Looking at the big picture: demonstrating benefits of Bayesian latent cluster spatio-temporal analysis for understanding maternal prescription opioids misuse

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BACKGROUND: As the opioid crisis rages, maternal prescription opioid misuse (MPOM) is particularly concerning because it influences both the mother and child’s wellbeing. However, little was known about the trend of MPOM and its county-level risk factors. Potentially varying relationships between MPOM and local environmental factors across time and geospatial context constitute analytic challenges. OBJECTIVES: (1) Employ Bayesian latent cluster modeling to detect spatial clusters in each of which the temporal association between MPOM rates and local structural risk factors is varying and unique. (2) Illustrate the spatio-temporal trend and hotspots of MPOM in Pennsylvania (PA) 2010–2013, and characterize their associations with key county-level environmental determinants. METHODS: Using Medicaid Analytic eXtract (MAX) data, 5,653 Medicaid-enrolled women who recently gave birth were identified with MPOM among 61,227 deliveries in PA during 2010–2013. County-level unemployment rate, poverty rate, race heterogeneity, population density, and total number of opioid prescribed were used as environmental risk factors. Spatial and temporal autocorrelation effects were integrated into a Bayesian latent clustering process. RESULTS: MPOM rates in Medicaid enrolled women increased from 7.9% to 10.6% during 2010–2013 in PA. Cluster models showed that there were three distinct spatial clusters: north and central rural counties, southwestern and southeastern metropolitan or suburban counties, and buffering counties located between the first two clusters. Hotspot counties for MPOM mainly belonged to the cluster including north and central rural counties. Spatio-temporal heterogeneity existed in the association between MPOM and environmental factors across clusters. CONCLUSION: This study demonstrates the utility of the Bayesian spatio-temporal clustering approach in investigating MPOM trend. With this latent clustering analytic method, it is possible to detect space specific patterns of MPOM incident risk and its relationship with key local areal risk factors. The varying relationships between MPOM and areal structural factors have important implications for state and county MPOM prevention measures.

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