, PhD!; Kevin J. Vagi, PhD!; 
Nicolas A. Suarez, MPH?; Marissa L. Zwald, PhD!; Richard Lowry, MD? 


1Division of Violence Prevention, National Center for Injury Prevention and Control, CDC; ? Division of Adolescent and School Health, National Center for HIV/AIDS, 
Viral Hepatitis, STD, and TB Prevention, CDC; 3 Office of the Director, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC 


Abstract 


Adolescent interpersonal violence victimization is an adverse childhood experience and a serious public health problem for 
youths, their families, and communities. Violence victimization includes dating violence, sexual violence, and bullying. Youth 
Risk Behavior Survey data for 2019 were used to examine physical and sexual dating violence; sexual violence by anyone; and 
bullying victimization, whether on school property or electronic, of U.S. high school students by sex, race/ethnicity, and sexual 
identity. In addition, this report explores frequency of dating violence and frequency of sexual violence among students who 
reported these forms of victimization and presents composites of dating violence and bullying. Findings reveal that 8.2% of 
students reported physical dating violence; 8.2% reported sexual dating violence; 10.8% reported sexual violence by anyone, of 
which 50% of cases were by a perpetrator other than a dating partner; 19.5% reported bullying on school property; and 15.7% 
reported electronic bullying victimization during the previous 12 months. Approximately one in eight students reported any 
dating violence, and one in four reported any bullying victimization. Female students; lesbian, gay, and bisexual students; and 
students not sure of their sexual identity reported the highest prevalence estimates across all five violence victimization types, any 
and both forms of dating violence, and any bullying victimization. Non-Hispanic white students reported the highest prevalence 
of bullying victimization. Among students experiencing physical or sexual dating violence or sexual violence by anyone, the most 
common frequency reported was one time during the previous year; higher frequency was more prevalent among male students 
compared with female students. These findings provide a contextual understanding of the prevalence of interpersonal violence 
of U.S. high school students, highlighting those with highest prevalence. Findings can be used by public health professionals to 
guide prevention efforts with youths in schools and communities. 


of students in grades 7-12 found that 56% of females and 48% 
of males reported some form of sexual violence victimization by 
a peer (e.g., unwelcome comments, touching, or being forced 
to do something sexual) during the 2010-11 school year (3). 
Approximately 20% of adolescents reported physical dating 
violence and 9% reported sexual dating violence (4). These 
studies indicate that sexual violence during adolescence occurs 
inside and outside of the dating context. In addition, 20% of 
students in grades 6-12 reported bullying victimization during 
the 2017 school year (5). 

Scientific literature indicates that certain groups (e.g., females, 
racial/ethnic minorities, and sexual minority youths) 


Introduction 


Interpersonal violence, or aggression perpetrated by another 
person, including dating violence, sexual violence, and bullying, 
is a serious problem for students, schools, and communities. 
Violence can reoccur across the lifespan and is associated with 
multiple health effects and negative health behaviors (e.g., 
risky sexual behaviors, substance misuse, and physical health 
symptoms) (7). Victimization often begins during adolescence 
and can be viewed as an adverse childhood experience (ACE). For 
example, nationally representative data from adults during 2015 
indicate that 43.2% of females and 51.3% of males who had 


Be faped weri Bisbepea berote ace 18 years ieee disproportionately experience interpersonal violence during 


studies ofadolescents confirm this finding. For example, a survey 


Corresponding author: Kathleen C. Basile, PhD, Division of Violence 
Prevention, National Center for Injury Prevention and Control, CDC. 


Telephone: 404-398-8317; E-mail: kbasile@cdc.gov. 





28 MMWR / August 21,2020 / Vol.69 / No.1 


adolescence (7). For instance, in a sample of northeastern 
10th-grade students, sexual minority youths reported more 
bullying, sexual violence, and dating violence victimization 
than heterosexual youths, with sexual minority females 
reporting particularly high levels (91% of sexual minority 
females and 79% of sexual minority males reported at least 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


one form of victimization) (6). Furthermore, in a study of 
sexual violence victimization of college students, females had 
higher odds of victimization than did males, and non-Hispanic 
black (black) students and students of other races/ethnicities 
had higher odds of victimization than did non-Hispanic 
white (white) students; moreover, these racial differences were 
greater for males. For females, Hispanics had lower odds of 
sexual violence victimization than whites, and for males, no 
substantial differences existed between Hispanics and whites 
(7). Understanding these disparities in the experience of 
violence victimization is crucial for identifying those at highest 
risk and for guiding prevention efforts. Contextual factors also 
are valuable in describing victimization (e.g., frequency of 
victimization or co-occurrences of violence subtypes). These 
factors increase understanding of these violence types and 
further contextualize prevalence estimates. For example, in 
a report using 2013 data, approximately 21% of female and 
10% of male high school students who reported dating in the 
previous year experienced sexual or physical dating violence, 
and 6% of females and 3% of males experienced both physical 
and sexual dating violence (8). 

This report presents 2019 prevalence estimates for dating 
violence, sexual violence, and bullying victimization of U.S. 
high school students by sex, race/ethnicity, and sexual identity, 
and includes frequency of dating violence and sexual violence 
victimization by demographic characteristics. Combined 
prevalence of different forms of dating violence and bullying 
also is presented to provide the most current estimates of each 
violence type. These findings can guide prevention efforts 
in addressing adolescent interpersonal violence at different 
levels of the social ecology (i.e., individual, relationship, and 
community or societal levels). 


Methods 


Data Source 


This report includes data from CDC’s 2019 Youth Risk 
Behavior Survey (YRBS), a cross-sectional, school-based 
survey conducted biennially since 1991. Each survey year, 
CDC collects data from a nationally representative sample 
of public and private school students in grades 9-12 in the 
50 U.S. states and the District of Columbia (N = 13,677). 
Additional information about YRBS sampling, data collection, 
response rates, and processing is available in the overview 
report of this supplement (9). The prevalence estimates for 
all violence questions for the overall study population and by 
sex, race/ethnicity, grade, and sexual orientation are available 
at https://nccd.cdc.gov/youthonline/App/Default.aspx. The 
full YRBS questionnaire is available at https://www.cdc.gov/ 
healthyyouth/data/yrbs/pdf/2019/2019_YRBS-National-HS- 
Questionnaire.pdf. 


Measures 


This analysis included five standard measures of violence 
victimization and three composite variables created from those 
standard measures. The standard measures included 1) having 
experienced physical dating violence, 2) having experienced 
sexual dating violence, 3) having experienced sexual violence 
by anyone, 4) having been bullied on school property, and 
5) having been bullied electronically (Table 1). For each of 
these five standard measures, dichotomous categories were 
created: >1 time versus 0 times for all sexual violence and dating 
violence measures and “yes” versus “no” for both bullying 


TABLE 1. Violence victimization measures — Youth Risk Behavior Survey, United States, 2019 


Violence victimization 


Physical dating violence 
victimization 


Questionnaire item 


“During the past 12 months, how many times did someone you were dating or going out 
with physically hurt you on purpose? (Count such things as being hit, slammed into 


Coding for analysis 


21 time versus 0 times; 
1 time, 2 or 3 times, 24 times 


something, or injured with an object or weapon.)” [Question excludes students who did 
not date or go out with anyone during the previous 12 months.] 


Sexual dating violence 
victimization* 


“During the past 12 months, how many times did someone you were dating or going out 
with force you to do sexual things that you did not want to do? (Count such things as 


21 time versus 0 times; 
1 time, 2 or 3 times, 24 times 


kissing, touching, or being physically forced to have sexual intercourse.)” [Question 
excludes students who did not date or go out with anyone during the previous 


12 months.] 


Sexual violence victimization 
by anyonet 
forced to have sexual intercourse.)” 


Bullied on school property 
Electronically bullied 


Abbreviation: YRBS = Youth Risk Behavior Survey. 


“During the past 12 months, how many times did anyone force you to do sexual things 
that you did not want to do? (Count such things as kissing, touching, or being physically 


“During the past 12 months, have you ever been bullied on school property?” 


“During the past 12 months, have you ever been electronically bullied?” 


21 time versus 0 times; 
1 time, 2 or 3 times, 24 times 


Yes versus no 


Yes versus no 


* A total of 3,324 students had missing data for this variable, mostly attributed to the use of different versions of the YRBS questionnaire that did not include the 


sexual violence questions in certain selected schools. 


t A total of 3,439 students had data missing for this variable, mostly attributed to the use of different versions of the YRBS questionnaire that did not include the sexual 


violence questions in certain selected schools. 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


MMWR / August 21, 2020 / Vol.69 / No.1 29 


Supplement 


victimization measures. The manner in which the data were 
collected (see Limitations) means that approximately 25% of 
respondents were missing data for sexual violence victimization 
by anyone (3,439) out of a sample of 13,677 students. The 
denominators for dating violence victimization measures are 
students who reported dating during the 12 months before 
the survey (66.1% [n = 8,703 students] for physical dating 
violence victimization and 66.2% [n = 6,847 students] for 
sexual dating violence victimization), whereas the denominator 
for the sexual violence by anyone and bullying victimization 
measures are the full sample of students for which data were 
available. Three of these standard measures included levels 
of victimization frequency. For each of three measures (i.e., 
physical dating violence, sexual dating violence, and sexual 
violence by anyone), frequencies were collapsed into three 
levels: 1 time, 2 or 3 times, or >4 times. 

The two dating violence victimization measures were 
combined into composite measures: experienced any dating 
violence victimization and experienced both physical and 
sexual dating violence victimization. Because of the manner 
in which the data were collected, approximately 25% of 
respondents were missing data for sexual dating violence 
victimization (3,324 observations out of a sample of 13,677 
students). When calculating the “any dating violence 
victimization” measure, responses missing data for either the 
sexual or the physical dating violence measure were removed 
from the analysis. Any “yes” responses to either the physical 
dating violence measure or the sexual dating violence measure 
were combined for the numerator, with all responses without 
missing data as the denominator. Similarly, to create the 
“both physical and sexual dating violence” measure, “yes” 
responses to both physical dating violence and sexual dating 
violence were required for the numerator, with all nonmissing 
responses in the denominator. A similar strategy was also used 
for creating a bullying victimization “any” measure. “Any 
bullying victimization” included any “yes” response to either 
experiencing bullying at school or experiencing electronic 
bullying, with all nonmissing responses in the denominator. 
The option of exploring “both bullying at school and electronic 
bullying” was not pursued. Use of personal electronic devices 
in the school setting is increasing; therefore, the amount of 
overlap between electronic bullying and bullying at school 
might be considerable and combining these items could result 
in an overestimate of their prevalence. Additional analysis 
examined overlap between the sexual dating violence measure 
and the sexual violence by anyone measure. 

Three demographic characteristics were included in the 
analyses: student sex (male or female), race/ethnicity (white, 
black, Hispanic, or other), and sexual identity (heterosexual; 
lesbian, gay, or bisexual [LGB]; or not sure). Although students 


30 MMWR / August 21,2020 / Vol.69 / No.1 


of multiple or other race/ethnicity are included in these 
analyses, data are not presented for this group because small 
sample sizes and unknown heterogeneity within this group 
resulted in limited interpretability. 


Analysis 


Weighted prevalence estimates and corresponding 95% 
confidence intervals were determined for all violence victimization 
measures. Comparisons by demographic characteristics were 
conducted with the chi-square test (p<0.05). When differences 
among groups were demonstrated, additional t-tests were 
performed to determine pairwise differences between groups. 
Differences between prevalence estimates were considered 
statistically significant if the +test p value was <0.05 for main 
effects (sex, race/ethnicity, or sexual identity). 


Results 


Among the approximately two thirds of U.S. high school 
students who reported dating during the 12 months before the 
survey, 8.2% reported experiencing physical dating violence, 
and 8.2% experienced sexual dating violence (Table 2). 
Sexual violence victimization perpetrated by anyone during 
the 12 months before the survey was reported by 10.8% of 
students. When comparing the sexual dating violence measure 
with the sexual violence by anyone measure, half (50%) of the 
10.8% of students who reported sexual violence by anyone 
were victimized only by someone other than a dating partner. 
Experiences of bullying victimization during the 12 months 
before the survey varied, with 15.7% of students reporting 
experiencing electronic bullying and 19.5% reporting bullying 
on school property. For all violence victimization measures, 
the prevalence varied by both sex and sexual identity, and 
variation by race/ethnicity was only observed for bullying 
victimization. Specifically, female students, LGB students, 
and students not sure of their sexual identity consistently 
had the highest prevalence across all five of the violence 
victimization indicators. In addition, compared with Hispanic 
or black students, white students had the highest prevalence 
of experiencing bullying victimization at school and electronic 
bullying. The prevalence of electronic bullying among Hispanic 
students was also significantly greater than the prevalence 
among black students. 

Among students who experienced physical dating violence, 
sexual dating violence, or sexual violence by anyone during the 
previous year, the most common frequency reported was | time for 
each (Figure). The pattern of frequency for violence victimization 
differed by type of victimization. The distribution of frequency 
for physical dating violence victimization was U-shaped, with 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 2. Percentage of high school students who experienced violence victimization,* by demographic characteristics and type of violence — 
Youth Risk Behavior Survey, United States, 2019 


Experienced sexual dating violence 


Experienced physical dating violencet 


Experienced sexual violence by anyone" 





Characteristic % (95% Cl) p value** % (95% Cl) p value** % (95% Cl) p value** 
Total 8.2 (7.2-9.4) NA 8.2 (7.4-9.1) NA 10.8 (9.9-11.7) NA 
Sex 

Female 9,3 (8.0-10.8) 0.01 12.6 (11.2-14.2) <0.01 16.6 (15.1-18.2) <0.01 
Male 7.0tt (5.8-8.4) NA 3.8tt (3.1-4.7) NA 5.2tt (4.4-6.1) NA 
Race/Ethnicity 

White, non-Hispanic 7.5 (6.4-8.7) 0.43 8.1 (6.9-9.6) 0.11 10.2 (9.1-11.4) 0.23 
Black, non-Hispanic 8.2 (6.1-10.8) NA 6.2 (4.5-8.6) NA 10.3 (8.0-13.1) NA 
Hispanic 8.9 (7.4-10.8) NA 8.7 (6.9-10.8) NA 12.2 (10.6-14.0) NA 
Sexual identity 

Heterosexual 7.2 (6.2-8.3) 0.01 6.7 (5.9-7.5) <0.01 9.0 (8.2-9.9) <0.01 
Lesbian, gay, or bisexual 13.188 (10.5-16.1) NA 16.488 (12.7-20.9) NA 21.588 (18,2-25.2) NA 
Not sure 16.955 (11.1-24.9) NA 15.088 (9.5-23.0) NA 16.258 (11.7-22.0) NA 
Characteristic Bullied on school property Electronically bullied — — 
Total 19.5 (18.2-20.9) NA 15.7 (14.6-16.9) NA — — 
Sex 

Female 23.6 (21.8-25.5) <0.01 20.4 (18.9-22.0) <0.01 — — 
Male 15.455 (14.0-16.9) NA 10.9tt (9.6-12.4) NA — — 
Race/Ethnicity 

White, non-Hispanic 23.1 (21.4-24.8) <0.01 18.6 (17.1-20.2) <0.01 — — 
Black, non-Hispanic 15.111 (13.1-17.4) NA 8.611 (7.4-10.0) NA — — 
Hispanic 14.811 (12.8-17.1) NA 12,7*** (11.1-14.5) NA — — 
Sexual identity 

Heterosexual 17.1 (15.7-18.7) <0.01 14.1 (12.9-15.4) <0.01 — — 
Lesbian, gay, or bisexual 32.055 (29.5-34.6) NA 26.655 (23.3-30.2) NA — — 
Not sure 26.988 (22.2-32.2) NA 19.455:ttt (15.5-24.0) NA — — 


Abbreviations: Cl = confidence interval; NA = not applicable; YRBS = Youth Risk Behavior Survey. 

* During the 12 months before the survey. 

t Being physically hurt on purpose (counting such things as being hit, slammed into something, or injured with an object or weapon) by someone they were dating 
or going out with, 21 time, among the 66.1% (n = 8,703) of students nationwide who dated or went out with someone during the 12 months before the survey. 

$ Being forced to do “sexual things” (counting such things as kissing, touching, or being physically forced to have sexual intercourse) they did not want to do by 
someone they were dating or going out with, =1 time, among the 66.2% (n = 6,847) of students nationwide who dated or went out with someone during the 
12 months before the survey. Of 13,677 students, this variable was missing for 3,324, mostly attributed to the use of different versions of the YRBS questionnaire 
that did not include the sexual violence questions in certain selected schools. This resulted in complete data for 10,353 students, of which 66.2% (6,847) reported 
dating in the 12 months before the survey. 

‘ Being forced to do “sexual things” (counting such things as kissing, touching, or being physically forced to have sexual intercourse) they did not want to do by 
anyone, >21 time, during the 12 months before the survey. Data were missing for 3,439 students for this variable, mostly attributed to the use of different versions 
of the YRBS questionnaire that did not include the sexual violence questions in certain selected schools. 

** Chi-square test (p<0.05). 
tt Significantly different from female students, based on t-test (p<0.05). 
SS Significantly different from heterosexual students, based on t-test (p<0.05). 
11 Significantly different from white students, based on t-test (p<0.05). 
*** Significantly different from black students, based on t-test (p<0.05). 
ttt Significantly different from lesbian, gay, or bisexual students, based on t-test (p<0.05). 


the highest levels of frequency at 1 time and >4 times, whereas 
for both sexual dating violence victimization and sexual violence 
victimization by anyone, the most common frequency was 1 time, 
with a decreasing prevalence as the frequency increased. 

The frequency of physical and sexual dating violence varied 
significantly by sex (Table 3). Specifically, the prevalence of 
physical dating violence was significantly greater at higher 
frequency levels (24 times) among male students compared 
with female students (41.6% versus 21.6%, respectively). 
This frequency distribution pattern was similar for sexual 
dating violence. The prevalence at the higher end of frequency 
for sexual dating violence was significantly greater for male 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


students compared with female students (41.0% versus 20.8%, 
respectively). Higher frequency (24 times) was also reported 
for sexual violence by anyone for male students compared 
with female students (33.9% versus 18.6%, respectively). No 
significant differences existed by race/ethnicity in frequency 
of physical and sexual dating violence or sexual violence by 
anyone. These analyses could not include sexual identity 
because of limited data (i.e., group counts <30). 

Overall, 12.2% of students experienced any type of dating 
violence victimization, and 3.0% experienced both types 
(Table 4). Both dating violence composite measures varied 
substantially by sex and sexual identity but not by race/ethnicity. 


MMWR / August 21, 2020 / Vol.69 / No.1 31 


Supplement 


The prevalence of the dating violence composite variables was 
significantly greater for female students compared with male 
students (16.4% versus 8.2% for any dating violence type; 
3.8% versus 2.1% for both dating violence types). Students 
who did not identify as heterosexual had substantially greater 
prevalence of both dating violence composites. For any type of 


FIGURE. Percentage of high school students who experienced violence, 
by type of victimization (physical dating violence, sexual dating 
violence, or sexual violence by anyone) and by number of times during 
the previous year — Youth Risk Behavior Survey, United States, 2019 


100 


O 1time 


Bi 2-3 times 
Hi >4 times 






D eal 
jo) oO 


Percentage 
w 


Sexual violence 
by anyone 
(n = 1,159) 


Physical dating 
violence 
(n = 799) 


Sexual dating 
violence 
(n = 558) 


Type of victimization 


dating violence, the prevalence was 22.3% for LGB students 
and 18.7% for students who were not sure of their sexual 
identity versus 10.5% for heterosexual students. For both types 
of dating violence, the prevalence was 5.8% for LGB students 
and 9.4% for students not sure of their sexual identity versus 
2.4% for heterosexual students. 

The prevalence of experiencing any type of bullying 
victimization was 24.8% (Table 4), and prevalence varied 
significantly by sex, race/ethnicity, and sexual identity. The 
prevalence of experiencing any bullying victimization was 
significantly greater for female students compared with male 
students (30.2% versus 19.2%, respectively) and significantly 
greater for white (28.8%) compared with black (18.0%) or 
Hispanic (19.2%) students. Both LGB students (39.5%) 
and students not sure of their sexual identity (32.7%) had 
significantly higher prevalence of any bullying compared with 
heterosexual students (22.2%), with LGB students reporting 
greater prevalence than students not sure of their sexual identity. 


Discussion 


This report describes the 2019 prevalence and frequency 
of different forms of interpersonal violence victimization 
experienced by U.S. high school students. Similar to 


TABLE 3. Frequency of types of violence victimization,* by demographic characteristics among high school students reporting experiencing 
specific types of violence — Youth Risk Behavior Survey, United States, 2019 


Race/Ethnicity 


Sex 
Male Female 
Type of violence victimization % (95% Cl) % (95% Cl) 
Experienced physical dating violence’ NA NA 


1 time 

2 or 3 times 

24 times 

Experienced sexual dating violence! 
1 time 

2 or 3 times 

24 times 

Experienced sexual violence by anyone** 
1 time 

2 or 3 times 

24 times 


38.0 (32.2-44.2) 

20.4 (14.2-28.4) 

41.6 (34.6-48.9) 
NA 

33.3 (23.8-44.4) 

25.7 (16.8-37.2) 

41.0 (28.0-55.3) 
NA 

36.6 (28.7-45.4) 

29.5 (21.8-38.6) 

33.9 (25.3-43.8) 


51.7 (44.2-59.2) 

26.7 (21.6-32.5) 

21.6 (16.9-27.1) 
NA 

44.0 (36.5-51.8) 

35.2 (28.4-42.6) 

20.8 (15.3-27.6) 
NA 

47.3 (42.8-52.0) 

34.1 (29.9-38.5) 

18.6 (15.2-22.5) 





Abbreviations: Cl = confidence interval; NA = not applicable; YRBS = Youth Risk Behavior Survey. 


White, non- 
Hispanic Black, non-Hispanic Hispanic 
p valuet % (95% Cl) % (95% Cl) % (95% Cl)  pvaluet 
<0.01 NA NA NA 0.21 
NA 47.9 (39.7-56.2) 47.5 (37.6-57.7) 40.9 (31.3-51.3) NA 
NA 25.5 (18.7-33.8) 16.7 (10.3-25.9) 27.0 (19.5-36.0) NA 
NA 26.6 (20.1-34.3) 35.8 (25.0-48.2) 32.1 (24.9-40.3) NA 
0.05 A NA NA 0.39 
NA 42.2 (33.7-51.2) 29.0 (15.5-47.6) 45.0 (33.3-57.3) NA 
NA 32.3 (25.8-39.4) 38.6 (23.8-56.0) 33.3 (22.3-46.5) NA 
NA 25.5 (18.8-33.7) 32.4 (15.4-55.7) 21.6 (14.4-31.2) NA 
0.006 A NA NA 0.36 
NA 47.6 (41.2-54.1) 39.7 (30.0-50.2) 44.0 (36.6-51.6) NA 
NA 31.2 (26.3-36.6) 34.9 (27.1-43.7) 34.5 (28.1-41.6) NA 
NA 21.2 (16.1-27.4) 25.4 (17.1-36.0) 21.5 (15.7-28.7) NA 


* During the 12 months before the survey. 

t Chi-square test (p<0.05). 

$ Being physically hurt on purpose (counting such things as being hit, slammed into something, or injured with an object or weapon) by someone they were dating 
or going out with, 21 time, among the 66.1% (n = 8,703) of students nationwide who dated or went out with someone during the 12 months before the survey. 

1 Being forced to do “sexual things” (counting such things as kissing, touching, or being physically forced to have sexual intercourse) they did not want to do by 
someone they were dating or going out with, =>1time, among the 66.2% (n = 6,847) of students nationwide who dated or went out with someone during the 
12 months before the survey. Of 13,677 students, this variable was missing for 3,324, mostly attributed to the use of different versions of the YRBS questionnaire 
that did not include the sexual violence questions in certain selected schools. This resulted in complete data for 10,353 students, of which 66.2% (6,847) reported 
dating in the 12 months before the survey. 

** Being forced to do “sexual things” (counting such things as kissing, touching, or being physically forced to have sexual intercourse) they did not want to do by 

anyone during the 12 months before the survey. These data were missing for 3,439 students for this variable, mostly attributed to the use of different versions of 
the YRBS questionnaire that did not include the sexual violence questions in certain selected schools. 


32 MMWR / August 21, 2020 / Vol.69 / No.1 US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 4. Percentage of high school students who experienced any dating violence or both physical and sexual dating violence* and any form 
of bullying victimization,t by demographic characteristics — Youth Risk Behavior Survey, United States, 2019 


Dating violence composite variables 


Experienced any dating violence’ 


Characteristic % (95% Cl) p valuett 
Total 12.2 (11.3-13.3) NA 
Sex 

Female 16.4 (14.7-18.2) <0.01 
Male 8.255 (7.1-9.4) NA 
Race/Ethnicity 

White, non-Hispanic 12.1 (10.8-13.5) 0.42 
Black, non-Hispanic 10.6 (7.9-14.1) NA 
Hispanic 12.7 (11.1-14.6) NA 
Sexual identity 

Heterosexual 10.5 (9.5-11.6) <0.01 
Lesbian, gay, or bisexual 22.3*** (17.9-27.5) NA 
Not sure 18.7*** (13.2-26.0) NA 


Abbreviations: Cl = confidence interval; NA = not applicable. 


Bullying victimization composite 


Experienced both physical and sexual 


dating violence! Experienced any bullying** 


% (95% Cl) p valuett % (95% Cl) p valuett 
3.0 (2.5-3.7) NA 24.8 (23.4-26.3) NA 
3.8 (3.0-5.0) 0.006 30.2 (28.4-32.1) <0.01 
2.188 (1.6-2.9) NA 19.255 (17.6-20.9) NA 
2.8 (2.2-3.5) 0.51 28.8 (26.9-30.7) <0.01 
3.0 (1.7-5.2) NA 18.011 (15.7-20.6) NA 
3.3 (2.1-5.1) NA 19.211 (17.4-21.1) NA 
2.4 (2.0-2.9) 0.007 22.2 (20.6-23.8) <0.01 
5.8*** (3.9-8.4) NA 39.5*** (36.6-42.5) NA 
9.4*** (5.0-16.9) NA 32.7*** ttt (27,6-38.3) NA 


* During the 12 months before the survey, among students who dated or went out with someone during the 12 months before the survey. 


t During the 12 months before the survey. 


$ Combined any “yes” responses to physical dating violence and sexual dating violence. Because of the manner in which this variable was calculated, missing values 
in both the physical dating violence and sexual dating violence measures resulted in 3,355 missing values in the “experienced any dating violence” composite measure. 
‘Combined where responses to both physical dating violence and sexual dating violence were “yes.” Because of the manner in which this variable was calculated, 
the missing values in both the physical dating violence and sexual dating violence measures resulted in 3,355 missing observations in the “experienced both 


physical and sexual dating violence” composite measure. 
** Combined any “yes” responses to bullied at school and electronic bullying. 
tt Chi-square test (p<0.05). 
$$ Significantly different from female students, based on t-test (p<0.05). 


11 Significantly different from white, non-Hispanic students, based on t-test (p<0.05). 


*** Significantly different from heterosexual students, based on t-test (p<0.05). 


tt Significantly different from lesbian, gay, or bisexual students, based on t-test (p<0.05). 


findings from previous YRBSs (https://www.cdc.gov/ 
violenceprevention/ pdf/20 12FindingsonSVinYouth-508.pdf), 
physical dating violence, sexual dating violence, sexual violence 
by anyone, bullying on school property, and electronic bullying 
victimization are adverse childhood experiences (ACEs) that are 
occurring at high rates. Examining their prevalence individually 
and in combination by key demographic characteristics 
provides an overall observation and contextual understanding 
of interpersonal violence experienced by U.S. high school 
students and helps identify disparities in health and safety 
among U.S. youths, which can guide prevention efforts. 

All five types of victimization, including any or both forms of 
dating violence and any form of bullying, were more common 
among female and sexual minority students, highlighting their 
more frequent victimization. These findings are consistent 
with previous studies that reported disparities in interpersonal 
violence victimization, particularly dating violence and sexual 
violence, by sex and sexual identity (6,7). Although findings 
did not reveal substantially greater prevalence for racial/ethnic 
minority youths for the forms of violence examined, research 
has consistently shown that racial/ethnic minority youths are 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


at greater risk for homicides and other community violence 
victimization (https://www.cdc.gov/violenceprevention/ 
pub/technical-packages.html). Disparities in health and risk 
for violence have been linked to sexism, homophobia, and 
structural disadvantage (10). 

Half of students who reported sexual violence victimization 
by anyone did not report sexual violence by a dating partner, 
indicating that students who experience sexual violence are often 
victimized by someone other than a dating partner. This finding 
is consistent with previous research (3) documenting that sexual 
violence happening in school during adolescence is frequently 
perpetrated by peers and not necessarily by dating partners. 
Indeed, perpetrators of sexual violence during youth can be 
acquaintances, family members, persons in a position of authority, 
and strangers, in addition to dating partners (https://www.cdc. 
gov/violenceprevention/pdf/2012FindingsonSVinYouth-508. 
pdf). This indicates that efforts might need to be focused on 
preventing sexual violence both inside and outside the context 
of dating relationships to be most helpful. 

