Abstract
Objective:
To expand knowledge of co-occurring alcohol use and disordered eating behaviors (DEB) among sexual minority (ie, nonheterosexual) youth.
Method:
Using pooled 2009–2015 US Youth Risk Behavior Surveys (322,687 students; 7.3% lesbian, gay, bisexual), multivariable logistic regression models examined: (1) associations of age of onset of drinking and past month binge drinking with past year DEB (fasting, diet pill use, purging, steroid use); and (2) effect modification by sexual orientation.
Results:
Alcohol use and sexual minority identity were independently associated with elevated odds for diet pill use and purging among females, and fasting and steroid use among males. Odds of fasting increased with greater frequency of monthly binge drinking among heterosexual adolescent females, and odds of diet pill use increased with greater frequency of monthly binge drinking among heterosexual adolescent males. DEB prevalence was particularly pronounced among adolescents who binge drank and who were not sure of their sexual orientation identity. Among males not sure of their sexual orientation identity, those who binge drank more than one day in the past month had 8.63–23.62 times the odds of using diet pills relative to those who did not binge drink, and 13.37–26.42 times the odds of purging relative to those who did not binge drink.
Conclusion:
More research is needed on psychosocial factors underlying alcohol use and DEB in youth of all sexual orientations.
Keywords: Adolescence, Sexual Minorities, Binge Drinking, Eating Disorders, Males and Females
INTRODUCTION
Alcohol use disorders and eating disorders are debilitating conditions that frequently co-occur. 1–3 Genetic factors contribute to vulnerability for alcohol dependence and disordered eating,4 yet do not fully explain the association.5, 6 Alcohol use and disordered eating behaviors (DEB; e.g., purging) typically onset during adolescence.7–9 Although estimates vary, one recent large-scale survey of US college students indicated that approximately 17% of males and 19% of females engage in some form of co-occurring DEB and binge drinking.10 Identifying population subgroups in which co-occurrence is elevated can help inform prevention efforts. One diverse subpopulation within which elevated risk of co-occurrence is likely is sexual minority (i.e., nonheterosexual) adolescents. Prior research indicates that sexual minority adolescents are more likely than their heterosexual peers to engage in substance abuse behaviors11, 12 and to engage in a diverse range of DEB, including use of diet pills13 and steroids,14 purging,15 and binge eating.15, 16 Despite abundant research on sexual orientation disparities in adolescent alcohol use and DEB, few studies examine sexual orientation differences in co-occurring alcohol use and DEB.
Alcohol use and DEB may co-occur among sexual minority adolescents due to shared risk factors. In particular, sexual minority youth are at risk for experiencing and internalizing stress related to their stigmatized sexual orientation identities (i.e., minority stress17). Prior research has found that sexual minority youth may be targets of homophobic and heterosexist victimization and discrimination in schools and other settings.18 Sexual minority youth may engage in alcohol use and DEB as a means of coping with minority stress processes.19, 20
Examining differences in alcohol use and DEB risk by gender and sexual orientation can differentiate priority subgroups of adolescents that require preventive interventions. In general, research on co-occurring alcohol use and DEB has focused on young women (ages 18–30), generally with no data on sexual orientation reported.3, 21 Prior studies indicate that gender stratified approaches are warranted, given that males generally report higher levels of alcohol use than females.22, 23 In addition, whereas females are more likely than males to engage in purging and diet pill use, males are more likely to engage in steroid use.24 Sexual orientation modifies the association between gender and alcohol use22, 25 and gender and DEB.13, 15, 26 Thus, the association between alcohol use and DEB may differ by sexual orientation across males and females.
The current study examined associations between early alcohol use, binge drinking, and DEB among heterosexual and sexual minority adolescent males and females. It was expected that earlier age of alcohol use and more frequent binge drinking would be associated with greater engagement in DEB. The association between alcohol use and DEB was hypothesized to be stronger in sexual minorities than among heterosexual youth.
METHOD
Data Source
The YRBS is a biennial national survey conducted by the Centers for Disease Control and Prevention (CDC) since 1991 to collect health data on students in grades 9–12.27 This study utilized local YRBS surveys, which are administered on a state, large urban school district, or county level by departments of education or health; in this implementation, jurisdictions use a two-stage cluster sample design to identify a sample of students.27 Schools are first selected with a probability proportional to their enrollment; then, classes are randomly selected, and all students within these classes are eligible to participate. A new sample is selected each year that the survey is administered; the same students are not followed over time.
Local YRBS data were pooled across multiple jurisdictions and years (biennially from 2009–2015). The entire dataset consists of 47 jurisdictions across 6 time points, and 411,409 students. There were a total of 114 jurisdiction-years (distinct surveys administered by a particular jurisdiction in a specific year) that assessed sexual identity (366,550 students). The present analysis used data from 2009–2015. Students were excluded if they were missing any of the primary demographic/covariate variables of interest (sexual identity: 4.85%; race/ethnicity: 3.36%; sex: 0.76% and age 0.35%; BMI percentile: 8.41%; not mutually exclusive) resulting in a sample size 322,687 students (51% female).
Measures
Sexual orientation identity was assessed by an item, “Which of the following best describes you?”: “Heterosexual (straight);” “Gay or lesbian;” “Bisexual;” or “Not sure.”
Fasting.
Participants were asked, “During the past 30 days, did you go without eating for 24 hours or more (also called fasting) to lose weight or keep from gaining weight?” (Yes/No).
Diet pills.
Participants were asked, “During the past 30 days, did you take any diet pills, powders, or liquids without a doctor’s advice to lose weight or to keep from gaining weight? (Do not include meal replacement products such as Slim Fast.)” (Yes/No).
Purging.
Participants were asked, “During the past 30 days, did you vomit or take laxatives to lose weight or to keep from gaining weight?” (Yes/No).
Steroid use.
Participants were asked, “During your life, how many times have you taken steroid pills or shots without a doctor’s prescription?” Response options ranged from 0 times to 40 or more times and were collapsed and dichotomized (Any use/No use).
Alcohol use was assessed with two items.
For age at first drink, participants were asked, “How old were you when you had your first drink of alcohol other than a few sips?” Items were coded to reflect those who “Never Drank”, drank “Before age 13” and who drank “At age 13 or older,” per the CDC coding used in the 2015 YRBS Combined Data Users Guide.28 For binge drinking, participants were asked, “ During the past 30 days, on how many days did you have 5 or more drinks of alcohol in a row, that is, within a couple of hours?” Response options (ranging from 0 days to 20+ days) were coded into three categories: “0 days;” “1–2 days;” and “3+ days.”
