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Patterns and risk factors of opioid-suspected EMS overdose in Houston metropolitan area, 2015-2019: A Bayesian spatiotemporal analysis
BACKGROUND: Opioid-related overdose deaths are the top accidental cause of death in the United States, and development of regional strategies to address this epidemic should begin with a better understanding of where and when overdoses are occurring. METHODS AND FINDINGS: In this study, we relied on...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7951926/ https://www.ncbi.nlm.nih.gov/pubmed/33705402 http://dx.doi.org/10.1371/journal.pone.0247050 |
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author | Bauer, Cici Champagne-Langabeer, Tiffany Bakos-Block, Christine Zhang, Kehe Persse, David Langabeer, James R. |
author_facet | Bauer, Cici Champagne-Langabeer, Tiffany Bakos-Block, Christine Zhang, Kehe Persse, David Langabeer, James R. |
author_sort | Bauer, Cici |
collection | PubMed |
description | BACKGROUND: Opioid-related overdose deaths are the top accidental cause of death in the United States, and development of regional strategies to address this epidemic should begin with a better understanding of where and when overdoses are occurring. METHODS AND FINDINGS: In this study, we relied on emergency medical services data to investigate the geographical and temporal patterns in opioid-suspected overdose incidents in one of the largest and most ethnically diverse metropolitan areas (Houston Texas). Using a cross sectional design and Bayesian spatiotemporal models, we identified zip code areas with excessive opioid-suspected incidents, and assessed how the incidence risks were associated with zip code level socioeconomic characteristics. Our analysis suggested that opioid-suspected overdose incidents were particularly high in multiple zip codes, primarily south and central within the city. Zip codes with high percentage of renters had higher overdose relative risk (RR = 1.03; 95% CI: [1.01, 1.04]), while crowded housing and larger proportion of white citizens had lower relative risks (RR = 0.9; 95% CI: [0.84, 0.96], RR = 0.97, 95% CI: [0.95, 0.99], respectively). CONCLUSIONS: Our analysis illustrated the utility of Bayesian spatiotemporal models in assisting the development of targeted community strategies for local prevention and harm reduction efforts. |
format | Online Article Text |
id | pubmed-7951926 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-79519262021-03-22 Patterns and risk factors of opioid-suspected EMS overdose in Houston metropolitan area, 2015-2019: A Bayesian spatiotemporal analysis Bauer, Cici Champagne-Langabeer, Tiffany Bakos-Block, Christine Zhang, Kehe Persse, David Langabeer, James R. PLoS One Research Article BACKGROUND: Opioid-related overdose deaths are the top accidental cause of death in the United States, and development of regional strategies to address this epidemic should begin with a better understanding of where and when overdoses are occurring. METHODS AND FINDINGS: In this study, we relied on emergency medical services data to investigate the geographical and temporal patterns in opioid-suspected overdose incidents in one of the largest and most ethnically diverse metropolitan areas (Houston Texas). Using a cross sectional design and Bayesian spatiotemporal models, we identified zip code areas with excessive opioid-suspected incidents, and assessed how the incidence risks were associated with zip code level socioeconomic characteristics. Our analysis suggested that opioid-suspected overdose incidents were particularly high in multiple zip codes, primarily south and central within the city. Zip codes with high percentage of renters had higher overdose relative risk (RR = 1.03; 95% CI: [1.01, 1.04]), while crowded housing and larger proportion of white citizens had lower relative risks (RR = 0.9; 95% CI: [0.84, 0.96], RR = 0.97, 95% CI: [0.95, 0.99], respectively). CONCLUSIONS: Our analysis illustrated the utility of Bayesian spatiotemporal models in assisting the development of targeted community strategies for local prevention and harm reduction efforts. Public Library of Science 2021-03-11 /pmc/articles/PMC7951926/ /pubmed/33705402 http://dx.doi.org/10.1371/journal.pone.0247050 Text en © 2021 Bauer et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Bauer, Cici Champagne-Langabeer, Tiffany Bakos-Block, Christine Zhang, Kehe Persse, David Langabeer, James R. Patterns and risk factors of opioid-suspected EMS overdose in Houston metropolitan area, 2015-2019: A Bayesian spatiotemporal analysis |
title | Patterns and risk factors of opioid-suspected EMS overdose in Houston metropolitan area, 2015-2019: A Bayesian spatiotemporal analysis |
title_full | Patterns and risk factors of opioid-suspected EMS overdose in Houston metropolitan area, 2015-2019: A Bayesian spatiotemporal analysis |
title_fullStr | Patterns and risk factors of opioid-suspected EMS overdose in Houston metropolitan area, 2015-2019: A Bayesian spatiotemporal analysis |
title_full_unstemmed | Patterns and risk factors of opioid-suspected EMS overdose in Houston metropolitan area, 2015-2019: A Bayesian spatiotemporal analysis |
title_short | Patterns and risk factors of opioid-suspected EMS overdose in Houston metropolitan area, 2015-2019: A Bayesian spatiotemporal analysis |
title_sort | patterns and risk factors of opioid-suspected ems overdose in houston metropolitan area, 2015-2019: a bayesian spatiotemporal analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7951926/ https://www.ncbi.nlm.nih.gov/pubmed/33705402 http://dx.doi.org/10.1371/journal.pone.0247050 |
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