Males who experienced dating violence or sexual violence 
reported high frequencies of victimization (24 times during 


MMWR / August 21, 2020 / Vol.69 / No.1 33 


Supplement 


the previous year) substantially more often than did females. 
That is, although male students do not report higher prevalence 
of victimization than do female students, when they do 
report it, they report experiencing it at a higher frequency. 
Previous research has documented that, among youths at 
high risk (i.e., previously exposed to violence in the home or 
community), adolescent males reported higher frequency of 
victimization than did females for sexual dating violence (11). 
However, male adolescents might also be more likely to disclose 
dating violence and sexual violence when the victimization has 
happened more than once. 

In this study, bullying victimization was the only type 
of violence victimization examined for which racial/ethnic 
differences existed, with substantially higher prevalence 
occurring among white students compared with black or 
Hispanic students. This result for bullying is supported in 
part by previous research (/2). In addition, Hispanic students 
reported substantially higher prevalence of electronic bullying 
victimization compared with black students. Other research 
has indicated that black students might underreport bullying 
victimization when presented with a definition-based measure 
of bullying that includes a form of the word “bully,” as is used 
in YRBS, as opposed to behaviorally specific measures that 
describe the victimization behaviors but do not use the word 
“bully” (13). The measurement of bullying in this study might 
have differentially affected reporting across racial/ethnic groups. 

Overall, these findings highlight the importance of early 
engagement in effective, evidence-based efforts for preventing 
violence victimization and perpetration before they begin or 
stopping them from continuing. Findings from this study 
also demonstrate substantial differences in exposure to these 
types of violence by sex, race/ethnicity, and sexual identity, 
highlighting the need for prevention efforts that address the 
unique needs of these groups. To help communities focus 
their prevention efforts on what works and to address risk 
and protective factors for violence and other ACEs across the 
social ecology, CDC developed a series of technical packages 
that identify key violence prevention strategies and approaches 
on the basis of the best available research evidence. (CDC's 
technical packages for violence prevention are available at 
https://www.cdc.gov/violenceprevention/pub/technical- 
packages.html.) This series includes packages focused on sexual 
violence, intimate partner violence (including dating violence), 
and youth violence (including bullying). Preventing Adverse 
Childhood Experiences (ACEs): Leveraging the Best Available 
Evidence compiles evidence focused on ACEs from across the 
technical packages (https://www.cdc.gov/violenceprevention/ 


pdf/preventingACES. pdf). 


34 MMWR / August 21, 2020 / Vol.69 / No.1 


Multiple evidence-based interpersonal violence prevention 
approaches are directly related to the findings in this study. 
For example, social-emotional learning programs that 
support development of skills for communication, emotion 
regulation, empathy, and respect and that target risk factors for 
interpersonal violence (e.g., impulsivity or drug use) have been 
reported to decrease adolescent sexual violence perpetration 
and homophobic name-calling, with indirect effects on peer 
bullying, cyberbullying, and sexual harassment perpetration 
when mediated by delinquency (14,15). By addressing shared 
risk and protective factors across types of violence, social- 
emotional learning programs can build the skills youths 
need for engaging in healthy relationships with family, peers, 
dating partners, and others, thus preventing multiple forms of 
adolescent interpersonal violence and long-term consequences 
into adulthood. In addition, bystander programs teach youths 
how to safely act when they see behaviors that increase risk 
for violence and change social norms within their peer groups. 
Although originally conceptualized as a means of challenging 
heterosexist attitudes to prevent sexual and dating violence 
(16), such programs might also prevent other forms of 
adolescent violence, including bullying and violence targeting 
sexual, gender, and racial minorities by focusing the training 
on recognizing and challenging these specific harmful attitudes 
and behaviors (17,18). 

Modifying the social and physical environment in schools 
and neighborhoods might improve safety and reduce risk 
for violence for more of the population than individual- or 
relationship-level approaches alone. For example, one school- 
based prevention approach that includes a building-level 
intervention (e.g., addressing physical areas in the school 
identified by students as less safe) has been reported to reduce 
sexual violence victimization and perpetration by peers 
and dating partners (19). In addition, the development of 
safe and supportive environments in schools that promote 
protective factors (e.g., school connectedness and professional 
development regarding lesbian, gay, bisexual, and transgender 
[LGBT] youths) can help create accepting school environments 
for LGBT youths and reduce the risk for bullying and other 
violence (20). Results from this report indicate that LGB 
youths, specifically, are at a disproportionately higher risk 
for interpersonal violence victimization compared with 
heterosexual youths. As of 2019, gender identity has not been 
assessed by the YRBS nationwide. However, during 2017, 
gender identity was assessed in YRBSs conducted in 10 states 
and nine large urban school districts; these data show that 
transgender students consistently report greater prevalence 
of violence victimization than their cisgender peers (2/). 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


Promotion of gay-straight alliances and support of LGBT 
students can help provide these youths with an accepting school 
environment, which might also reduce the risk for school-based 
violence against these youths (22). (Information about CDC’s 
current school health programs is available at https://www. 
cdc.gov/healthyyouth/fundedprograms/1807/resources.htm.) 

CDC is engaged in ongoing research and programmatic 
activities for expanding the research evidence and adding to 
the knowledge base of effective primary prevention programs, 
policies, and practices available to communities for preventing 
interpersonal violence among youths. For example, CDC’s 
Dating Matters: Strategies to Promote Healthy Teen Relationships 
is a comprehensive adolescent dating violence prevention 
model. Dating Matters includes multiple integrated prevention 
strategies that address risk factors for youths and their families, 
schools, and neighborhoods with demonstrated effects on 
adolescent dating violence, bullying, and peer violence in 
middle school. (Additional information about Dating Matters 
is available at https://www.cdc.gov/violenceprevention/ 
intimatepartnerviolence/datingmatters/index.html.) 

In addition, since 2001, CDC has provided funding for 
primary prevention of sexual violence through the Rape 
Prevention and Education Program to state health departments 
in all 50 states, the District of Columbia, and four U.S. 
territories. Funded organizations implement initiatives that 
address youths in their communities, including community- 
and societal-level approaches (e.g., improving education and 
leadership opportunities for girls). (Additional information 
about the Rape Prevention and Education Program is available 
at https://www.cdc.gov/violenceprevention/sexualviolence/rpe/ 
index.html.) CDC also sponsors youth violence prevention 
research through its National Centers of Excellence in Youth 
Violence Prevention. Their goal is to build the scientific 
infrastructure and community partnerships necessary for 
stimulating new youth violence prevention research and 
practice across the country, including a focus on the impact 
of structural factors (e.g., housing, education, or systemic 
discrimination) that limit access to positive social determinants 
of health. 

Prevention of interpersonal violence among adolescents 
might be most successful when a comprehensive strategy is 
used that addresses these ACEs at multiple levels of the social 
ecology simultaneously and recognizes that these different 
forms of victimization can be co-occurring (/). The findings 
reported here also highlight the importance of acknowledging 
the disproportionate prevalence of these forms of victimization 
on certain youths (i.e., females and sexual minorities) and 
addressing these disparities in prevention efforts. 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Limitations 


General limitations for the YRBS are available in the overview 
report of this supplement (9).The findings in this report are 
subject to at least five additional limitations. First, substantial 
overlap likely existed in the measures that examined experiences 
of sexual violence victimization (i.e., sexual dating violence 
victimization and sexual violence victimization by anyone), 
and among the bullying victimization measures (i.e., electronic 
bullying and bullied at school). For these reasons, composites 
for the sexual violence measures and a “both” composite for 
bullying (i.e., experienced both electronic bullying and bullying 
at school) were not created. Second, because of the breadth of 
topics included in the YRBS, violence victimization subtype 
measures included in the YRBS tend to be broad in nature and, 
in this study, were assessed by single items. More specific and 
detailed measures of violence victimization would allow for a 
comprehensive analysis of the prevalence and overlap between 
different forms of interpersonal violence victimization. Third, 
the YRBS bullying items include the word “bullied,” which 
might have decreased disclosure (13). Fourth, the interpersonal 
violence victimization types that could be included in this 
study (i.e., dating violence, sexual violence, and bullying), as 
a whole, do not reflect the breadth of interpersonal violence 
victimization experienced by youths (i.e., other forms of youth 
violence experienced in the community) and might partially 
explain why few racial/ethnic differences were found. Finally, 
the sexual violence measures (and composite measures that 
were created with the sexual violence measures) in this report 
had a relatively large amount of missing data (approximately 
3,400 observations) in 2019. Most of this missing data can 
be attributed to the use of different versions of the YRBS 
questionnaire that did not include the sexual violence questions 
in certain selected schools. Consequently, not all students in 
the national sample were given the opportunity to answer the 
sexual violence questions and were counted as missing. When 
constructing the composite measures for any dating violence, 
and both physical and sexual dating violence victimization, the 
analytic sample was restricted to students who had complete 
data for both physical and sexual dating violence victimization, 
which reduced the potential for biased estimates. 


Future Directions 


To increase understanding of the differential experiences 
of adolescent interpersonal violence victimization, future 
research that focuses in more detail on the demographic 
groups highlighted in this study can be beneficial. For 
example, on the basis of these findings, additional research 


MMWR / August 21, 2020 / Vol.69 / No.1 35 


Supplement 


to better understand the characteristics and consequences 
of these forms of interpersonal violence on sexual minority 
youths is warranted. Research exploring sex differences in the 
frequency of victimization across additional types of violence 
can add to the findings reported here. Future studies that 
include more detailed measures of dating violence, sexual 
violence, and bullying for capturing and isolating understudied 
subtypes of these forms of violence (e.g., psychological dating 
violence, nonconsensual sexting, or relational bullying) would 
increase knowledge of the full prevalence of these forms of 
violence among youths. Finally, studies that examine the 
co-occurrence and cumulative impact of different forms of 
violence victimization during adolescence and into adulthood 
can guide more comprehensive prevention efforts. 


Conclusion 


Interpersonal violence victimization experiences of high 
school students are a form of ACEs and represent a substantial 
public health problem in the United States. Multiple forms 
of interpersonal violence, including dating violence, sexual 
violence, and bullying, negatively affect youths and can 
continue to have damaging effects throughout a person’s 
life. The findings in this report are consistent with those in 
previous studies about disparities in interpersonal violence 
victimization by demographic characteristics; the report also 
provides additional insight about the specific groups of students 
who are at highest risk for particular types of interpersonal 
violence and who might benefit most from prevention 
efforts. In addition, the findings increase understanding of 
the contextual factors associated with interpersonal violence 
victimization (e.g., frequency, location, and co-occurrence of 
subtypes) and can guide how violence prevention professionals 
select and implement prevention approaches for addressing 
dating violence, sexual violence, and bullying. Prevention 
approaches at the individual, relationship, and school or 
community levels (e.g., those that seek to increase youths skills 
in preventing violence, change social norms related to violence, 
and modify the physical and social environment in schools and 
communities to increase protection against violence) are crucial 
for building a comprehensive strategy to reduce interpersonal 
violence victimization among youths. 


Conflicts of Interest 


All authors have completed and submitted the International 
Committee of Medical Journal Editors form for disclosure of 
potential conflicts of interest. No potential conflicts of interest 
were disclosed. 


36 MMWR / August 21, 2020 / Vol.69 / No.1 


10. 


11. 


T2; 


13: 


14. 


15. 


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MMWR / August 21, 2020 / Vol.69 / No.1 37 


Supplement 


Prescription Opioid Misuse and Use of Alcohol and Other Substances 
Among High School Students — Youth Risk Behavior Survey, 
United States, 2019 


Christopher M. Jones, PharmD, DrPH}; Heather B. Clayton, PhD2; Nicholas P. Deputy, PhD23; Douglas R. Roehler, PhD‘; Jean Y. Ko, PhD>; 
Marissa B. Esser, PhD®; Kathryn A. Brookmeyer, PhD’, Marci Feldman Hertz, MS? 


! Office of the Director, National Center for Injury Prevention and Control, CDC; ?Division of Adolescent and School Health, National Center for HIV/AIDS, 
Viral Hepatitis, STD, and TB Prevention, CDC; 3Epidemic Intelligence Service, CDC; 4Division of Overdose Prevention, National Center for Injury Prevention 
and Control, CDC; *Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, CDC; Division of Population 
Health, National Center for Chronic Disease Prevention and Health Promotion, CDC; 7Division of STD Prevention, National Center for HIV/AIDS, Viral 
Hepatitis, STD, and TB Prevention, CDC 


Abstract 


Adolescence is an important period of risk for substance use initiation and substance use—related adverse outcomes. To examine 
youth substance use trends and patterns, CDC analyzed data from the 2009-2019 Youth Risk Behavior Survey. This report 
presents estimated prevalence of current (i.e., previous 30-days) marijuana use, prescription opioid misuse, alcohol use, and binge 
drinking and lifetime prevalence of marijuana, synthetic marijuana, cocaine, methamphetamine, heroin, injection drug use, and 
prescription opioid misuse among U.S. high school students. Logistic regression and Joinpoint analyses were used to assess 2009- 
2019 trends. Prevalence of current and lifetime substance use by demographics, frequency of use, and prevalence of co-occurrence 
of selected substances among students reporting current prescription opioid misuse are estimated using 2019 data. Multivariable 
logistic regression analysis was used to determine demographic and substance use correlates of current prescription opioid misuse. 
Current alcohol, lifetime cocaine, methamphetamine, heroin, and injection drug use decreased during 2009-2019. Lifetime use 
of synthetic marijuana (also called synthetic cannabinoids) decreased during 2015-2019. Lifetime marijuana use increased during 
2009-2013 and then decreased during 2013-2019. In 2019, 29.2% reported current alcohol use, 21.7% current marijuana use, 
13.7% current binge drinking, and 7.2% current prescription opioid misuse. Substance use varied by sex, race/ethnicity, grade, 
and sexual minority status (lesbian, gay, or bisexual). Use of other substances, particularly current use of alcohol (59.4%) and 
marijuana (43.5%), was common among students currently misusing prescription opioids. Findings highlight opportunities for 
expanding evidence-based prevention policies, programs, and practices that aim to reduce risk factors and strengthen protective 
factors related to youth substance use, in conjunction with ongoing initiatives for combating the opioid crisis. 


Introduction vulnerability to reinforcing and rewarding effects of substances 
(2,3). Preventing or delaying substance use initiation among 
youths can reduce later risk for substance use and substance 
use disorders (1,3,4). Beyond the individual negative effects 
of substance use during youth and into adulthood, substance 
use among youths also increases the likelihood for negative 
consequences that affect peers, families, and communities 
(5). Youth substance use is associated with increased risk for 
delinquency, academic underachievement, teenage pregnancy, 
sexually transmitted diseases, perpetrating or experiencing 
violence, injuries, and mental health problems (1,3—6). 

As the United States confronts its decades-long opioid 
overdose epidemic (1,2), preventing opioid misuse among 
youth is a public health imperative. Previous research has 
documented that misuse of prescription opioids among 
youths is associated with multiple adverse health outcomes 
CDC. Telephone: 404-498-0756; E-mail: fjr0@cde.gov. and risk behaviors, including use of alcohol and other illicit 
drugs, injection drug use, suicidal ideation, youth violence, 


Substance use and associated adverse outcomes contribute 
to substantial morbidity, mortality, and economic costs to 
society each year in the United States (7). Data from national 
surveys indicate the majority of adolescents will engage in 
some form of substance use before they graduate from high 
school (https://www.samhsa.gov/data/report/2018-nsduh- 
detailed-tables). During adolescence, areas of the brain 
associated with emotional responses and reward systems 
develop before those associated with executive functioning, 
judgement, and decision making (2). This uneven maturation 
results in increased susceptibility for engaging in risky and 
impulsive behaviors, including substance use, and increases 


Corresponding author: Christopher M. Jones, PharmD, DrPH, Office 


of the Director, National Center for Injury Prevention and Control, 





38 MMWR / August 21,2020 / Vol.69 / No.1 US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


delinquency, having four or more lifetime sexual partners, not 
using a condom at last sexual intercourse, increased risk for 
acquisition of human immunodeficiency virus infection and 
sexually transmitted diseases (6), and increasing overdoses (7). 
Studies also have demonstrated that prescription opioid misuse 
among youths is strongly linked with subsequent initiation 
and use of heroin and increased risk for injecting prescription 
opioids and developing an opioid use disorder (8-10). 
Preventing substance use among youths is necessary because 
of the health and social effects of youth substance use. To 
inform substance use prevention initiatives and to improve 
understanding of youth substance use patterns, including 
misuse of prescription opioids and other substances, this 
analysis 1) examines trends and patterns in substance use 
among high school students overall and by demographic 
characteristics, 2) characterizes the frequency of use of 
specific substances among high school students, 3) explores 
co-occurring substance use among high school students who 
misuse prescription opioids, and 4) examines the demographic 
and substance use correlates of prescription opioid misuse 
among high school students. Findings from this analysis can 
help inform efforts by public health practitioners, clinicians, 
and the substance use prevention community to expand 
the implementation of evidence-based prevention policies, 
programs, and practices that aim to reduce risk factors and 
strengthen protective factors related to youth substance use. 


Methods 


Data Source 


This report includes data from CDC’s 2009-2019 Youth 
Risk Behavior Survey (YRBS), a cross-sectional, school-based 
survey conducted biennially since 1991. Each survey year, 
CDC collects data from a nationally representative sample 
of public- and private-school students in grades 9-12 in 
the 50 U.S. states and the District of Columbia. Additional 
information about YRBS sampling, data collection, response 
rates, and processing is available in the overview report of this 
supplement (11). The prevalence estimates for all substance 
use questions for the overall study population and by sex, 
race/ethnicity, grade, and sexual orientation are available at 
https://nccd.cdc.gov/youthonline/App/Default.aspx. The 
full YRBS questionnaire is available at https://www.cdc.gov/ 
healthyyouth/data/yrbs/pdf/2019/2019_YRBS-National-HS- 
Questionnaire.pdf. 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Measures 


This report addresses four current (i.e., previous 30 days 
before the survey) and seven lifetime substance use behaviors. 
The four current substance use behaviors include 1) marijuana 
use (ascertained by the question, “During the past 30 days, 
how many times did you use marijuana?”), 2) alcohol use 
(“During the past 30 days, on how many days did you have 
at least one drink of alcohol?”), 3) binge drinking (“During 
the past 30 days, on how many days did you have 4 or more 
drinks of alcohol in a row, that is, within a couple of hours 
[if you are a female] or 5 or more drinks of alcohol in a row, 
that is, within a couple of hours [if you are a male]?”), and 
4) prescription opioid misuse (“During the past 30 days, how 
many times have you taken prescription pain medicine without 
a doctor's prescription or differently than how a doctor told you 
to use it?”). The current prescription opioid misuse question 
is new for the 2019 YRBS, providing opportunities to explore 
substance use patterns and individual characteristics associated 
with this variable for the first time. 

The seven lifetime substance use behaviors include 
1) marijuana use (“During your life, how many times have you 
used marijuana?”), 2) synthetic marijuana (also called synthetic 
cannabinoids) use (“During your life, how many times have you 
used synthetic marijuana?”), 3) cocaine use (“During your life, 
how many times have you used any form of cocaine, including 
powder, crack, or freebase?”), 4) methamphetamine use (“During 
your life, how many times have you used methamphetamines 
[also called speed, crystal meth, crank, ice, or meth]?”), 5) heroin 
use (“During your life, how many times have you used heroin 
[also called smack, junk, or China White]?”), 6) prescription 
opioid misuse (“During your life, how many times have you 
taken prescription pain medicine without a doctor’s prescription 
or differently than how a doctor told you to use it?”), and 
7) injection drug use (“During your life, how many times have 
you used a needle to inject any illegal drug into your body?”). 

Substance use behaviors were dichotomized to indicate 
current or lifetime use versus no use. With three exceptions, 
frequency of use for each substance was categorized as 
1-2 times, 3-9 times, 10-39 times, or 240 times. Frequency of 
current alcohol use and current binge drinking were categorized 
as 1-2 days, 3-9 days, 10-19 days, or >20 days. For injection 
drug use, frequency of use was categorized as 1 time or 22 times. 

Four demographic characteristics were included in the analysis: 
sex (male or female), race/ethnicity (non-Hispanic white [white], 
non-Hispanic black [black], Hispanic, or other), grade (9/10 
or 11/12), and sexual identity (heterosexual; lesbian, gay, or 
bisexual; or not sure). Students reporting “other” race/ethnicity 
are included in all analyses; however, data are not presented for 
that group because of limited interpretability. 


MMWR / August 21, 2020 / Vol.69 / No.1 39 


Supplement 


Analysis 


First, annual prevalence of each substance use behavior was 
estimated for all years with available data. Second, to identify 
temporal trends, logistic regression analyses were used to 
model linear and quadratic time effects while controlling for 
sex, grade, and racial/ethnic group changes over time; for 
significant quadratic time effects, Joinpoint software was used 
to identify the year the trend changed direction (//). Trends 
were assessed during 2009-2019 for current alcohol, current 
marijuana, lifetime marijuana, lifetime cocaine, lifetime 
methamphetamine, lifetime heroin, and lifetime injection 
drug use. Synthetic marijuana use was first assessed by YRBS in 
2015; therefore, trend analysis for this variable was conducted 
for 2015-2019. Third, to identify 2-year changes in substance 
use behaviors, prevalence estimates from 2017 and 2019 were 
compared by using ¢tests; changes were considered statistically 
different if the p value was <0.05. 

Four additional analyses were conducted by using 2019 
YRBS data only. First, prevalence estimates and associated 
95% confidence intervals (CIs) for each substance use behavior 
were calculated by demographic characteristics. Statistically 
significant pairwise differences between demographic groups 
for each of the substance use behaviors were determined by 
t-tests; differences were considered statistically significant if the 
p value was <0.05. Second, to examine frequency of use among 
students who reported engaging in each substance use behavior, 
prevalence estimates and 95% Cls of students reporting each 
frequency of use category were calculated. Third, prevalence 
estimates of co-occurring use of selected substances among 
students reporting current prescription opioid misuse were 
estimated. Finally, multivariable logistic regression analysis 
was used to determine demographic and substance use 
correlates of current prescription opioid misuse. Because the 
use of one substance is generally strongly associated with use 
of one or more other substances, it is important to account 
for multiple substance use behaviors during the modeling 
process. Therefore, all demographic and substance use variables 
were included in a single model to examine the independent 
effect of each variable on current prescription opioid misuse. 
This modeling strategy is consistent with previous research 
examining substance use behaviors among youths (6). 

To improve model stability during multivariable analyses, 
three composite substance use variables were created. A 
composite variable regarding alcohol consumption was created 
with three levels: 1) no previous 30-day use, 2) previous 
30-day use (current drinking but no binge drinking), and 
3) previous 30-day binge alcohol use. A marijuana composite 
variable also was created with three levels: 1) no lifetime use, 
2) lifetime use but no previous 30-day use, and 3) previous 


40 MMWR / August 21, 2020 / Vol.69 / No.1 


30-day use. A composite lifetime use of cocaine, heroin, 
or methamphetamine variable was created by combining 
answers of “1 or more times” for each of the three constituent 
variables. Because substance use variables are known to be 
highly correlated with each other, the Variance Inflation Factor 
was used to assess multicollinearity. None was observed (i.e., 
no values >10). 

Adjusted prevalence ratios (aPRs) and corresponding 
95% CIs were calculated; estimates were considered 
statistically significant if the 95% CI did not include 1.0. All 
analyses were conducted by using SAS-callable SUDAAN 
(version 11.0.1; RTI International) to account for survey 
weights and the complex sample design of the YRBS. No 
imputation methods were used for data that were missing. 


Results 


Substance use was common among U.S. high school 
students during 2019 and varied by substance, year, and 
demographic groups (Table 1). Among current substance 
use measures, the highest prevalence estimates were for 
alcohol (29.2%) and marijuana use (21.7%). Current binge 
drinking was reported by 13.7% of high school students, 
and 7.2% reported current prescription opioid misuse. 
Among lifetime use measures, marijuana use was reported 
by 36.8% of high school students, followed by misuse of 
prescription opioids (14.3%) and use of synthetic marijuana 
(7.3%), cocaine (3.9%), methamphetamine (2.1%), or heroin 
(1.8%). Lifetime injection drug use was reported by 1.6% of 
high school students. 

Trend data were available for eight of the 11 substance 
use measures included in the analyses. Among these 
measures, current alcohol use, lifetime cocaine, lifetime 
methamphetamine, lifetime heroin, and lifetime injection drug 
use decreased during 2009-2019. Lifetime use of synthetic 
marijuana decreased during 2015-2019. The prevalence 
of lifetime marijuana use increased during 2009-2013 
(36.8%-40.7%) and then decreased during 2013-2019 
(40.7%-36.8%). No statistically significant changes from 2017 
to 2019 were observed for any of the substance use behaviors. 

Compared with females, males had a significantly higher 
prevalence of lifetime use of cocaine (4.9% versus 2.7%), 
methamphetamine (2.7% versus 1.5%), heroin (2.3% versus 
1.0%), and injection drug use (2.1% versus 1.1%) (Table 2). 
Compared with males, females had a significantly higher 
prevalence of current alcohol use (31.9% versus 26.4%), binge 
drinking (14.6% versus 12.7%), current prescription opioid 
misuse (8.3% versus 6.1%), and lifetime prescription opioid 
misuse (16.1% versus 12.4%). Among racial/ethnic groups, 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 1. Prevalence of and trends in prevalence of lifetime and current use of specific substances and use behaviors among high school 
students — Youth Risk Behavior Survey, United States, 2009-2019 


Prevalence Change 
from 
Behavior 2009 2011 2013 2015 2017 2019 Linear change* Quadratic change* 2017 to 2019+ 
Current use 
Marijuana 20.8 23.1 23.4 21.7 19.8 21.7 No change No change No change 
Alcohol 41.8 38.7 34.9 32.8 29.8 29.2 Decreased 2009-2019 No change No change 
Binge drinking — — — — 13.5 13.7 NA‘ NA! No change 
Prescription opioid misuse — — — — — 7.2 NA‘ NA! NA‘ 
Lifetime use 
Marijuana 36.8 39.9 40.7 38.6 35.6 36.8 No change Increased 2009-2013 No change 
Decreased 2013-2019 
Cocaine 6.4 6.8 55 5.2 48 3.9 Decreased 2009-2019 No change No change 
Methamphetamine 4.1 3.8 3.2 3.0 2.5 2.1 Decreased 2009-2019 No change No change 
Heroin 2.5 2.9 2.2 2.1 1.7 1.8 Decreased 2009-2019 No change No change 
Injection drug use 2.1 2.3 1.7 1.8 1.5 1.6 Decreased 2009-2019 No change No change 
Synthetic marijuana — — — 9.2 6.9 7.3 Decreased 2015-2019 NA! No change 
Prescription opioid misuse — — — — 14.0 14.3 NA‘ NA! No change 


Abbreviation: NA = not available. 

* Based on trend analyses by using a logistic regression model controlling for sex, race/ethnicity, and grade (p<0.05). 
t Based on t-test analysis (p<0.05). 

S Previous 30 days before the survey. 

