David Kline1, Staci Hepler2, Andrea Bonny3, Erin McKnight3. 1. Center for Biostatistics, Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH. Electronic address: david.kline@osumc.edu. 2. Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, NC. 3. Division of Adolescent Medicine, Nationwide Children's Hospital, Columbus, OH; Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, OH.
Abstract
PURPOSE: Opioid misuse is a national epidemic, and Ohio is one of the states most impacted by this crisis. Ohio collects county-level counts of opioid-associated deaths and treatment admissions. We jointly model these two outcomes and assess the association of each rate with social and structural factors. METHODS: We use a joint spatial rates model of death and treatment counts using a generalized common spatial factor model. In addition to covariate effects, we estimate a spatial factor for each county that characterizes structural factors not accounted for by other covariates in the model that are associated with both outcomes. RESULTS: We observed an association of health professional shortage area with death rates and the rate of people 18-64 on disability with treatment rates. The proportion of single female households was associated with both outcomes. We estimated the presence of unmeasured risk factors in the southwestern part of the state and unmeasured protective factors in the eastern region. CONCLUSIONS: We described associations of social and structural covariates with the death and treatment rates. We also characterized counties with latent risk that can provide a launching point for future investigations to determine potential sources of that risk.
PURPOSE: Opioid misuse is a national epidemic, and Ohio is one of the states most impacted by this crisis. Ohio collects county-level counts of opioid-associated deaths and treatment admissions. We jointly model these two outcomes and assess the association of each rate with social and structural factors. METHODS: We use a joint spatial rates model of death and treatment counts using a generalized common spatial factor model. In addition to covariate effects, we estimate a spatial factor for each county that characterizes structural factors not accounted for by other covariates in the model that are associated with both outcomes. RESULTS: We observed an association of health professional shortage area with death rates and the rate of people 18-64 on disability with treatment rates. The proportion of single female households was associated with both outcomes. We estimated the presence of unmeasured risk factors in the southwestern part of the state and unmeasured protective factors in the eastern region. CONCLUSIONS: We described associations of social and structural covariates with the death and treatment rates. We also characterized counties with latent risk that can provide a launching point for future investigations to determine potential sources of that risk.
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