Sex.
Participants identified their sex with the item “What is your sex?” (Female/Male).
Covariates.
The YRBS assessed race/ethnicity by self-report, with response options combined using CDC classification guidelines into the following racial/ethnic groups: “American Indian or Alaska Native;” “Asian;” “Black or African American;” “Native Hawaiian/Other Pacific Islander;” “White;” “Hispanic/Latino;” and “Multiple-Non-Hispanic.” Age was measured with the question, “How old are you?” coded into five categories: “14 or younger;” “15 y.o.;” “16 y.o.;” “17 y.o.;” and “18 and older.” BMIpercentile was calculated based on percentile for body mass index (BMI) by age and sex, based on self-reported height and weight,29 and entered as a continuous variable for multivariable analyses. Survey year was coded as the year of survey completion. Substance use was measured using a series of items about participants’ use of marijuana, cocaine, inhalants, heroin, methamphetamines, and ecstasy. Endorsement of any marijuana use was coded as marijuana use; any other drug was coded as non-marijuana illicit drug use. Cigarette use was measured by asking participants “During the past 30 days, on how many days did you smoke cigarettes?” Response options ranged from 0 days to 29 days, and were dichotomized (Any use/No use). Models examining steroid abuse also considered potential steroid use related to the treatment of asthma, a common chronic health condition in childhood and adolescence. Asthma was measured using the item, “Has a doctor or nurse ever told you that you have asthma?” (Yes/No).
Analysis
Analyses were conducted in SAS Version 9.4 (SAS Institute, Cary, NC), using SAS-Callable SUDAAN Version 11.0.1 (RTI International, Research Triangle Park, NC) to weight estimates and account for the complex sampling design of the YRBS. YRBS data weights adjust for student non-response and distribution of students by grade, sex, and race/ethnicity in each jurisdiction.27
First, descriptive statistics were calculated for all variables by sex and sexual orientation identity (Table 1). Next, DEB outcomes were regressed on the alcohol use indicators and sexual orientation in sex-stratified, multivariable logistic regression models (adjusting for all covariates). To test whether associations between alcohol use and DEB outcomes differed by sexual orientation subgroup, interaction terms (sexual orientation X alcohol use) were tested. If significant interaction effects were detected, sexual orientation-stratified models were estimated. Bar graphs plotting sexual orientation subgroup-specific raw prevalence of DEB by level of alcohol use aided in the interpretation of interactions.
Table 1.
Weighted Prevalence Estimates of Key Variables from the 2009–2015 Pooled Youth Risk Behavior Surveys
| FEMALE PARTICIPANTS (N=163,153) | ||||||||
|---|---|---|---|---|---|---|---|---|
| Heterosexual n= 139,714 |
Lesbian n= 3,286 |
Bisexual n= 14,431 |
Not Sure n= 6,132 |
|||||
| n | % | n | % | n | % | n | % | |
| Alcohol Use | ||||||||
| Age of First Drink | ||||||||
| Never Drank | 55400 | 41.2 | 829 | 28.6 | 3076 | 21.8 | 2531 | 45.7 |
| 13 or older | 59394 | 45.8 | 1446 | 44.1 | 6565 | 51.3 | 1977 | 34.5 |
| Before age 13 | 18548 | 13.0 | 851 | 27.4 | 4050 | 26.9 | 1256 | 19.8 |
| Binge Drinking (past 30 days) | ||||||||
| 0 days | 113249 | 84.9 | 2242 | 80.8 | 10262 | 76.8 | 4906 | 85.6 |
| 1–2 days | 13551 | 10.2 | 494 | 13.1 | 2121 | 15.2 | 556 | 9.8 |
| 3+ days | 5882 | 4.9 | 204 | 6.0 | 1071 | 7.9 | 281 | 4.6 |
| Disordered Eating Behaviors | ||||||||
| Fasting | 6213 | 14.1 | 217 | 27.5 | 1074 | 28.0 | 371 | 24.4 |
| Diet Pills | 2427 | 5.4 | 114 | 15.3 | 422 | 10.8 | 172 | 9.8 |
| Purging (vomiting or laxatives) | 3187 | 5.4 | 153 | 16.7 | 642 | 13.3 | 236 | 11.8 |
| Steroid Use | 1085 | 1.5 | 190 | 9.3 | 395 | 4.7 | 181 | 2.8 |
| Covariates | ||||||||
| Substance use | ||||||||
| Illicit Drugs | 11531 | 8.4 | 794 | 24.2 | 3421 | 22.8 | 1096 | 16.2 |
| Marijuana | 32229 | 34.2 | 1321 | 54.2 | 5684 | 60.6 | 1508 | 32.0 |
| Cigarette Use (past 30 days) | 10878 | 7.3 | 660 | 21.0 | 3222 | 24.0 | 700 | 8.9 |
| Demographics | ||||||||
| Age | ||||||||
| 14 or younger | 21556 | 12.4 | 336 | 6.8 | 2085 | 13.1 | 1194 | 21.3 |
| 15 | 36193 | 25.5 | 722 | 26.2 | 3922 | 25.4 | 1744 | 31.9 |
| 16 | 36091 | 25.3 | 909 | 24.7 | 3900 | 24.0 | 1483 | 22.7 |
| 17 | 32612 | 24.0 | 858 | 25.6 | 3171 | 22.2 | 1169 | 16.4 |
