Examining sexual identity stability and change over time and associations with tobacco use in a nationally representative US sample.

BACKGROUND: Epidemiologic research has found sexual minority identifying individuals are disproportionately burdened by tobacco use and tobacco use disorder (TUD). However, these studies often conceptualize sexual identity as time-invariant. This study examined sexual identity over time and whether a transition to a sexual minority identity was associated with tobacco outcomes. METHODS: This study used data from Waves 1-4 of the Population Assessment of Tobacco and Health (2013/14-2016/18) study (adolescents and adults aged ≥ 14 years; n = 26,553). We examined associations of sexual identity stability and change with changes in the number of tobacco products used and TUD symptoms. RESULTS: Males and females who experienced two or more changes in sexual identity and females who changed from a heterosexual to a sexual minority identity were more likely to have an increase in two or more tobacco products and increase TUD symptoms compared to heterosexual-stable males and females. Gay-stable males were less likely to increase TUD symptoms compared to heterosexual-stable males. CONCLUSIONS: Experiencing a change in sexual identity may be a particularly vulnerable period for increases in tobacco products used and TUD. It may be important to provide tobacco use intervention and support resources to individuals coming out as a sexual minority and those fluid in their sexual identity.

Latent class trajectories: U.S. adolescents' nicotine use and its association with nicotine dependence.

INTRODUCTION: Twenty-seven percent of adolescents used a nicotine/tobacco product in 2018. Our study analyzed three waves from the Population Assessment of Tobacco and Health (PATH) Study and examined adolescent nicotine/tobacco use trajectories over time to determine which latent classes were associated with symptoms of nicotine dependence. METHODS: The PATH Study used a four-stage, stratified area probability sample and annual household interviews with adolescents (12-17 years). Adolescents who indicated past 30-day nicotine/tobacco use at least once were included (n = 1101). We used latent class analysis (LCA) to identify nicotine/tobacco trajectories across three waves of PATH data and their association with six symptoms consistent with nicotine dependence from the Wisconsin Inventory of Smoking Dependence Motives (WISDM-68). RESULTS: All types of past 30-day nicotine/tobacco use increased across the three waves. An LCA model fit was assessed using both the CIV and entropy measures in conjunction with the Vuong-Lo-Mendell-Rubin LRT. A five latent class solution had the lowest BIC value (BIC = 9784.272), and resulted in: (1) "Stable/consistent multiproduct use trajectory", (2) "Increasing cigarette use trajectory", (3) "Increasing e-cigarette use trajectory", (4) "Experimental (poly-nicotine/tobacco) use trajectory", and (5) "Increasing other nicotine/tobacco use trajectory". The most prevalent was the "Experimental (poly-nicotine/tobacco) use trajectory" (33.8%) although sex, race, and social class were associated with different trajectories. Those represented by the "Increasing cigarette use trajectory" (19.4%) reported significantly more past-year nicotine dependence symptoms (b = 1.73, p < 0.001) compared to the "Increasing e-cigarette use trajectory". Findings varied by sex and race. CONCLUSIONS: Results indicate that the relationship between e-cigarette use and other forms of nicotine/tobacco and substance use is complex and that adolescent nicotine/tobacco users are a heterogenous group with different risks for nicotine dependence. Findings can inform tailored prevention education and messaging for different groups of youth.

Why Are National Estimates So Different? A Comparison of Youth E-Cigarette Use and Cigarette Smoking in the MTF and PATH Surveys.

OBJECTIVE: We compared estimates of adolescents' nicotine product use and perceptions of harm from two national surveys: Monitoring the Future (MTF) and Population Assessment of Tobacco and Health (PATH). We explored one explanation for the different estimates for nicotine product use and adolescents' perceptions of harm. METHOD: We used data source triangulation examining 30-day e-cigarette use and cigarette smoking, beliefs about harm, and friends' use of these products in two samples of adolescents from the 2015-2016 MTF and PATH samples. RESULTS: Differences were found, with MTF reporting higher prevalence rates in both past-30-day e-cigarette use (12.4% vs. 6.7%) and cigarette smoking (8.6% vs. 5.1%) when compared with PATH. Differences were significant at the .001 alpha level. MTF respondents were less likely than PATH respondents to view both e-cigarettes (17.7% vs. 48.6%) and cigarettes (75.6% vs. 82.4%) as harmful. The unadjusted odds ratio (OR) shows that PATH respondents had significantly lower odds of indicating either e-cigarette (OR = 0.509, 95% confidence interval [CI] [0.400, 0.648]) or cigarette smoking (OR = 0.571, 95% CI [0.433, 0.753]) when compared with MTF respondents. However, these differences in e-cigarette use (adjusted odds ratio [AOR] = 0.849, 95% CI [0.630, 1.144]) and cigarette smoking (AOR = 0.829, 95% CI = [0.578, 1.189]) were mediated when additional predictors were included in the model (i.e., friends use, risk of harm). CONCLUSIONS: Substantial differences were found between national surveys estimating population rates of e-cigarette use and cigarette smoking. Data source triangulation allowed for new explanations for several of the disparate nicotine use estimates between MTF and PATH.

Friends and relatives as sources of prescription opioids for misuse among young adults: The significance of physician source and race/ethnic differences.

Background: Given the public health issues associated with prescription opioid misuse, there is surprisingly little research on sources of prescription opioids for misuse. We know that free from friends/relatives is the most common source, that source is associated with patterns of misuse, and that sources vary based on the sociodemographic characteristics. The current research assesses how friends/relatives obtain the prescription opioids they freely share with others. Methods: Data were from the 2009-2014 National Survey on Drug Use and Health and focused on respondents aged 18 to 25 (N = 106,845), as they report the highest prevalence of opioid misuse and are also more likely to obtain prescription opioids free from friends/relatives. Analyses used weighted cross-tabulations and design-based logistic regression to examine the relationships between sources of prescription opioids shared by friends/relatives and sociodemographic characteristics, substance use disorders, and risk behavior. Results: The most common source was from one physician (68.8%), and 18.4% of respondents reported multiple sources of opioids. We also found significant differences in friend/relative sources based on school enrollment, sex, and race/ethnicity of the person obtaining the opioids. Notably, white respondents were more likely to report theft/fake prescription, purchases, and multiple sources. Finally, friend/relative sources were significantly associated with substance use disorders and other risk behaviors. Conclusions: The current research assesses the sharing of prescription opioids between friends/relatives, highlights physicians as a major source, and identifies whites as a vulnerable group. Policy implications associated with the sharing of prescription opioids among friends/relatives are discussed.

Medical and nonmedical use of prescription pain medication by youth in a Detroit-area public school district.

The purpose of this descriptive study was to examine the nonmedical use of prescription pain medications in a sample of public school students in the Detroit metropolitan area. The Web-based survey occurred during a two-week period in May 2003 and drew on a diverse population between the ages of 10 and 18 years. An 87% response rate was achieved, providing a final sample of 1017 students. Twenty-two percent of girls and 10% of boys reported lifetime nonmedical use of a pain medication (chi2 = 25.2, d.f. = 1, p < 0.001). Fifteen percent of girls and 7% of boys reported past year nonmedical use (chi2 = 12.7, d.f. = 1, p < 0.01). Compared to nonusers, nonmedical users of prescription pain medication were seven times more likely to smoke cigarettes, five times more likely to drink alcohol and smoke marijuana, almost four times more likely to binge drink, and eight times more likely to have used other illicit drugs. Of those reporting lifetime nonmedical use of prescription pain medication, 70% noted how they obtained the drugs; 34% reported getting the drugs from a family member, 17% from a friend and 14% from a dealer or theft.