1 Insufficient years of data to assess trends. 


TABLE 2. Prevalence of lifetime and current use of specific substances and use behaviors among high school students, by demographic 
characteristics — Youth Risk Behavior Survey, United States, 2019 





Sex Race/Ethnicity Grade Sexual identity 
White, Black, 
Male Female non-Hispanic non-Hispanic Hispanic 9/10 11/12 Heterosexual LGB Not sure 
(n=6,641) (n=6,885) (n=6,668) (n=2,040) (n=3,038) (n=7,354) (n=6,172) (n=10,853) (n=1,531) (n=591) 
% % % % % % % % % % 
Behavior (95% Cl) (95% Cl) (95% Cl) (95% Cl) (95% Cl) (95% Cl) (95% Cl) (95% Cl) (95% Cl) (95% Cl) 
Current use* 
Marijuana 22.5 20.8 22.1 21.7 22.4 17.1 26.6t 20.9 31.18 19,51 
(20.6-24.5) (18.7-23.1)  (19.9-24.6)  (19.1-24.5) (20.4-24.6)  (15.5-18.8)  (23.6-29.7)  (19.0-23.0)  (27.4-35.1) (14.8-25.3) 
Alcohol 26.4 31.9" 34.2 16.8tt 28.4188 22.8 36.0t 28.8 33.98 25.31 
(24.4-28.6) (29.6-34.3) — (31.7-36.8) (13.5-20.7) — (26.1-30.8)  (20.6-25.2) (33.8-38.3)  (26.8-30.8)  (29.8-38.2) (20.0-31.4) 
Binge drinking 12.7 14.6" 17.3 6.2tt 12.4tt,55 8.9 18.8t 13.4 15.6 13.1 
(11.0-14.6) (13.2-16.2) (15.1-19.7) (4.2-9.2) (11.0-14.0) (7.4-10.7) (17.0-20.8) (12.0-15.0) (12.8-18.8) (9.0-18.8) 
Prescription 6.1 8.3" 5.5 8.7tt 9.8tt 7.0 7.3 6.4 12.08 11.58 
opioid misuse (5.3-7.1) (7.0-9.9) (4.4-6.9) (6.5-11.6) (8.2-11.6) (5.8-8.4) (6.1-8.8) (5.4-7.5) (9.6-14.9) (8.2-15.9) 
Lifetime use 
Marijuana 37.0 36.5 36.8 37.5 39.2 29.2 44.8t 36.0 49.65 27.584 
(34.2-40.0) (34.1-38.9)  (33.9-39.8)  (34.0-41.1)  (36.5-41.9)  (26.7-31.8)  (41.5-48.2)  (33.3-38.7) (45.1-54.1) (22.4-33.3) 
Cocaine 49 2.7" 2.9 4.0 5.6tt 2.8 5.0t 3.3 7.08 7.68 
(4.2-5.8) (2.0-3.7) (2.2-3.7) (2.7-5.9) (4.5-6.9) (2.0-3.7) (4.1-6.1) (2.7-4.0) (4.8-10.1)  (4.3-12.9) 
Methamphetamine 27 15” 1.2 3.8tt 2.717 1.5 2.6t 15 5.08 6.18 
(2.1-3.4) (1.0-2.2) (0.9-1.6) (2.4-6.0) (1.8-4.0) (1.0-2.3) (1.9-3.3) (1.2-1.9) (3.1-7.9)  (3.4-10.8) 
Heroin 23 1.0° 0.9 3.4tt 2.4tt 1.6 1.8 1.2 3.88 6.28 
(1.8-3.1) (0.6-1.8) (0.6-1.2) (2.2-5.3) (1.5-3.9) (1.0-2.5) (1.3-2.5) (0.9-1.6) (2.1-7.0)  (3.4-11.0) 
Injection drug use 2.1 E i 0.8 2.9tt 2.5tt 1.6 1.5 1.1 3.55 5.15 
(1.5-2.9) (0.6-1.9) (0.6-1.2) (1.5-5.5) (1.8-3.5) (1.1-2.3) (1.0-2.4) (0.8-1.6) (2.1-5.7)  (2.5-10.2) 
Synthetic marijuana 7.2 7.4 6.7 5.7 9.8tt,5S 6.2 8.3t 6.7 11.68 10.4 
(6.2-8.4) (6.2-8.7) (5.6-8.0) (4.4-7.4) (8.6-11.3) (5.3-7.3) (7.2-9.7) (5.8-7.7) (9.0-14.7)  (6.9-15.5) 
Prescription 12.4 16.1" 12.7 15.3 16.0 13.6 14.9 12.7 23.98 19.15 
opioid misuse (11.0-14.1) (14.1-18.4) (10.9-14.7) (12.9-18.1) (13.5-18.8) (11.9-15.5) (13.2-16.7) (11.2-14.4) (19.9-28.3) (14.6-24.5) 
Abbreviations: Cl = confidence interval; LGB = lesbian, gay, or bisexual. 
* Previous 30 days before the survey. 
t Significantly different from 9/10 grade students, based on t-test analysis (p<0.05). 
$ Significantly different from heterosexual students, based on t-test analysis (p<0.05). 
1 Significantly different from lesbian, gay, or bisexual students, based on t-test analysis (p<0.05). 
™ Significantly different from male students, based on t-test analysis (p<0.05). 
+ Significantly different from white students, based on t-test analysis (p<0.05). 
88 Significantly different from black students, based on t-test analysis (p<0.05). 
US Department of Health and Human Services/Centers for Disease Control and Prevention MMWR / August 21, 2020 / Vol.69 / No.1 41 


Supplement 


notable differences in prevalence estimates were identified for 
current use of alcohol, binge drinking, current prescription 
opioid misuse, and lifetime use of cocaine, methamphetamine, 
heroin, injection drug use, and synthetic marijuana. However, 
no clear pattern emerged. For example, the prevalence of 
current prescription opioid misuse was significantly lower 
among white students (5.5%) compared with black (8.7%) 
or Hispanic students (9.8%). Conversely, the prevalence of 
current alcohol use was lower among black students (16.8%) 
compared with white (34.2%) or Hispanic students (28.4%). 

Approximately half of the substance use behaviors varied 
substantially by grade, with consistently higher prevalence 
among 11th- and 12th-grade students compared with 9th- and 
10th-grade students for current marijuana use, current alcohol 
use and binge drinking, lifetime marijuana use, lifetime cocaine 
use, lifetime methamphetamine use, and lifetime synthetic 
marijuana. Prevalence of all but one of the substance use 
behaviors (i.e., binge drinking) varied considerably by sexual 
identity. Students who identified as lesbian, gay, or bisexual 
had a higher prevalence of all substance use behaviors, except 
binge drinking, compared with students who identified as 
heterosexual. Similarly, students who identified as not sure of 
their sexual identity also had higher prevalence of approximately 


half of the substance use behaviors compared with heterosexual 
students, including current prescription opioid misuse, lifetime 
cocaine use, lifetime methamphetamine use, lifetime heroin 
use, lifetime injection drug use, and lifetime prescription opioid 
misuse. However, students who identified as not sure of their 
sexual identity had lower prevalence of certain substance use 
behaviors compared with students identifying as lesbian, gay, 
or bisexual, including current marijuana use, current alcohol 
use, and lifetime marijuana use. 

Frequency of use (i.e., number of times used or number 
of days used) varied across specific substance use behaviors 
(Table 3). Among students reporting marijuana use during the 
30 days before the survey (i.e., current use), 18.0% reported 
using it >40 times; 23.5%, 10-39 times; 21.8%, 3—9 times; 
and 36.7%, 1-2 times. For current prescription opioid misuse, 
9.8% reported misuse 240 times; 13.7%, 10-39 times; 23.3%, 
3-9 times, and 53.2%, 1-2 times. Among students reporting 
lifetime use of specific substances, marijuana had the highest 
percentage of students reporting use 240 times (33.6%), 
followed by heroin (32.9%), methamphetamine (27.9%), 
and cocaine (16.1%). Lifetime prescription opioid misuse and 
lifetime synthetic cannabinoid use were the two substances 
with the highest percentages reporting use 1—2 times (48.8% 


TABLE 3. Frequency of lifetime and current use among high school students reporting use of specific substances — Youth Risk Behavior Survey, 


United States, 2019 


Behavior 


Current use* 

Marijuana (n = 2,946) 

Prescription opioid misuse (n = 661) 
Lifetime use 

Marijuana (n = 4,219) 

Prescription opioid misuse (n = 2,000) 
Synthetic marijuana (n = 955) 
Cocaine (n = 557) 

Methamphetamine (n = 351) 

Heroin (n = 316) 


Behavior 


Current use 
Alcohol use (n = 3,669) 
Binge drinking (n = 1,657) 


Behavior 


Lifetime use 
Injection drug use (n = 200) 


Abbreviation: Cl = confidence interval. 


* Previous 30 days before the survey. 


42 MMWR / August 21,2020 / Vol.69 / No.1 


Frequency 
1-2 times 3-9 times 10-39 times 240 times 
% (95% Cl) % (95% Cl) % (95% Cl) % (95% Cl) 
36.7 (33.7-39.8) 21.8 (19.7-24.1) 23.5 (21.4-25.8) 18.0 (15.1-21.3) 
53.2 (47.9-58.5) 23.3 (18.9-28.3) 13.7 (11.0-16.9) 9.8 (6.3-14.8) 
24.6 (22.4-27.1) 20.9 (19.3-22.6) 20.8 (19.2-22.5) 33.6 (30.5-36.9) 
48.8 (45.7-51.9) 24.7 (22.4-27.2) 15.9 (14.0-18.1) 10.6 (8.8-12.7) 
48.8 (44.9-52.6) 20.8 (17.8-24.3) 18.5 (14.9-22.8) 11.9 (9.2-15.3) 
45.0 (38.7-51.5) 20.3 (15.7-25.9) 18.5 (14.4-23.6) 16.1 (11.8-21.7) 
42.9 (34.7-51.5) 15.5 (10.8-21.7) 13.7 (9.0-20.3) 27.9 (19.1-39.0) 
31.7 (24.4-40.1) 18.6 (12.8-26.2) 16.9 (11.8-23.6) 32.9 (21.7-46.3) 
Frequency 
1-2 days 3-9 days 10-19 days 220 days 
% (95% Cl) % (95% Cl) % (95% Cl) % (95% Cl) 
54.8 (52.6-57.0) 36.6 (34.4-38.8) 5.1 (4.1-6.4) 3.5 (2.6-4.7) 
61.2 (56.5-65.7) 31.1 (27.7-34.7) 4.1 (2.7-6.2) 3.6 (2.4-5.3) 
Frequency 
1 time 22 times 


% (95% Cl) % (95% Cl) 


47.8 (35.4-60.4) 52.2 (39.6-64.6) 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


each), followed by cocaine (45.0%), and methamphetamine 
(42.9%). Among students reporting current alcohol use or 
current binge drinking, more than half of students (54.8% 
and 61.2%, respectively) reported those behaviors on 1-2 days. 
Among students who had ever injected drugs (1.2%), 47.8% 
reported injecting drugs 1 time, and 52.2% reported injecting 
drugs 22 times. 

Students reporting current prescription opioid misuse 
commonly indicated use of other substances (Figure). Overall, 
43.5% of students reporting current prescription opioid misuse 
also reported current marijuana use, 59.4% reported current 
alcohol use, and 30.3% reported current binge drinking. 
Lifetime use of other substances among students reporting 
current prescription opioid misuse was 62.9% for marijuana, 
30.3% for synthetic marijuana, 20.5% for cocaine, 15.0% 
for methamphetamine, and 14.0% for heroin. Approximately 
12.4% of students reporting current prescription opioid misuse 
also reported lifetime injection drug use. 

In adjusted analyses, current prescription opioid misuse 
varied by sex, race/ethnicity, and sexual identity (Table 4). 
Specifically, males were significantly less likely to report 
engaging in current prescription opioid misuse (aPR: 0.69; 
95% CI: 0.57—0.84) compared with females (referent group); 
black and Hispanic students were significantly more likely to 
have engaged in prescription opioid misuse (black students, 
aPR: 1.49; 95% CI: 1.05-2.10; Hispanic students, aPR: 1.52; 
95% CI: 1.12-2.05) compared with white students (referent 
group); and students identifying as lesbian, gay, or bisexual 
were more likely to report current prescription opioid misuse 
(aPR: 1.35; 95% CI: 1.02-1.79) compared with students 
identifying as heterosexual (referent group). All substance 
use behaviors included in the model, except for marijuana 
use, were significantly associated with current prescription 
opioid misuse, ranging from aPR = 2.13 (95% CI: 1.59- 
2.86) for lifetime synthetic marijuana use and aPR = 2.13 
(95% CI: 1.58-2.86) for previous 30-day binge drinking, to 
aPR = 5.08 (95% CI: 2.72-9.49) for lifetime injection drug use. 


Discussion 


This report provides key insights into substance use 
behaviors of U.S. high school students during 2009-2019. 
Encouraging findings include decreasing prevalence of current 
alcohol use and decreases in the prevalence of lifetime use of 
marijuana, cocaine, methamphetamine, heroin, synthetic 
marijuana, and injection drug use. However, the findings in 
this report underscore that substance use among high school 
students remains common, with approximately one in three 
students reporting current alcohol use, one in five reporting 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


current marijuana use, and one in seven reporting current 
binge drinking. Because of the ongoing U.S. opioid crisis, of 
particular concern are the high rates of lifetime (one in seven 
students) and current prescription opioid misuse (one in 14 
students) and high rates of co-occurring substance use among 
students currently misusing prescription opioids. 

Notable demographic differences and patterns in substance 
use among high school students are identified in this report. 
Specifically, males had substantially higher rates of cocaine, 
methamphetamine, heroin, and injection drug use compared 
with females, and females had substantially higher rates of 
current alcohol use and current binge drinking. In addition, 
females had higher rates of current prescription opioid misuse 
compared with males, and this pattern persisted in multivariable 
models where males had lower adjusted prevalence ratios for 
current prescription opioid misuse compared with females. 
Differences also occurred in substance use patterns across 
racial/ethnic groups. For example, black and Hispanic students 
reported higher rates of current prescription opioid misuse 
compared with white students; in contrast, white students 
reported the highest rates of current alcohol use and binge 
drinking, followed by Hispanic and black students. These 
substance use patterns by racial/ethnic groups are similar to 
those identified in other U.S. youth substance use surveys 
(https://www.samhsa.gov/data/report/20 18-nsduh-detailed- 
tables). This heterogeneity in substance use patterns among 
demographic groups can be used to guide development of 
tailored and targeted prevention messages and interventions. 

Particularly noteworthy were the universally elevated rates 
of substance use among self-identified sexual minority youths 
compared with heterosexual youths, which is consistent with 
previous research (12). In addition to findings regarding 
broader substance use patterns, this report provides actionable 
information on prescription opioid misuse among high school 
students that can be applied to ongoing efforts for preventing 
opioid misuse, use disorders, and overdoses. Specifically, the 
high rates of co-occurring substance use, especially alcohol and 
marijuana use, among students currently misusing prescription 
opioids highlights the importance of prevention efforts that focus 
on general substance use risk and protective factors. Notably, 
these associations are not limited to high school students because 
binge drinking and marijuana use are associated with increased 
prescription opioid misuse among both adults and adolescents 
(13). Finally, sexual minority youths also had significantly 
higher prevalence of current prescription opioid misuse even 
after controlling for other demographic and substance use 
characteristics, which is consistent with their overall pattern 
of higher rates of substance use in this study. It also further 
emphasizes the importance of identifying tailored prevention 
strategies to address disparities among this vulnerable population. 


MMWR / August 21, 2020 / Vol.69 / No.1 43 


Supplement 


FIGURE. Percentage of co-occurring substance use behaviors among high school students who reported previous 30-day prescription opioid 


misuse* — Youth Risk Behavior Survey, United States, 2019 
100 


90 
80 
70 
60 


50 


Percentage 


40 


30 


20 


10 


Previous Previous Previous Lifetime 
30-day 30-day 30-day marijuana 
marijuana alcohol binge use 
use use alcohol 
use 





] 95% Confidence interval 


Lifetime Lifetime Lifetime Lifetime Lifetime 

synthetic cocaine meth- heroin injection 

marijuana use amphetamine use drug 
use use use 


Substance use behaviors 


* Unweighted N = 661. 


Scientific evidence for the effective prevention of substance 
use indicates the importance of interventions that target risk 
and protective factors at the individual, family, and community 
levels to maximize their public health impact (1-3). Risk 
factors include adverse childhood experiences at the individual 
level, limited parental monitoring and involvement and 
active substance use in the home at the family level, and easy 
availability and accessibility to alcohol and other substances 
and community norms favorable toward use of alcohol and 
other substances at the community level (7,2,5). In addition, 
studies have demonstrated that youth alcohol use is associated 
with adult alcohol use, and that both community-level and 
individual-level alcohol use are affected by population-level 
alcohol policies (e.g., those that reduce the availability and 
accessibility of alcohol and increase its price) (14. 

The ability to reach young persons during early elementary 
ages, before they begin using substances, and throughout 
adolescence makes the school environment well-suited 
for prevention programming. School-based substance use 
prevention programs that focus on broad-based skill building 
(e.g., psychosocial development, life-skills development, and 


44 MMWR / August 21,2020 / Vol.69 / No.1 


social-emotional learning and connectedness) have greater 
promise than substance-specific programs (15,16). In addition, 
multifaceted programs that incorporate aspects of individual, 
school, and family interventions (e.g., the Promoting School- 
community-university Partnerships to Enhanced Resilience 
[PROSPER] program and Communities That Care [CTC]) 
have demonstrated effectiveness at reducing or preventing 
youth substance use (17,18). 

Broader prevention policies for changing the environment 
in which youths live (e.g., those that reduce the availability of 
substances) can also be used as part of a comprehensive approach 
for reducing youth substance use. The U.S. Community 
Preventive Services Task Force recommends certain population- 
level strategies (e.g., increasing alcohol taxes and regulating 
the number and concentration of places that sell alcohol as 
interventions for reducing excessive alcohol use, including 
alcohol use among youths) (https://www.thecommunityguide. 
org/resources/what-works-preventing-excessive-alcohol- 
consumption). Enhanced enforcement of existing substance 
use policies (e.g., prescription drug monitoring programs 
that are used universally with near-real-time data and laws 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 4. Multivariable logistic regression model examining 
individual-level characteristics associated with previous 30-day 
prescription opioid misuse among high school students — Youth 
Risk Behavior Survey, United States, 2019 











Adjusted* 
prevalence ratios 

Characteristic (95% Cl) 
Demographics 
Sex 

Female Referent 

Male 0.69 (0.57-0.84) 
Race/Ethnicity 

White, non-Hispanic Referent 

Black, non-Hispanic 1.49 (1.05-2.10) 

Hispanic 1.52 (1.12-2.05) 
Grade 

9 or 10 Referent 

11 or 12 0.85 (0.66-1.10) 
Sexual identity 

Heterosexual Referent 

Lesbian, gay, or bisexual 1.35 (1.02-1.79) 

Not sure 1.37 (0.86-2.17) 
Substance use and use behaviors 
Alcohol use 

No previous 30-day use Referent 

Previous 30-day nonbinge drinking 2.28 (1.63-3.19) 

Previous 30-day binge drinking 2.13 (1.58-2.86) 
Marijuana use 

No lifetime use Referent 

Lifetime use, but no previous 30-day use 1.21 (0.89-1.65) 

Previous 30-day use 1.31 (0.95-1.80) 
Lifetime synthetic marijuana use 

o Referent 

Yes 2.13 (1.59-2.86) 
Lifetime use of cocaine, heroin, or methamphetamine 

No Referent 

Yes 2.49 (1.89-3.27) 
Lifetime injection drug use 

No Referent 

Yes 5.08 (2.72-9.49) 


Abbreviation: CI = confidence interval. 
* Adjusted prevalence ratios were calculated from a single logistic regression 
model that included all covariates listed in this table. 


prohibiting sales of alcohol to persons aged <21 years) also 
can help reduce substance use among youths (19,20). In 
addition, strategies for expanding access to evidence-based pain 
treatment and improving prescribing of prescription opioids 
through safer prescribing practices can help reduce opioid 
misuse and overdoses. Improving opioid prescribing can have 
dual benefits by reducing the environmental availability of 
prescription opioids for diversion and misuse and reducing 
the risk for misuse associated with the prescription of opioids 
to youths (2). 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Limitations 


General limitations for the YRBS are available in the overview 
report of this supplement (//). The findings in this report are 
subject to at least three additional limitations. First, the questions 
assessing lifetime and current prescription opioid misuse refer 
to prescription pain medicine; however, the questions provide 
examples of opioid-containing prescription medications only. 
Therefore, if students considered nonopioid prescription pain 
medications when answering, an overestimation of prescription 
opioid misuse prevalence might have occurred. Second, many 
of the substance use questions included common street names 
for drugs; however, newly introduced street names or street 
names specific to certain geographic areas were not included, 
which might have resulted in underreporting of substance 
use behaviors. Finally, there was variation in the amount of 
missing data for some substance use variables (e.g., the largest 
amount missing was for current prescription opioid misuse 
[5,000 missing observations]). Missing data might result from 
a variety of factors, such as students choosing not to answer 
questions or inconsistent responses to similar questions that 
are set to missing during the data cleaning process (//). In 
addition, schools selected to participate in the national YRBS 
and in a state or local YRBS only complete the local version 
of the survey; as a result, questions included on the national 
survey but not the local survey are set to missing. 


Conclusion 


The findings in this report indicate that youth substance use 
has declined in recent years; however, substance use, including 
misuse of prescription opioids, remains common among 
U.S. high school students. Opportunities exist for bringing 
to scale evidence-based policies, programs, and practices 
that aim to reduce risk factors and strengthen protective 
factors among youths in conjunction with initiatives already 
underway for combating the U.S. opioid overdose epidemic. 
Disproportionately affected populations (e.g., sexual minority 
youths) might benefit from tailored substance use interventions 
combined with more widespread implementation of broader 
population-level policy strategies. 


Conflicts of Interest 


All authors have completed and submitted the International 
Committee of Medical Journal Editors form for disclosure of 
potential conflicts of interest. No potential conflicts of interest 
were disclosed. 


MMWR / August 21, 2020 / Vol.69 / No.1 45 


10. 


46 


Supplement 


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US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


Suicidal Ideation and Behaviors Among High School Students — 
Youth Risk Behavior Survey, United States, 2019 


Asha Z. Ivey-Stephenson, PhD!; Zewditu Demissie, PhD?; Alexander E. Crosby, MD!; Deborah M. Stone, ScD!; Elizabeth Gaylor, MPH!; 
Natalie Wilkins, PhD?; Richard Lowry, MD?; Margaret Brown, DrPH! 


1 Division of Injury Prevention, National Center for Injury Prevention and Control, CDC; ? Division of Adolescent and School Health, National Center for HIV/AIDS, 
Viral Hepatitis, STD, and TB Prevention, CDC; 3 Office of the Director, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC 


Abstract 


Suicide is the second leading cause of death among high school-aged youths 14-18 years after unintentional injuries. This 
report summarizes data regarding suicidal ideation (i.e., seriously considered suicide) and behaviors (i.e., made a suicide plan, 
attempted suicide, and made a suicide attempt requiring medical treatment) from CDC’s 2019 Youth Risk Behavior Survey. 
Results are reported overall and by sex, grade, race/ethnicity, sexual identity, and sex of sexual contacts, overall and within sex 
groups. Trends in suicide attempts during 2009-2019 are also reported by sex, race/ethnicity, and grade. During 2009-2019, 
prevalence of suicide attempts increased overall and among female, non-Hispanic white, non-Hispanic black, and 12th-grade 
students. Data from 2019 reflect substantial differences by demographics regarding suicidal ideation and behaviors. For example, 
during 2019, a total of 18.8% of students reported having seriously considered suicide, with prevalence estimates highest among 
females (24.1%); white non-Hispanic students (19.1%); students who reported having sex with persons of the same sex or with 
both sexes (54.2%); and students who identified as lesbian, gay, or bisexual (46.8%). Among all students, 8.9% reported having 
attempted suicide, with prevalence estimates highest among females (11.0%); black non-Hispanic students (11.8%); students 
who reported having sex with persons of the same sex or with both sexes (30.3%); and students who identified as lesbian, gay, or 
bisexual (23.4%). Comprehensive suicide prevention can address these differences and reduce prevalence of suicidal ideation and 
behaviors by implementing programs, practices, and policies that prevent suicide (e.g., parenting programs), supporting persons 
currently at risk (e.g., psychotherapy), preventing reattempts (e.g., emergency department follow-up), and attending to persons 
who have lost a friend or loved one to suicide. 


increased by 61.7% from 6.0 to 9.7 per 100,000 population 
(2). Although suicide is a major public health problem, many 
more youths make suicide attempts and struggle with suicidal 
ideation. For example, during 2018, according to data from 
a nationally representative sample of emergency departments 
(EDs), approximately 95,000 youths aged 14-18 years visited 
EDs for self-harm injuries (2). 

One objective of the Healthy People 2020 Mental Health 
and Mental Disorders is to reduce suicide attempts by 
adolescents that resulted in an injury, poisoning, or overdose 
that had to be treated by a doctor or nurse (3). The Youth Risk 
Behavior Survey (YRBS) monitors six categories of priority 
health behaviors and experiences among adolescents, with 
four questions specifically related to suicide (4). This report 
summarizes 2019 YRBS data regarding suicidal ideation and 
behaviors among high school students and presents trends in 
suicide attempts among this population during 2009-2019. 
The report is intended for decision-makers, prevention 


Introduction 


Suicidal behavior presents a major challenge to public 
health in the United States and globally (/). Although fatal 
(i.e., suicide) and nonfatal (e.g., suicide attempts) suicidal 
behaviors are a public health concern across the life span, 
they are of particular concern for youths and young adults 
aged 10-24 years. During 2018, a total of 48,344 persons 
(all ages) died from suicide, and suicide was the 10th leading 
cause of death overall in the United States, accounting for 
approximately 1.7% of all deaths (2). Among high school- 
aged youths (14-18 years), 2,039 suicides occurred that year, 
making it the second leading cause of death for this age group 
after unintentional injuries (n = 2,590). Suicide accounted 
for approximately 33.9% or approximately one of every 
three injury-related deaths among this age group (2). During 
2009-2018, suicide rates among youths aged 14-18 years 


Corresponding author: Asha Z. Ivey-Stephenson, PhD, Division of 
Injury Prevention, National Center for Injury Prevention and Control, 


CDC. Telephone: 770-488-0940; E-mail: aivey@cdc.gov. 





US Department of Health and Human Services/Centers for Disease Control and Prevention 


program practitioners, and those who work in youth-serving 
organizations so that they can identify vulnerable youths and 
take appropriate action to direct prevention resources to those 
young persons. 


MMWR / August 21, 2020 / Vol.69 / No.1 47 


Supplement 


Methods 


Data Source 


This report includes data from the 2009-2019 cycles of 
the YRBS, a cross-sectional, school-based survey conducted 
biennially since 1991. Each survey year, CDC collects data 
from a nationally representative sample of public and private 
school students in grades 9-12 in the 50 U.S. states and the 
District of Columbia. Additional information about YRBS 
sampling, data collection, response rates, and processing 
is available in the overview report of this supplement (4). 
The overview report also includes information about the 
classification of sexual identity and sex of sexual contacts and 
standard data analysis methods. The prevalence estimates for 
all suicidal ideation and behavior questions for the overall 
study population and by sex, race/ethnicity, grade, and sexual 
orientation are available at https://nccd.cdc.gov/youthonline/ 
App/Default.aspx. The full YRBS questionnaire is available at 
https://www.cdc.gov/healthyyouth/data/yrbs/pdf/2019/2019_ 
YRBS-National-HS-Questionnaire.pdf. 


Measures 


Four suicidal ideation and behavior variables are included in 
this report. Suicidal ideation was measured with the question, 
“During the past 12 months, did you ever seriously consider 
attempting suicide?” Making a suicide plan was measured 
with the question, “During the past 12 months, did you 
make a plan about how you would attempt suicide?” (These 
two questions had “yes” or “no” response options.) Suicide 
attempts were measured with the question, “During the 
past 12 months, how many times did you actually attempt 
suicide?” Suicide attempts were assessed by frequency of 
attempts, but the variable was dichotomized into yes or no 
responses for analytic purposes. Lastly, students were asked, 
“If you attempted suicide during the past 12 months, did any 
attempt result in an injury, poisoning, or overdose that had to 
be treated by a doctor or nurse?” This question is referred to 
in this report as, “made a suicide attempt requiring medical 
treatment.” The response options for the last question were, 
“I did not attempt suicide during the past 12 months,” “yes,” 
or “no”; however, this variable was also dichotomized into yes 
or no responses for analysis. 


Analysis 


Analyses of these suicidal ideation and behavior variables 
included examining associations between each item and 
demographic characteristics, including sex (male/female), race/ 
ethnicity (non-Hispanic white [white], non-Hispanic black 


48 MMWR / August 21,2020 / Vol.69 / No.1 


[black], or Hispanic), grade (9, 10, 11, or 12), sexual identity 
(heterosexual; lesbian, gay, or bisexual [LGB]; or not sure), or 
sex of sexual contacts (sexual contact with only the opposite 
sex, sexual contact with only the same sex or both sexes, and 
no sexual contact). Associations by race/ethnicity, grade, sexual 
identity, and sex of sexual contacts were calculated for the 
overall study population but also separately for male and female 
students. Statistical differences were determined by using 
chi-square analyses at the p <0.05 level of significance. Linear 
trends for 2009-2019 were examined for attempted suicide by 
sex, race/ethnicity, and grade. All analyses of suicidal ideation 
and behaviors were conducted among the full sample, and 
analysis of behavior variables was not limited to students who 
reported suicidal ideation (i.e., analysis conducted among the 
full sample). Additional information about the methods used 
to conduct YRBS trend analyses are provided in the overview 
report of this supplement (4). 


Results 


Suicidal Ideation and Behaviors, 
Overall and by Sex 


During the 12 months before the survey, 18.8% of students 
nationwide reported seriously considered attempting suicide 
(prevalence significantly higher among female [24.1%] than 
male [13.3%] students), and among students nationwide, 
15.7% of students had made a plan about how they would 
attempt suicide (prevalence significantly higher among female 
[19.9%] than male [11.3%] students), and 8.9% of students 
had attempted suicide >1 time (prevalence significantly higher 
among female [11.0%] than male [6.6%] students) (Table 1). 
Nationwide, 2.5% of students had made a suicide attempt 
requiring medical treatment, with a prevalence significantly 


higher among female (3.3%) than male (1.7%) students. 