| 18 or older | 13262 | 12.8 | 461 | 16.7 | 1353 | 15.4 | 542 | 7.7 |
| Race | ||||||||
| American Indian/Alaskan Native | 2075 | 1.0 | 70 | 0.9 | 329 | 1.2 | 122 | 0.9 |
| Asian | 9545 | 5.7 | 87 | 2.0 | 485 | 4.5 | 593 | 8.2 |
| Black | 22274 | 14.7 | 892 | 25.8 | 2814 | 18.5 | 1050 | 13.7 |
| Hispanic/Latino | 32569 | 26.4 | 970 | 25.3 | 4455 | 30.6 | 1701 | 25.1 |
| Hawaiian/Pacific Islander | 2205 | 0.7 | 52 | 0.3 | 231 | 0.5 | 112 | 2.9 |
| White | 64533 | 48.3 | 1004 | 42.1 | 5129 | 40.4 | 2159 | 43.2 |
| Multiple-Races (non-Hispanic) | 6513 | 3.2 | 211 | 3.5 | 988 | 4.4 | 395 | 6.1 |
| Health | ||||||||
| Asthma | 24052 | 22.4 | 848 | 36.8 | 3275 | 30.1 | 1152 | 23.4 |
| Weight Statusa | ||||||||
| Underweight | 3421 | 2.4 | 92 | 1.9 | 333 | 2.1 | 175 | 3.0 |
| Healthy Weight | 104086 | 74.2 | 2018 | 58.7 | 8959 | 63.4 | 3986 | 67.9 |
| Overweight/Obese | 32207 | 23.4 | 1176 | 39.4 | 5139 | 34.5 | 1971 | 29.1 |
| MALE PARTICIPANTS (N=155,263) | ||||||||
| Heterosexual n= 143,869 |
Gay n= 143,869 |
Bisexual n= 3,836 |
Not Sure n= 4,125 |
|||||
| n | % | n | % | n | % | n | % | |
| Alcohol Use | ||||||||
| Age of First Drink | ||||||||
| Never Drank | 59293 | 43.5 | 1152 | 38.5 | 1157 | 36.4 | 1888 | 55.4 |
| 13 or older | 51221 | 39.1 | 1101 | 36.7 | 1301 | 37.4 | 975 | 20.4 |
| Before age 13 | 25873 | 17.4 | 955 | 24.9 | 1161 | 26.3 | 1020 | 24.3 |
| Binge Drinking (past 30 days) | ||||||||
| 0 days | 112033 | 83.0 | 2237 | 82.5 | 2606 | 76.1 | 2860 | 83.6 |
| 1–2 days | 13900 | 10.5 | 414 | 10.5 | 477 | 16.5 | 376 | 9.0 |
| 3+ days | 8460 | 6.5 | 255 | 7.0 | 337 | 7.3 | 384 | 7.5 |
| Disordered Eating Behaviors | ||||||||
| Fasting | 3347 | 7.2 | 233 | 24.8 | 204 | 20.6 | 200 | 17.7 |
| Diet Pills | 2030 | 4.5 | 169 | 15.4 | 106 | 13.3 | 124 | 11.9 |
| Purging (vomiting or laxatives) | 1998 | 3.1 | 231 | 17.3 | 160 | 12.5 | 182 | 14.3 |
| Steroid Use | 2235 | 2.3 | 394 | 13.1 | 356 | 12.4 | 368 | 10.7 |
| Covariates | ||||||||
| Substance use | ||||||||
| Illicit Drugs | 15280 | 10.3 | 1136 | 26.5 | 1227 | 26.9 | 1133 | 25.7 |
| Marijuana | 38910 | 39.3 | 950 | 42.8 | 1217 | 45.5 | 1040 | 36.1 |
| Cigarette Use (past 30 days) | 15720 | 11.2 | 653 | 16.8 | 819 | 25.7 | 695 | 14.6 |
| Demographics | ||||||||
| Age | ||||||||
| 14 or younger | 19332 | 11.2 | 359 | 9.5 | 495 | 11.0 | 606 | 14.0 |
| 15 | 36343 | 25.2 | 803 | 26.3 | 940 | 21.3 | 986 | 27.4 |
| 16 | 37339 | 25.3 | 854 | 23.1 | 984 | 23.8 | 1022 | 23.4 |
| 17 | 33857 | 23.7 | 886 | 24.4 | 946 | 28.5 | 954 | 19.4 |
| 18 or older | 16998 | 14.6 | 531 | 16.7 | 471 | 15.4 | 557 | 16.0 |
| Race | ||||||||
| American Indian/Alaskan Native | 2685 | 1.3 | 85 | 1.3 | 111 | 2.3 | 121 | 2.3 |
| Asian | 9800 | 6.0 | 189 | 4.1 | 203 | 3.3 | 426 | 9.2 |
| Black | 22461 | 14.1 | 661 | 17.8 | 578 | 15.2 | 611 | 13.0 |
| Hispanic/Latino | 33727 | 26.2 | 991 | 33.1 | 1083 | 28.8 | 1077 | 31.6 |
| Hawaiian/Pacific Islander | 2470 | 0.9 | 88 | 2.9 | 84 | 0.6 | 116 | 0.7 |
| White | 66467 | 48.7 | 1243 | 36.5 | 1513 | 45.9 | 1555 | 39.5 |
| Multiple-Races (non-Hispanic) | 6259 | 2.9 | 176 | 4.4 | 264 | 3.8 | 219 | 3.7 |
| Health | ||||||||
| Asthma | 26332 | 24.1 | 770 | 25.3 | 796 | 25.7 | 812 | 28.7 |
| Weight Status | ||||||||
| Underweight | 5237 | 3.6 | 197 | 6.3 | 225 | 6.9 | 247 | 5.2 |
| Healthy Weight | 95100 | 65.2 | 2127 | 59.0 | 2213 | 54.9 | 2469 | 59.4 |
| Overweight/Obese | 43532 | 31.2 | 1109 | 34.7 | 1398 | 38.2 | 1409 | 35.4 |
Note: Multivariable models included body mass index (BMI) percentile (continuous variable) based on participant’s age and sex.
For descriptive purposes, weight status was calculated from body mass index z-scores.
RESULTS
Female Youth
Among females (Table 2), alcohol use and minority sexual orientation identity were independently associated with elevated odds of diet pill use and purging. Drinking in adolescence, regardless of age of onset (vs. never drinking) was associated with 1.69–2.11 times the odds of diet pill use and 1.73–1.80 times the odds of purging. In addition, binge drinking at least one day in the past month was associated with 1.33–1.99 times the odds of using diet pills and 1.24–1.54 times the odds of purging. Compared to heterosexual adolescent females, lesbian adolescents had 1.67 times the odds of using diet pills (95% CI= 1.07, 2.60), and females who identified as lesbian, bisexual, or who were not sure of their sexual orientation identity had 1.772.75 times the odds of purging.
Table 2.
