Multivariate spatiotemporal modeling of drug- and alcohol-poisoning deaths in New York City, 2009-2014

A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research

With the advancement of spatial analysis approaches, methodological research addressing the technical and statistical issues related to joint spatial and spatiotemporal models has increased. Despite the benefits of spatial modelling of several interrelated outcomes simultaneously, there has been no published systematic review on this topic, specifically when such models would be useful. This systematic review therefore aimed at reviewing health research published using joint spatial and spatiotemporal models. A systematic search of published studies that applied joint spatial and spatiotemporal models was performed using six electronic databases without geographic restriction. A search with the developed search terms yielded 4077 studies, from which 43 studies were included for the systematic review, including 15 studies focused on infectious diseases and 11 on cancer. Most of the studies (81.40%) were performed based on the Bayesian framework. Different joint spatial and spatiotemporal models were applied based on the nature of the data, population size, the incidence of outcomes, and assumptions. This review found that when the outcome is rare or the population is small, joint spatial and spatiotemporal models provide better performance by borrowing strength from related health outcomes which have a higher prevalence. A framework for the design, analysis, and reporting of such studies is also needed.

A spatio-temporal Bayesian model to estimate risk and evaluate factors related to drug-involved emergency department visits in the greater Baltimore metropolitan area

The ongoing opioid overdose epidemic in the United States presents a major public health challenge. Opioid-involved morbidity, especially nonfatal emergency department (ED) visits, are a key opportunity to prevent mortality and measure the extent of the problem in the local substance use landscape. Data on the rate of ED visits is normally distributed by federal agencies. However, state- and substate-level rates of ED visit demonstrate significant geographic variation. This study uses an ongoing sample of ED visits from four hospitals in the University of Maryland Medical System from January 2016 to December 2019 to provide locally sensitive information on ED visit rates and risk for drug-related health outcomes. Using exploratory spatial data analysis and spatio-temporal Bayesian models, this study analyzes both the frequency and risk of heroin-, methadone-, and cocaine-involved ED visits across the greater Baltimore Maryland area at the Zip Code Tabulation Area-level (ZCTA). The Global Moran's I for total heroin-, methadone-, and cocaine-involved ED visits in 2019 was 0.44, 0.56, and 0.53, demonstrating strong positive spatial autocorrelation. Spatio-temporal Bayesian models indicated that ZCTA with a higher score in a deprivation index, with a higher share of Centers for Medicare Services claims, and adjacent to a sampled UMMS hospital had an increased risk of ED visits, with variation in the magnitude of this increased risk depending on the drug-demographic strata. Modeled disease risk surfaces - including posterior median risk and posterior exceedance probabilities - showed distinctly different risk surfaces between the substances of interest, probabilistically identifying ZCTA with a lower or higher risk of ED visits. The modeling approach used a sample of ED visits from a larger health system to estimate recent, locally sensitive drug-related morbidity across a large metropolitan area.