Suicidal Ideation and Behaviors by Race/ 
Ethnicity and Grade, Overall and by Sex 


Overall, a significant difference occurred in having seriously 
considered attempting suicide by race/ethnicity (white: 19.1%; 
black: 16.9%; Hispanic: 17.2%) (Table 2), with a significant 
difference by race/ethnicity among male students (white: 
13.8%; black: 10.7%; Hispanic: 11.4%) but not among female 
students. No significant differences (overall or by sex) occurred 
in having seriously considered attempting suicide by grade. 

Among students reporting having made a suicide plan, a 
significant difference occurred by race and ethnicity overall 
(white: 15.7%; black: 15.0%; Hispanic: 14.7%) but not among 
male or female students. No significant difference occurred in 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 1. Percentage of high school students who had seriously considered attempting suicide, had made a suicide plan, had attempted 
suicide, or had made a suicide attempt requiring medical treatment during the 12 months before the survey, by sex — Youth Risk Behavior 


Survey, United States, 2019 


Female Male Total Chi-square 

Behavior % (95% Cl) % (95% Cl) % (95% Cl) (p value) 

Seriously considered attempting suicide 97.922 (<0.001) 
Yes 24.1 (22.3-26.0) 13.3 (12.2-14.5) 18.8 (17.6-20.0) NA 
No 75.9 (74.0-77.7) 86.7 (85.5-87.8) 81.2 (80.0-82.4) NA 
Made a suicide plan 109.568 (<0.001) 
Yes 19.9 (18.4-21.6) 11.3 (10.3-12.4) 15.7 (14.6-16.9) NA 
No 80.1 (78.4-81.6) 88.7 (87.6-89.7) 84.3 (83.1-85.4) NA 
Attempted suicide 27.037 (<0.001) 
Yes 11.0 (9.7-12.5) 6.6 (5.5-8.1) 8.9 (7.9-10.0) NA 
No 89.0 (87.5-90.3) 93.4 (91.9-94.5) 91.1 (90.0-92.1) NA 
Made a suicide attempt requiring medical treatment* 10.313 (0.003) 
Yes 3.3 (2.6-4.2) 1.7 (1.3-2.3) 2.5 (2.1-3.0) NA 
No 96.7 (95.8-97.4) 98.3 (97.7-98.7) 97.5 (97.0-97.9) NA 


Abbreviations: Cl = confidence interval; NA = not applicable. 


* Made a suicide attempt that resulted in an injury, poisoning, or overdose that had to be treated by a doctor or nurse. 


having made a suicide plan by grade overall or among female 
students, but a significant difference was identified among male 
students (9th grade: 9.5%; 10th grade: 10.4%; 11th grade: 
12.1%; 12th grade: 13.6%). Students who had attempted 
suicide were significantly different by race/ethnicity overall 
(white: 7.9%; black: 11.8%; Hispanic: 8.9%) and among 
female students (white: 9.4%; black: 15.2%; Hispanic: 11.9%) 
but not among male students. No significant differences 
existed in having attempted suicide by grade (overall or by 
sex). In addition, no significant difference in having made a 
suicide attempt requiring medical treatment was noted by race/ 
ethnicity or grade, overall or by sex. 


Suicidal Ideation and Behaviors by Sexual 
Identity and Sex of Sexual Contacts, 
Overall and by Sex 


A significant difference occurred in having seriously 
considered attempting suicide by sexual identity overall 
(heterosexual: 14.5%; LGB: 46.8%; not sure: 30.4%) (Table 3) 
and among both female (heterosexual: 18.0%; LGB: 49.0%; 
not sure: 35.9%) and male (heterosexual: 11.4%; LGB: 
40.4%; not sure: 21.7%) students. Similarly, having seriously 
considered attempting suicide varied by sex of sexual contacts, 
overall (had sexual contact with only the opposite sex: 19.4%; 
had sexual contact with only the same sex or both sexes: 
54.2%; had no sexual contact: 13.0%), among female (had 
sexual contact with only the opposite sex: 25.3%; had sexual 
contact with only the same sex or both sexes: 59.2%; had no 
sexual contact: 16.2%), and among male (had sexual contact 
with only the opposite sex: 14.6%; had sexual contact with 
only the same sex or both sexes: 39.1%; had no sexual contact: 


9.7%) students. 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Overall, a significant difference occurred in having made 
a suicide plan by sexual identity (heterosexual: 12.1%; LGB: 
40.2%; not sure: 23.9%), with a significant difference among 
both female (heterosexual: 14.6%; LGB: 42.4%; not sure: 
28.1%) and male (heterosexual: 9.9%; LGB: 33.0%; not sure: 
17.4%) students. Similarly, a significant difference was noted 
among students having made a suicide plan by sex of sexual 
contacts, overall (had sexual contact with only the opposite 
sex: 16.5%; had sexual contact with only the same sex or both 
sexes: 44.0%; had no sexual contact: 10.9%), with a significant 
difference among both female (had sexual contact with only 
the opposite sex: 20.7%; had sexual contact with only the same 
sex or both sexes: 48.2%; had no sexual contact: 13.8%) and 
male (had sexual contact with only the opposite sex: 12.9%; 
had sexual contact with only the same sex or both sexes: 31.2%; 
had no sexual contact: 7.9%) students. 

A significant difference existed in having attempted suicide 
by sexual identity, overall (heterosexual: 6.4%; LGB: 23.4%; 
not sure: 16.1%) and among both female (heterosexual: 7.9%; 
LGB: 23.6%; not sure: 15.2%) and male (heterosexual: 5.1%; 
LGB: 23.8%; not sure: 16.4%) students. Similarly, a significant 
difference was identified in having attempted suicide by sex 
of sexual contacts, overall (had sexual contact with only the 
opposite sex: 9.3%; had sexual contact with only the same 
sex or both sexes: 30.3%; no sexual contact: 4.8%), with a 
significant difference among both female (had sexual contact 
with only the opposite sex: 11.4%; had sexual contact with 
only the same sex or both sexes: 31.4%; no sexual contact: 
6.1%) and male (had sexual contact with only the opposite 
sex: 7.5%; had sexual contact with only the same sex or both 
sexes: 26.5%; no sexual contact: 3.5%) students. 

Finally, a significant difference occurred in having made a 
suicide attempt requiring medical treatment by sexual identity, 


MMWR / August 21, 2020 / Vol.69 / No.1 49 


TABLE 2. Percentage of high school students who had seriously considered attempting suicide, had made a suicide plan, had attempted 
suicide, or had made a suicide attempt requiring medical treatment during the 12 months before the survey, by sex, race/ethnicity, and 


grade — Youth Risk Behavior Survey, United States, 2019 


Behavior 


Female 
% (95% Cl) 


Seriously considered attempting suicide 


Race/Ethnicity 
White, non-Hispanic 
Black, non-Hispanic 
Hispanic 

Grade 


Made a suicide plan 

Race/Ethnicity 
White, non-Hispanic 
Black, non-Hispanic 
Hispanic 

Grade 


Attempted suicide 

Race/Ethnicity 
White, non-Hispanic 
Black, non-Hispanic 
Hispanic 

Grade 


* 


24.3 (21.9-26.9) 
23.7 (20.7-27.1) 
22.7 (19.3-26.5) 


20.7-27.0) 
20.3-27.3) 
22.5-27.6) 
20.7-27.6) 


23.7 
23.6 
24.9 
24.0 


19.2 (16.9-21.8) 
20.4 (17.6-23.5) 
19.6 (16.9-22.6) 


17.9-23.2) 
17.2-23.7) 
17.6-23.5) 
15.7-21.6) 


20.4 
20.3 
20.4 
18.5 


9.4 (7.8-11.3) 
15.2 (10.8-20.9) 
11.9 (9.0-15.6) 


12.8 (10.7-15.3) 
11.0 (9.1-13.3) 
10.4 (8.1-13.3) 

9.4 (6.9-12.6) 


Chi-square 
(p value) 


1.504 (0.230) 


0.209 (0.889) 


1.652 (0.194) 


0.461 (0.711) 


2.973 (0.044) 


1.878 (0.150) 


Made a suicide attempt requiring medical treatmentt 


Race/Ethnicity 
White, non-Hispanic 
Black, non-Hispanic 
Hispanic 

Grade 


2.9 (1.9-4.4) 
3.8 (2.3-6.2) 
3.6 (2.6-4.9) 


2.3-4.8) 
2.3-5.5) 
1.7-4.3) 
2.2-5.3) 


3.3 
3.6 
2.7 
3.4 


Abbreviation: Cl = Confidence interval. 


* Not applicable. 


0.446 (0.721) 


0.406 (0.750) 


Supplement 


Male 
% (95% Cl) 


13.8 (12.3-15.3) 
10.7 (8.2-13.7) 
11.4 (9.8-13.3) 


11.9 (9.9-14.2) 
13.2 (11.1-15.8) 
13.6 (11.5-16.0) 
14.9 (12.4-17.7) 


12.0 (10.6-13.5) 
10.1 (7.3-13.9) 
9.6 (8.0-11.4) 


9.5 (7.9-11.4) 
10.4 (8.6-12.4) 
12.1 (10.3-14.2) 
13.6 (11.4-16.1) 


6.4 (5.1-7.8) 
8.5 (5.6-12.9) 
5.5 (3.9-7.6) 


6.0 (4.5-7.9) 
6.5 (4.7-9.0) 
6.7 (5.2-8.8) 
7.3 (5.2-10.0) 


1.2 (0.8-1.9) 
2.9 (1.5-5.5) 
2.3 (1.4-3.9) 


7-2.3) 
9-3.3) 
.2-3.2) 
.0-3.9) 


1.3 (0. 
1.7 (0. 
2.0 (1 
1.9(1 


Chi-square 
(p value) 


4.989 (0.005) 


0.790 (0.507) 


2.358 (0.087) 


3.195 (0.035) 


1.505 (0.229) 


0.384 (0.765) 


1.583 (0.210) 


0.571 (0.638) 


Total 
% (95% Cl) 


19.1(17.6-20.8) 
16.9 (15.3-18.7) 
17.2 (15.2-19.4) 


17.7 (15.7-19.8) 
18.5 (16.1-21.1) 
19.3 (17.7-21.1) 
19.6 (17.5-21.9) 


15.7 (14.1-17.4) 
15.0 (12.9-17.5) 
14.7 (13.0-16.7) 


14.8 (13.1-16.6) 
15.4 (13.4-17.7) 
16.4 (14.5-18.5) 
16.2 (14.3-18.3) 


7.9 (6.9-9.1) 
11.8 (8.7-15.9) 
8.9 (7.1-11.1) 


9.4 (7.9-11.1) 
8.8 (7.4-10.5) 
8.6 (7.1-10.4) 
8.5 (6.8-10.6) 


2.1 (1.5-2.8) 
3.3 (2.2-4.9) 
3.0 (2.3-3.8) 


2.3 (1.7-3.1) 
2.7 (1.8-3.9) 
2.3 (1.7-3.3) 
2.7 (2.0-3.7) 


Chi-square 
(p value) 


5.870 (0.002) 


0.820 (0.491) 


3.043 (0.041) 


0.652 (0.587) 


2.866 (0.050) 


0.311 (0.817) 


1.387 (0.262) 


0.274 (0.844) 


t Made a suicide attempt that resulted in an injury, poisoning, or overdose that had to be treated by a doctor or nurse. 


overall (heterosexual: 1.7%; LGB: 6.3%; not sure: 5.2%) and 
among both female (heterosexual: 2.3%; LGB: 6.6%; not 
sure: 3.8%) and male (heterosexual: 1.3%; LGB: 5.9%; not 
sure: 7.6%) students. A significant difference also was noted 
in having made a suicide attempt requiring medical treatment 
by sex of sexual contacts, overall (had sexual contact with only 
the opposite sex: 2.6%; had sexual contact with only the same 
sex or both sexes: 10.2%; had no sexual contact: 1.0%) and 
among both female (had sexual contact with only the opposite 
sex: 3.4%; had sexual contact with only the same sex or both 


50 MMWR / August 21,2020 / Vol.69 / No.1 


sexes: 10.4%; had no sexual contact: 1.4%) and male (had 
sexual contact with only the opposite sex: 1.9%; had sexual 
contact with only the same sex or both sexes: 9.4%; had no 
sexual contact: 0.5%) students. 


Trends in Suicide Attempts, Overall and by 
Sex, Race/Ethnicity, and Grade 


Among the total student population, the percentage of 
students who had attempted suicide 21 time during the 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 3. Percentage of high school students who had seriously considered attempting suicide, had made a suicide plan, had attempted sui- 
cide, or had made a suicide attempt requiring medical treatment during the 12 months before the survey, by sex, sexual identity, and sex of 


sexual contacts — Youth Risk Behavior Survey, United States, 2019 


Female Chi-square Male Chi-square Total Chi-square 

Behavior % (95% Cl) (p value) % (95% Cl) (p value) % (95% Cl) (p value) 

Seriously considered attempting suicide 

Sexual identity — 75.728 (<0.001) — 22.231 (<0.001) — 88.194 (<0.001) 
Heterosexual 18.0 (16.3-20.0) —* 11.4 (10.4-12.6) — 14.5 (13.4-15.7) — 

LGB 49.0 (44.8-53.3) — 40.4 (33.9-47.1) — 46.8 (43.1-50.6) — 
Not sure 35.9 (29.5-42.9) — 21.7 (14.8-30.5) — 30.4 (25.4-35.9) — 

Sex of sexual contacts — 64.007 (<0.001) — 13.972 (<0.001) — 66.938 (<0.001) 
Opposite sex only 25.3 (22.8-28.0) — 14.6 (12.9-16.5) — 19.4 (17.6-21.4) — 

Same sex only or both sexes 59.2 (52.5-65.6) — 39.1 (29.3-49.9) — 54.2 (49.0-59.3) — 
No sexual contact 16.2 (14.2-18.3) — 9.7 (8.1-11.7) — 13.0 (11.8-14.3) — 

Made a suicide plan 

Sexual identity — 66.568 (<0.001) — 19.732 (<0.001) — 90.368 (<0.001) 
Heterosexual 14.6 (13.2-16.0) — 9.9 (8.9-11.0) — 12.1 (11.1-13.1) — 

LGB 42.4 (38.4-46.4) — 33.0 (26.4-40.3) — 40.2 (36.6-44.0) — 
Not sure 28.1 (22.1-35.0) — 17.4 (11.8-24.8) — 23.9 (19.4-29.0) — 

Sex of sexual contacts — 56.442 (<0.001) — 18.435 (<0.001) — 62.470 (< 0.001) 
Opposite sex only 20.7 (18.4-23.3) — 12.9 (11.5-14.6) — 16.5 (14.9-18.1) — 
Same sex only or both sexes 48.2 (42.8-53.6) — 31.2 (23.8-39.7) — 44.0 (39.7-48.4) — 

No sexual contact 13.8 (12.3-15.6) — 7.9 (6.7-9.4) — 10.9 (9.8-12.1) — 

Attempted suicide 

Sexual identity — 26.919 (<0.001) — 15.972 (<0.001) — 40.352 (<0.001) 
Heterosexual 7.9 (6.6-9.4) — 5.1 (4.2-6.3) — 6.4 (5.6-7.4) — 

LGB 23.6 (20.0-27.6) — 23.8 (17.8-31.1) — 23.4 (20.0-27.1) — 
Not sure 15.2 (9.6-23.3) — 16.4 (9.9-26.0) — 16.1 (11.1-22.8) — 

Sex of sexual contacts — 58.123 (<0.001) — 12.379 (<0.001) — 66.202 (<0.001) 
Opposite sex only 11.4 (9.5-13.5) — 7.5 (5.8-9.6) — 9.3 (7.9-10.8) — 

Same sex only or both sexes 31.4 (27.0-36.1) — 26.5 (17.5-38.0) — 30.3 (25.9-35.0) — 
No sexual contact 6.1 (4.8-7.8) — 3.5 (2.6-4.8) — 4.8 (4.0-5.8) — 

Made a suicide attempt requiring medical treatmentt 

Sexual identity — 7.893 (0.001) — 5.592 (0.008) — 13.034 (<0.001) 
Heterosexual 2.3 (1.6-3.2) — 1.3 (0.9-1.9) — 1.7 (1.4-2.2) — 

LGB 6.6 (5.0-8.7) — 5.9 (3.2-10.6) — 6.3 (4.8-8.3) — 
Not sure 3.8 (1.6-8.4) — 7.6 (3.6-15.2) — 5.2 (3.0-9.0) — 

Sex of sexual contacts — 14.728 (<0.001) — 10.517 (<0.001) — 23.046 (<0.001) 
Opposite sex only 3.4 (2.4-4.8) — 1.9 (1.3-2.9) — 2.6 (2.0-3.3) — 

Same sex only or both sexes 10.4 (7.5-14.2) — 9.4 (4.9-17.6) — 10.2 (7.6-13.4) — 
No sexual contact 1.4 (0.8-2.4) — 0.5 (0.3-1.1) — 1.0 (0.6-1.5) — 

Abbreviations: Cl = confidence interval; LGB = lesbian, gay, or bisexual. 

* Not applicable. 

t Made a suicide attempt that resulted in an injury, poisoning, or overdose that had to be treated by a doctor or nurse. 

12 months before the survey experienced a significant linear Discussion 


increase from 6.3% during 2009 to 8.9% during 2019 
(Figure 1-3). Among female students, a significant linear 
increase (from 8.1% to 11.0%) occurred in the prevalence of 
having attempted suicide. No significant linear change was 
observed for the prevalence of having attempted suicide among 
male students. By race/ethnicity, significant linear increases in 


having attempted suicide were observed for white (from 5.0% 
to 7.9%) and black (from 7.9% to 11.8%) but not Hispanic 


During 2019, approximately one in five (18.8%) youths had 
seriously considered attempting suicide, one in six (15.7%) 
had made a suicide plan, one in 11 (8.9%) had made an 
attempt, and one in 40 (2.5%) had made a suicide attempt 
requiring medical treatment. Linear trends in suicide attempts 
have increased during 2009-2019 overall and among certain 
demographic groups. 


The 2019 YRBS data highlight considerable differences 
in suicidal ideation, plans, attempts, and attempts requiring 
medical treatment. Consistent with previous research, during 
2019, females had more suicidal ideation, suicide plans, 
and suicide attempts, including attempts requiring medical 


students. By grade, a significant linear increase in having 
attempted suicide was observed only for 12th-grade students 
(from 4.2% to 8.5%). 


US Department of Health and Human Services/Centers for Disease Control and Prevention MMWR / August 21,2020 / Vol.69 / No.1 51 


Supplement 


FIGURE 1. Percentage of high school students who attempted suicide during the 12 months before the survey, overall and by sex — Youth Risk 
Behavior Survey, United States, 2009-2019 


100 


= Overall 


16 
== = Female 


14 = = a Male 


12 


10 


Percentage 





2009 2011 2013 2015 2017 2019 


Year 


FIGURE 2. Percentage of high school students who attempted suicide during the 12 months before the survey, by race/ethnicity — Youth Risk 
Behavior Survey, United States, 2009-2019 


100 
16 === Hispanic 

= = Black, non-Hispanic 

14 === White, non-Hispanic 


12 


10 


Percentage 





2009 2011 2013 2015 2017 2019 


Year 


52 MMWR / August 21,2020 / Vol.69 / No.1 US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


FIGURE 3. Percentage of high school students who attempted suicide during the 12 months before the survey, by grade — Youth Risk Behavior 


Survey, United States, 2009-2019 


100 


16 w= 9th 
== == 10th 

14 === 11th 
= = 12th 


12 


10 


Percentage 


. 
6 = 


2009 2011 2013 


treatment, than males (5). Certain racial/ethnic differences 
also were identified. For example, black male students had 
the lowest prevalence estimates of suicidal ideation. Regarding 
suicide attempts, black students (male and female) had the 
highest prevalence estimates. This finding is consistent with 
previous research (6). Also consistent with previous research are 
the study findings regarding sexual orientation and sex of sexual 
contacts (5). Namely, prevalence estimates of suicidal ideation, 
suicide plans, attempts, and attempts requiring medical 
treatment were highest among sexual minority youths, those 
who identified as LGB, and youths who reported having had 
sexual contact with the same or with both sexes during 2019. 

Adolescence is a developmental stage often characterized 
by rapid and extensive physical and psychosocial changes (7). 
It also represents a time for expanded identity development, 
with sexual identity development representing a complex, 
multidimensional, and often stressful process for youths (8). 
The potential dissonance between sexual identity and behavior 
and the social rejection sexual minority youths often experience 
can contribute to increased suicidal ideation and behaviors 
along with an increased risk for suicide (9,10). Because of the 
high prevalence of suicidal ideation and behaviors among sexual 
minority youths, additional research is needed to determine 
how best to support this vulnerable group. Such research 
might evaluate strategies designed to reduce sexual minority 
stress (e.g., discrimination and victimization resulting from 
sharing one’s sexual orientation) (77) and unhealthy behaviors 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


————————————_——— i 
a 





2015 2017 2019 


Year 


(e.g., substance use) and the resultant impact on suicidal 
ideation and behaviors. 

Suicide attempts are a known risk factor for and the greatest 
predictor of death by suicide (12), which is the rationale for 
investigating trends only on this outcome. The number of 
children and adolescents who sought medical treatment at EDs 
for suicide attempts increased sharply from 2007 (540,000) 
to 2015 (960,000) (13). Either a linear increase or no change 
in suicide attempts by variables reported here (i.e., sex, race/ 
ethnicity, and grade) was identified for 2009-2019. Although 
for a different period (1991-2017), other researchers also 
have reported that suicide attempts among black students 
increased significantly (6). More specifically, previous findings 
indicated that suicide attempts increased at an accelerating rate 
among black females, and black male youths had a substantial 
increase in attempts requiring medical treatment during the 
period (6). Future studies are needed to continue monitoring 
trends in suicidal ideation and behavior for black students and 
other race/ethnicity groups. For example, such studies might 
include more detailed analyses among the American Indian/ 
Alaska Native youth population who have been reported to 
be at increased risk for suicidal behaviors (6). 

In this analysis, one notable finding emerged by sex and 
grade; a substantial increase in making a suicide plan occurred 
among males as grade increased. To address this trend, schools 
can consider a sex-by-grade—specific approach to implementing 
suicide prevention or intervention activities. 


MMWR / August 21, 2020 / Vol.69 / No.1 53 


Supplement 


Limitations 


General limitations for the YRBS are available in the 
overview report of this supplement (4). The findings in this 
report are subject to at least one additional limitation. This 
analysis is conducted among all students (i.e., does not separate 
ideation from behaviors); suicide patterns might differ between 
those who experienced suicidal ideation and those who did not. 


Future Directions 


To address the health differences in suicidal ideation and 
behaviors observed by student demographics and to decrease 
these outcomes overall, a comprehensive approach to suicide 
prevention, including programs, practices, and policies based 
on the best available evidence, is needed. Such an approach 
addresses the range of risk and protective factors occurring 
across the individual, relationship, community, and societal 
levels. A comprehensive approach also seeks to prevent suicide 
risk, identify and support youths at increased risk, prevent 
attempts and reattempts, and help survivors of suicide loss (i.e., 
those grieving the death of a friend or loved one). States and 
communities, including school communities, can use strategies 
with such best available evidence as that documented in the 
CDC Preventing Suicide Technical Package (14). 

Preventing adverse childhood experiences (e.g., child 
maltreatment) can help reduce suicide risk among adolescents 
through strategies that promote safe, stable, nurturing 
relationships and environments in childhood (15). Other 
strategies in a comprehensive approach to suicide prevention 
include supporting families by strengthening economic 
supports and teaching coping and problem-solving skills 
among children, adolescents, and their parents; promoting 
connectedness between youths and their schools, teachers, 
peers, and family; creating protective environments in schools 
and at home (e.g., limiting access to such lethal means among 
students at risk as medications and firearms); promoting help- 
seeking behaviors; reducing stigma; and training teachers and 
adults in recognizing signs of suicide and responding effectively 
through referrals to evidence-based treatment (e.g., cognitive- 
behavioral therapy) (14). Finally, schools and the media should 
respond to and report on suicides in ways that are supportive 
and responsible (e.g., not sensationalizing deaths), thereby 
avoiding additional suicides (i.e., suicide contagion) (14. 


54 MMWR / August 21,2020 / Vol.69 / No.1 


Conclusion 


Suicide is a leading cause of death among youths; however, 
many more youths are at risk for suicide as a result of 
experiencing suicidal ideation, making suicide plans, and 
attempting suicide, making a focus on nonfatal suicidal 
behavior a crucial public health priority. During 2009-2019, 
trends in suicide attempts among adolescents increased overall 
and among many demographic groups. Prevalence estimates 
of suicidal ideation, suicide plans, attempts, and attempts 
requiring medical treatment were highest among sexual 
minority youths and youths who reported having had sexual 
contact with the same or with both sexes. Regarding differences 
by race/ethnicity, black students had the highest prevalence 
estimates for attempted suicide. Factors at the individual, 
relationship, community, and societal levels likely contribute 
to the differences in suicide attempts among different racial/ 
ethnic groups and sexual minority youths and the differences 
observed by sex and grade. More research is needed to better 
understand the risk and protective factors to determine which 
suicide prevention strategies might best serve each group. 
The findings in this report underscore the importance of a 
comprehensive approach to suicide prevention, which would 
provide necessary support to those at risk, decrease suicidal 
ideation and behaviors, and ultimately prevent suicide among 
youths and save lives. 


Conflicts of Interest 


All authors have completed and submitted the International 
Committee of Medical Journal Editors form for disclosure of 
potential conflicts of interest. No potential conflicts of interest 
were disclosed. 


References 


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2. CDC. Web-based Injury Statistics Query and Reporting System 
(WISQARS). Atlanta, GA: US Department of Health and Human 
Services, CDC, National Center for Injury Prevention and Control; 
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Washington, DC: US Department of Health and Human Services, 
Office of Disease Prevention and Health Promotion; 2020. https://www. 
healthypeople.gov 

4. Underwood JM, Brener N, Thornton J, et al. Overview and methods 
for the Youth Risk Behavior Surveillance System—United States, 2019. 
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Suppl 2020;69(No. Suppl 1). 


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among high school students in the United States: 1991-2017. Pediatrics 
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development construct: a conceptual review. ScientificWorldJournal 
2012;2012:529691. https://doi-org/10.1100/2012/529691 


. Rosario M, Schrimshaw EW, Hunter J, Braun L. Sexual identity 


development among lesbian, gay, and bisexual youths: consistency and 
change over time. J Sex Res 2006;43:46-58. https://doi. 
org/10.1080/00224490609552298 


. Annor FB, Clayton HB, Gilbert LK, et al. Sexual orientation discordance 


and nonfatal suicidal behaviors in U.S. high school students. Am J Prev 
Med 2018;54:530-8. https://doi.org/10.1016/j.amepre.2018.01.013 
Hong JS, Espelage DL, Kral MJ. Understanding suicide among sexual 
minority youth in America: an ecological systems analysis. J Adolesc 
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Meyer IH. Minority stress and mental health in gay men. J Health Soc 
Behav 1995;36:38-56. https://doi.org/10.2307/2137286 

Franklin JC, Ribeiro JD, Fox KR, et al. Risk factors for suicidal thoughts 
and behaviors: A meta-analysis of 50 years of research. Psychol Bull 
2017;143:187—232. https://doi.org/10.1037/bul0000084 

Burstein B, Agostino H, Greenfield B. Suicidal attempts and ideation 
among children and adolescents in US emergency departments, 2007— 
2015. JAMA Pediatr 2019;173:598-600. https://doi.org/10.1001/ 
jamapediatrics.2019.0464 

Stone DM, Holland KM, Bartholow B, Crosby AE, Davis S, Wilkins 
N. Preventing suicide: a technical package of policies, programs, and 
practices. Atlanta, GA: US Department of Health and Human Services, 
CDC, National Center for Injury Prevention and Control; 2017. https:// 
www.cdc.gov/violenceprevention/pdf/suicideTechnicalPackage.pdf 
CDC. Preventing adverse childhood experiences (ACEs): leveraging the 
best available evidence. Atlanta, GA: US Department of Health and 
Human Services, CDC, National Center for Injury Prevention and 
Control; 2019. https://www.cdc.gov/violenceprevention/pdf/ 
preventingACES. pdf 


MMWR / August 21, 2020 / Vol.69 / No.1 55 


Supplement 


Tobacco Product Use Among High School Students — Youth Risk 
Behavior Survey, United States, 2019 


MeLisa R. Creamer, PhD}; Sherry Everett Jones, PhD, JD?; Andrea S. Gentzke, PhD!; Ahmed Jamal, MBBS!; Brian A. King, PhD! 


