Tracking the dynamics of the opioid crisis in the United States over space and time

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dc.contributor.advisorStewart, Kathleenen_US
dc.contributor.authorXia, Zhiyueen_US
dc.contributor.departmentGeographyen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2023-02-02T06:31:54Z
dc.date.available2023-02-02T06:31:54Z
dc.date.issued2022en_US
dc.description.abstractMillions of adolescents and adults in the United States suffer from drug problems such as substance use disorder, referring to clinical impairments including mental illnesses and disabilities caused by drugs. The Substance Abuse and Mental Health Services Administration reported the estimated number of illicit drug users increased to 59.3 million in 2020, or 21.4% of the U.S. population, which made drug misuse one of the most concerning public health issues. Opioids are a category of drugs that can be highly addictive, including heroin and synthetic drugs such as fentanyl. Centers for Disease Control and Prevention (CDC) indicated that about 74.8% of drug overdose deaths involved opioids in 2020. The opioid crisis has hit American cities hard, spreading across the U.S. beginning with the west coast, and then expanding to heavily impact the central, mid-Atlantic, and east coast of the U.S. as well as states in the southeast. In this dissertation, I work on three studies to track the dynamics of the opioid crisis in the U.S. over space and time from a geographic perspective using spatiotemporal data science methods including clustering analysis, time-series models and machine learning approaches. The first study focused on the geospatial patterns of illicit drug-related activities (e.g., possession, delivery, and manufacture of opioids) in a typical U.S. city (Chicago as a case study area). By analyzing more than 52,000 reported drug activities, I built a data-driven machine learning model for predicting opioid hot zones and identifying correlated built environment and sociodemographic factors that drove the opioid crisis in an urban setting. The second study of my dissertation is to analyze the opioid crisis in the context of the global pandemic of SARS-CoV-2 (COVID-19). In 2020, COVID-19 outbroke and affected hundreds of millions of people across the globe. The COVID-19 pandemic is also impacting the community of opioid misusers in the U.S. The major research objective of Study 2 is to understand how the opioid crisis is impacted by the COVID-19 pandemic and to find neighborhood characteristics and economic factors that have driven the variations before and during the pandemic. Study 3 focuses on analyzing the crisis risen by synthetic opioids (including fentanyl) that are more potent and dangerous than other drugs. This study analyzed the geographic patterns of synthetic opioids spreading across the U.S. between 2013 and 2020, a period when synthetic opioids rose to be a major risk factor for public health. The significance of this dissertation is that the three studies investigate the opioid crisis in the U.S. in a comprehensive manner and these studies can facilitate public health stakeholders with effective decision making on healthcare planning relating to drug problems. Tracking the dynamics of the opioid crisis by drug type, including modeling and predicting the geographic patterns of opioid misuse involving particular opioids (e.g, heroin and synthetic opioids), can provide an important basis for applying further treatment services and mitigation efforts, and also be useful for assessing current services and efforts.en_US
dc.identifierhttps://doi.org/10.13016/54ox-t85c
dc.identifier.urihttp://hdl.handle.net/1903/29672
dc.language.isoenen_US
dc.subject.pqcontrolledGeographic information science and geodesyen_US
dc.subject.pqcontrolledPublic healthen_US
dc.subject.pqcontrolledGeographyen_US
dc.subject.pquncontrolleddrug overdoseen_US
dc.subject.pquncontrolledmachine learningen_US
dc.subject.pquncontrolledopioid crisisen_US
dc.subject.pquncontrolledspatial data scienceen_US
dc.subject.pquncontrolledspatial modellingen_US
dc.subject.pquncontrolledspatiotemporal analysisen_US
dc.titleTracking the dynamics of the opioid crisis in the United States over space and timeen_US
dc.typeDissertationen_US

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