Multivariable Logistic Regression Models Estimating Odds of Disordered Eating Behaviors by Adolescent Alcohol Use Among Heterosexual and Sexual Minority Females in the 2009–2015 Pooled Youth Risk Behavior Surveys
| Overall Sample | Heterosexual | Lesbian | Bisexual | Not Sure | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI | |
| Fastinga | ||||||||||
| Age of First Drink | ||||||||||
| Never Drank (Referent) | ||||||||||
| 13 or older | 1.40*** | (1.24, 1.59) | 1.46*** | (1.27, 1.67) | 0.51 | (0.23, 1.15) | 1.00 | (0.67, 1.51) | 1.45 | (0.81, 2.60) |
| Before age 13 | 1.77*** | (1.53, 2.06) | 1.79*** | (1.52, 2.09) | 0.94 | (0.38, 2.34) | 1.46 | (0.91, 2.34) | 2.61** | (1.33, 5.13) |
| Binge Drinking (30 days) | ||||||||||
| 0 days (Referent) | ||||||||||
| 1–2 days | 1.47*** | (1.30, 1.65) | 1.48*** | (1.29, 1.70) | 1.23 | (0.50, 3.01) | 1.17 | (0.80, 1.70) | 3.38** | (1.39, 8.24) |
| 3+ days | 1.39c | (1.14, 1.69) | 1.61*** | (1.30, 1.99) | 0.73 | (0.23, 2.28) | 0.68 | (0.39, 1.17) | 1.51 | (0.62, 3.73) |
| Any illicit drug use | 1.95*** | (1.67, 2.27) | 2.09*** | (1.74, 2.51) | 1.56 | (0.68, 3.55) | 1.52** | (1.10, 2.11) | 2.21** | (1.24, 3.97) |
| Any marijuana use | 1.12 | (0.99, 1.27) | 1.10 | (0.96, 1.26) | 1.10 | (0.50, 2.42) | 1.25 | (0.89, 1.75) | 0.89 | (0.51, 1.53) |
| Any cigarettes (30 days) | 1.50*** | (1.31, 1.73) | 1.47*** | (1.23, 1.75) | 1.65 | (0.73, 3.74) | 1.76** | (1.26, 2.46) | 1.18 | (0.56, 2.47) |
| Sexual Orientation | ||||||||||
| Heterosexual (Referent) | ||||||||||
| Lesbian | ||||||||||
| Bisexual | ||||||||||
| Not Sure | ||||||||||
| Overall Sample | Heterosexual | Lesbian | Bisexual | Not Sure | ||||||
| OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI | |
| Diet Pills | ||||||||||
| Age of First Drink | ||||||||||
| Never Drank (Referent) | ||||||||||
| 13 or older | 1.69*** | (1.32, 2.14) | ||||||||
| Before age 13 | 2.11*** | (1.60, 2.78) | ||||||||
| Binge Drinking (30 days) | ||||||||||
| 0 days (Referent) | ||||||||||
| 1–2 days | 1.33** | (1.09, 1.63) | ||||||||
| 3+ days | 1.99*** | (1.49, 2.66) | ||||||||
| Any illicit drug use | 1.75*** | (1.42, 2.15) | ||||||||
| Any marijuana use | 1.14 | (0.92, 1.41) | ||||||||
| Any cigarettes (30 days) | 1.66*** | (1.31, 2.10) | ||||||||
| Sexual Orientation | ||||||||||
| Heterosexual (Referent) | ||||||||||
| Lesbian | 1.67* | (1.07, 2.60) | ||||||||
| Bisexual | 1.16 | (0.91, 1.49) | ||||||||
| Not Sure | 1.43 | (0.98, 2.08) | ||||||||
| Overall Sample | Heterosexual | Lesbian | Bisexual | Not Sure | ||||||
| OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI | |
| Purging |
||||||||||
| Age of First Drink | ||||||||||
| Never Drank (Referent) | ||||||||||
| 13 or older | 1.80*** | (1.45, 2.24) | ||||||||
| Before age 13 | 1.73*** | (1.34, 2.23) | ||||||||
| Binge Drinking (30 days) | ||||||||||
| 0 days (Referent) | ||||||||||
| 1–2 days | 1.24c | (1.02, 1.49) | ||||||||
| 3+ days | 1.54** | (1.17, 2.03) | ||||||||
| Any illicit drug use | 2.17*** | (1.80, 2.61) | ||||||||
| Any marijuana use | 1.13 | (0.96, 1.33) | ||||||||
| Any cigarettes (30 days) | 1.55*** | (1.29, 1.87) | ||||||||
| Sexual Orientation | ||||||||||
| Heterosexual (Referent) | ||||||||||
| Lesbian | 2.75*** | (1.85, 4.08) | ||||||||
| Bisexual | 1.77*** | (1.40, 2.23) | ||||||||
| Not Sure | 1.85** | (1.33, 2.58) | ||||||||
| Overall Sample | Heterosexual | Lesbian | Bisexual | Not Sure | ||||||
| OR | 95% CI | OR 95% CI | OR 95% CI | OR 95% CI | OR | 95% CI | ||||
| Steroids |
||||||||||
| Age of First Drink | ||||||||||
| Never Drank (Referent) | ||||||||||
| 13 or older | 0.69 | (0.27, 1.74) | ||||||||
| Before age 13 | 0.93 | (0.38, 2.29) | ||||||||
| Binge Drinking (30 days) | ||||||||||
| 0 days (Referent) | ||||||||||
| 1–2 days | 1.14 | (0.68, 1.90) | ||||||||
| 3+ days | 1.00 | (0.61, 1.64) | ||||||||
| Any illicit drug use | 4.47*** | (2.76, 7.23) | ||||||||
| Any marijuana use | 2.98*** | (1.76, 5.06) | ||||||||
| Any cigarettes (30 days) | 2.37*** | (1.49, 3.76) | ||||||||
| Sexual Orientation | ||||||||||
| Heterosexual (Referent) | ||||||||||
| Lesbian | 1.31 | (0.52, 3.31) | ||||||||
| Bisexual | 1.40 | (0.84, 2.33) | ||||||||
| Not Sure | 0.79 | (0.29, 2.12) | ||||||||
Note: Models adjust for age, race/ethnicity, body mass index (BMI) percentile, asthma (steroid use model only), and survey wave. Sexual orientation-stratified results are reported when significant sexual orientation X alcohol use interaction effects are identified. OR = odds ratio.
Sexual orientation estimates in the Overall Sample model for fasting are not reported due to the significant interaction effect.
p < 0.05,
p < 0.01,
p < 0.001
A significant interaction (p=0.017) indicated that the effect of binge drinking on fasting differed by sexual orientation. Stratified models (Table 2) and prevalence plots (Figure 1A) revealed that, among heterosexual females, those who binge drank 1–2 days in the past month had 1.48 times the odds of those who did not binge drink of engaging in fasting, and those who binge drank 3+ days in the past month had 1.61 times the odds of fasting. Among females who were not sure of their sexual orientation identity, those who binge drank 1–2 times in the past 30 days had 3.38 times the odds of fasting.
