Investigating site-level longitudinal effects of population health interventions: Gay-Straight Alliances and school...

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.