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Spatio-Temporal Predictive Modeling for Placement of Substance Use Disorder Treatment Facilities in the Midwestern U.S

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Pervasive Computing Technologies for Healthcare (PH 2022)

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Abstract

The inappropriate use of illegal and prescription drugs is an ongoing public health crisis across the United States and beyond. The demand for treatment services quickly outstrips the available supply, limiting access to care. Thus, a data-driven approach to assessing where new treatment facilities are to be built is an essential way to ensure new investments are strategically and optimally located. In this exploratory research, we report the findings of using 24 different public data sets to create three index variables used within a spatio-temporal modeling approach to predict what urban, suburban, and rural counties would most benefit from new substance use disorder treatments across the state of Indiana in the United States. Finally, we discuss the importance and potential limitations of taking this type of approach to develop policies that address complex societal issues.

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Author information

Authors and Affiliations

  1. Parkview Research Center, Parkview Health, Fort Wayne, IN, USA

    Jessica A. Pater, Shion Guha, Rachel Pfafman & Tammy Toscos

  2. Faculty of Information and Department of Computer Science, University of Toronto, Toronto, Canada

    Shion Guha

  3. Parkview Behavioral Health, Parkview Health, Fort Wayne, IN, USA

    Connie Kerrigan

Authors
  1. Jessica A. Pater
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  2. Shion Guha
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  3. Rachel Pfafman
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  4. Connie Kerrigan
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  5. Tammy Toscos
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Corresponding author

Correspondence to Jessica A. Pater .

Editor information

Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Athanasios Tsanas

  2. Centre for Research and Technology Hellas, Thessaloniki, Greece

    Andreas Triantafyllidis

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Pater, J.A., Guha, S., Pfafman, R., Kerrigan, C., Toscos, T. (2023). Spatio-Temporal Predictive Modeling for Placement of Substance Use Disorder Treatment Facilities in the Midwestern U.S. In: Tsanas, A., Triantafyllidis, A. (eds) Pervasive Computing Technologies for Healthcare. PH 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-031-34586-9_28

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  • DOI: https://doi.org/10.1007/978-3-031-34586-9_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34585-2

  • Online ISBN: 978-3-031-34586-9

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