Figure 1.
Raw Prevalence Plots of Fasting (Female Participants) and Diet Pill Use and Purging (Males Participants) by Sexual Orientation and Alcohol Use Note: (Figure 1A) Female Participants: Fasting by binge drinking behavior and sexual orientation. (Figure 1B) Male Participants: Purging by binge drinking behavior and sexual orientation. (Figure 1C) Male Participants: Diet pill use by age of first drink and sexual orientation. (Figure 1D) Male Participants: Diet pill use by binge drinking behavior and sexual orientation.
Male Youth
Among males (Table 3), alcohol use and sexual minority identity were independently associated with fasting and steroid use. Compared to those who did not drink, males who reported drinking prior to age 13 had 1.55 greater odds of fasting and 2.19 times the odds of steroid use. In addition, males who were gay, bisexual, or not sure of their identity had 2.43–3.07 times the odds of heterosexual males of reporting fasting. Males who identified as bisexual or not sure of their identity had 2.29–2.42 times the odds of using steroids compared to heterosexual males.
Table 3.
Multivariable Logistic Regression Models Estimating Odds of Disordered Eating Behaviors by Adolescent Alcohol Use Among Heterosexual and Sexual Minority Males in the 2009–2015 Pooled Youth Risk Behavior Surveys
| Overall Sample | Heterosexual | Gay | Bisexual | Not Sure | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| OR | 95% CI | OR | 95% CI | OR | 95% CI | OR 95% CI | OR | 95% CI | ||
| Fasting | ||||||||||
| Age of First Drink | ||||||||||
| Never Drank (Referent) | ||||||||||
| 13 or older | 1.11 | (0.93, 1.33) | ||||||||
| Before age 13 | 1.55*** | (1.29, 1.86) | ||||||||
| Binge Drinking (30 days) | ||||||||||
| 0 days (Referent) | ||||||||||
| 1–2 days | 1.11 | (0.87, 1.42) | ||||||||
| 3+ days | 1.22 | (0.94, 1.60) | ||||||||
| Any illicit drug use | 1.49*** | (1.20, 1.85) | ||||||||
| Any marijuana use | 0.93 | (0.79, 1.11) | ||||||||
| Any cigarettes (30 days) | 1.55*** | (1.27, 1.88) | ||||||||
| Sexual Orientation | ||||||||||
| Heterosexual (Referent) | ||||||||||
| Gay | 3.07*** | (2.07, 4.58) | ||||||||
| Bisexual | 2.54*** | (1.84, 3.50) | ||||||||
| Not Sure | 2.43*** | (1.75, 3.37) | ||||||||
| Overall Sample | Heterosexual | Gay | Bisexual | Not Sure | ||||||
| OR | 95% CI | OR | 95% CI | OR | 95% CI | OR 95% CI | OR | 95% CI | ||
| Diet Pillsa |
||||||||||
| Age of First Drink | ||||||||||
| Never Drank (Referent) | ||||||||||
| 13 or older | 1.15 | (0.89, 1.48) | 1.18 | (0.91, 1.54) | 0.78 | (0.25, 2.38) | 3.26 | (0.57, 18.61) | 0.16* | (0.03, 0.72) |
| Before age 13 | 1.18 | (0.87, 1.61) | 1.22 | (0.86, 1.74) | 1.19 | (0.37, 3.83) | 0.95 (0.17, 5.45) | 0.21 | (0.02, 2.02) | |
| Binge Drinking (30 days) | ||||||||||
| 0 days (Referent) | ||||||||||
| 1–2 days | 1.44c | (1.07, 1.92) | 1.56** | (1.15, 2.12) | 0.21 | (0.05, 0.87) | 0.05 | (0.01, 0.20) | 8.63** | (1.56, 47.84) |
| 3+ days | 1.84*** | (1.39, 2.43) | 1.73*** | (1.27, 2.36) | 0.68 | (0.18, 2.60) | 4.93* | (1.06, 23.02) | 23.62*** | (4.70, 118.64) |
| Any illicit drug use | 1.44** | (1.12, 1.84) | 1.35* | (1.02, 1.79) | 4.15* | (1.28, 13.46) | 1.48 | (0.36, 6.06) | 4.41* | (1.29, 15.12) |
| Any marijuana use | 1.08 | (0.86, 1.35) | 1.07 | (0.84, 1.35) | 2.02 | (0.65, 6.30) | 1.14 | (0.25, 5.28) | 0.66 | (0.15, 2.94) |
| Any cigarettes (30 days) | 1.31 | (0.98, 1.74) | 1.33 | (0.97, 1.84) | 0.96 | (0.28, 3.32) | 2.14 | (0.67, 6.91) | 0.63 | (0.21, 1.91) |
| Sexual Orientation | ||||||||||
| Heterosexual (Referent) | ||||||||||
| Gay | ||||||||||
| Bisexual | ||||||||||
| Not Sure | ||||||||||
| Overall Sample | Heterosexual | Gay | Bisexual | Not Sure | ||||||
| OR | 95% CI | OR | 95% CI | OR | 95% CI | OR 95% CI | OR | 95% CI | ||
| Purgingb |
||||||||||
| Age of First Drink | ||||||||||
| Never Drank (Referent) | ||||||||||
| 13 or older | 0.83 | (0.60, 1.14) | 0.81 | (0.57, 1.15) | 0.82 | (0.17, 3.84) | 0.69 | (0.21, 2.26) | 0.61 | (0.20, 1.90) |
| Before age 13 | 0.99 | (0.69, 1.41) | 1.00 | (0.67, 1.51) | 1.69 | (0.44, 6.51) | 0.32 | (0.10, 1.02) | 0.36 | (0.10, 1.28) |
| Binge Drinking (30 days) | ||||||||||
| 0 days (Referent) | ||||||||||
| 1–2 days | 1.34 | (0.92, 1.93) | 1.30 | (0.88, 1.91) | 2.67 | (0.49, 14.62) | 0.11** | (0.03, 0.45) | 13.37* | (1.63, 109.72) |
| 3+ days | 1.36 | (0.95, 1.96) | 1.17 | (0.80, 1.72) | 1.98 | (0.38, 10.21) | 4.50* | (1.31, 15.41) | 26.42** | (3.73, 187.03) |
| Any illicit drug use | 1.85*** | (1.40, 2.45) | 1.76*** | (1.29, 2.41) | 2.17 | (0.72, 6.54) | 1.50 | (0.56, 4.05) | 6.07*** | (2.23, 16.49) |
| Any marijuana use | 0.89 | (0.65, 1.22) | 0.96 | (0.69, 1.34) | 0.50 | (0.14, 1.82) | 1.34 | (0.29, 6.12) | 0.06 | (0.01, 0.36) |
| Any cigarettes (30 days) | 1.56* | (1.09, 2.24) | 1.68* | (1.13, 2.51) | 0.94 | (0.22, 4.02) | 0.79 | (0.25, 2.53) | 2.60 | (0.73, 9.27) |
| Sexual Orientation | ||||||||||
| Heterosexual (Referent) | ||||||||||
| Gay | ||||||||||
| Bisexual | ||||||||||
| Not Sure | ||||||||||
| Overall Sample | Heterosexual | Gay | Bisexual | Not Sure | ||||||
| OR | 95% CI | OR 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI | ||
| Steroids |
||||||||||
| Age of First Drink | ||||||||||
| Never Drank (Referent) | ||||||||||
