Multi-dimensional measures of geography and the opioid epidemic: place, time and context

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2019

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The opioid crisis has hit the United States hard in recent years. Behavioral patterns and social environments associated with opioid use and misuse vary significantly across communities. It is important to understand the geospatial prevalence of opioid overdoses and other impacts related to the crisis in order to provide a targeted response at different locations. This dissertation contributes a framework for understanding spatial and temporal patterns of drug prevalence, treatment services access and associated socio-environmental factors for opioid use and misuse. This dissertation addresses three main questions related to geography and the opioid epidemic: 1) How did drug poisoning deaths involving heroin evolve over space and time in the U.S. between 2000-2016; 2) How did access to opioid use disorder treatment facilities and emergency medical services vary spatially in New Hampshire during 2015-2016; and 3) What were the relations between socio-environmental factors and numbers of emergency department patients with drug-related health problems over space and time in Maryland during 2016-2018. For the first study, this dissertation developed a spatial and temporal data model to investigate trends of heroin mortality over a 17-year period (2000-2016). The research presented in this dissertation also involved developing a composite index to analyze spatial accessibility to both opioid use disorder treatment facilities and emergency medical services and compared these locations with the locations of deaths involving fentanyl to identify possible gaps in services. In the third study for this dissertation, I utilized socially-sensed data to identify neighborhood characteristics and investigated spatial and temporal relationships with emergency department patients with drug-related health problems admitted to the four hospitals in the western Baltimore area in Maryland during 2016 to 2018, in order to identify the dynamic patterns of the associations in terms of various socio-environmental factors.

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