I Office on Smoking and Health, National Center for Chronic Disease Prevention and Health Promotion, CDC; 2 Division of Adolescent and School Health, 
National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC 


Abstract 


Tobacco product use is the leading cause of preventable disease, disability, and death in the United States. This report used data 
from the 2019 Youth Risk Behavior Survey to assess the following among U.S. high school students: ever use of cigarettes and 
electronic vapor products, current use (21 day during the 30 days before the survey) of tobacco products, frequent use (220 days 
during the 30 days before the survey) among current users of tobacco products, trends in use over time, and usual source of 
electronic vapor products among current electronic vapor product users. In 2019, a total of 50.1% of U.S. high school students 
had ever used electronic vapor products, and 24.1% had ever tried cigarette smoking. Current electronic vapor product use was 
32.7%, current cigarette smoking was 6.0%, current cigar smoking was 5.7%, and current smokeless tobacco use was 3.8%. 
Approximately 36.5% of students were current users of any tobacco product, and 8.2% were current users of two or more tobacco 
products. Frequent use among users of individual products was 32.6% for electronic vapor products, 28.5% for smokeless tobacco, 
22.2% for cigarettes, and 18.4% for cigars. Among current electronic vapor product users who were aged <17 years, the most 
commonly reported source was borrowing them from someone else (42.8%). Significant decreases occurred in current cigarette 
smoking (1991: 27.5%; 2019: 6.0%), cigar smoking (1997: 22.0%; 2019: 5.7%), and smokeless tobacco use (2017: 5.5%; 
2019: 3.8%). However, significant increases occurred in current electronic vapor product use (2015: 24.1%; 2019: 32.7%) 
and any tobacco product use (2017: 19.5%; 2019: 36.5%). Although current cigarette smoking, cigar smoking, and smokeless 
tobacco use has decreased among high school students, the increased prevalence of electronic vapor product use among youths 
is concerning. Continued surveillance for all tobacco product use is warranted for guiding and evaluating public health policy at 
the local, state, tribal, and national levels. 


Introduction allow higher levels of nicotine to be inhaled more easily by 
the user (6). Sales of these newer generation, or “pod-mod,” 
products have increased in the United States during recent 
years. For example, sales of JUUL, the most commonly sold 


Ge he Previ Sa e-cigarette in the United States since December 2017, increased 
smoking before age 18 years (2). Previous reports indicate approximately 600% during 2016-2017 from 2.2 million 


decreases in current cigarette smoking (i.e., use during the init cales ta 162 million u rales (C: By Docanber D018, 


30 days before the sarea anong eae high school students JUUL accounted for an estimated 76% of the $322.1 million 
from a high of 36.4% in 1997 to 8.8% in 2017 (3). However, 


there are a variety of tobacco products, including smokeless 
tobacco products, cigars, and most recently, electronic vapor 
products (e.g., e-cigarettes). 

Electronic vapor products have evolved since entering the 
U.S. marketplace in 2007. Initial products were disposable, 
resembled the size and shape of conventional cigarettes, 
and used free-base nicotine; however, newer products are 


Smoking is the leading cause of preventable premature 
disease and death in the United States (/). An estimated 88% 
of adult daily cigarette smokers report first trying cigarette 


total e-cigarettes sales that occurred that month in the United 
States (8). The popularity of these electronic vapor products 
among youths is likely the result of multiple factors, including 
advertising exposure, availability of youth-appealing flavors, 
curiosity, and social exposure through friends and others (4-6). 
In 2014, prevalence of electronic vapor product use among 
high school students surpassed prevalence of cigarette smoking 
f (9), and according to data from the 2017 Youth Risk Behavior 
rechargeable, resemble common objects (e.g., USB flash Curver RBS): 19.2% af hich school students hadvused 
f i , eae y , 13.2% of high school students had use 
drives), and typically deliver nicotine salts (4,5), which electronic vapor products during the previous 30 days (3). 
These findings align with increases in use observed in other 


Corresponding author: Andrea S. Gentzke, PhD, Office on Smoking and national surveys of youth in the United States. For example, 


Health, National Center for Chronic Disease Prevention and Health . he Nati Youth Ti NYT 
Promotion, CDC. Telephone: 404-498-1795; E-mail: AGentzke@cdc.gov. peor totke National Newly Tobasco: Saruy 4 ah 





56 MMWR / August 21,2020 / Vol.69 / No.1 US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


current electronic vapor product use among high school students 
increased 78% (11.7% to 20.8%) during 2017-2018 (9). 

Youth use of tobacco products in any form is unsafe (1,4. 
Cigarette smoking harms nearly every organ in the body, and 
smokeless tobacco product use is associated with multiple health 
risks, including cancers of the mouth (7). Moreover, the aerosol 
in electronic vapor products can contain harmful ingredients, 
including heavy metals, ultrafine particles, and nicotine (4). 
Nicotine is highly addictive, can harm the developing adolescent 
brain, and can prime the brain for addiction to other drugs 
(4,10). In addition, a growing body of scientific literature 
suggests that youths who use e-cigarettes are more likely to smoke 
conventional cigarettes in the future (4,/0). 

Surveillance for tobacco product use among youths is crucial 
for guiding and evaluating tobacco control strategies at local, 
state, tribal, and national levels. This report presents the latest 
data from the 2019 YRBS to assess the following among U.S. 
high school students: ever use of cigarettes and electronic 
vapor products; current use (21 day during the 30 days before 
the survey) of tobacco products (electronic vapor products, 
cigarettes, cigars [cigars/cigarillos/little cigars], smokeless 
tobacco [chewing tobacco, snuff, dip, snus, or dissolvable 
tobacco products], any tobacco product, and two or more 
products); frequent use (220 days during the 30 days before 
the survey) of tobacco products among current users of those 
products; trends in tobacco product use over time; and usual 
source of obtaining electronic vapor products among current 
electronic vapor product users. 


Methods 


Data Source 


This report includes data from the 1991-2019 cycles of 
CDC's national YRBS, a cross-sectional, school-based survey 
conducted biennially since 1991. Each survey year, CDC 
collects data from a nationally representative sample of public 
and private school students in grades 9-12 in the 50 U.S. states 
and the District of Columbia. Additional information about 
YRBS sampling, data collection, response rates, and processing 
is available in the overview report for this supplement (11). The 
prevalence estimates for all tobacco product use questions for 
the overall study population and by sex, race/ethnicity, grade, 
and sexual orientation are available at https://nccd.cdc.gov/ 
youthonline/App/Default.aspx. The full YRBS questionnaire 
is available at https://www.cdc.gov/healthyyouth/data/yrbs/ 
pdf/2019/2019_YRBS-National-HS-Questionnaire.pdf. 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Measures 


Ever use, which was defined as having used the product 
at least one time during their lifetime, was assessed for two 
distinct tobacco products: cigarettes and electronic vapor 
products. Ever cigarette smoking was assessed by the question, 
“Have you ever tried cigarette smoking, even one or two 
puffs?” Ever electronic vapor product use was assessed by the 
question, “Have you ever used an electronic vapor product?” 
with a preamble that read, “The next 3 questions ask about 
electronic vapor products, such as JUUL, Vuse, MarkTen, and 
blu. Electronic vapor products include e-cigarettes, vapes, vape 
pens, e-cigars, e-hookahs, hookah pens, and mods.” 

Current use (21 day during the 30 days before the survey) 
was assessed for four tobacco products: 1) current electronic 
vapor product use was assessed by the question, “During the 
past 30 days, on how many days did you use an electronic 
vapor product?” 2) current cigarette smoking was assessed. by 
the question, “During the past 30 days, on how many days did 
you smoke cigarettes?” 3) current cigar smoking was assessed 
by the question, “During the past 30 days, on how many days 
did you smoke cigars, cigarillos, or little cigars?” and 4) current 
smokeless tobacco use was assessed by the question, “During 
the past 30 days, on how many days did you use chewing 
tobacco, snuff, dip, snus, or dissolvable tobacco products, such 
as Copenhagen, Grizzly, Skoal, or Camel Snus? (Do not count 
any electronic vapor products.)” Response options for each of 
the four questions were 0 days, 1-2 days, 3—5 days, 6-9 days, 
10-19 days, 20-29 days, and all 30 days. Among current users 
of each individual product, frequent use was also calculated. 
Frequent use was defined as having used the respective product 
on 220 days during the 30 days before the survey. 

Two composite measures were also investigated in this 
analysis. Any current tobacco product use was defined as any 
use of electronic vapor products, cigarettes, cigars, or smokeless 
tobacco during the 30 days before the survey. Use of two or 
more products was defined as current use of two or more of 
the four assessed tobacco products. 

Respondents also were asked how they usually obtained 
electronic vapor products by the question (referred to as source 
hereinafter), “During the past 30 days, how did you usually 
get your own electronic vapor products? (Select only one 
response.)” Response options were as follows: I did not use any 
electronic vapor products during the past 30 days; I bought 
them in a store such as a convenience store, supermarket, 
discount store, gas station, or vape store; I got them on the 
Internet; I gave someone else money to buy them for me; I 
borrowed them from someone else; a person who can legally 
buy these products gave them to me; I took them from a store 


MMWR / August 21, 2020 / Vol.69 / No.1 57 


Supplement 


or another person; or I got them some other way. Analysis of this 
variable was limited to current electronic vapor product users. 

The demographic characteristics of students analyzed for 
this report included sex (female or male), grade (9, 10, 11, or 
12), age (<15 years, 16 or 17 years, or 218 years), and sexual 
identity (heterosexual; lesbian, gay, or bisexual; or not sure). 
In addition, students were classified into four racial/ethnic 
categories: non-Hispanic white (white); non-Hispanic black 
(black); Hispanic or Latino of any race (Hispanic); and other 
or multiple races (non-Hispanic). The numbers of students 
in the other or multiple racial/ethnic groups were too small 
to produce statistically stable estimates; therefore, those data 
are not presented as a separate group in this report but were 
retained in the overall analytic sample. 


Analysis 


Prevalence of use for each respective tobacco product was 
estimated for all years for which data were available. For 2019, 
statistically significant pairwise differences by sex, grade, race/ 
ethnicity, age, and sexual identity were determined for each of 
the assessed tobacco product use behaviors by using tests. For 
each tobacco product, changes in prevalence were compared for 
2017 and 2019 by using tests. In addition, t-tests were used 
to compare how students who were <17 years and 218 years 
usually obtained their electronic vapor products; these age 
groups were used because age 18 years was the federal legal 
age of sale for tobacco products at the time of the survey. 
Prevalence estimates were considered statistically different if 
the p value was <0.05. 

To identify temporal trends, logistic regression analyses 
were used to model linear and quadratic time effects while 
controlling for sex, grade, and race/ethnicity. Linear time 
effects were analyzed for current electronic vapor products 
use (2015-2019), and both linear and quadratic time effects 
were analyzed for current cigarette smoking (1991-2019) and 
current cigar smoking (1997-2019). Because of substantial 
changes in the question wording for smokeless tobacco 
products in 2017, trends were not assessed for smokeless 
tobacco. Additional information about the methods used to 
conduct YRBS trend analyses are provided in the overview 
report of this supplement (11). 


Results 
Among U.S. high school students in 2019, a total of 50.1% 
(95% confidence interval [CI]: 48.1-52.2) had ever used 
electronic vapor products, and 24.1% (CI: 21.3-27.0) had 
ever tried cigarette smoking (data not shown). Prevalence of 
current use was 32.7% for electronic vapor products, 6.0% for 


58 MMWR / August 21, 2020 / Vol.69 / No.1 


cigarettes, 5.7% for cigars, and 3.8% for smokeless tobacco. In 
addition, 36.5% of students had currently used any tobacco 
products, and 8.2% had currently used two or more tobacco 
products (Table 1). 

Prevalence of tobacco product use varied by demographic 
groups, with current use of cigarettes, cigars, smokeless tobacco, 
and two or more tobacco products being higher among male 
students than female students. Although differences in tobacco 
product use varied by grade, prevalence of current use of each 
individual product, any tobacco product, and two or more 
tobacco products was higher among 12th-grade students than 
9th-grade students. Prevalence of current use of electronic 
vapor products, cigarettes, any tobacco product, and two or 
more tobacco products was higher among white and Hispanic 
students than black students, and the prevalence of electronic 
vapor products and any tobacco product use was higher among 
white than Hispanic students. Prevalence of current cigar use 
was higher among students aged >18 years than those aged 16 
or 17 years and those aged <15 years. For all other individual 
products, any tobacco product, and two or more tobacco 
products, prevalence increased in each age category. Among 
sexual identity groups, prevalence of electronic vapor product 
use was higher among heterosexual students and lesbian, gay, or 
bisexual students than not-sure students. Prevalence of current 
use of cigarettes, cigars, any tobacco product, and two or more 
tobacco products was higher among lesbian, gay, or bisexual 
students than heterosexual students. Finally, the prevalence 
of any tobacco product use was higher among lesbian, gay, or 
bisexual students than not-sure students. 

In 2019, among the 32.7% of current electronic vapor 
product users, 32.6% were frequent users; among the 5.7% 
current cigarette smokers, 22.2% were frequent users; among 
the 3.8% current cigar smokers, 18.4% were frequent users; 
and among the 6.0% current smokeless tobacco product users, 
28.5% were frequent users. From 2017 to 2019, among current 
electronic vapor product users, a significant increase occurred 
in frequent use (from 25.1% to 32.6%), and among current 
cigarette smokers, a significant decrease occurred in frequent 
use (from 30.0% in 2017 to 22.2% in 2019) (Figure 1). No 
significant changes in frequent use of smokeless tobacco or 
cigars were observed among users of these products from 
2017 to 2019. 

The usual source of electronic vapor products among current 
users varied by age (Table 2). Among current electronic vapor 
product users who were aged <17 years, the most commonly 
reported usual source of electronic vapor products was 
borrowing them from someone else (42.8%). Among those 
aged 218 years, the most commonly reported source was 
buying them in a store (56.4%). Compared with students 
aged <17 years, a higher prevalence of students aged >18 years 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 1. Percentage of high school students who were current tobacco users, by selected characteristics and type of tobacco product — Youth 
Risk Behavior Survey, United States, 2019 


Electronic vapor products* Cigarettest Cigars§ Smokeless tobacco" Any tobacco product** >2 productstt 
Characteristic % (95% Cl) % (95% Cl) % (95% Cl) % (95% Cl) % (95% Cl) % (95% Cl) 
Total 32.7 (30.7-34.8) 6.0 (5.0-7.2) 5.7 (4.8-6.7) 3.8 (3.2-4.6) 36.5 (33.6-39.5) 8.2 (7.0-9.5) 
Sex$$§ 
Male 32.0 (29.7-34.3) 6.9 (5.7-8.4) 7.4 (6.4-8.6) 5.8 (4.7-7.1) 36.3 (33.3-39.3) 10.4 (9.0-11.9) 
Female 33.5 (30.9-36.1) 4.9 (3.8-6.4) 3.8 (2.8-5.1) 1.6 (1.2-2.1) 36.6 (33.1-40.2) 5.8 (4.5-7.5) 
Grade" 
9 25.0 (22.8-27.4) 3.8 (2.8-5.1) 3.8 (2.7-5.2) 2.0 (1.4-3.0) 27.7 (24.8-30.9) 5.3 (4.2-6.6) 
10 30.5 (27.3-33.8) 5,2 (3.9-6.9) 4.7 (3.5-6.2) 3.6 (2.6-5.0) 34.3 (30.3-38.6) 7.3 (5.6-9.6) 
11 35.9 (32.3-39.8) 5.9 (4.5-7.7) 6.0 (4.6-7.8) 3.9 (3.0-5.1) 39.8 (35.7-44.1) 8.4 (6.7-10.4) 
12 40.4 (37.5-43.4) 9.0 (7.6-10.7) 8.5 (6.9-10.4) 5.5 (4.3-7.1) 45.0 (41.3-48.7) 11.9 (10.3-13.7) 
Race/Ethnicity*** 
Black, non-Hispanic 19.7 (16.9-22.8) 3.3 (2.3-4.6) 5.3 (4.1-6.8) 2.8 (1.8-4.4) 24.7 (21.3-28.4) 4.8 (3.7-6.2) 
Hispanic 31.2 (28.6-33.8) 6.0 (4.3-8.4) 6.1 (4.7-8.0) 3.1 (2.3-4.3) 33.8 (31.1-36.7) 7.9 (6.2-10.0) 
White, non-Hispanic 38.3 (36.0-40.7) 6.7 (5.3-8.4) 5.9 (4.7-7.4) 4.4 (3.3-5.7) 42.0 (38.3-45.9) 9.5 (7.8-11.5) 
Age group (yrs) ttt 
<15 25.9 (24.1-27.9) 4.2 (3.2-5.4) 4.2 (3.1-5.6) 2.7 (2.0-3.8) 29.1 (26.2-32.1) 5.8 (4.6-7.2) 
16 or 17 35.2 (32.3-38.3) 6.0 (4.8-7.4) 5.7 (4.5-7.0) 3.7 (3.0-4.6) 38.8 (35.2-42.4) 8.4 (6.8-10.1) 
218 42.8 (39.0-46.7) 10.9 (8.6-13.6) 10.2 (8.1-12.7) 7.2 (5.5-9.2) 49.1 (44.9-53.4) 14.2 (12.0-16.7) 
Sexual identity$$$ 
Heterosexual 32.8 (30.5-35.2) 5.2 (4.3-6.3) 5.2 (4.4-6.1) 3.7 (3.1-4.4) 36.1 (33.1-39.2) 7.8 (6.7-9.0) 
Lesbian, gay, or bisexual 34.1 (30.8-37.6) 10.4 (7.8-13.7) 8.1 (5.9-11.1) 3.2 (2.0-5.2) 40.3 (36.2-44.4) 10.4 (8.0-13.5) 
Not sure 24.9 (19.8-30.7) 7.4 (4.8-11.3) 7.2 (4.3-12.0) 5.5 (3.1-9.5) 30.0 (23.3-37.6) 8.1 (5.4-11.9) 


Abbreviation: Cl = confidence interval. 

* Percentage of students who used an electronic vapor product, including e-cigarettes, e-cigars, e-pipes, vape pipes, vaping pens, e-hookahs, and hookah pens 
(e.g. blu, NJOY, Vuse, MarkTen, Logic, Vapin Plus, eGo, and Halo), on =1 day during the 30 days before the survey. 

t Percentage of students who smoked cigarettes on >1 day during the 30 days before the survey. 

$ Percentage of students who smoked cigars, cigarillos, or little cigars on >1 day during the 30 days before the survey. 

4 Percentage of students who used smokeless tobacco, including chewing tobacco, snuff, dip, snus, or dissolvable tobacco products (e.g., Red Man, Levi Garrett, 
Beechnut, Skoal, Skoal Bandits, Copenhagen, Camel Snus, Marlboro Snus, General Snus, Ariva, Stonewall, or Camel Orbs), but not including any electronic vapor 
products, on =1 day during the 30 days before the survey. 

** Percentage of students who smoked cigarettes or cigars or used smokeless tobacco or an electronic vapor product, on =1 day during the 30 days before the survey. 

tt Percentage of students who used 22 of the following tobacco products: cigarettes, cigars (cigars, cigarillos, or little cigars), an electronic vapor product, or smokeless 
tobacco, on =1 day during the 30 days before the survey. 

SS Sex pairwise comparisons assessed by t-test (p<0.05): for cigarettes, cigars, smokeless tobacco, and >2 products, male students were significantly different (p<0.05) 
from female students. 

11 Grade pairwise comparisons assessed by t-test (p<0.05): for electronic vapor products and any tobacco product: all pairwise comparisons were significantly 
different (p<0.05); for cigarettes, cigars, and =2 products: 12th grade was significantly different (p<0.05) than 9th, 10th, and 11th grades; 11th grade was significantly 
different (p<0.05) than 9th grade; for smokeless tobacco: 12th grade was significantly different (p<0.05) than 9th, 10th, and 11th grades; 10th and 11th grades 
were significantly different (p<0.05) than 9th grade. 

*** Race/ethnicity pairwise comparisons assessed by t-test (p<0.05): for electronic vapor products and any tobacco product: all pairwise comparisons were significantly 
different (p<0.05); for cigarettes and =>2 products: white and Hispanic were significantly different (p<0.05) than black. 

ttt Age pairwise comparisons assessed by t-test (p<0.05): for electronic vapor products, cigarettes, smokeless tobacco, any tobacco product, and >2 products: all 
pairwise comparisons were significantly different (p<0.05); for cigars: >18 years was significantly different (p<0.05) than 16-17 years and <15 years. 

588 Sexual identity pairwise comparisons assessed by t-test (p<0.05): for electronic vapor products: heterosexual and lesbian, gay, or bisexual were significantly 
different (p<0.05) than not-sure students; for cigarettes, cigars, and =>2 products: lesbian, gay, or bisexual was significantly different (p<0.05) than heterosexual; 
for any tobacco product: lesbian, gay, or bisexual was significantly different (p<0.05) than heterosexual and not-sure students. 


usually bought electronic vapor products ina store. In contrast, 
compared with older students, a higher prevalence of students 


smoking also was identified: a 6-year increase in prevalence 


(from 27.5% in 1991 to 36.4% in 1997) was followed by 


aged <17 years got them on the Internet, gave someone else 
money to buy them, borrowed them from someone else, got 
them from a person who could legally buy them, or got them 
some other way. 

Trend analyses indicated that during 2015-2019, a significant 
linear increase occurred in prevalence of current electronic 
vapor products use (from 24.1% to 32.7%) (Figure 2). Trend 
analyses also indicated that during 1991-2019, a significant 
linear decrease in current cigarette smoking was observed (from 
27.5% to 6.0%). A significant quadratic trend in cigarette 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


a 22-year decrease (from 36.4% in 1997 to 6.0% in 2019). 
Additionally, during 1997-2019, a significant linear decrease 
(from 22.0% to 5.7%) occurred in the overall prevalence 
of current cigar smoking. A significant quadratic trend also 
was identified: a 16-year decrease in prevalence (from 22.0% 
in 1997 to 12.6% in 2013) was followed by another 6-year 
decrease, but at a different rate of decrease (from 12.6% in 
2013 to 5.7% in 2019). 

During 2017-2019, a significant increase occurred in current 
electronic vapor products use (from 13.2% to 32.7%) and 


MMWR / August 21, 2020 / Vol.69 / No.1 59 


Supplement 


FIGURE 1. Prevalence of frequent tobacco use* among current users, by type of tobacco product? — Youth Risk Behavior Survey, United States, 
2017 and 20198 


100 


O 2017 
40 E 2019 


35 


30 


25 
20 
15 
10 

0 


Electronic vapor products 


Prevalence (%) 


wal 


Cigarettes Cigars Smokeless tobacco 


Tobacco product 


* Frequent use was defined as use on 220 days during the 30 days before the survey. 

t Frequent use was assessed among respondents who reported current use (on >1 day during the 30 days before the survey) of each tobacco product. In 2017, among 
the 13.2% of students nationwide who used electronic vapor products on >21 day during the 30 days before the survey; among the 8.8% of students nationwide 
who smoked cigarettes on =1 day during the 30 days before the survey; among the 8.0% of students nationwide who smoked cigars on =1 day during the 30 days 
before the survey; among the 5.5% of students nationwide who used smokeless tobacco on =1 day during the 30 days before the survey. In 2019, among the 32.7% 
of students nationwide who used electronic vapor products on =1 day during the 30 days before the survey; among the 6.0% of students nationwide who smoked 
cigarettes on =1 day during the 30 days before the survey; among the 5.7% of students nationwide who smoked cigars on =1 day during the 30 days before the 
survey; among the 3.8% of students nationwide who used smokeless tobacco on =1 day during the 30 days before the survey. 

$ Differences from 2017 to 2019 were assessed by t-test (p<0.05): A significant increase occurred in frequent use of electronic vapor products; a significant decrease 
occurred in frequent use of cigarettes; and no change occurred in frequent use of cigars/cigarillos/little cigars and smokeless tobacco. 


any tobacco product use (from 19.5% to 36.5%). During 
2017-2019, significant decreases were observed in current 
cigarette smoking (from 8.8% to 6.0%), current cigar smoking 
(from 8.0% to 5.7%), and current smokeless tobacco use (from 
5.5% to 3.8%). No change occurred in use of two or more 
tobacco products during 2017-2019. 


Discussion 


In 2019, a total of 36.5% of high school students currently 
used any tobacco product, with electronic vapor products being 
the most commonly used product. This reflects an increase in 
use of electronic vapor products from 2017 to 2019, findings 
that are consistent with those from other national surveillance 


60 MMWR / August 21,2020 / Vol.69 / No.1 


systems, including NYTS (9,12) and Monitoring the Future 
(13). For example, NYTS results demonstrated that, among 
high school students, e-cigarette use increased from 11.7% 
in 2017 to 27.5% in 2019 (9,12). These increases align 
with the increasing popularity of newer electronic vapor 
product devices, including JUUL (7). The dramatic increase 
in electronic vapor product use among high school students 
has led to increases in overall tobacco product use among 
U.S. youths, erasing gains made in previous years and leading 
the U.S. Surgeon General to declare youth e-cigarette use an 
epidemic in the United States (/0). 

Use of any tobacco product among youth is unsafe, regardless 
of frequency of use or number of products used. Although the 
2019 national YRBS results indicate that most current youth 
tobacco product users are infrequent users, variations exist by 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 2. Usual source* of obtaining electronic vapor products among 
current electronic vapor product users,t by age — Youth Risk 
Behavior Survey, United States, 2019 


Age group’ 
218 yrs <17 yrs 

Usual source % (95% Cl) % (95% Cl) 
Bought them in a store (e.g., a 56.4 (51.0-1.6) 8.1 (6.8-9.6) 

convenience store, supermarket, 

discount store, gas station, or 

vape store) 
Got them on the Internet 1.8 (0.9-3.4) 3.6 (2.8-4.6) 
Gave someone else money to buy 3.1 (1.5-6.1) 21.3 (19.5-23.2) 


them for me 


Borrowed them from someone else 27.5 (23.4-32.0) 42.8 (40.2-45.4) 


A person who can legally buy these 3.9 (2.4-6.3) 11.1 (9.9-12.3) 
products gave them to me 

Took them from a store or 2.0 (0.8-5.0) 1.6 (1.1-2.4) 
another person 

Got them some other way 5.4 (3.3-8.8) 11.6 (10.1-13.4) 


Abbreviation: Cl = confidence interval. 

* Students were limited to selecting only one response. 

t Including e-cigarettes, e-cigars, e-pipes, vape pipes, vaping pens, e-hookahs, 
or hookah pens (e.g., blu, NJOY, Vuse, MarkTen, Logic, Vapin Plus, eGo, and 
Halo) among students who used electronic vapor products during the 30 days 
before the survey. 

$ Comparisons between age groups were assessed by t-test (p<0.05). All 
comparisons were statistically different with the exception of “took them from 
a store or another person.” 


product; for example, frequent use ranged from 18.4% for 
cigars to 32.6% for electronic vapor products. In addition, these 
results indicate that frequent use of electronic vapor products 
increased during 2017-2019; whereas frequent use of other 
products decreased or did not change. Even infrequent tobacco 
product use, particularly cigarette smoking, is predictive of 
progression to daily smoking (14. Nearly all tobacco products 
include nicotine, and even infrequent use of tobacco products 
has been linked to symptoms of nicotine dependence (15). 
Further, 8.2% of high school students currently used two or 
more tobacco products in 2019. Multiple tobacco product use is 
associated with substance use disorders (16) and might increase 
nicotine exposure and risk for nicotine dependence (15). 

In 2019, electronic vapor product users aged <17 years 
usually obtained their products from social sources (e.g., 
by borrowing them from someone). This is consistent with 
results from both the Population Assessment of Tobacco and 
Health Study and NYTS, which also determined that social 
sources were the most common way for adolescents to obtain 
electronic vapor products (17,18). These social sources might 
include older students who are of legal age for purchasing 
the products in their state or community. In 2016, electronic 
vapor products were deemed to be tobacco products under 
the Family Smoking Prevention Tobacco Control Act (https:// 
www.federalregister.gov/documents/2016/05/10/2016-10685/ 
deeming-tobacco-products-to-be-subject-to-the-federal-food- 
drug-and-cosmetic-act-as-amended-by-the), thus setting the 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


federal minimum purchase age for electronic vapor products 
at 18 years. However, on December 20, 2019, federal 
legislation increased the minimum age of sales of tobacco 
products from 18 to 21 years nationwide; the law does not 
preempt more stringent state or local age of sale laws (https:// 
www.fda.gov/tobacco-products/retail-sales-tobacco-products/ 
selling-tobacco-products-retail-stores). Before this federal 
law, 19 states, the District of Columbia, Guam, and Palau 
had enacted laws that increased the age of sale for tobacco 
products to 21 years, including 13 laws enacted during 2019 
(19). Such laws might limit the ability for high school students 
to obtain tobacco products from their peers, including those 
older students who were of legal age to purchase these products 
in their state or community before the law’s implementation. 