| 13 or older | 1.43 | (0.91, 2.24) | ||||||||
| Before age 13 | 2.19*** | (1.38, 3.47) | ||||||||
| Binge Drinking (30 days) | ||||||||||
| 0 days (Referent) | ||||||||||
| 1–2 days | 1.80* | (1.13, 2.86) | ||||||||
| 3+ days | 3.47 | (2.12, 5.69) | ||||||||
| Any illicit drug use | 6.00*** | (4.34, 8.31) | ||||||||
| Any marijuana use | 1.66c | (1.10, 2.49) | ||||||||
| Any cigarettes (30 days) | 1.25 | (0.89, 1.76) | ||||||||
| Sexual Orientation | ||||||||||
| Heterosexual (Referent) | ||||||||||
| Gay | 1.00 | (0.55, 1.81) | ||||||||
| Bisexual | 2.29* | (1.19, 4.41) | ||||||||
| Not Sure | 2.42*** | (1.57, 3.73) | ||||||||
Note: Models adjust for age, race/ethnicity, body mass index (BMI) percentile, asthma (steroid use model only), and survey wave. Sexual orientation-stratified results are reported when significant sexual orientation X alcohol use interaction effects are identified. OR = Odds ratio.
Sexual orientation estimates in the Overall Sample model for diet pills and purging are not reported due to significant interaction effects.
p < 0.05,
p < 0.01,
p < 0.001
Significant interactions revealed that the associations between binge drinking and purging (p=0.0005), drinking age and diet pills (p=0.0056), and binge drinking and diet pills (p<0.0001) differed by sexual orientation. Stratified models (Figure 1B; Table 3) indicated that, among males not sure of their sexual orientation identity, those who binge drank 1 −2 days in the past month had 13.37 times the odds of those who did not binge drink of purging (95% CI=1.63, 109.72), and those who binge drank 3+ days had 26.42 times the odds of purging (95% CI=3.73, 187.03). Among bisexual males, males who binge drank 1–2 days had 0.11 times the odds of purging; however, bisexual males who binge drank 3+ days had 4.5 times the odds of purging (95% CI=1.31, 15.41).
Among males who were not sure of their sexual orientation identity, those who drank at age 13 or later (vs. never drank) had 0.16 times the odds of using diet pills (Figure 1C; Table 3).
However, those who binge drank 1–2 days in the past month had 8.63 times the odds of those who did not binge drink of using diet pills (95% CI=1.56, 47.84), and those who binge drank 3+ days in the past month had 23.62 times the odds of using diet pills (95% CI=4.70, 118.64) (Figure 1D; Table 3). Among bisexual males, those who binge drank 3+ days in the past month had 4.93 times the odds of those who did not binge drink of using diet pills.
Although heterosexual males overall reported a low prevalence of diet pill use at all levels of binge drinking (Figure 1D), heterosexual males who binge drank 1–2 days had 1.56 times the odds of using diet pills relative to those who did not binge drink, and those who binge drank 3+ days had 1.73 times the odds of using diet pills.
DISCUSSION
The results of the current study align with previous research detecting elevated DEB among adolescents who consumed more alcohol,30 and higher risk for DEB among sexual minorities.13, 31, 32 The findings support the clinical practice of screening for alcohol use among youth who engage in DEB, and being responsive to the potentially unique experiences of sexual minority youth. For females, ever drinking and more frequent binge drinking were associated with higher odds of diet pill use and purging. In addition, lesbian and bisexual females had higher odds of using diet pills and purging. Among males, earlier onset of drinking was associated with higher odds of fasting and steroid use, and more frequent binge drinking was associated with higher odds of steroid use. In addition, gay and bisexual males had higher odds of engaging in fasting, and bisexual males had higher odds of using steroids compared to heterosexual males.
The results present novel findings on youth who were not sure of their sexual orientation identity, who are sometimes excluded from research. Because sexual orientation identity is just one dimension of sexual orientation, which typically forms later in adolescence than other dimensions of sexual orientation, such as same-gender attraction,33 some youth who identified as “not sure” could identify as gay, lesbian, bisexual, or a different sexual minority identity later in adolescence or young adulthood.34 Irrespective of their future identity, results indicate that youth who are not sure of their sexual orientation identity are at elevated risk for DEB, particularly in the context of alcohol use. Compared to their heterosexual peers, females who were not sure of their sexual orientation identity had 1.85 times the odds of purging, and males who were not sure had more than a two-fold increase in the odds of fasting and using steroids. Stratified analyses among males revealed that if youth who are not sure about their sexual orientation also engage in more frequent monthly binge drinking, they may also have dramatically increased odds of also engaging in diet pill use and purging. Future research may include assessments of other dimensions of sexual orientation, such as attractions, or indicators of minority stress (e.g., victimization) in order to understand whether the experiences of youth who are not sure of their sexual orientation identity can be contextualized under minority stress paradigms.
