Aims To estimate associations between both current-and prior-year medical cannabis dispensary densities and hospitalizations for cannabis use disorder in California, USA between 2013 and 2016. Design Spatial analysis of ZIP code-level hospitalization discharge data using Bayesian Poisson hierarchical space-time models over 4 years. Setting and cases California, USA from 2013 to 2016 (6832 space-time ZIP code units). Measurements We assessed associations of annual hospitalizations for cannabis use disorder [assignment of a primary or secondary code for cannabis abuse and/or dependence using ICD-9-CM or ICD-10-CM (outcome)] with the total number of medical cannabis dispensaries per square mile in a ZIP code as well as dispensary temporal and spatial lags (primary exposures). Other exposure covariates included alcohol outlet densities, manual labor and retail sales densities and ZIP code-level economic and demographic conditions.Findings One additional dispensary per square mile was associated with a median risk ratio of 1.021 (95% credible interval 1.001, 1.041). Prior-year dispensary density did not appear to be associated with hospitalizations (median risk ratio = 1.006, 95% CrI = 0.986, 1.026). Higher median household income, higher unemployment, greater off-premises alcohol outlet density and lower on-premises alcohol outlet density and poverty were all associated with decreased ZIP code-level risk of cannabis abuse/dependence hospitalizations. Conclusions In California, USA, the increasing density of medical cannabis dispensaries appears to be positively associated with same-year but not next-year hospitalizations for cannabis use disorder.
Population analyses of the correlates of neighborhood crime implicitly assume that a single spatial unit can be used to assess neighborhood effects. However, no single spatial unit may be suitable for analyses of the many social determinants of crime. Instead, effects may appear at multiple spatial resolutions with some determinants acting broadly, others locally, and yet others as some function of both global and local conditions. We provide a multi-resolution spatial analysis that simultaneously examines Census block, block group, and tract effects of alcohol outlets and drug markets on violent crimes in Oakland, California, incorporating spatial lag effects at the two smaller spatial resolutions. Using call data from the Oakland Police Department from 2010-2015, we examine associations between assaults, burglaries, and robberies with multiple resolutions of alcohol outlet types, and compare the performance of single (block-level) vs. multi-resolution models. Multi-resolution models performed better than the block models, reflected in improved Deviance and Watanabe-Akaike Information Criteria and well-supported multi-resolution associations. By considering multiple spatial scales and spatial lags in a Bayesian framework, researchers can explore multi-resolution processes, providing more detailed tests of expectations from theoretical models and leading the way to more effective intervention efforts.
Opioid use disorder (OUD) and overdose rates have been sharply on the rise in the United States. Although systematic patterns of geographic variation in OUD and opioid overdose have been identified, the factors that explain why opioid-related hospitalizations increase in certain areas are not well understood. Method: We examined Pennsylvania Health Care Cost Containment Council (PHC4) hospital inpatient discharge data at the ZIP code level to measure the geographic growth and spread of OUD as measured by 44 quarters of inpatient hospitalization data (from 2004 through 2014) for the entire state of Pennsylvania (n = 16,275 ZIP codes). We assessed the relative contribution of specific attributes of areas (e.g., population density) to patterns of OUD, heroin poisonings, and non-heroin opioid poisonings. Unit misalignment and spatial autocorrelation were corrected for using Bayesian space-time conditional autoregressive models. Results: The associations between a greater density of manual labor establishments and all opioid-related hospitalizations were well supported and positive. A dose-response relationship between population density and opioid-related hospitalizations existed, with a stronger association for heroin poisonings (relative rate, densest quintile vs. least dense: 3.40 [95% credible interval 2.68, 4.39]). Conclusions: Posterior distributions from these models enabled the identifi cation of locations most vulnerable to problems related to the opioid epidemic in Pennsylvania. Understanding spatial patterns of OUD and poisonings can enhance the development and implementation of effective prevention programs.
Background: The rapid growth of opioid abuse and the related mortality across the United States has spurred the development of predictive models for the allocation of public health resources. These models should characterize heterogeneous growth across states using a drug epidemic framework that enables assessments of epidemic onset, rates of growth, and limited capacities for epidemic growth. Methods: We used opioid overdose mortality data for 146 North and South Carolina counties from 2001 through 2014 to compare the retrodictive and predictive performance of a logistic growth model that parameterizes onsets, growth, and carrying capacity within a traditional Bayesian Poisson space–time model. Results: In fitting the models to past data, the performance of the logistic growth model was superior to the standard Bayesian Poisson space–time model (deviance information criterion: 8,088 vs. 8,256), with reduced spatial and independent errors. Predictively, the logistic model more accurately estimated fatality rates 1, 2, and 3 years in the future (root mean squared error medians were lower for 95.7% of counties from 2012 to 2014). Capacity limits were higher in counties with greater population size, percent population age 45–64, and percent white population. Epidemic onset was associated with greater same-year and past-year incidence of overdose hospitalizations. Conclusion: Growth in annual rates of opioid fatalities was capacity limited, heterogeneous across counties, and spatially correlated, requiring spatial epidemic models for the accurate and reliable prediction of future outcomes related to opioid abuse. Indicators of risk are identifiable and can be used to predict future mortality outcomes.
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