Multiple factors continue to promote and influence tobacco 
product use among youths, including exposure to tobacco 
product advertising and imagery through media, as well as the 
availability of flavored tobacco products. The sustained and 
comprehensive implementation of population-based strategies, 
in coordination with the regulation of tobacco products by 
the U.S. Food and Drug Administration (FDA), can reduce 
all forms of tobacco product use and initiation among U.S. 
youths. Such strategies include increasing the price of tobacco 
products, implementing comprehensive smoke-free policies, 
implementing advertising and promotion restrictions and 
national antitobacco public education media campaigns, 
restricting youth access to flavored tobacco products, and 
implementing policies that increase the minimum age of 
purchase for tobacco products to 21 years (1,2,4,10). In 
addition to population-level policies for preventing and 
reducing initiation of tobacco product use among youths, 
tools from the National Cancer Institute (e.g., https:// 
teen.smokefree.gov) and the Truth Initiative (e.g., https:// 
truthinitiative.org/thisisquitting) provide resources to help 
youth quit tobacco product use. 


Limitations 


Limitations for YRBS overall are available in the overview 
report of this supplement (//). This report is subject to at least 
three additional limitations. First, changes in question wording 
for smokeless tobacco use in 2017 prohibit comparability with 
previous years’ data and long-term trend analyses for prevalence 
of smokeless tobacco use, any tobacco product use, and use of 
two or more tobacco products. Second, the question addressing 
how students usually obtained electronic vapor products requires 
that respondents select only one response, although they might 
have obtained these products through multiple sources; therefore, 
the full scope of the sources students use to access these products 


MMWR / August 21, 2020 / Vol.69 / No.1 61 


Supplement 


FIGURE 2. Prevalence of current tobacco product use, by year — Youth Risk Behavior Survey, United States, 1991-2019* 


100 
40 
35 
30 
£ 
o 25 
v 
= 
L 
© 
Go 20 
a 
15 Any tobacco product 
Cigarettes 
10 Cigars/cigarillos/little cigars 
Electronic vapor products 
Smokeless tobacco 
5 >2 products 
0 


1991 1993 1995 1997 1999 2001 2003 





2005 2007 2009 2011 2013 2015 2017 2019 


Year 


* Logistic regression analyses were used to model linear and quadratic time effects while controlling for sex, grade, and race/ethnicity. Electronic vapor products: 
significant linear increase (2015-2019); cigarettes: significant linear decrease (1991-2019); significant quadratic trend: increase during 1991-1997, decrease during 
1997-2019; cigars/cigarillos/little cigars: significant linear decrease (1997-2019); significant quadratic trend: decrease 1997-2013; decrease 2013-2019 (different 
rate of decrease). Differences from 2017 to 2019 were assessed by t-test (p<0.05): A significant increase occurred in use of electronic vapor products and any tobacco 
product; a significant decrease occurred in use of cigarettes, cigars/cigarillos/little cigars and smokeless tobacco; and no change occurred in use of =2 products. 


might not have been addressed. Finally, the questions related to 
electronic vapor products and cigars do not specifically exclude 
the possibility of marijuana use in either product (e.g., blunt use). 


Conclusion 


Although current use of cigarettes, cigars, and smokeless 
tobacco among U.S. high school students has decreased, 
tobacco product usage has evolved, and the increasing 
prevalence of electronic vapor product use among youths 
during recent years is concerning. Implementing evidence- 
based tobacco control strategies, combined with FDA’s 
regulatory efforts, is important for preventing and reducing 
all forms of tobacco product use among youths. In addition, 
continued surveillance of all tobacco products is warranted for 
guiding and evaluating public health policy at the local, state, 
tribal, and national levels. 


62 MMWR / August 21, 2020 / Vol.69 / No.1 


Conflicts of Interest 


All authors have completed and submitted the International 
Committee of Medical Journal Editors form for disclosure of 
potential conflicts of interest. No potential conflicts of interest 
were disclosed. 


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mmwr.mm6907a3 


MMWR / August 21, 2020 / Vol.69 / No.1 63 


Supplement 


Dietary and Physical Activity Behaviors Among High School 
Students — Youth Risk Behavior Survey, United States, 2019 


Caitlin L. Merlo, MPH!; Sherry Everett Jones, PhD, JD?; Shannon L. Michael, PhD!; Tiffany J. Chen, MSPH3; 
Sarah A. Sliwa, PhD}; Seung Hee Lee, PhD’; Nancy D. Brener, PhD2?; Sarah M. Lee, PhD!; Sohyun Park, PhD? 


' Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, CDC; ? Division of Adolescent and School Health, 
National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC; 3 Division of Nutrition, Physical Activity, and Obesity, National Center for 
Chronic Disease Prevention and Health Promotion, CDC 


Abstract 


Establishing healthy dietary and physical activity patterns among youths is an important public health strategy for improving 
health and preventing chronic diseases; however, few adolescents meet U.S. government recommendations for dietary or physical 
activity behaviors, and disparities by sex and race/ethnicity exist. CDC analyzed data from the 2019 Youth Risk Behavior Survey to 
update estimates of dietary and physical activity behaviors among U.S. high school students overall and by sex and race/ethnicity. 
In addition, 2-year comparisons (2017 and 2019) and trends in prevalence of these behaviors during 2009-2019 were examined. 
In 2019, overall, during the 7 days before the survey, 41.8% of students had eaten fruit or drunk 100% fruit juices <1 time/day; 
40.7% had eaten vegetables <1 time/day; and 16.7% had not eaten breakfast on all 7 days. Moreover, although 57.4% of students 
had played on 21 sports team during the 12 months before the survey, less than half of students had been physically active for 
260 minutes/day on all 7 days (23.2%), had exercised to strengthen or tone their muscles on 23 days/week (49.5%), had met both 
aerobic and muscle-strengthening physical activity guidelines (16.5%), or had attended physical education classes on all 5 days in 
an average school week (25.9%). Trend data indicate limited progress in shifting dietary and physical activity behaviors. That is, 
with the exception of decreases in the percentage of students who had consumed soda 21 time/day (2009: 29.2%; 2019: 15.1%), 
sports drinks 21 time/day (2015: 13.8%; 2019: 10.6%), and <3 glasses/day of plain water (2015: 50.5%; 2019: 44.6%), high 
school students’ dietary and physical activity behaviors have not improved and, in certain cases, have worsened. These findings 
support the need for multicomponent approaches, including policy and environmental changes, and opportunities for adolescents 
to learn about and practice making healthy choices. 


Introduction for Americans 2015-2020 outlines key recommendations for 
following a healthy eating pattern within calorie levels that are 
appropriate for a person’s age, sex, height, weight, and physical 
activity level (7). Recommendations include eating a variety 
of vegetables, fruits, and whole grains, and limiting sodium, 
added sugars, and saturated and trans fats. 

The Physical Activity Guidelines for Americans, 2nd Edition, 
recommends that children and adolescents ages 6-17 years 
engage in 260 minutes of moderate-to-vigorous physical 
activity daily (2). Most of this daily physical activity should 
be aerobic activity, whereas muscle- and bone-strengthening 
physical activity should each be done >3 days each week (2). 

Previous data indicate that most adolescents are not meeting 
recommendations for healthy eating (Z) or physical activity 
(3), which increases the risk for chronic diseases later in life. 
In addition, disparities by sex and race/ethnicity exist (4). 
To update national estimates of dietary and physical activity 
behaviors among U.S. high school students overall and by 


Nutrition and physical activity are important for optimal 
growth and development and chronic disease prevention. 
Approximately half of U.S. adults have a chronic disease that 
is related to inadequate diet quality and physical activity, 
including type 2 diabetes, cardiovascular diseases, or obesity 
(1). Such diseases can affect productivity and quality of life and 
contribute to high health-care costs. Dietary and physical activity 
behaviors develop during childhood and can create a trajectory 
that continues into adulthood (7,2). Establishing healthy dietary 
and physical activity behaviors early in life is a vital public health 
strategy for promoting lifelong physical health. 

The U.S. government establishes recommendations for 
healthy dietary and physical activity patterns for persons of 
different ages, including adolescents. The Dietary Guidelines 


Corresponding author: Caitlin L. Merlo, MPH, Division of Population a : i 
Health, National Center for Chronic Disease Prevention and Health sex and race/ ethnicity and to determine how these behaviors 


Promotion, CDC. Telephone: 770-488-6171; E-mail: ihb7@cdc.gov. have changed over time, CDC analyzed data from the 2019 
Youth Risk Behavior Survey (YRBS) and examined trends in 





64 MMWR / August 21,2020 / Vol.69 / No.1 US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


prevalence of these behaviors during the previous 10 years. 
Public health and school health researchers and practitioners 
can use these findings to inform policies and practices that 
support healthy eating and physical activity among adolescents. 


Methods 


Data Source 


This report includes data from the 2009-2019 cycles of 
the YRBS, a cross-sectional, school-based survey conducted 
biennially since 1991. Each survey year, CDC collects data 
from a nationally representative sample of public and private 
school students in grades 9-12 in the 50 U.S. states and the 
District of Columbia. Additional information about YRBS 
sampling, data collection, response rates, and processing is 
available in the overview report of this supplement (5). The 
prevalence estimates for all physical activity, nutrition, and 
body weight questions for the overall study population and by 
sex, race/ethnicity, grade, and sexual orientation are available 
at https://nccd.cdc.gov/youthonline/App/Default.aspx. The 
full YRBS questionnaire is available at https://www.cdc.gov/ 
healthyyouth/data/yrbs/pdf/2019/2019_YRBS-National-HS- 
Questionnaire.pdf. 


Measures 


The student demographic characteristics analyzed included 
sex (female or male) and race/ethnicity. Students were classified 
into four racial/ethnic categories: non-Hispanic white (white); 
non-Hispanic black (black); Hispanic or Latino of any race 
(Hispanic); and other or multiple races. The numbers of 
students in the other or multiple racial/ethnic groups were 
too small for meaningful analysis; therefore, findings for 
those groups are not presented; however, the corresponding 
data remain in the analytic sample. This analysis included six 
dietary variables and five physical activity variables (Table 1). 
The dietary variables included the following: during the 7 days 
before the survey, had eaten fruit or drunk 100% fruit juices 
<1 time/day, had eaten vegetables <1 time/day, had not eaten 
breakfast on all 7 days, had drunk soda or pop 21 time/day 
(not counting diet soda or diet pop), had drunk a sports drink 
21 time/day, and had drunk <3 glasses/day of plain water. The 
physical activity variables included the following: during the 
7 days before the survey, had been physically active for a total 
of >60 minutes/day on all 7 days, had exercised to strengthen 
or tone muscles on 23 days, had met both aerobic and muscle- 
strengthening physical activity guidelines (defined as being 
physically active for a total of 260 minutes/day on all 7 days 
and doing exercises to strengthen or tone muscles on 23 days), 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


had attended physical education classes on all 5 days in an 
average school week, and had played on 21 sports team during 
the 12 months before the survey. 


Analysis 


Prevalence estimates and 95% confidence intervals for each 
2019 dietary and physical activity behavior were calculated 
overall and for each sex and racial/ethnic group. Statistically 
significant pairwise differences by sex and race/ethnicity were 
determined by tests. In addition, prevalence of each dietary 
and physical activity behavior was compared for 2017 with 
2019 by using tests. Differences between prevalence estimates 
were considered statistically significant if the +test p value 
was <0.05. 

To identify 10-year temporal trends, logistic regression analyses 
were used to model linear and quadratic time effects while 
controlling for sex, grade (9, 10, 11, and 12), and racial/ethnic 
changes over time (6). All variables had data available for 
2009-2019, except for did not eat breakfast on all 7 days; were 
physically active for a total of >60 minutes/day on all 7 days; did 
exercises to strengthen or tone muscles on 23 days and met both 
aerobic and muscle-strengthening physical activity guidelines, 
which had data for 2011-2019 only; and drank a sports drink 
21 time/day and drank <3 glasses/day of plain water, which had 
data for 2015-2019 only. Additional information about the 
methods used to conduct YRBS trend analyses are provided in 
the overview report of this supplement (5). 


Results 


Dietary Behaviors 


Overall 


In 2019, nationwide, 41.8% of students had eaten fruit 
or drunk 100% fruit juices <1 time/day; 40.7% had eaten 
vegetables <1 time/day; 16.7% had not eaten breakfast on 
all 7 days; 15.1% had drunk sugar-sweetened soda or pop 
21 time/day (not counting diet soda or diet pop); 10.6% 
had drunk a sports drink >1 time/day; and 44.6% had drunk 
<3 glasses/day of plain water (Table 2). A higher percentage of 
male students than female students had drunk sugar-sweetened 
soda or pop 21 time/day (18.2% versus 11.7%) and had drunk 
a sports drink 21 time/day (14.0% versus 7.1%). A higher 
percentage of black students than white and Hispanic students 
had eaten fruit or drunk 100% fruit juices <1 time/day (47.8% 
versus 42.1% and 39.5%, respectively), had eaten vegetables 
<1 time/day (54.8% versus 35.5% and 46.8%, respectively), 
had drunk a sports drink 21 time/day (15.6% versus 9.3% 


MMWR / August 21, 2020 / Vol.69 / No.1 65 


Supplement 


TABLE 1. Question wording and details for included dietary and physical activity behavior variables — Youth Risk Behavior Survey, United 


States, 2019 


Variable Question 


Dietary behaviors 

Ate fruit or drank 
100% fruit juices 
<1 time/day 


During the past 7 days, how many times did you... 

+ drink 100% fruit juices such as orange juice, 
apple juice, or grape juice? (Do not count 
punch, Kool-Aid, sports drinks, or other 
fruit-flavored drinks.) 

+ eat fruit? (Do not count fruit juice.) 


Ate vegetables 
<1 time/day 


During the past 7 days, how many times 
did you eat... 

e green salad? 

+ potatoes? (Do not count French fries, fried 
potatoes, or potato chips.) 

e carrots? 

+ other vegetables? (Do not count green salad, 
potatoes, or carrots.) 


Did not eat breakfast 
on all 7 days 


During the past 7 days, on how many days did 
you eat breakfast? 


Drank soda or pop 
21 time/day 


During the past 7 days, how many times did you 
drink a can, bottle, or glass of soda or pop, such 
as Coke, Pepsi, or Sprite? (Do not count diet 
soda or diet pop.) 


Drank a sports drink 
21 time/day 


During the past 7 days, how many times did you 
drink a can, bottle, or glass of a sports drink, 
such as Gatorade or Powerade? (Do not count 
low-calorie sports drinks such as Propel or G2.) 


Drank <3 glasses/day During the past 7 days, how many times did you 
of plain water drink a bottle or glass of plain water? (Count 
tap, bottled, and unflavored sparkling water.) 


Physical activity behaviors 
Were physically During the past 7 days, on how many days were 
active foratotalof you physically active for a total of at least 
260 minutes/day 60 minutes per day? (Add up all the time you 
on all 7 days spent in any kind of physical activity that 
increased your heart rate and made you 
breathe hard some of the time.) 


Did exercises to 
strengthen or tone 
muscles on 23 days 


During the past 7 days, on how many days did you 
do exercises to strengthen or tone your muscles, 
such as push-ups, sit-ups, or weightlifting? 


Met both aerobic 
and muscle- 
strengthening 


[See “were physically active for a total of 
260 minutes/day on all 7 days” and “did 
exercises to strengthen or tone muscles on 


physical activity 23 days.”] 
guidelines 
Attended physical In an average week when you are in school, on 
education classes how many days do you go to physical 
on all 5 days education (PE) classes? 


During the past 12 months, on how many sports 
teams did you play? (Count any teams run by 
your school or community groups.) 


Played on =1 sports 
team 


66 MMWR / August 21,2020 / Vol.69 / No.1 


Years of data 
available for 10-year 


Response options trend analysis 


| did not [drink 100% fruit juice]/[eat 2009-2019 
fruit] during the past 7 days, 1-3 times 

during the past 7 days, 4-6 times 

during the past 7 days,1 time/day, 

2 times/day, 3 times/day, or 


24 times/day 


| did not eat [green salad]/[potatoes]/ 2009-2019 
[carrots]/[other vegetables] during 

the past 7 days, 1-3 times during the 

past 7 days, 4-6 times during the past 

7 days, 1 time/day, 2 times/day, 


3 times/day, or =4 times/day 


0 days, 1 day, 2 days, 3 days, 4 days, 2011-2019 


5 days, 6 days, or 7 days 


| did not drink soda or pop during the 2009-2019 
past 7 days, 1-3 times during the past 

7 days, 4-6 times during the past 

7 days, 1 time/day, 2 times/day, 


3 times/day, or =4 times/day 


| did not drink sports drinks during the 2015-2019 
past 7 days, 1-3 times during the past 

7 days, 4-6 times during the past 

7 days, 1 time/day, 2 times/day, 


3 times/day, or 24 times/day 


| did not drink water during the past 2015-2019 
7 days, 1-3 times during the past 

7 days, 4-6 times during the past 

7 days, 1 time per day, 2 times per day, 


3 times/day, or 24 times/day 


0 days, 1 day, 2 days, 3 days, 4 days, 2011-2019 


5 days, 6 days, or 7 days 


0 days, 1 day, 2 days, 3 days, 4 days, 2011-2019 


5 days, 6 days, or 7 days 


Not applicable 2011-2019 


0 days, 1 day, 2 days, 3 days, 4 days, or 2009-2019 


5 days 


0 teams, 1 team, 2 teams, or >3 teams 2009-2019 


Coding for analysis 


<1 time/day versus 
21 time/day 


<1 time/day versus 
21 time/day 


<7 days versus 7 days 


21 time/day versus 
<1 time/day 


21 time/day versus 
<1 time/day 


23 times/day versus 
<3 times/day 


7 days versus <7 days 


>3 days versus <3 days 


Physically active for 
260 minutes/day on all 
7 days and did exercises 
to strengthen or tone 
muscles on 23 days 
versus physically active 
for <60 minutes/day on 
all 7 days or did exercises 
to strengthen or tone 
muscles on <3 days 


>5 days versus <5 days 


21 team versus <1 team 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 2. Percentage of high school students who engaged in selected dietary and physical activity behaviors, by sex and race/ethnicity — 
Youth Risk Behavior Survey, United States, 2019 


Variable 


Dietary behaviors 

Ate fruit or drank 100% fruit juices 
<1 time/day* 

Ate vegetables <1 time/day! 

Did not eat breakfast on all 7 days during the 
7 days before the survey 

Drank sugar-sweetened soda or pop =1 
time/day** 

Drank a sports drink >1 time/dayS$ 

Drank <3 glasses/day of plain water"! 


Total 
% (95% Cl) 


41.8 (39.8-43.8) 


40.7 (38.0-43.4) 
16.7 (15.3-18.1) 


15.1 (13.1-17.2) 


10.6 (9.2-12.3) 
44.6 (42.7-46.5) 


Sex 


Female 
% (95% Cl) 


43.0 (40.7-45.4) 


40.4 (37.2-43.6) 
16.7 (15.2-18.3) 


11.7 (9.9-13.8) 


7.1 (5.7-8.8) 
44.1 (42.0-46.1) 


Male 
% (95% Cl) 


40.6 (38.2-43.1) 


41.1 (38.1-44.3) 
16.6 (14.9-18.4) 


18.2tt (15.9-20.8) 


14.01 (11.9-16.4) 
45.0 (42.3-47.6) 


White, 
non-Hispanic 
% (95% Cl) 


42.1 (39.2-45.1) 


35.5 (33.2-37.8) 
15.3 (13.9-16.8) 


15.2 (12.7-18.0) 


9.3 (7.7-11.2) 
44.2 (41.7-46.7) 


Race/Ethnicity 


Black, 
non-Hispanic 
% (95% Cl) 


47.815 (43.6-51.9) 


54.818 (50.1-59.4) 
21.11 (17.3-25.6) 


16.9 (13.5-21.0) 


15.618 (12.9-18.8) 
54.818 (49.0-60.4) 


Hispanic 
% (95% Cl) 


39.5 (36.7-42.3) 


46.81 (41.8-52.0) 
16.9 (14.1-20.0) 


16.1 (13.1-19.6) 


11.9t (10.2-13.8) 
44.2 (41.8-46.7) 


Physical activity behaviors 

Were physically active for a total of 
260 minutes/day on all 7 days*** 

Did exercises to strengthen or tone muscles 
on 23 daysttt 

Met both aerobic and muscle-strengthening 
physical activity guidelines$$§ 

Went to physical education classes on 
all 5 days!" 

Played on =1 sports team**** 


23.2 (21.9-24.6) 
49.5 (47.6-51.3) 
16.5 (14.6-18.6) 
25.9 (21.5-31.0) 


57.4 (54.3-60.4) 


Abbreviation: Cl = confidence interval. 


15.4 (14.2-16.6) 
39.7 (37.2-42.4) 

10.1 (8.7-11.6) 
22.8 (17.9-28.5) 


54.6 (51.1-58.0) 


30.9tt (28.9-33.1) 25.6 (24.1-27.2) 21.11 (17.6-25.2) 20.91 (18.6-23.5) 


59.01t (56.8-61.0) 50.8 (48.2-53.4) 47.0 (42.7-51.2) 48.1 (44.5-51.9) 


23.11 (20.4-26.0) 18.4 (15.8-21.4) 13.4t (9.5-18.4) 16.0 (13.7-18.6) 


28.91t (24.6-33.7) 24.3 (18.8-30.7) 23.8 (17.4-31.7) 29.9 (24.5-36.0) 





60.2tt (56.9-63.4) 62.0(58.1-65.7) 56.1t (51.4-60.7) 51.61 (46.5-56.6) 


* Such as orange juice, apple juice, or grape juice, not counting punch, Kool-Aid, sports drinks, or other fruit-flavored drinks during the 7 days before the survey. 


t Significantly different than white students based on t-test analysis (p<0.05). 

$ Significantly different than Hispanic students based on t-test analysis (p<0.05). 

‘Green salad, potatoes (not counting French fries, fried potatoes, or potato chips), carrots, or other vegetables during the 7 days before the survey. 
** Such as Coke, Pepsi, or Sprite, not counting diet soda or diet pop, during the 7 days before the survey. 

tt Significantly different than female students based on t-test analysis (p<0.05). 

S$ Such as Gatorade or PowerAde, not counting low-calorie sports drinks such as Propel water or G2, during the 7 days before the survey. 

11 Counting tap, bottled, and unflavored sparkling water during the 7 days before the survey. 


*** Adding up time spent in any kind of physical activity that increased their heart rate and made them breathe hard some of the time during the 7 days before the survey. 


ttt Such as push-ups, sit-ups, or weightlifting during the 7 days before the survey. 


SSS Were physically active for >60 minutes/day on all 7 days and did exercises to strengthen or tone muscles on 23 of the 7 days before the survey. 





499 In an average week when the student was in school. 


**** Counting any teams run by their school or community groups during the 12 months before the survey. 


and 11.9%, respectively), and had drunk <3 glasses/day of 
plain water (54.8% versus 44.2% and 44.2%, respectively). 
In addition, a higher percentage of Hispanic students than 
white students had eaten vegetables <1 time/day (46.8% versus 
35.3%) and had drunk a sports drink 21 time/day (11.9% 
versus 9.3%), and a higher percentage of black students than 
white students had not eaten breakfast on all 7 days (21.1% 
versus 15.3%). 


Trends 


Trend analyses indicated that, during 2009-2019, a 
significant linear increase occurred in the percentage of students 
who had eaten fruit or drunk 100% fruit juices <1 time/day 
overall and among female, male, white, black, and Hispanic 
students (Table 3). Significant quadratic trends were not 
identified except among black students. The percentage of 
black students who had eaten fruit or drunk 100% fruit 
juices <1 time/day did not change during 2009-2015 and 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


then increased during 2015-2019. During 2017-2019, the 
percentage of students who had eaten fruit or drunk 100% 
fruit juices <1 time/day increased among male students and 
black students. 

During 2009-2019, a significant linear increase occurred 
in the percentage of students who had eaten vegetables 
<1 time/day overall and among male, white, and black students. 
Significant quadratic trends were not identified, except among 
black students. The percentage of black students who had eaten 
vegetables <1 time/day did not change during 2009-2015 and 
then increased during 2015-2019. 

During 2011-2019, a significant linear increase occurred in 
the percentage of students who had not eaten breakfast on all 
7 days overall and among female, male, and white students. 
During 2017-2019, the percentage of students who had not 
eaten breakfast on all 7 days increased among students overall 
and among female, male, white, and black students. 


MMWR / August 21, 2020 / Vol.69 / No.1 67 


Supplement 


TABLE 3. Percentage of high school students who engaged in selected dietary behaviors, by sex, race/ethnicity, and survey year — Youth Risk 
Behavior Survey, United States, 2009-2019 


Prevalence (%) 


SS] Change during 
Behavior 2009 2011 2013 2015 2017 2019 Linear change* Quadratic change* 2017-20191 
Ate fruit or drank 100% fruit juices <1 time/day$ 
Total 35.2 36.0 37.4 36.7 39.2 41.8 Increased during None None 
2009-2019 
Female 37.6 38.4 40.0 37.9 41.8 43.0 Increased during None None 
2009-2019 
Male 33.0 33.9 34.7 35.4 36.7 40.6 Increased during None Increased 
2009-2019 
White, non-Hispanic 34.4 35.8 39.3 37.0 40.4 42.1 Increased during None None 
2009-2019 
Black, non-Hispanic 39.2 36.4 36.5 37.8 39.3 47.8 Increased during None during Increased 
2009-2019 2009-2015 
Increased during 
2015-2019 
Hispanic 35.6 35.3 35.0 35.9 37.6 39.5 Increased during None None 
2009-2019 
Ate vegetables <1 time/dayî 
Total 37.3 37.7 38.5 39.0 40.6 40.7 Increased during None None 
2009-2019 
Female 38.4 38.4 38.7 40.0 40.7 40.4 None None None 
Male 36.3 37.2 38.5 38.0 40.6 41.1 Increased during None None 
2009-2019 
White, non-Hispanic 32.7 34.3 35.2 35.8 37.2 35.5 Increased during None None 
2009-2019 
Black, non-Hispanic 48.8 45.7 48.1 475 50.6 54.8 Increased during None during None 
2009-2019 2009-2015 
Increased during 
2015-2019 
Hispanic 45.9 43.6 43.1 43.5 43.9 46.8 None None None 
Did not eat breakfast on all 7 days during the 7 days before the survey 
Total —** 13.1 13.7 13.8 14.1 16.7 Increased during —tt Increased 
2011-2019 
Female _* 13.9 13.8 14.2 14.5 16.7 Increased during —tt Increased 
2011-2019 
Male _—* 12.3 13.5 13.3 13.6 16.6 Increased during —tt Increased 
2011-2019 
White, non-Hispanic _* 12.0 11.5 12.0 12.8 15.3 Increased during —tt Increased 
2011-2019 
Black, non-Hispanic _—* 16.1 16.0 18.0 15.2 21.1 None —tt Increased 
Hispanic _* 14.4 174 14.7 16.0 16.9 None —tt None 
Drank sugar-sweetened soda or pop >21 time/dayS$ 
Total 29.2 27.8 27.0 20.4 18.7 15.1 Decreased during None during Decreased 
2009-2019 2009-2013 
Decreased during 
2013-2019 
Female 23.3 24.0 24.1 16.4 15.4 11.7 Decreased during None during Decreased 
2009-2019 2009-2013 
Decreased during 
2013-2019 
Male 34.6 31.4 29.9 24.3 22.3 18.2 Decreased during None Decreased 
2009-2019 
White, non-Hispanic 29.0 28.8 29.0 19.7 19.6 15.2 Decreased during None Decreased 
2009-2019 
Black, non-Hispanic 33.7 28.0 30.2 227 21.5 16.9 Decreased during None None 
2009-2019 
Hispanic 28.1 27.0 22.6 21.7 17.0 16.1 Decreased during None None 
2009-2019 


See table footnotes on the next page. 


68 MMWR / August 21, 2020 / Vol.69 / No.1 US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 3. (Continued) Percentage of high school students who engaged in selected dietary behaviors, by sex, race/ethnicity, and survey year — 


Youth Risk Behavior Survey, United States, 2009-2019 


Prevalence (%) 


Behavior 2009 2011 2013 2015 2017 


Drank a sports drink >1 time/day™! 


Total —** —** —** 13.8 12.4 
Female —** —** = 8.8 8.2 
Male —** _** _** 18.7 16.9 
White, non-Hispanic _** —** —** 12.4 10.7 
Black, non-Hispanic _** —** —** 19.7 21.1 
Hispanic —** —** =" 157 135 


Drank <3 glasses/day of plain water*** 


Total _* _* _* 50.5 48.7 
Female —** —** —** 51.9 48.8 
Male _* —** —** 49.0 48.6 
White, non-Hispanic —** —** —** 50.1 48.8 
Black, non-Hispanic _** —** —** 60.9 52.7 
Hispanic _* —** _* 49.7 475 


Change during 

2019 Linear change* Quadratic change* 2017-2019t 

10.6 Decreased during —tt None 
2015-2019 

7.1 None —tt None 

14.0 Decreased during —tt Decreased 

2015-2019 
9.3 Decreased during —tt None 

2015-2019 

15.6 None —tt Decreased 

11.9 Decreased during —tt None 
2015-2019 

44.6 Decreased during —tt Decreased 
2015-2019 

44.1 Decreased during —tt Decreased 
2015-2019 

45.0 Decreased during —tt Decreased 
2015-2019 

44.2 Decreased during —tt Decreased 
2015-2019 

54.8 None —tt None 

44.2 Decreased during —tt Decreased 
2015-2019 


* Based on trend analyses by using a logistic regression model controlling for sex, race/ethnicity, and grade (p<0.05). 


t Based on t-test analysis (p<0.05). 