Notably, among females who identified as heterosexual or not sure, the prevalence of fasting increased with greater frequency of binge drinking. The odds of diet pill use also increased with greater levels of binge drinking among heterosexual males, and markedly so among males not sure of their sexual orientation identity (over an eight-fold increase in odds). However, among lesbian and bisexual females, the prevalence of fasting was consistently high at all levels of binge drinking, and diet pill use and purging were consistently high at all levels of binge drinking and age of onset of drinking among gay males. Although future research must replicate and explore patterns of association in greater depth, the results suggest that specific DEB and alcohol use behaviors may be less correlated among certain sexual orientation subgroups. Nevertheless, the findings indicate that potentially deleterious associations between alcohol use and specific DEB exist among all sexual orientation subgroups, including heterosexual adolescent females and males, even after adjusting for any illicit drug use, marijuana use, or past month cigarette use. Additional research is needed on psychological and contextual factors accounting for differential patterns. For example, emerging studies suggest that young adult females, in particular, may be at risk for engaging in dietary restriction behaviors, including fasting, in conjunction with binge drinking in an effort to adjust for overall daily calorie intake (i.e., the phenomenon of “drunkorexia”).35 Whether this pattern holds more consistently for different sexual orientation groups across genders requires further study. In addition, studies should test for possible common underlying sources of causation, such as emotional dysregulation,2, 5, 6 and for mechanisms underlying potential sexual orientation differences in associations between DEB and alcohol use, such as comorbid depressive distress.20
The current study represents one of the largest to examine co-occurring alcohol use and DEB among heterosexual and sexual minority adolescents. Nevertheless, there are limitations that merit consideration in interpreting results. Analyses were cross-sectional, preventing determination of directionality of effects. Despite the pooling of datasets, which permitted tests of interactions, point estimates in the stratified models are potentially inflated and should be interpreted with some caution, as noted by the wide confidence intervals. However, as displayed in the prevalence estimates and within-group raw prevalence plots, the high odds ratio estimates are likely tied to the high prevalence of the DEB outcomes within the sexual minority subgroups at each level of alcohol use (>10%) and the use of logit modeling.36 The YRBS did not include binge eating, which is among the more common forms of DEB among adolescents, and which has been shown in research among women, in particular, to be one of the key eating disorder symptoms associated with alcohol use.3, 7 In addition, the YRBS only includes adolescents that were in school on the day of survey administration, which could introduce bias. Sexual minority youth may be more likely than their heterosexual peers to experience school absence due to victimization.13
In conclusion, future research should examine psychosocial factors underlying alcohol use and DEB among adolescents of all sexual orientation identities, including youth who are currently not sure of their sexual orientation identity. The findings yielded evidence of associations between alcohol use and sexual orientation with DEB that are of clinical relevance. Alcohol use and minority sexual orientation were generally uniquely associated with higher odds of disordered eating behaviors among adolescent females and males. However, evidence of effect modification indicated that clinicians and researchers should attend to and explore unique patterns of co-occurrence by sexual orientation subgroup.
Acknowledgments
The Youth Risk Behavior Surveys are funded by the Centers for Disease Control and Prevention (CDC). Dr. Calzo was supported by K01DA034753 from the National Institute on Drug Abuse of the National Institutes of Health (NIH). Dr. Phillips, Ms. Turner, and Ms. Marro were supported by R01AA024409 from the National Institute on Alcohol Abuse and Alcoholism of NIH. The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC or NIH.
This study was presented as an abstract at the International Conference on Eating Disorders, Chicago, IL, April 19–21, 2018.
Ms. Turner served as the statistical expert for this research.
Footnotes
Disclosure: Drs. Calzo and Phillips and Mss. Turner and Marro report no biomedical financial interests or potential conflicts of interest.
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Contributor Information
Jerel P. Calzo, the Division of Health Promotion and Behavioral Science, San Diego State University Graduate School of Public Health, CA, and the Institute for Behavioral and Community Health, San Diego State University Research Foundation, CA..
Blair C. Turner, the Feinberg School of Medicine, Northwestern University, Chicago, IL..
Rachel Marro, the Feinberg School of Medicine, Northwestern University, Chicago, IL..
Gregory L. Phillips, II, the Feinberg School of Medicine, Northwestern University, Chicago, IL..
REFERENCES
- 1.Micali N, Solmi F, Horton NJ, et al. Adolescent Eating Disorders Predict Psychiatric, High-Risk Behaviors and Weight Outcomes in Young Adulthood. JAm Acad Child Adolesc Psychiatry. 2015;54(8):652–659. doi: 10.1016/j.jaac.2015.05.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Fouladi F, Mitchell JE, Crosby RD, et al. Prevalence of Alcohol and Other Substance Use in Patients with Eating Disorders. Eur Eat Disord Rev. 2015;23(6):531–536. [DOI] [PubMed] [Google Scholar]
- 3.Gadalla T, Piran N. Co-occurrence of eating disorders and alcohol use disorders in women: A meta analysis. Arch Womens Ment Health. 2007;10(4):133–140. [DOI] [PubMed] [Google Scholar]
- 4.Munn-Chernoff MA, Duncan AE, Grant JD, et al. A twin study of alcohol dependence, binge eating, and compensatory behaviors. J Stud Alcohol Drugs. 2013;74(5):664–673. http://www.ncbi.nlm.nih.gov/pubmed/23948525. Accessed November 16, 2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Trace SE, Thornton LM, Baker JH, et al. A behavioral-genetic investigation of bulimia nervosa and its relationship with alcohol use disorder. Psychiatry Res. 2013;208(3):232–237. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Grilo C, Sinha R, O’Malley S. Eating Disorders and Alcohol Use Disorders. https://pubs.niaaa.nih.gov/publications/arh26-2/151-160.htm. Published 2002. Accessed August 27, 2018.
