Spatiotemporal modeling of opioid abuse and dependence outcomes using Bayesian hierarchical methods

Opioid addiction is a major public health concern that presents a significant disease burden. In the past decade, drug overdose rates have soared. More research is necessary to inform policy and to ensure provision of proper care to individuals and communities in need. This thesis explores spatiotemporal models to assess ecological and demographic factors associated with opioid addiction risk on a ZIP-code level in Pennsylvania. Bayesian hierarchical models are commonly used to explore complex spatiotemporal disease trends. Markov chain Monte Carlo (MCMC) simulations are a valuable albeit computationally costly tool in fitting models of this class. A newer method, integrated nested Laplace approximation (INLA), offers improved computational efficiency with comparable results for models with latent Gaussian fields. For example, a 2014 cross-sectional model discussed in this thesis took 5581 seconds to run using MCMC simulations, while INLA offered comparable results in seven seconds. Cross-sectional and longitudinal misalignment models with opioid abuse and dependence outcomes are compared using both methods. Higher outcome risk is associated with areas with greater proportions of 45- to 64-year-olds, higher density, more retail clutter and manual labor establishments per square mile, higher unemployment, lower median income, and greater proportion of residents below the 150% poverty line. As regional needs differ, identifying high-risk community-level factors and locations carries great public health significance. Interventions and preventive efforts could then be tailored specifically to areas where the disease burden is greatest

Alcohol-attributable mortality before and during the early phases of the COVID-19 pandemic

Excessive alcohol use is a leading preventable cause of death. During the COVID-19 pandemic, alcohol use patterns changed for many U.S. residents. In this dissertation, we used nationally representative, comprehensive mortality data to study both individual- and population-level temporal trends and correlates of multiple categories of alcohol-attributable mortality before and during the early phases of the pandemic. In Aim 1, we assessed six mortality outcomes: chronic fully alcohol-attributable deaths, poisonings, motor vehicle accidents (MVAs), suicides, homicides, and falls. We performed descriptive and logistic regression analyses for adult decedents between 2017 and 2020. Compared to 2019, 2020 rates of chronic fully alcohol-attributable deaths, homicides, poisonings, and falls increased, while mortality due to MVAs and suicide decreased. Relative to dying by any other cause, the odds of death by chronic fully alcohol-attributable causes and poisonings were higher across 2020 vs. 2019. In Aim 2, we assessed the same six alcohol-attributable mortality outcomes for 2019-2020 at the county level using Bayesian hierarchical spatial models. The year 2020 was positively associated with all outcomes except for suicides. In the spring of 2020, MVAs and suicides decreased above and beyond usual year and season effects. Higher county median household income was associated with reduced risk for most outcomes. Other effects differed by outcome (e.g., counties with greater proportions of older residents had increased risk for falls but decreased risk for most other outcomes). In Aim 3, we investigated associations between substance use treatment facility densities and three categories of fully alcohol-attributable mortality: chronic fully alcohol-attributable causes, alcohol poisonings, and suicides by alcohol exposure. Greater county-level densities of treatment facilities were associated with increased risk for chronic alcohol-attributable mortality and poisonings. Estimates were similar for models run separately for 2019 and 2020, suggesting that the relationship between facility densities and alcohol-attributable mortality did not substantively change early in the pandemic. Findings from this work describe both individual- and population-level correlates and temporal trends of multiple categories of alcohol-attributable mortality. In part, our findings can help reduce alcohol-attributable mortality by informing geographically specific public health interventions that account for each county’s specific characteristics and mortality risks