Risk factors for persistent fatal opioid-involved overdose hotspots in Massachusetts 2011- 2021: A spatial statistical analysis with socio-economic, access and prescription factors

Abstract

Abstract Background: Fatal opioid-involved overdose rates increased precipitously from 5.0 per 100,000 population to 33.5 in Massachusetts between 1999 and 2022. Methods: We use spatial rate smoothing techniques to identify persistent opioid overdose fatality clusters at the ZIP Code Tabulation Area (ZCTA) level. Rate smoothing techniques were effective in reducing variance common with zero-inflated rates locations where population counts are low such as rural and suburban areas which were affected by the epidemic in Massachusetts such as Worcester, Fall River, New Bedford, and Wareham. We use Getis-Ord hotspot analyses with the smoothed incidence rates to identify locations of persistent risk from 2011-2021. We constructed measures of the socio-built environment and potentially inappropriate prescribing (PIP) using principal components analysis (PCA). The resulting measures were used as covariates in autologistic, zero-inflated Poisson, negative binomial and Conditional Autoregression (CAR) Bayesian regression models to predict if a ZCTA was part of an opioid-involved smoothed hotspot cluster for fatal overdose rates as well as the number of times that it was part of a hotspot. Results: Persistent hotspot clusters in Massachusetts had higher mean percentages of Black and Hispanic residents, and residents experiencing poverty. PCA helped in identifying unique socio-environmental factors, such as poverty and minority presence by combining socioeconomic, built environment and prescription variables that were highly correlated with each other. Fatal opioid-involved overdose hotspots were found to be significantly more likely to be ZCTA with high poverty levels and high percentages of people from minoritized populations. Regressions models that corrected for spatial autocorrelation were necessary to avoid model misspecification. Conclusion: Conducting spatially robust analyses may help inform policies to identify community-level risks for opioid-involved overdose deaths. The results can help inform policy makers and planners about locations of persistent risk.