The optimization of harm reduction services in Massachusetts through the use of GIS: Location-allocation analyses, 2019-2021.

BACKGROUND: Fatal opioid-related overdoses (OOD) continue to be a leading cause of preventable death across the US. Opioid Overdose Education and Naloxone Distribution programs (OENDs) play a vital role in addressing morbidity and mortality associated with opioid use, but access to such services is often inequitable. We utilized a geographic information system (GIS) and spatial analytical methods to inform prioritized placement of OEND services in Massachusetts. METHODS: We obtained addresses for OEND sites from the Massachusetts Department of Public Health and address-level fatal OOD data for January 2019 to December 2021 from the Massachusetts Registry of Vital Records and Statistics. Using location-allocation approaches in ArcGIS Pro, we created p-median models using locations of existing OEND sites and fatal OOD counts to identify areas that should be prioritized for future OEND placement. Variables included in our analysis were transportation mode, distance from public schools, race and ethnicity, and location feasibility. RESULTS: Three Massachusetts communities - Athol, Dorchester, and Fitchburg - were identified as priority sites for new OEND locations using location-allocation models based on capacity to maximize OOD prevention. Communities identified by the models for OEND placement had similar demographics and overdose rates (42.8 per 100,000 vs 40.1 per 100,000 population) to communities with existing OEND programs but lower naloxone kit distribution rates (2589 doses per 100,000 vs 3704 doses per 100,000). Further models demonstrated differential access based on location and transportation. CONCLUSION: Our analyses identified key areas of Massachusetts with greatest need for OEND services. Further, these results demonstrate the utility of using spatial epidemiological methods to inform public health recommendations.

Risk factors for persistent fatal opioid-involved overdose clusters in Massachusetts 2011-2021: a spatial statistical analysis with socio-economic, accessibility, and prescription factors.

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 used spatial rate smoothing techniques to identify persistent opioid overdose-involved fatality clusters at the ZIP Code Tabulation Area (ZCTA) level. Rate smoothing techniques were employed to identify locations of high fatal opioid overdose rates where population counts were low. In Massachusetts, this included areas with both sparse data and low population density. We used Local Indicators of Spatial Association (LISA) cluster analyses with the raw incidence rates, and the Empirical Bayes smoothed rates to identify clusters from 2011 to 2021. We also estimated Empirical Bayes LISA cluster estimates to identify clusters during the same period. We constructed measures of the socio-built environment and potentially inappropriate prescribing using principal components analysis. The resulting measures were used as covariates in Conditional Autoregressive Bayesian models that acknowledge spatial autocorrelation to predict both, if a ZCTA was part of an opioid-involved cluster for fatal overdose rates, as well as the number of times that it was part of a cluster of high incidence rates. RESULTS: LISA clusters for smoothed data were able to identify whether a ZCTA was part of a opioid involved fatality incidence cluster earlier in the study period, when compared to LISA clusters based on raw rates. PCA helped in identifying unique socio-environmental factors, such as minoritized populations and poverty, potentially inappropriate prescribing, access to amenities, and rurality by combining socioeconomic, built environment and prescription variables that were highly correlated with each other. In all models except for those that used raw rates to estimate whether a ZCTA was part of a high fatality cluster, opioid overdose fatality clusters in Massachusetts had high percentages of Black and Hispanic residents, and households experiencing poverty. The models that were fitted on Empirical Bayes LISA identified this phenomenon earlier in the study period than the raw rate LISA. However, all the models identified minoritized populations and poverty as significant factors in predicting the persistence of a ZCTA being part of a high opioid overdose cluster during this time period. CONCLUSION: Conducting spatially robust analyses may help inform policies to identify community-level risks for opioid-involved overdose deaths sooner than depending on raw incidence rates alone. The results can help inform policy makers and planners about locations of persistent risk.

Traffic Exposure and Breast Cancer Mortality by Area of Residence: Incorporating Clinical and Socioeconomic Data.

Incorporating clinical and environmental data holds promise for monitoring vulnerable populations at the community level. This spatial epidemiology study explores the link between traffic-related air pollution and breast cancer mortality in Seoul, using public socioeconomic and clinical data from Samsung Medical Center's registry (N=6,089). Traffic and socioeconomic status were collected from official sources and integrated for spatial analysis. The findings revealed a significant association between adult breast cancer mortality and districts with high road density, NO2 emissions, and family income (p<0.05). Significant spatial autocorrelation of residuals was observed (Moran's I test p<0.001).

Applied risk mapping and spatial analysis of address-level decedent data to inform opioid overdose interventions: The Massachusetts HEALing Communities Study.