- 7.Swanson SA, Crow SJ, LeGrange D, Swendsen J, Merikangas KR. Prevalence and correlates of eating disorders in adolescents. Arch Gen Psychiatry. 2011. doi: 10.1001/archgenpsychiatry.2011.22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Johnston LD, O’Malley PM, Miech RA, Bachman JG, Schulenberg JE. Monitoring the Future National Survey Results on Drug Use, 1975–2016: Overview, Key Findings on Adolescent Drug Use; Ann Arbor, MI, MI; 2017. [Google Scholar]
- 9.Pisetsky EM, May Chao Y, Dierker LC, May AM, Striegel-Moore RH. Disordered eating and substance use in high-school students: Results from the Youth Risk Behavior Surveillance System. Int JEat Disord. 2008;41(5):464–470. doi: 10.1002/eat.20520. [DOI] [PubMed] [Google Scholar]
- 10.Rush CC, Curry JF, Looney JG. Alcohol expectancies and drinking behaviors among college students with disordered eating. J Am Coll Heal. 2016;64(3): 195–204. doi: 10.1080/07448481.2015.1107837. [DOI] [PubMed] [Google Scholar]
- 11.Talley AE, Sher KJ, Littlefield AK. Sexual orientation and substance use trajectories in emerging adulthood. Addiction. 2010;105(7):1235–1245. doi: 10.1111/j.1360-0443.2010.02953.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Corliss HL, Rosario M, Wypij D, Wylie SA, Frazier AL, Austin SB. Sexual orientation and drug use in a longitudinal cohort study of U.S. adolescents. Addict Behav. 2010;35(5):517–521. doi: 10.1016/j.addbeh.2009.12.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Austin SB, Nelson LA, Birkett MA, Calzo JP, Everett B. Eating disorder symptoms and obesity at the intersections of gender, ethnicity, and sexual orientation in US high school students. Am J Public Heal. 2013;103(2):e16–22. doi: 10.2105/ajph.2012.301150. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Blashill AJ, Safren SA. Sexual orientation and anabolic-androgenic steroids in U.S. adolescent boys. Pediatrics. 2014;133(3):469–475. doi: 10.1542/peds.2013-2768. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Austin SB, Ziyadeh NJ, Corliss HL, et al. Sexual Orientation Disparities in Purging and Binge Eating From Early to Late Adolescence. JAdolesc Heal. 2009;45(3):238–245. doi: 10.1016/j.jadohealth.2009.02.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Matthews-Ewald MR, Zullig KJ, Ward RM. Sexual orientation and disordered eating behaviors among self-identified male and female college students. Eat Behav. 2014;15(3):441–444. doi: 10.1016/j.eatbeh.2014.05.002. [DOI] [PubMed] [Google Scholar]
- 17.Meyer IH. Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: conceptual issues and research evidence. Psychol Bull. 2003;129(5):674–697. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Russell ST, Toomey RB, Ryan C, Diaz RM. Being out at school: the implications for school victimization and young adult adjustment. Am J Orthopsychiatry. 2014;84(6):635–643. doi: 10.1037/ort0000037. [DOI] [PubMed] [Google Scholar]
- 19.Phillips G, Turner B, Salamanca P, et al. Victimization as a mediator of alcohol use disparities between sexual minority subgroups and sexual majority youth using 2015 National youth Risk Behavior Survey. Drug Alcohol Depend. 2017;178:355–362. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Katz-Wise SL, Scherer EA, Calzo JP, et al. Sexual Minority Stressors, Internalizing Symptoms, and Unhealthy Eating Behaviors in Sexual Minority Youth. Ann Behav Med. July 2015. doi: 10.1007/s12160-015-9718-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Luce KH, Engler PA, Crowther JH. Eating disorders and alcohol use: group differences in consumption rates and drinking motives. EatBehav. 2007;8(2):177–184. doi: 10.1016/j.eatbeh.2006.04.003. [DOI] [PubMed] [Google Scholar]
- 22.McCabe SE, Hughes TL, Bostwick WB, West BT, Boyd CJ. Sexual orientation, substance use behaviors and substance dependence in the United States. Addiction. 2009;104:1333–1345. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Erol A, Karpyak VM. Sex and gender-related differences in alcohol use and its consequences: Contemporary knowledge and future research considerations. Drug Alcohol Depend. 2015;156:1–13. [DOI] [PubMed] [Google Scholar]
- 24.Calzo JP, Sonneville KR, Scherer EA, Jackson B, Austin SB. Gender Conformity and Use of Laxatives and Muscle-Building Products in Adolescents and Young Adults. Pediatrics. 2016; 138(2). doi: 10.1542/peds.2015-4073. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Corliss HL, Rosario M, Wypij D, Fisher LB, Austin SB. Sexual Orientation Disparities in Longitudinal Alcohol Use Patterns Among Adolescents: Findings From the Growing Up Today Study. Arch Pediatr Adolesc Med. 2008;162(11):1071–1078. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Diemer EW, Grant JD, Munn-Chernoff MA, Patterson DA, Duncan AE. Gender Identity, Sexual Orientation, and Eating-Related Pathology in a National Sample of College Students. JAdolesc Health. 2015;57(2):144–149. doi: 10.1016/j.jadohealth.2015.03.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Brener ND, Kann L, Shanklin S, et al. Methodology of the youth risk behavior surveillance system—2013. MMWRMorb Mortal Wkly Rep. 2013;62(1):1–20. [PubMed] [Google Scholar]
- 28.Centers for Disease Control and Prevention. Youth Risk Behavior Surveillance System 2015 YRBS national, State, and District Combined Datasets User’s Guide. www.cdc.gov/yrbs. Published 2017.
- 29.Kuczmarski RJ, Ogden CL, Guo SS, et al. 2000. CDC Growth Charts for the United States: Methods and Development. Vital Health Stat 11 (246).; 2002. [PubMed] [Google Scholar]
- 30.Croll J, Neumark-Sztainer D, Story M, Ireland M. Prevalence and risk and protective factors related to disordered eating behaviors among adolescents: relationship to gender and ethnicity. J Adolesc Health. 2002;31(2):166–175. http://www.ncbi.nlm.nih.gov/pubmed/12127387. Accessed March 23, 2018. [DOI] [PubMed] [Google Scholar]
- 31.Watson RJ, Adjei J, Saewyc E, Homma Y, Goodenow C. Trends and disparities in disordered eating among heterosexual and sexual minority adolescents. Int J Eat Disord. July 2016. doi: 10.1002/eat.22576. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Calzo JP, Austin SB, Micali N. Sexual orientation disparities in eating disorder symptoms among adolescent boys and girls in the UK. Eur Child Adolesc Psychiatry. March 2018. doi: 10.1007/s00787-018-1145-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Igartua K, Thombs BD, Burgos G, Montoro R. Concordance and Discrepancy in Sexual Identity, Attraction, and Behavior Among Adolescents. JAdolesc Heal 2009;45(6):602–608. doi: 10.1016/j.jadohealth.2009.03.019. [DOI] [PubMed] [Google Scholar]
- 34.Calzo JP, Masyn KE, Austin SB, Jun H-J, Corliss HL. Developmental latent patterns of identification as mostly heterosexual versus lesbian, gay, or bisexual. J Res Adolesc. 2016;27(1):246–253. doi: 10.1111/jora.12266. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Hunt TK, Forbush KT. Is “drunkorexia” an eating disorder, substance use disorder, or both? EatBehav. 2016;22:40–45. doi: 10.1016/j.eatbeh.2016.03.034. [DOI] [PubMed] [Google Scholar]
- 36.Zou G A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7):702–706. http://www.ncbi.nlm.nih.gov/pubmed/15033648. Accessed May 23, 2018. [DOI] [PubMed] [Google Scholar]