Purpose: Using representative school-based data and community-level primary data, we investigated how environmental factors (e.g., school and community climate) might be protective against substance use behaviors among a vulnerable population of adolescents. Methods: We analyzed a sample of 2678 sexual minority adolescents using a combination of student-level data (British Columbia Adolescent Health Survey) and primary community-level data (assessing lesbian, gay, bisexual, transgender, and queer [LGBTQ]-specific community and school environments). Using multilevel logistic regression models, we examined associations between lifetime substance use (alcohol, illegal drugs, marijuana, nonmedical use of prescription drugs, and smoking) and community-level predictors (community and school LGBTQ supportiveness). Results: Above and beyond student characteristics (e.g., age and years living in Canada), sexual minority adolescents residing in communities with more LGBTQ supports (i.e., more supportive climates) had lower odds of lifetime illegal drug use (for boys and girls), marijuana use (for girls), and smoking (for girls). Specifically, in communities with more frequent LGBTQ events (such as Pride events), the odds of substance use among sexual minority adolescents living in those communities was lower compared with their counterparts living in communities with fewer LGBTQ supports. Conclusions: The availability of LGBTQ community-level organizations, events, and programs may serve as protective factors for substance use among sexual minority adolescents. In particular, LGBTQ-supportive community factors were negatively associated with substance use, which has important implications for our investment in community programs, laws, and organizations that advance the visibility and rights of LGBTQ people.

Item response tree (IRTree) models are recently introduced as an approach to modeling response data from Likert-type rating scales. IRTree models are particularly useful to capture a variety of individuals’ behaviors involving in item responding. This study employed IRTree models to investigate response styles, which are individuals’ tendencies to prefer or avoid certain response categories in a rating scale. Specifically, we introduced two types of IRTree models, descriptive and explanatory models, perceived under a larger modeling framework, called explanatory item response models, proposed by De Boeck and Wilson. This extends the typical application of IRTree models for studying response styles. As a demonstration, we applied the descriptive and explanatory IRTree models to examine acquiescence and extreme response styles in Rosenberg’s Self-Esteem Scale. Our findings suggested the presence of two distinct extreme response styles and acquiescence response style in the scale.

There is limited research on evaluating nonrandomized population health interventions. We aimed to introduce a new approach for assessing site-level longitudinal effects of population health interventions (SLEPHI) by innovatively applying multiple group multilevel (MG-ML) modeling to repeated cycles of cross-sectional data collected from different individuals of the same sites at different times, a design commonly employed in public health research. For illustration, we used this SLEPHI method to examine the influence of Gay-Straight Alliances (GSAs) on school-level perceived safety among lesbian, gay, and bisexual (LGB) and heterosexual (HET) adolescents. Individual-level data of perceived school safety came from 1625 LGB students (67.4% female; mean age, 15.7 years) and 37,597 HET students (50.2% female; mean age, 15.4 years) attending Grades 7–12 in 135 schools, which participated in 3 British Columbia Adolescent Health Surveys (BCAHS: 2003, 2008, 2013) in Canada. School-level data of GSA length since established were collected by telephone in 2008 and 2014. Nested MG-ML models suggested that after accounting for secular trend, cohort effects, measurement error, measurement equivalence, and student age, GSA length linearly related to increased school-level perceived safety among LGB students (b = 1.57, SE = 0.21, p < .001, β = 0.32) and also among HET students (β = 0.34 in 2003 & 2013, β = 0.32 in 2008) although statistical differences between years for HET youth were likely due to the large sample size. By conducting MG-ML analysis on repeated cross-sectional surveys, this SLEPHI method accounted for many confounding factors and followed schools for a longer period than most longitudinal designs can follow individuals. Therefore, we drew a stronger conclusion than previous observational research about GSAs and LGB students’ well-being. The SLEPHI method can be widely applied to other repeated cycles of cross-sectional data in public health research.

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