A Spatiotemporal Analysis of Heroin-Related Calls for Emergency Medical Services and Community-Health Centers in Boston, Massachusetts


Using a combination of data derived from the U.S. Census Bureau, the Substance Abuse and Mental Health Service Administration’s Behavioral Health Systems Locator and the Boston Police Department’s Incident Reports, this study examines the spatiotemporal distribution of calls for medical assistance following a heroin-related injury between 2015 and 2018. As well, an examination is conducted regarding the accessibility of Health Care Centers (HCCs) in relation to individuals in need of medical assistance. Distance-based measures of spatial association, including nearest neighbor and K-function analysis, were used to determine the clustering of calls at various spatial scales. Structural neighborhood characteristics (i.e., distance to the closest HCC) and physical and social vulnerability were used to predict the rate of calls for a heroinrelated injury per 1,000 persons in the city of Boston using a spatial autoregressive model. HCC catchment areas for the set of medical emergency calls were created using Voronoi tessellations. Results showed that the average nearest neighbor distance (NND) of each call for medical assistance was approximately 187 m or .12 miles. Calls for assistance became more probable and increasingly more spatially concentrated over the study period. The K-function analysis revealed that the calls clustered at different spatial scales and in proximity to HCCs. Average distance to the nearest HCC and Household/Disability vulnerability were significantly associated with the census tract call rate per 1,000 persons. The average travel distance between the calls for medical assistance and the nearest HCC was 941 m, or just over a half-mile. Policy implications for the provision, through HCCs, of a focused and comprehensive community-based support system in a large urban city for individuals with serious drug problems are discussed in context.

Applied Spatial Analysis and Policy, 13(2) 507–525. https://doi.org/10.1007/s12061-019-09315-5