$ Such as orange juice, apple juice, or grape juice, not counting punch, Kool-Aid, sports drinks, or other fruit-flavored drinks, during the 7 days before the survey. 
‘Green salad, potatoes (not counting French fries, fried potatoes, or potato chips), carrots, or other vegetables during the 7 days before the survey. 


** Data not available. Question not asked in that year. 
tt Insufficient years of data to assess quadratic trends. 


SS Such as Coke, Pepsi, or Sprite, not counting diet soda or diet pop, during the 7 days before the survey. 
11 Such as Gatorade or PowerAde, not counting low-calorie sports drinks such as Propel water or G2, during the 7 days before the survey. 
*** Counting tap, bottled, and unflavored sparkling water during the 7 days before the survey. 


During 2009-2019, a significant linear decrease occurred 
in the percentage of students who had drunk sugar-sweetened 
soda or pop 21 time/day overall and among female, male, white, 
black, and Hispanic students (Figure 1). Significant quadratic 
trends were identified overall and among female students. 
Overall and among female students, the percentage of students 
who had drunk sugar-sweetened soda or pop 21 time/day did 
not change during 2009-2013 and then decreased during 
2013-2019. During 2017-2019, the percentage of students who 
had drunk sugar-sweetened soda or pop 21 time/day decreased 
overall and among female, male, and white students. 

During 2015-2019, a significant linear decrease occurred 
in the percentage of students who had drunk a sports drink 
21 time/day overall and among male, white, and Hispanic 
students (Figure 2). During 2017-2019, the percentage of 
students who had drunk a sports drink 21 time/day decreased 
among male students and black students. 

During 2015-2019, a significant linear decrease occurred 
in the percentage of students who had drunk <3 glasses/day 
of plain water overall and among female, male, white, and 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Hispanic students. During 2017-2019, the percentage of 
students who had drunk <3 glasses/day of plain water decreased 
overall and among female, male, white, and Hispanic students. 


Physical Activity Behaviors 


Overall 


In 2019, nationwide, 23.2% of students had been physically 
active for >60 minutes/day on all 7 days; 49.5% had exercised 
to strengthen or tone their muscles on 23 days/week; 16.5% 
had met both aerobic and muscle-strengthening physical 
activity guidelines; 25.9% had attended physical education 
classes on all 5 days in an average school week; and 57.4% had 
played on 21 sports team (Table 2). A higher percentage of 
male students than female students had been physically active 
for 260 minutes/day on all 7 days (30.9% versus 15.4%), had 
exercised to strengthen or tone muscles on 23 days (59.0% 
versus 39.7%), had met both aerobic and muscle-strengthening 
physical activity guidelines (23.1% versus 10.1%), had 


MMWR / August 21, 2020 / Vol.69 / No.1 69 


Supplement 


FIGURE 1. Percentage of high school students who had drunk sugar-sweetened soda or pop 21 time per day during the 7 days before the 
survey, overall and by sex and race/ethnicity* — Youth Risk Behavior Survey, United States, 2009-2019 





2015 2017 2019 


Year 


100 
= Overall 
== == Female 
40 === Male 
a eee ee 
Dn oe i a aan ata oe 
S 30 A 
c ey 
(oD) "aay 
Ko “an, 
D — e e e e e å å å å å å oy an 
a 
20 
10 
0 
2009 2011 2013 
100 
=== White, non-Hispanic 
== = Black, non-Hispanic 
40 se Hispanic 
v 
3 30 
£ 
i= 
oO 
g 
v 
a 


20 


10 


2009 2011 2013 





2015 2017 2019 


Year 


* During 2009-2019, a significant linear decrease was observed in the percentage of students who had drunk sugar-sweetened soda or pop =1 time/day overall and among 
female, male, white, black, and Hispanic students. Based on trend analyses by using a logistic regression model controlling for sex, race/ethnicity, and grade (p<0.05). 


attended physical education classes on all 5 days in an average 
school week (28.9% versus 22.8%), and had played on 
21 sports team (60.2% versus 54.6%) (Figure 3). A higher 
percentage of white students than black students had been 
physically active for 260 minutes/day on all 7 days (25.6% 
versus 21.1%), had met both aerobic and muscle-strengthening 
physical activity guidelines (18.4% versus 13.4%), and had 
played on 21 sports team (62.0% versus 56.1%). In addition, 


70 MMWR / August 21,2020 / Vol.69 / No.1 


a higher percentage of white students than Hispanic students 
had been physically active for >60 minutes/day on all 7 days 
(25.6% versus 20.9%) and had played on =1 sports team 
(62.0% versus 51.6%) (Table 2). 


Trends 


During 2011-2019, a significant linear decrease occurred 
in the percentage of students who had been physically active 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


FIGURE 2. Percentage of high school students who had drunk a sports drink 21 time per day during the 7 days before the survey, overall and 
by sex and race/ethnicity* — Youth Risk Behavior Survey, United States, 2015-2019 


100 


25 





20 
(oD) 
od) 
B 
o 15 
Y 
w 
a 
10 
5 = Overall 
== == Female 
oa Male 
0 
2015 2017 2019 
Year 


1 


Percentage 


00 


25 





20 
15 
10 

5 === White, non-Hispanic 

== = Black, non-Hispanic 

=u Hispanic 
o 
2015 2017 2019 
Year 


* During 2015-2019, a significant linear decrease was observed in the percentage of students who had drunk a sports drink >21 time/day overall and among male, 
white, and Hispanic students. Based on trend analysis by using a logistic regression model controlling for sex, race/ethnicity, and grade (p<0.05). 


for >60 minutes/day on all 7 days overall and among female, 
male, white, black, and Hispanic students (Table 4). During 
2017-2019, the percentage of students who had been 
physically active for >60 minutes/day on all 7 days decreased 
overall and among male students and Hispanic students. 

During 2011-2019, a significant linear decrease occurred in 
the percentage of students who had exercised to strengthen or 
tone their muscles on 23 days/week overall and among male, 
white, black, and Hispanic students. During 2017-2019, no 
significant changes occurred in the percentage of students 
who had exercised to strengthen or tone their muscles on 
23 days/week overall or among the sex or racial/ethnic groups. 

During 2011-2019, a significant linear decrease occurred 
in the percentage of students who had met both aerobic and 
muscle-strengthening physical activity guidelines overall 
and among female, male, white, and black students. During 
2017-2019, the percentage of students who had met both 
aerobic and muscle-strengthening physical activity guidelines 
did not significantly change overall but decreased among male 
students and Hispanic students. 

During 2009-2019 and during 2017-2019, no significant 
linear changes occurred in the percentage of students who had 
attended physical education classes on all 5 days in an average 
school week or had played on 21 sports team overall or among 
the sex and racial/ethnic groups, except among female students. 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Among female students, a significant linear decrease occurred 
in the percentage who had attended physical education classes 
on all 5 days in an average school week. 


Discussion 


With the exception of decreases in the percentages of 
students who had consumed soda 21 time/day, sports drinks 
21 time/day, and <3 glasses/day of plain water, high school 
students’ dietary and physical activity behaviors have not 
improved during the previous 10 years and, in certain cases, 
have worsened. This is cause for concern because healthy 
dietary and physical activity behaviors are important for 
growth and development, academic outcomes, and prevention 
of chronic diseases, including type 2 diabetes, heart disease, 
hypertension, and obesity (1,7). Recent data demonstrate 
that approximately one in five adolescents have prediabetes, 
which increases the risk for type 2 diabetes and cardiovascular 
diseases (8). In addition, data from the National Health and 
Nutrition Examination Survey reveal that, in the United States 
during 2007-2008, approximately 18.1% of youths aged 
12-19 years had obesity and this increased to 20.6% during 
2015-2016 (9). In this analysis, in which differences by race/ 
ethnicity exist, black and Hispanic high school students have 
poorer dietary and physical activity behaviors, compared 


MMWR / August 21, 2020 / Vol.69 / No.1 71 


Supplement 


FIGURE 3. Percentage* of high school students who had engaged in physical activityt and physical education during the 7 days before the 
survey, overall and by sex — Youth Risk Behavior Survey, United States, 2019 


100 


65 


O Overall 
E Female 
E Male 


60 


Percentage 


ial 





Did exercises to 
strengthen or tone 
muscles on 23 days 


Were physically active 
for a total of 
260 minutes/day 
onall 7 days 


Met both aerobic 
and muscle-strengthening 
physical activity guidelines 





Attended physical 
education classes 
onall 5 days 


Played on 21 
sports team 
during the past 
12 months 


Activity 


* Bars represent the percentage of respondents with a “yes” response, overall and by sex. 

t The “met both aerobic and muscle-strengthening physical activity guidelines” variable is defined as being physically active for a total of >60 minutes/day on all 
7 days and doing exercises to strengthen or tone muscles on 23 days during the 7 days before the survey (Source: U.S. Department of Health and Human Services. 
Physical activity guidelines for Americans. 2nd ed. Washington, DC: US Department of Health and Human Services; 2018. https://www.hhs.gov/fitness/be-active/ 
physical-activity-guidelines-for-americans/index.html). 

5 In 2019, a significantly higher percentage of male than female students had been physically active for >60 minutes/day on all 7 days during the 7 days before the 
survey, had exercised to strengthen or tone muscles on 23 days during the 7 days before the survey, had met the aerobic and muscle-strengthening physical activity 
guidelines during the 7 days before the survey, had attended physical education classes on all 5 days in an average school week when the student was in school, 


and had played on 21 sports team during the past 12 months. Based on t-test analysis (p<0.05). 


with white high school students. These findings also indicate 
that male students have poorer dietary behaviors but better 
physical activity behaviors than do female students. Addressing 
dietary and physical activity behaviors can benefit all students 
and is especially important for those with increased risk for 
chronic diseases (e.g., students from low-income families and 
racial/ethnic minorities). 


Dietary Behaviors 


No improvements occurred in fruit or vegetable consumption 
during 2009-2019 and, in many cases, have worsened. Overall, 
consumption of fruits and vegetables remained low in 2019. 
For example, four of 10 high school students had eaten fruit 


72 MMWR / August 21, 2020 / Vol.69 / No.1 


or drunk 100% fruit juices <1 time/day. Similarly, four of 10 
had eaten vegetables <1 time/day. Although the prevalence of 
having eaten fruit or drunk 100% fruit juice <1 time/day and 
having eaten vegetables <1 time/day is similar for male students 
and female students, recommended daily intakes differ by age 
and sex. Females and males aged 14—18 years need 1.5 cups 
and 2 cups, respectively, of fruits, and 2.5 cups and 3 cups, 
respectively, of vegetables (https://www.choosemyplate.gov/ 
resources/MyPlatePlan). Although YRBS measures frequency 
of intake and not the amount consumed, children and 
adolescents who meet the recommended amounts typically 
consume fruits and vegetables multiple times throughout the 
day (10); therefore, consuming fruits or vegetables <1 time/day 
is likely insufficient. Strategies that encourage adolescents to 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 4. Percentage of high school students who engaged in selected physical activity behaviors, by sex, race/ethnicity, and survey year — 


Youth Risk Behavior Survey, United States, 2009-2019 


Prevalence (%) 





Behavior 2009 2011 2013 2015 2017 
Were physically active for a total of >60 minutes/day on all 7 days® 

Total —' 28.7 27.1 27.1 26.1 
Female —1 18.5 17.7 17.7 17.5 
Male —1 38.3 36.6 36.0 35.3 
White, non-Hispanic i 30.4 28.2 29.0 27.1 
Black, non-Hispanic —i 26.0 26.3 24.2 24.5 
Hispanic i 26.5 25.5 24.6 25.8 


Did exercises to strengthen or tone muscles on 23 daystt 


Total i 55.6 51.7 53.4 51.1 
Female i 43.8 41.6 42.7 40.8 
Male —1 66.7 61.8 63.7 62.1 
White, non-Hispanic —S 55.7 52.4 54.5 50.6 
Black, non-Hispanic —i 54.0 48.8 52.3 51.0 
Hispanic —i 56.6 53.3 52.4 52.3 


Met guidelines for aerobic and muscle-strengthening physical activity$$ 





Total 1 21.9 21.6 20.5 20.0 
Female i 12.7 13.0 12.2 12.1 
Male i 30.7 30.3 28.6 28.5 
White, non-Hispanic i 23.9 22.6 22.7 20.8 
Black, non-Hispanic =i 18.4 20.6 15.7 17.7 
Hispanic —1 18.9 20.5 18.7 20.0 


See table footnotes on the next page. 


increase the quantity of fruits and vegetables each time they 
consume them are likely needed to help them meet the daily 
recommendations (70). For example, schools can offer students 
multiple fruit and vegetable choices each day through school 
meal programs, including through grab-and-go salads (72). 
Sugar-sweetened beverages (SSBs) are the primary source 
of added sugars in U.S. youths’ diets (7). Frequently 
drinking SSBs is associated with health conditions, including 
obesity, type 2 diabetes, heart disease, and tooth decay (12). 
Alternatively, drinking enough water every day is good for 
overall health and is associated with higher Healthy Eating 
Index scores among adolescents (13). (More information about 
the Healthy Eating Index is available at https://www.fns.usda. 
gov/resource/healthy-eating-index-hei.) YRBS asks about two 
specific types of SSBs, soda or pop and sports drinks. This study 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Change during 

2019 Linear change* Quadratic change* 2017-2019t 

23.2 Decreased during —** Decreased during 
2011-2019 2017-2019 

15.4 Decreased during —** None 
2011-2019 

30.9 Decreased during —** Decreased during 
2011-2019 2017-2019 

25.6 Decreased during —** None 
2011-2019 

21.1 Decreased during —** None 
2011-2019 

20.9 Decreased during —** Decreased during 
2011-2019 2017-2019 

49.5 Decreased during —** None 
2011-2019 

39.7 None _* None 

59.0 Decreased during —** None 
2011-2019 

50.8 Decreased during —** None 
2011-2019 

47.0 Decreased during —** None 
2011-2019 

48.1 Decreased during —** None 
2011-2019 

16.5 Decreased during —** None 
2011-2019 

10.1 Decreased during —** None 
2011-2019 

23.1 Decreased during —** Decreased during 
2011-2019 2017-2019 

18.4 Decreased during —** None 
2011-2019 

13.4 Decreased during —** None 
2011-2019 

16.0 None —** Decreased during 


2017-2019 


identified substantial decreases in the percentage of students 
who had drunk soda or pop 21 time/day overall and among all 
sex and racial/ethnic groups. In addition, decreases occurred 
in the percentage of students who had drunk a sports drink 
21 time/day overall and among female, white, and Hispanic 
students. Despite these improvements in soda and sports drink 
consumption, consumption of these beverages is common. 
Differences also existed by sex and race/ethnicity. Similar to 
this study, previous studies reported that SSB intake was higher 
among males than among females (/4) and among black and 
Hispanic adolescents than among white adolescents (75). One 
possible explanation for the differences between racial/ethnic 


groups is that beverage companies disproportionately market 
SSBs to black and Hispanic youths (16). 


MMWR / August 21, 2020 / Vol.69 / No.1 73 


Supplement 


TABLE 4. (Continued) Percentage of high school students who engaged in selected physical activity behaviors, by sex, race/ethnicity, and survey 


year — Youth Risk Behavior Survey, United States, 2009-2019 


Prevalence (%) 


Behavior 2009 2011 2013 2015 2017 


Went to physical education classes on all 5 days"! 


Total 33.3 31.5 29.4 29.8 29.9 
Female 31.9 27.2 24.0 25.5 25:3 
Male 34.6 35.5 34.9 33.8 34.7 
White, non-Hispanic 30.6 33.0 27.1 25.4 27.2 
Black, non-Hispanic 37.0 27.6 26.6 35.8 28.5 
Hispanic 40.5 30.0 37.7 37.7 37.4 
Played on 21 sports team*** 

Total 58.3 58.4 54.0 57.6 54.3 
Female 52:3 52.6 48.5 53.0 49.3 
Male 63.8 64.0 59.6 62.2 59.7 
White, non-Hispanic 61.1 60.9 55.2 62.4 54.5 
Black, non-Hispanic 57.3 57.0 55.2 57.6 59.1 
Hispanic 53.2 54.1 51.2 48.5 52.2 


Change during 
2019 Linear change* Quadratic change* 2017-2019t 
25.9 None None None 
22.8 Decreased during None None 
2009-2019 
28.9 None None None 
24.3 None None None 
23.8 None None None 
29.9 None None None 
57.4 None None None 
54.6 None None None 
60.2 None None None 
62.0 None None None 
56.1 None None None 
51.6 None None None 


* Based on trend analyses by using a logistic regression model controlling for sex, race/ethnicity, and grade (p<0.05). 


t Based on t-test analysis (p<0.05). 


$ Adding up time spent in any kind of physical activity that increased their heart rate and made them breathe hard some of the time during the 7 days before the survey. 


4 Data not available. Question not asked in that year. 
** Insufficient years of data to assess quadratic trends. 


tt Such as push-ups, sit-ups, or weightlifting during the 7 days before the survey. 


SS Were physically active for >60 minutes/day on all 7 days and did exercises to strengthen or tone muscles on >3 of the 7 days before the survey. 


11 In an average week when the student was in school. 


*** Counting any teams run by their school or community groups during the 12 months before the survey. 


During the 2014-15 school year, the Smart Snacks in School 
nutrition standards were implemented, which decreased 
students access to SSBs at school. (More information about 
the Smart Snacks in School nutrition standards is available at 
https://www.gpo.gov/fdsys/pkg/FR-2013-06-28/pdf/2013- 
15249.pdf.) Additional policy and educational approaches 
(e.g., health education classes or communitywide campaigns) 
might help further reduce SSB access in schools and other 
settings and help adolescents choose healthier beverage options, 
including plain water. 


Physical Activity Behaviors 


Overall, prevalence of health-promoting physical activity 
behaviors was low in 2019 and either decreased or did not 
change during the previous 10 years. Healthy People 2020 
monitors four of the five physical activity behaviors included 
in this study (https://www.healthypeople.gov/), and these 
behaviors will continue to be monitored with Healthy People 
2030. Healthy People 2020 objective PA-3 aims to increase the 
proportion of adolescents who meet federal physical activity 
guidelines for aerobic physical activity to 231.6% (PA-3.1), 
muscle-strengthening activity to 261.2% (PA-3.2), and both 
aerobic physical activity and muscle-strengthening activity to 
224.1% (PA-3.3). The proportions of students meeting the 
aerobic, muscle-strengthening, or both guidelines decreased 


74 MMWR / August 21, 2020 / Vol.69 / No.1 


during 2011-2019, and 2019 data indicate that adolescents 
continue to fall short of achieving these targets. 

One of the Healthy People 2020 objectives (PA-5) is to increase 
the proportion of adolescents who participate in daily school 
physical education to 236.6%. Given no increase in this behavior 
during 2009-2019 and that only 25.9% of high school students 
attended daily physical education class during 2019, the target 
for this objective is unlikely to be met in 2020. Students can 
accumulate approximately 40% of their daily physical activity 
through participation in physical education (17), demonstrating 
that physical education at school is an effective strategy for helping 
high school students meet the federal physical activity guidelines. 
During 2015-2016, although the majority of U.S. states required 
public high schools to provide physical education, few states 
mandated a time requirement for high school students, and many 
states permitted students to substitute other activities for their 
physical education requirement. (More information about the 
status of physical education in the United States is available at 
https://www.shapeamerica.org/MemberPortal/SHAPE_Sign_I. 
aspx? WebsiteKey=c03f2b5 1-3ee7-46fa-b587-de1 82 13dcae5&Lo 
ginRedirect=true&returnurl=%2fadvocacy%2fson%2f.) 

The 2019 release of the National Youth Sports Strategy 
highlighted youth sports participation for its physical activity, 
psychosocial, and academic achievement benefits. (More 
information about the National Youth Sports Strategy is 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


available at https://health.gov/our-work/physical-activity/ 
national-youth-sports-strategy.) Despite these benefits, only 
57.4% of high school students reported participating in 
sports. The National Survey of Children’s Health also assesses 
participation in youth sports, with similar estimates to YRBS 
for youths aged 14-17 years (3). 

Across all the physical activity behaviors, a higher percentage 
of males than females met aerobic, muscle-strengthening, or 
both guidelines, participated in daily physical education, and 
played on 21 sports team. These differences might be caused 
by gender stereotypes, self-efficacy, self-consciousness, or 
social influences (18). When overcoming barriers to physical 
activity, particularly for adolescent females, strategies that 
span the Social-Ecological Model by addressing individual, 
interpersonal, organizational, community, and societal 
components might need to be considered. 


Addressing Dietary and 
Physical Activity Behaviors 


Improving dietary and physical activity behaviors among 
adolescents requires efforts across multiple settings. For 
example, schools can implement policies and practices (e.g., 
local school wellness policies) (https://www.fns.usda.gov/tn/ 
local-school-wellness-policy) that support healthy eating and 
physical activity, including ensuring the following: 1) that 
foods and beverages sold during the school day meet Smart 
Snacks in School nutrition standards, 2) that school meals are 
appealing and include menu items that students enjoy, and 
3) that students have access to free drinking water during the 
school day (J 1). Schools can also help students meet the federal 
physical activity guidelines by providing physical activity 
opportunities before, during, and after the school day. This 
can be achieved by developing, implementing, and evaluating 
a comprehensive school physical activity program, which serves 
as a national framework for physical education and physical 
activity in schools. (More guidance on comprehensive school 
physical activity programs is available at https://www.cdc.gov/ 
healthyschools/physicalactivity/pdf/13_242620-A_CSPAP_ 
SchoolPhysActivityPrograms_Final_508_12192013.pdf.) 

Health education is another way that schools can help students 
develop the knowledge and skills needed for making health- 
enhancing decisions. These school efforts can be addressed and 
coordinated through the Whole School, Whole Community, 
Whole Child Model, which highlights the interconnectedness 
of multiple health behaviors and outcomes and promotes 
collaboration among diverse partners, including mental health 
professionals, school leaders, school nurses, physical and health 
educators, and parents for promoting health and well-being for 
all students. (More information about the Whole School, Whole 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Community, Whole Child approach is available at https://www. 
cdc.gov/healthyschools/wscc/index.htm.) 

Community members and parents can reinforce the 
messages promoted within the school and can participate on 
the school wellness or school health teams that are addressing 
healthy eating, physical education, and physical activity. (More 
information about parent engagement in school health is 
available at https://www.cdc.gov/healthyyouth/protective/ 
pdf/parent_engagement_strategies.pdf.) In addition, parents 
and community members can engage in physical activity 
with adolescents, provide social supports for adolescents that 
increase physical activity while decreasing sedentary behaviors, 
and make choices that support healthy eating. 

Community-based interventions that address healthy eating 
and physical activity through policy and environmental 
changes can improve dietary and physical activity behaviors 
and weight-status outcomes among youths (79-21). These 
kinds of community-based approaches often adopt multiple 
strategies, including providing information (e.g., messaging 
campaigns and healthy recipe demonstrations), providing 
incentives, and improving access to opportunities for practicing 
healthy behaviors through policy and systems changes. Having 
multiple activities that target specific behaviors and using a 
mix of behavioral change strategies appear to be important 
for making health behavior changes (19). Community- 
based interventions that also include the school setting are 
more effective in influencing outcomes among youths than 
interventions that occur only in the community (20). 


Limitations 


General limitations for the YRBS are available in the 
overview report of this supplement (5). The findings in this 
report are subject to at least one additional limitation. Certain 
questions about dietary behaviors (e.g., fruit consumption) 
ask about frequency rather than portion size; therefore, these 
data cannot directly determine whether students are meeting 
specific recommendations for age and sex (22). 


Conclusion 


Because of the limited progress in increasing the prevalence 
of healthy dietary and physical activity behaviors among U.S. 
high school students, multicomponent approaches, including 
policy and environmental changes and opportunities for 
adolescents to learn about and practice making healthy choices, 
are needed to facilitate healthy dietary and physical activity 
patterns. Schools, communities, and families can work together 
in creating healthy environments where adolescents thrive. 


MMWR / August 21, 2020 / Vol.69 / No.1 i 


Supplement 


Conflicts of Interest 


All authors have completed and submitted the International 


Committee of Medical Journal Editors form for disclosure of 
potential conflicts of interest. No potential conflicts of interest 
were disclosed. 


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Ritchie LD, Woodward-Lopez G, Au LE, et al; Healthy Communities 
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children’s dietary intakes: the Healthy Communities Study. Pediatr Obes 
2018;13(Suppl 1):14—26. https://doi.org/10.1111/ijpo.12440 

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programmes work? A systematic review and meta-analysis. Obes Rev 
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Pate RR, Frongillo EA, Mclver KL, et al; Healthy Communities Study 
Team. Associations between community programmes and policies and 
children’s physical activity: the Healthy Communities Study. Pediatr 
Obes 2018;13(Suppl 1):72-81. https://doi.org/10.1111/ijpo.12426 
Eaton DK, Olsen EO, Brener ND, et al. A comparison of fruit and 
vegetable intake estimates from three survey question sets to estimates 
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2013;113:1165-74. https://doi.org/10.1016/j.jand.2013.05.013 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


Transportation Risk Behaviors Among High School Students — 
Youth Risk Behavior Survey, United States, 2019 


Merissa A. Yellman, MPH!; Leah Bryan, MPH?; Erin K. Sauber-Schatz, PhD!; Nancy Brener, PhD2 


! Division of Injury Prevention, National Center for Injury Prevention and Control, CDC; *Division of Adolescent and School Health, National Center for 
HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC 


Abstract 


Motor-vehicle crashes are a leading cause of death and nonfatal injury among U.S. adolescents, resulting in approximately 2,500 
deaths and 300,000 nonfatal injuries each year. Risk for motor-vehicle crashes and resulting injuries and deaths varies, depending 
on such behaviors as seat belt use or impaired or distracted driving. Improved understanding of adolescents’ transportation risk 
behaviors can guide prevention efforts. Therefore, data from the 2019 Youth Risk Behavior Survey were analyzed to determine 
prevalence of transportation risk behaviors, including not always wearing a seat belt, riding with a driver who had been drinking 
alcohol (riding with a drinking driver), driving after drinking alcohol, and texting or e-mailing while driving. Differences by 
student characteristics (age, sex, race/ethnicity, academic grades in school, and sexual identity) were calculated. Multivariable 
analyses controlling for student characteristics examined associations between risk behaviors. Approximately 43.1% of U.S. high 
school students did not always wear a seat belt and 16.7% rode with a drinking driver during the 30 days before the survey. 
Approximately 59.9% of students had driven a car during the 30 days before the survey. Among students who drove, 5.4% had 
driven after drinking alcohol and 39.0% had texted or e-mailed while driving. Prevalence of not always wearing a seat belt was 
higher among students who were younger, black, or had lower grades. Riding with a drinking driver was higher among Hispanic 
students or students with lower grades. Driving after drinking alcohol was higher among students who were older, male, Hispanic, 
or had lower grades. Texting while driving was higher among older students or white students. Few differences existed by sexual 
identity. Multivariable analyses revealed that students engaging in one transportation risk behavior were more likely to engage in 
other transportation risk behaviors. Traffic safety and public health professionals can use these findings to reduce transportation 
risk behaviors by selecting, implementing, and contextualizing the most appropriate and effective strategies for specific populations 
and for the environment. 


Introduction alcohol) increase the risk for injury or death in a crash or 
risk for a crash itself. Seat belt use among adolescents and 
young adults is typically lower than among adults of other 
age groups (J) (https://crashstats.nhtsa.dot.gov/Api/Public/ 
ViewPublication/812781). For instance, the National 
Occupant Protection Use Survey Controlled Intersection 
Study uses a probability-based sample of observational surveys 
conducted on an annual basis to produce estimates of seat belt 
use nationwide at a typical daylight moment. Results during 
2016-2018 indicate that seat belt use among adolescents and 
young adults aged 16—24 years was approximately 87% each 
year, whereas seat belt use among adults aged >25 years was 
90% or higher (https://crashstats.nhtsa.dot.gov/Api/Public/ 
ViewPublication/812781). Previous research also demonstrates 
that high school students put themselves at risk by riding with 
drivers who have been drinking alcohol (2). 

Per mile driven, drivers aged 16-19 years have crash rates 
approximately four times greater than those of drivers aged 
220 years (1); a leading contributor is driver inexperience 
Telephone: 404-498-5299; E-mail: myellman@cdc.gov. (1,3). Because of this elevated crash risk, engagement in 
driver-related transportation risk behaviors (e.g., driving after 


Motor-vehicle crashes are predictable and preventable. 
However, in the United States, they remain the second leading 
cause of death among adolescents and the fourth leading 
cause of nonfatal injury. During 2018, approximately 2,500 
adolescents (persons aged 12—19 years) died in motor-vehicle 
crashes; of those deaths, >75% were occupants of passenger 
vehicles (i.e., cars, pickup trucks, vans, or sport utility vehicles) 
(1). Motor-vehicle crashes also resulted in approximately 
297,000 nonfatal injuries among adolescents during 2018. 
Moreover, fatal and nonfatal motor-vehicle—crash injuries 
among adolescents resulted in approximately $12 billion in 
medical and work-loss costs during 2018 (https://www.cdc. 
gov/injury/wisqars). 

