Statistical Analysis of County-Level Contributing Factors to Opioid-Related Overdose Deaths in the United States

Haedong Kim, Hui Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper examines county-level characteristic factors contributing to opioid-related overdose deaths in the United States. We categorized factors into three groups: demographic, socio-economic, and health care environmental group. These features were used as predictors to model the overdose deaths from all types of opioids including prescription (e.g., oxycodone and hydrocodone) and illicit opioids (e.g., heroin and fentanyl) to investigate general trend, as well as separate models for heroin and fentanyl. Multilevel mixed-effect regression was adopted to adequately model grouping effect across counties.

Original languageEnglish (US)
Title of host publication42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationEnabling Innovative Technologies for Global Healthcare, EMBC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5860-5863
Number of pages4
ISBN (Electronic)9781728119908
DOIs
StatePublished - Jul 2020
Event42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada
Duration: Jul 20 2020Jul 24 2020

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2020-July
ISSN (Print)1557-170X

Conference

Conference42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
CountryCanada
CityMontreal
Period7/20/207/24/20

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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