BACKGROUND: Death certificate data provide powerful and sobering records of the opioid overdose crisis. In Massachusetts, where address-level decedent data are publicly available upon request, mapping and spatial analysis of fatal overdoses can provide valuable insights to inform prevention interventions. We describe how we used this approach to support a community-level intervention to reduce opioid-involved overdose mortality. METHODS: We developed a method to clean and geocode decedent data that substituted injury locations (the likely location of fatal overdoses) for deaths recorded in hospitals. After geomasking for greater privacy protection, we created maps to visualize the spatial distribution of decedent residence addresses, alone and juxtaposed with drive and walk-time distances to opioid treatment programs (OTPs), and place of death by overdose address. We used spatial statistical analyses to identify locations with significant clusters of overdoses. RESULTS: In the 8 intervention communities, 785 individuals died from opioid-involved overdoses between 2017 and 2020. We found that 19.7% of fatal overdoses were recorded in hospitals, 50.2% occurred at the decedent's residence, and 30.1% at another location. We identified overdose hotspots in study communities. By juxtaposing decedent residence data with drive- and walk-time analyses, we highlighted actionable spatial gaps in access to OTP treatment. CONCLUSION: To better understand local fatal opioid overdose risk environments and inform the development of community-level prevention interventions, we used publicly available address-level decedent data to conduct nuanced spatial analyses. Our approach can be replicated in other jurisdictions to inform overdose prevention responses.

Spatial access to buprenorphine-waivered prescribers in the HEALing communities study: Enhanced 2-step floating catchment area analyses in Massachusetts, Ohio, and Kentucky.

INTRODUCTION: The opioid overdose epidemic continues to impact a large swath of the population in the US. Medications for opioid use disorders (MOUD) are an effective resource to combat the epidemic; however, there is limited research on MOUD treatment access that accounts for both supply of and demand for services. We aimed to examine access to buprenorphine prescribers in the HEALing Communities Study (HCS) Wave 2 communities in Massachusetts, Ohio, and Kentucky during 2021, and the association between buprenorphine access and opioid-related incidents, specifically fatal overdoses and opioid-related responses by emergency medical services (EMS). METHODS: We calculated Enhanced 2-Step Floating Catchment Area (E2SFCA) accessibility indices for each state, as well as Wave 2 communities in each state, based on the location of providers (buprenorphine-waivered clinicians from the US Drug Enforcement Agency Active Registrants database), population-weighted centroids at the census block group level, and catchment areas defined by the state or community's average commute time. In advance of intervention initiation, we quantified the opioid-related risk environment of communities. We assessed gaps in services by using bivariate Local Moran's I analysis, incorporating accessibility indices and opioid-related incident data. RESULTS: Massachusetts Wave 2 HCS communities had the highest rates of buprenorphine prescribers per 1000 patients (median: 165.8) compared to Kentucky (38.8) and Ohio (40.1). While urban centers in all three states had higher E2SFCA index scores compared to rural communities, we observed that suburban communities often had limited access. Through bivariate Local Moran's I analysis, we identified numerous locations with low buprenorphine access surrounded by high opioid-related incidents, particularly in communities that surrounded Boston, Massachusetts; Columbus, Ohio; and Louisville, Kentucky. CONCLUSION: Rural communities demonstrated a great need for additional access to buprenorphine prescribers. However, policymakers should also direct attention toward suburban communities that have experienced significant increases in opioid-related incidents.

Relationships between places of residence, injury, and death: Spatial and statistical analysis of fatal opioid overdoses across Massachusetts.

Understanding the factors associated with where people who use opioids live, where their fatal overdoses occur, and where deaths are recorded can improve our knowledge of local risk environments and inform intervention planning. Through geospatial analyses of death certificate data between 2015 and 2017, we found that a majority of opioid-involved fatal overdoses in Massachusetts occurred at home. Age (adjusted odds ratio [AOR], 1.03; 95% confidence interval [CI], 1.02-1.04), living in a census tract with a higher percentage of crowded households (AOR, 1.04; 95% CI, 1.01-1.08), households without vehicles (AOR, 1.01; 95% CI, 1.00-1.02), and Hispanic ethnicity (AOR, 0.56; 95% CI, 0.42-0.74) were independently associated with fatal overdose at home. Using geographically weighted regression, we identified locations where these associations were stronger and could benefit most from home-based and culturally sensitive overdose prevention efforts, including expanded overdose education and naloxone distribution.

Mass incarceration and the impact of prison release on HIV diagnoses in the US South.

BACKGROUND: The purpose of this study was to determine the impact of prison release on HIV incidence in the southern region of the United States, the region with the highest rates of both incarceration and new HIV diagnoses nationwide. METHODS: 5-year HIV diagnoses rates were calculated at the ZIP code level for nine cities and metropolitan statistical areas in the US South (ZIP codes, N = 600). Multilevel regression models were constructed and adjusted rate ratios (ARRs) were estimated for overall, male and female HIV diagnoses rates. RESULTS: Across the nine cities, in multilevel, multivariate analysis, controlling for income inequality (GINI coefficient), percent living in poverty and percent Non-Hispanic Black population, the ZIP code level overall HIV diagnosis rate was significantly associated with prison release [ARR 1.004 (95%CI 1.0007, 1.006), p<0.01]. A 10-person increase in prison release rate would result in a 4% increase in overall 5-year HIV diagnosis rate-approximately 9.4 additional cases per 100,000 population. In gender-stratified models, prison release rate was significantly associated with the ZIP code level HIV diagnosis rate for males [ARR 1.004 (95%CI 1.0004, 1.007), p<0.01], but not for females. CONCLUSIONS: In the southern region of the US, prison release is significantly associated with HIV incidence. HIV prevention interventions should promote timely linkage to ongoing treatment for released inmates living with HIV.