Passenger-related transportation risk behaviors (e.g., nonuse 
of seat belts or riding with a driver who had been drinking 


Corresponding author: Merissa A. Yellman, MPH, Division of Injury 
Prevention, National Center for Injury Prevention and Control. 





US Department of Health and Human Services/Centers for Disease Control and Prevention MMWR / August 21, 2020 / Vol.69 / No.1 ri 


Supplement 


drinking alcohol or texting or e-mailing while driving) puts 
adolescents at even higher risk. For example, drinking alcohol 
negatively affects a person’s ability to drive safely regardless of 
age. However, even at the same blood alcohol concentration 
(BAC), drivers aged 16-20 years have a much higher risk for 
being involved in a crash than older drivers (1,4). Similarly, the 
negative effects of driver inexperience on driving performance 
are worsened by cell phone-related driver distraction (5). 

For this report, 2019 data from the Youth Risk Behavior 
Survey (YRBS) were analyzed by student characteristics to 
determine the prevalence of four transportation risk behaviors 
among U.S. high school students. Associations between 
engagement in multiple transportation risk behaviors also were 
calculated. This study provides an update on which adolescent 
groups have an elevated prevalence of engaging in transportation 
risk behaviors and reveals the extent to which adolescents engage 
in multiple transportation risk behaviors. The findings can help 
traffic safety and public health professionals appropriately select, 
tailor, and implement effective strategies to have a greater impact 
on reducing risk behaviors, thereby preventing crashes, injuries, 
and deaths among adolescents. 


Methods 


Data Source 


This report includes data from CDC’s 2019 YRBS, a cross- 
sectional, school-based survey conducted biennially since 
1991. Each survey year, CDC collects data from a nationally 
representative sample of public and private school students in 
grades 9-12 in the 50 U.S. states and the District of Columbia. 
Additional information about YRBS sampling, data collection, 
response rates, and processing is available in the overview 
report of this supplement (6). The prevalence estimates for all 
unintentional injury questions for the overall study population 
and by sex, race/ethnicity, grade, and sexual orientation are 
available at https://nccd.cdc.gov/youthonline/App/Default. 
aspx. The full YRBS questionnaire is available at https:// 
www.cdc.gov/healthyyouth/data/yrbs/pdf/2019/2019_YRBS- 
National-HS-Questionnaire. pdf. 


Measures 


This study examined two passenger- and two driver-related 
transportation risk behaviors among U.S. high school students. 
The overall analytic sample was used for the passenger-related 
risk behaviors, which included not always wearing a seat belt 
when riding in a car driven by someone else and riding with a 
driver who had been drinking alcohol (riding with a drinking 
driver). Not always wearing a seat belt was assessed with the 


78 MMWR / August 21,2020 / Vol.69 / No.1 


question, “How often do you wear a seat belt when riding in 
a car driven by someone else?” Response options included 
“always,” “most of the time,” “sometimes,” “rarely,” or “never,” 
with any response other than “always” being defined as not 
always wearing a seat belt. Riding with a drinking driver was 
assessed with the question, “During the past 30 days, how many 
times did you ride in a car or other vehicle driven by someone 
who had been drinking alcohol?” Responses were dichotomized 
(0 times versus 21 time). Students who reported riding with a 
drinking driver at least once during the previous 30 days were 
classified as having engaged in the behavior. 

Driver-related transportation risk behaviors included driving 
when they had been drinking alcohol (driving after drinking 
alcohol) and texting or e-mailing while driving (texting while 
driving). Driving after drinking alcohol was assessed with 
the question, “During the past 30 days, how many times did 
you drive a car or other vehicle when you had been drinking 
alcohol?” Texting while driving was assessed with the question, 
“During the past 30 days, on how many days did you text or 
e-mail while driving a car or other vehicle?” Students who 
indicated they had not driven a car or other vehicle during the 
past 30 days on each respective question were excluded from 
the analysis for these questions. Responses among drivers were 
categorized as 0 times or days versus 21 time or day. 

An approximation of driving prevalence among students is 
presented to provide context for the driver-related behaviors. 
However, driving prevalence is not directly captured in the 2019 
YRBS. For this approximation, students who chose a response 
other than “I did not drive a car or other vehicle during the past 
30 days” for both driver-related questions (driving after drinking 
alcohol and texting while driving) were classified as drivers, and 
students who indicated that they did not drive a car or other 
vehicle during the past 30 days were classified as nondrivers. 
Driver classification was independent of students’ responses to 
the two questions about passenger-related transportation risk 
behaviors because students who drove during the past 30 days 
could also be passengers when they were not driving during the 
same 30-day period. 

All transportation risk behaviors were analyzed by self- 
reported student characteristics, including age (14, 15, 
16, 17, or 218 years), sex (male or female), race/ethnicity 
(non-Hispanic white [white]; non-Hispanic black [black]; or 
Hispanic or Latino of any race [Hispanic]), academic grades in 
school (mostly As or Bs versus mostly Cs, Ds, or Fs), and sexual 
identity (heterosexual; lesbian, gay, or bisexual; or not sure). 
Although data from students in other or multiple racial/ethnic 
groups were collected, the numbers were too small to produce 
statistically stable estimates specific to other or multiple racial/ 
ethnic groups; therefore, these data are not presented as a 
separate group in this report but were retained in the analytic 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


sample. In addition, students aged <14 years (n = 87) were not 
included in the analysis by age because the sample of students 
in this age category was too small for meaningful analysis and 
because these students cannot legally drive anywhere in the 
United States (/). 


Analysis 


For this report, unadjusted weighted prevalence estimates and 
corresponding 95% confidence intervals were calculated, and 
posthoc ż-tests were used to assess between-group differences. 
Differences between prevalence estimates were considered 
statistically significant if the t-test p value was <0.05. In the 
results, only statistically significant differences in prevalence 
estimates are reported. 

Logistic regression models that controlled for age, sex, 
race/ethnicity, academic grades in school, and sexual 
identity produced adjusted prevalence ratios and examined 
the associations between transportation risk behaviors. For 
passenger-related transportation risk behaviors, students who 
did not engage in the risk behaviors were designated as the 
referent group. For driver-related transportation risk behaviors, 
students who drove but did not engage in the risk behaviors 
were designated as the referent group. Adjusted prevalence 
ratios were considered statistically significant if their pairwise 
comparison between groups (risk versus referent) was p <0.05. 


Results 


In 2019, a total of 43.1% of U.S. high school students had 
not always worn a seat belt and 16.7% had ridden with a 
drinking driver during the 30 days before the survey (Table 1). 
Among the 59.9% of respondents who had driven a car or other 
vehicle during the 30 days before the survey, 5.4% had driven 
after drinking alcohol and 39.0% had texted while driving. 

Both driving after drinking alcohol and texting while driving 
usually increased with age. Specifically, prevalence of driving 
after drinking alcohol was higher among students aged 218 
years (8.9%) than among students aged 16 (4.0%), 15 (2.6%), 
or 14 (2.7%) years (Table 1). In addition, prevalence was higher 
among students aged 17 (5.9%) years than among those aged 
15 (2.6%) years. For texting while driving, prevalence was 
higher among students aged 218 (59.5%) years than among 
students aged 17 (50.9%), 16 (30.5%), 15 (15.5%), or 
14 (15.5%) years. Prevalence also was higher among students 
aged 17 years than among those aged 16, 15, or 14 years and 
higher among students aged 16 years than among those aged 
15 or 14 years. 

Conversely, not always wearing a seat belt usually decreased 
with age. Prevalence of not always wearing a seat belt was lower 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


among students aged >18 years (39.4%) than among students 
aged 16 (43.5%), 15 (46.9%), or 14 (45.7%) years. Similarly, 
prevalence was lower among students aged 17 (38.9%) years 
than among all younger students. For riding with a drinking 
driver, no differences occurred by age. 

Differences by race/ethnicity were detected for all four 
transportation risk behaviors but did not demonstrate a 
consistent pattern. Prevalence of not always wearing a seat 
belt was higher among black students (61.7%) than among 
Hispanic students (48.2%) or white students (36.6%). In 
addition, prevalence among Hispanic students was higher than 
among white students. For the alcohol-related transportation 
risk behaviors, Hispanic students (20.8%) had a higher 
prevalence of riding with a drinking driver than black students 
(15.9%) or white students (15.1%), and Hispanic students 
(6.6%) had a higher prevalence of driving after drinking alcohol 
than black students (4.1%). In contrast, prevalence of texting 
while driving was higher among white students (43.9%) than 
among black students (29.5%) or Hispanic students (35.2%). 
Students whose academic grades in school were mostly Cs, Ds, 
or Fs had a higher prevalence of not always wearing a seat belt 
(57.0%), riding with a drinking driver (20.1%), and driving 
after drinking alcohol (7.4%) than students whose academic 
grades in school were mostly As or Bs (38.8%, 15.3%, and 
4.7%, respectively); however, prevalence of texting while 
driving did not differ by this characteristic. 

Few differences were identified when examining behaviors by 
sex and by sexual identity. Only alcohol-related transportation 
risk behaviors demonstrated differences. Among students who 
had driven during the 30 days before the survey, male students 
(7.0%) had a higher prevalence of driving after drinking alcohol 
than female students (3.6%). By sexual identity, students who 
were not sure of their sexual identity (21.9%) had a higher 
prevalence of riding with a drinking driver than heterosexual 
students (15.7%); however, the prevalence was not different 
from lesbian, gay, or bisexual students (19.2%). 

Multivariable analyses indicated that, for each transportation 
risk behavior, students engaging in that behavior were more likely 
to engage in each of the other transportation risk behaviors, 
after controlling for age, sex, race/ethnicity, academic grades 
in school, and sexual identity (Table 2). For passenger-related 
transportation risk behaviors, students who did not always 
wear a seat belt were 1.80 times as likely to have ridden with a 
drinking driver, 2.73 times as likely to have driven after drinking 
alcohol, and 1.29 times as likely to have texted while driving than 
students who always wore a seat belt. Students who had ridden 
with a drinking driver during the 30 days before the survey were 
1.42 times as likely to not always wear a seat belt, 9.87 times as 
likely to have driven after drinking alcohol, and 1.50 times as 
likely to have texted while driving than students who had not 


MMWR / August 21, 2020 / Vol.69 / No.1 79 


Supplement 


TABLE 1. Unweighted number and unadjusted weighted prevalence estimates of high school students* who engaged in transportation risk 
behaviors, by selected characteristics — Youth Risk Behavior Survey, United States, 2019 


Rode witha driver who Drove when they had 


Did not always wear a had been drinking been drinking Texted or e-mailed while 
Total seat belt! alcohol** alcohol**:tt drivingtt-$8 
Characteristic No.t % (95% Cl) No.§ % (95% Cl) No.8 % (95% Cl) No.S %(95%Cl) No. % (95% Cl) 
Total 13,677 NA 4,852 43.1(40.2-45.9) 2,214 16.7(15.2-18.2) 423 5.4 (4.5-6.5) 2,784 39.0 (36.4-41.7) 
Age (yrs)"™ 
14 1,699 11.9 (10.9-13.0) 573 45.7 (40.9-50.5) 276 16.4 (13.9-19.1) 14 2.7 (0.9-7.5) 51 15.5 (11.2-21.0) 
15 3,473 24.8 (23.5-26.0) 1,283 46.9 (42.7-51.1) 557 16.7 (14.6-19.1) 49 2.6 (1.7-3.9) 211 15.5 (11.8-20.2) 
16 3,628 25.6 (24.5-26.7) 1,318 43.5 (39.5-47.6) 564 16.0 (13.8-18.5) 112 4.0 (2.8-5.6) 730 30.5 (25.8-35.5) 
17 3,102 23.7 (22.5-24.8) 1,045 38.9 (35.6-42.4) 481 16.0 (13.8-18.5) 138 5.9 (4.3-7.9) 1,072 50.9 (46.5-55.3) 
218 1,616 13.7 (12.6-14.9) 574 39.4 (36.6-42.4) 279 18.4 (15.4-21.7) 91 8.9(6.4-12.4) 672 59.5 (54.9-63.9) 
Sex 
Male 6,641 50.6 (49.1-52.1) 2,369 43.3 (40.0-46.7) 1,015 15.6 (14.1-17.2) 257 7.0 (5.6-8.8) 1,434 39.6 (36.6-42.6) 
Female 6,885 49.4 (47.9-50.9) 2,440 42.7 (39.7-45.7) 1,141 17.5 (15.6-19.5) 149 3.6 (2.8-4.6) 1,311 38.4 (35.5-41.4) 
Race/Ethnicity*** 
White, 6,668 51.2 (46.4-56.0) 2,079 36.6 (33.8-39.6) 986 15.1 (13.5-16.8) 207 5.1 (3.9-6.5) 1,608 43.9 (40.4—-47.5) 
non-Hispanic 
Black, 2,040 12.2 (10.2-14.6) 901 61.7 (56.3-66.8) 325 15.9 (13.3-18.7) 47 4.1 (2.6-6.4) 312 29.5 (24.3-35.2) 
non-Hispanic 

Hispanic 3,038 26.1 (21.8-30.9) 1,237 48.2 (45.0-51.4) 605 20.8 (18.7-23.1) 107 6.6 (5.2-8.5) 562 35.2 (30.8-39.8) 
Academic gradesttt 
Mostly As or Bs 9,785 75.1 (72.2-77.8) 3,152 38.8 (36.0-41.6) 1,449 15.3 (13.8-17.0) 248 4.7 (3.8-5.9) 2,070 40.4 (37.8-43.1) 


Mostly Cs, Ds, or Fs 2,677 20.6 (18.3-23.2) 1,226 57.0 (53.4-60.5) 547 20.1 (17.7-22.8) 133 7.4 (5.7-9.6) 548 37.1 (32.2-42.4) 
Sexual identity 


Heterosexual 10,853 84.4 (83.4-85.3) 3,741 42.1 (39.1-45.2) 1,656 15.7 (14.1-17.4) 322 5.2 (4.2-6.4) 2,268 39.6 (36.6-42.6) 

Lesbian, gay, or 1,531 11.2 (10.4-12.0) 564 44.7 (39.4-50.1) 283 19.2 (16.0-22.9) 39 4.7 (2.4-9.0) 257 34.7 (28.4-41.7) 
bisexual 

Not sure 591 4.5 (3.9-5.0) 208 43.3 (37.6-49.2) 125 21.9 (16.8-28.1) 24 9.5 (4.8-17.7) 93 31.7 (22.0-43.4) 


Abbreviations: Cl = confidence interval; NA = not applicable. 

* Unadjusted weighted prevalence estimates and corresponding 95% Cls were calculated and are presented in the table. Posthoc t-tests were used to assess 
between-group differences. Differences were considered statistically significant if the t-test p value was <0.05. Statistical significance is not indicated in the table 
due to the large number of different pairwise comparisons; however, all significant differences are described in the results. 

t The unweighted number of students for each characteristic only includes students who selected a response on the survey question pertaining to that characteristic. 
Students who did not select a response were not included in the analysis for that characteristic but were retained in the analytic sample for every question on 
which they provided a response. 

$ Students who selected any response on the survey question pertaining to a risk behavior were included in the analysis for that behavior; however, only the 
unweighted numbers of students who engaged in that behavior are presented in the table. Students who did not select a response were not included in the 
analysis for that behavior but were retained in the analytic sample for every question on which they provided a response. 

‘ Most of the time, sometimes, rarely, or never wore a seat belt when riding in a car driven by someone else. 

** >1 time during the 30 days before the survey. 
tt Among students who had driven a car or other vehicle during the 30 days before the survey. 
55 On 21 day during the 30 days before the survey. 
11 The total column percentages for age do not add up to 100% because students aged <14 years are not presented because they cannot drive legally in any U.S. state. 
*** The total column percentages for race/ethnicity do not add up to 100% because other non-Hispanic race categories are not presented. 
ttt The total column percentages for academic grades do not add up to 100% because students who were not sure about their grades or who responded “none of 
these grades” are not presented. 


ridden with a drinking driver. For driver-related transportation as likely to have driven after drinking alcohol than students who 
tisk behaviors, students who had driven after drinking alcohol had not texted while driving. 

at least once during the 30 days before the survey were 1.65 

times as likely to not always wear a seat belt, 4.91 times as likely 

to have ridden with a drinking driver, and 2.38 times as likely Discussion 

to have texted while driving than students who had not driven 
after drinking alcohol. Students who had texted while driving 
on at least one day during the 30 days before the survey were 
1.32 times as likely to not always wear a seat belt, 1.96 times as 
likely to have ridden with a drinking driver, and 12.64* times 


‘Transportation risk behaviors varied by student characteristics, 
with age, race/ethnicity, and academic grades demonstrating 
the most differences. Increased engagement in driver-related 
transportation risk behaviors as students become older has been 
reported in other studies (7—9). This finding is not surprising 
stats cho uld be tare tate wick ean eae ha T because adolescents engage in certain risky driver—related 

interval is wide. behaviors less often when an adult supervisor is present in the 


80 MMWR / August 21,2020 / Vol.69 / No.1 US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


TABLE 2. Adjusted prevalence ratios* for high school students who engaged in multiple transportation risk behaviors — Youth Risk Behavior 
Survey, United States, 2019 


Rode with a driver who Texted or 
Did not always wear had been drinking Drove when they had been e-mailed while 
a seat belts alcohol! drinking alcohol™** driving**tt 
Transportation risk behaviort aPR (95% Cl) aPR (95% Cl) aPR (95% Cl) aPR (95% Cl) 
Did not always wear a seat belt§ NA 1.80 (1.59-2.04) 2.73 (1.81-4.11) 1.29 (1.19-1.41) 
Rode with a driver who had been drinking alcohol! 1.42 (1.32-1.53) NA 9.87 (7.14-13.64) 1.50 (1.37-1.65) 
Drove when they had been drinking alcohol"** 1.65 (1.40-1.95) 4.91 (4.17-5.77) NA 2.38 (2.15-2.63) 
Texted or e-mailed while driving***tt 1.32 (1.20-1.44) 1.96 (1.69-2.27) 12.64 (8.45-18.91)88 NA 


Abbreviations: aPR = adjusted prevalence ratio; Cl = confidence interval; NA = not applicable. 

* Multivariable logistic regression models that controlled for age, sex, race/ethnicity, academic grades, and sexual identity were used to produce the aPRs and 
corresponding 95% Cls presented in the table. The aPRs were considered statistically significant if the p value of their pairwise comparison between groups (risk 
versus referent) was <0.05. All aPRs in the table are significant. 

t Students who engaged in protective behaviors (i.e., always wearing a seat belt) or did not engage in risk behaviors (i.e., riding with a driver who had been drinking 
alcohol, driving when they had been drinking alcohol among students who had driven, or texting or e-mailing while driving among students who had driven) 


were the referent group. 


S Most of the time, sometimes, rarely, or never wore a seat belt when riding in a car driven by someone else. 


1 >1 time during the 30 days before the survey. 


** Among students who had driven a car or other vehicle during the 30 days before the survey. 


tt On 21 day during the 30 days before the survey. 
S$ Estimate should be interpreted with caution because the 95% CI is wide. 


vehicle, as is required when adolescents possess a driver's permit 
(https://aaafoundation.org/distracted-driving-among-newly- 
licensed-teen-drivers). As adolescents age, begin to drive without 
adult supervision, and gain driving experience, driver-related 
risk behaviors can be more common (9) (https://aaafoundation. 
org/distracted-driving-among-newly-licensed-teen-drivers). 
The positive association between age and texting while driving 
illustrates the need to sustain attention to preventing the behavior 
throughout adolescence (9). On the other hand, the prevalence 
of not always wearing a seat belt decreased by age, possibly 
indicating that although adolescents are typically more willing 
to engage in risky transportation behaviors as they become older, 
they still maintain a sense of self-preservation and risk perception 
and therefore take precautions by wearing seat belts. 

This study demonstrated that Hispanic students had a higher 
prevalence of riding with a drinking driver and driving after 
drinking alcohol than white students or black students. One 
study described similar findings about drinking and driving 
among Hispanics in the literature (/0). Additional research 
to explore which Hispanic populations might be at higher 
risk found that U.S.-born Hispanic youths were more likely 
to initiate drinking and driving behavior compared with first- 
generation immigrant Hispanic youths, even after adjusting 
for demographic variables (70). Additional research is needed 
to determine whether different strategies to reduce alcohol- 
impaired driving should be selected for or tailored to specific 
Hispanic populations based on nativity status. 

Other studies have reported that students with lower 
academic grades were more likely to engage in other health- 
related risk behaviors (e.g., risky sexual behaviors or substance 
use) (71). The 2019 YRBS illustrates that this association 
extends to engagement in transportation risk behaviors. Lower 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


academic achievement might be indicative of an underlying 
tendency to make riskier decisions, or risky behaviors 
themselves might lead to lower academic achievement. More 
research into a potential causal association and the temporality 
of that association is warranted. Of note, texting while driving 
was the one transportation risk behavior that did not differ 
by academic achievement. One potential explanation is that 
although adolescents understand that texting while driving 
is unsafe, the perceived benefits of texting while driving and 
the motivations for engaging in the behavior often differ 
from other transportation risk behaviors and can outweigh 
the perceived risks for adolescents at the moment when they 
choose to do it (8,9). 

In this study, students engaging in any given transportation 
risk behavior were more likely to engage in each of the other 
measured transportation risk behaviors, even after controlling 
for student characteristics. Associations with alcohol- 
related behaviors were highest, particularly for driving after 
drinking alcohol. Students who engaged in any of the other 
transportation risk behaviors were approximately 3—13 times 
as likely to have also engaged in driving after drinking alcohol 
at least once during the 30 days before the survey. This might 
signify a general willingness to engage in risky behaviors among 
students who choose to drink and drive. This finding is also 
concerning because of the potential additive effects of these 
transportation risk behaviors. For example, adolescents who 
drive after drinking alcohol, thus increasing their risk for a 
crash, are also more likely to not always wear a seat belt, which 
increases their risk for injury or death during a crash. 

Because students engaged in multiple transportation 
risk behaviors, interventions designed to address multiple 
transportation risk behaviors might concurrently help reduce 


MMWR / August 21, 2020 / Vol.69 / No.1 81 


Supplement 


those behaviors. Existing infrastructure and resources for 
comprehensive school and community programs designed 
to address different health behaviors could be leveraged to 
expand the benefits of these programs to transportation risk 
behaviors. For example, programs that already rely on family 
engagement could incorporate safe driving, because parental 
involvement is crucial for teaching adolescents how to drive 
by providing varied practice opportunities, promulgating 
safe driver behaviors, and instilling the importance of 
avoiding transportation risk behaviors (https://www.cdc. 
gov/parentsarethekey/parents/index.html). Programs that 
provide counseling and social services for adolescents could 
incorporate brief alcohol interventions, which are promising 
for reducing drinking and driving among adolescents at high 
risk for engaging in the behavior (12,13). 

Engagement in all of these transportation risk behaviors 
across the United States remains high. Considering that 
adolescent drivers (16-19 years of age) have the highest crash 
rates (Z), the fact that only six of 10 adolescents in this study 
always wore seat belts is concerning. Measures that are effective 
for increasing seat belt use, such as primary enforcement seat 
belt laws that allow police to ticket drivers or passengers for 
being unrestrained even in the absence of other violations (13), 
also can be beneficial for preventing crashes or crash injuries 
involving other contributing factors. For example, evidence 
indicates that primary enforcement seat belt laws are effective 
for reducing fatal alcohol-related crashes among underage 
drivers aged 15-20 years (14. 

Although this study did not find many differences in riding 
with a drinking driver by student characteristics, approximately 
one of every five students engaged in the behavior. Riding with 
a drinking driver is intrinsically unsafe and also is associated 
with adolescent drinking and driving (/5). Longitudinal 
research has revealed that adolescent passengers who are 
exposed to drinking and driving at a young age are more likely 
to engage in drinking and driving themselves as they become 
older and begin to drive (16). Additional research about 
the drinking drivers with whom adolescents ride and their 
relationships with the drinking drivers (e.g., parents, other 
family members, or peers) might be useful for designing and 
implementing targeted interventions. 

In every U.S. state, minimum legal drinking age (MLDA) 
laws stipulate that drinking alcohol is illegal for anyone aged 
<21 years, as is driving after drinking any amount of alcohol 
(zero tolerance laws) (1,13). Despite these laws, approximately 
one fifth of drivers aged 16-20 years killed in crashes during 
2018 had BACs of [0.08% (J). This study found that in 2019, 
a total of 5.4% of students who drove did so after drinking 


82 MMWR / August 21, 2020 / Vol.69 / No.1 


alcohol at least once in the previous 30 days. Driving after 
drinking alcohol is risky and unacceptable at any age; however, 
the risk is even higher among adolescent drivers aged 16-20 
years, even at BACs below the legal limit for adults (4). Zero 
tolerance laws (7,13,14), graduated driver licensing systems 
(7), and MLDA laws (7,13, 14) are effective in helping reduce 
drinking and driving and alcohol-related crashes and injuries 
among adolescents, and they should continue to remain 
universally implemented. Other general population deterrent 
approaches that are effective for preventing alcohol-impaired 
driving overall also can be beneficial for specifically preventing 
adolescent drinking and driving. For example, publicized 
sobriety checkpoints are highly effective for reducing drinking 
and driving overall (73), and evidence indicates that they can 
reduce alcohol-impaired driving (17) and alcohol-related 
crashes among underage drivers (14). 

Consistent with two recent studies, this analysis determined 
that texting while driving among adolescents remains high, 
increases with age, and is more common among white students 
than students of other races/ethnicities (8,9). Similar to the 
other studies, this analysis also determined that adolescents 
who engage in texting while driving are more likely to engage 
in other transportation risk behaviors (8,9). Awareness 
campaigns, education, and changes in policy related to texting 
while driving have had mixed effectiveness (9,13). Because of 
this, such technologic interventions as in-vehicle cell phone 
blocking technologies can serve as potential solutions; however, 
the effectiveness and acceptability of such solutions require 
more research (9,13). 

Lack of parental monitoring and supervision is a common 
underlying contributor to many health risk behaviors, and 
parental involvement can be especially important for reducing 
transportation risk behaviors. For example, one study found 
that adolescents with supportive parents who monitor their 
behavior were less likely to engage in multiple passenger- and 
driver-related transportation risk behaviors, including seat belt 
nonuse, cell phone use while driving, and drinking and driving, 
than adolescents with uninvolved parents (18). Parents/guardians 
also can play a vital role in teaching adolescents to drive by 
helping ensure they gain valuable driving experience and by 
setting rules and expectations for adolescent drivers, including 
rules and expectations for not engaging in transportation 
risk behaviors. Parent-teen driving agreements (https://www. 
cdc.gov/parentsarethekey/parents/index.html) can formalize 
those expectations and demonstrate a commitment between 
parents and adolescents to adhere to safe driving practices while 
adolescents gain new driving privileges over time. 


US Department of Health and Human Services/Centers for Disease Control and Prevention 


Supplement 


Limitations 


General limitations for the YRBS are available in the overview 
report of this supplement (6). The findings in this report are 
subject to at least two additional limitations. First, YRBS does 
not quantify driving or riding exposure in general or during the 
30 days before the survey. How many trips each student takes as 
a driver or as a passenger and the amount of time each student 
spends on the road are unknown. High school students who 
take more frequent trips or drive for longer times or distances 
might have more opportunity to engage in transportation risk 
behaviors because of a higher exposure that is not captured by the 
survey. Second, for riding with a driver who had been drinking 
alcohol, the relationship between the student and the drinking 
driver (e.g., parent/guardian, other family member, a peer, or 
someone else) is unknown. The nature of this relationship 
might have implications for designing potential strategies and 
prevention messages for empowering adolescents so that they 
can intervene (15). 


Conclusion 


Motor-vehicle—crash injuries remain a leading cause of 
death among adolescents. Despite this, passenger- and driver- 
related transportation risk behaviors that increase the risk for 
crashes, injuries, and deaths remain too common. Reducing 
transportation risk behaviors among adolescents by using proven 
strategies, especially those that can target multiple transportation 
tisk behaviors, can help prevent crashes, reduce injuries, and 
save lives. Because driver-related transportation risk behaviors 
increased with age, continued emphasis on implementation 
of effective strategies for preventing these behaviors with high 
school juniors and seniors should be considered. 


Conflicts of Interest 


All authors have completed and submitted the International 
Committee of Medical Journal Editors form for disclosure of 
potential conflicts of interest. No potential conflicts of interest 
were disclosed. 


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ISSN: 2380-8950 (Print)