Kara E Rudolph1, Jonathan Levy2, Nicole M Schmidt3, Elizabeth A Stuart4,5,6, Jennifer Ahern2. 1. From the Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY. 2. Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA. 3. Department of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN. 4. Department of Mental Health, Johns Hopkins School of Public Health, Baltimore, MD. 5. Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD. 6. Department of Health Policy and Management, Johns Hopkins School of Public Health, Baltimore, MD.
Abstract
BACKGROUND: Randomized trials may have different effects in different settings. Moving to Opportunity (MTO), a housing experiment, is one such example. Previously, we examined the extent to which MTO's overall effects on adolescent substance use and mental health outcomes were transportable across the sites to disentangle the contributions of differences in population composition versus differences in contextual factors to site differences. However, to further understand reasons for different site effects, it may be beneficial to examine mediation mechanisms and the degree to which they too are transportable across sites. METHODS: We used longitudinal data from MTO youth. We examined mediators summarizing aspects of the school environment over the 10-15 year follow-up. Outcomes of past-year substance use, mental health, and risk behavior were assessed at the final timepoint when participants were 10-20 years old. We used doubly robust and efficient substitution estimators to estimate (1) indirect effects by MTO site and (2) transported indirect effects from one site to another. RESULTS: Differences in indirect effect estimates were most pronounced between Chicago and Los Angeles. Using transport estimators to account for differences in baseline covariates, likelihood of using the voucher to move, and mediator distributions partially to fully accounted for site differences in indirect effect estimates in 10 of the 12 pathways examined. CONCLUSIONS: Using transport estimators can provide an evidence-based approach for understanding the extent to which differences in compositional factors contribute to differences in indirect effect estimates across sites, and ultimately, to understanding why interventions may have different effects when applied to new populations.
BACKGROUND: Randomized trials may have different effects in different settings. Moving to Opportunity (MTO), a housing experiment, is one such example. Previously, we examined the extent to which MTO's overall effects on adolescent substance use and mental health outcomes were transportable across the sites to disentangle the contributions of differences in population composition versus differences in contextual factors to site differences. However, to further understand reasons for different site effects, it may be beneficial to examine mediation mechanisms and the degree to which they too are transportable across sites. METHODS: We used longitudinal data from MTO youth. We examined mediators summarizing aspects of the school environment over the 10-15 year follow-up. Outcomes of past-year substance use, mental health, and risk behavior were assessed at the final timepoint when participants were 10-20 years old. We used doubly robust and efficient substitution estimators to estimate (1) indirect effects by MTO site and (2) transported indirect effects from one site to another. RESULTS: Differences in indirect effect estimates were most pronounced between Chicago and Los Angeles. Using transport estimators to account for differences in baseline covariates, likelihood of using the voucher to move, and mediator distributions partially to fully accounted for site differences in indirect effect estimates in 10 of the 12 pathways examined. CONCLUSIONS: Using transport estimators can provide an evidence-based approach for understanding the extent to which differences in compositional factors contribute to differences in indirect effect estimates across sites, and ultimately, to understanding why interventions may have different effects when applied to new populations.
Authors: Kara E Rudolph; Oleg Sofrygin; Nicole M Schmidt; Rebecca Crowder; M Maria Glymour; Jennifer Ahern; Theresa L Osypuk Journal: Epidemiology Date: 2018-07 Impact factor: 4.822
Authors: Daniel Westreich; Jessie K Edwards; Catherine R Lesko; Elizabeth Stuart; Stephen R Cole Journal: Am J Epidemiol Date: 2017-10-15 Impact factor: 4.897
Authors: Theresa L Osypuk; Nicole M Schmidt; Lisa M Bates; Eric J Tchetgen-Tchetgen; Felton J Earls; M Maria Glymour Journal: Pediatrics Date: 2012-08-20 Impact factor: 7.124
Authors: Aaron R Folsom; Richard A Kronmal; Robert C Detrano; Daniel H O'Leary; Diane E Bild; David A Bluemke; Matthew J Budoff; Kiang Liu; Steven Shea; Moyses Szklo; Russell P Tracy; Karol E Watson; Gregory L Burke Journal: Arch Intern Med Date: 2008-06-23
Authors: Kara E Rudolph; Nicole M Schmidt; M Maria Glymour; Rebecca Crowder; Jessica Galin; Jennifer Ahern; Theresa L Osypuk Journal: Epidemiology Date: 2018-03 Impact factor: 4.822