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Spatial clustering of heroin-related overdose incidents: a case study in Cincinnati, Ohio
BACKGROUND: Drug overdose is one of the top leading causes of accidental death in the U.S., largely due to the opioid epidemic. Although the opioid epidemic is a nationwide issue, it has not affected the nation uniformly. METHODS: We combined multiple data sources, including emergency medical servic...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9233379/ https://www.ncbi.nlm.nih.gov/pubmed/35752791 http://dx.doi.org/10.1186/s12889-022-13557-3 |
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author | Choi, Jung Im Lee, Jinha Yeh, Arthur B. Lan, Qizhen Kang, Hyojung |
author_facet | Choi, Jung Im Lee, Jinha Yeh, Arthur B. Lan, Qizhen Kang, Hyojung |
author_sort | Choi, Jung Im |
collection | PubMed |
description | BACKGROUND: Drug overdose is one of the top leading causes of accidental death in the U.S., largely due to the opioid epidemic. Although the opioid epidemic is a nationwide issue, it has not affected the nation uniformly. METHODS: We combined multiple data sources, including emergency medical service response, American Community Survey data, and health facilities datasets to analyze distributions of heroin-related overdose incidents in Cincinnati, Ohio at the census block group level. The Ripley’s K function and the local Moran’s I statistics were performed to examine geographic variation patterns in heroin-related overdose incidents within the study area. Then, conditional cluster maps were plotted to examine a relationship between heroin-related incident rates and sociodemographic characteristics of areas as well as the resources for opioid use disorder treatment. RESULTS: The global spatial analysis indicated that there was a clustered pattern of heroin-related overdose incident rates at every distance across the study area. The univariate local spatial analysis identified 7 hot spot clusters, 27 cold spot clusters, and 1 outlier cluster. Conditional cluster maps showed characteristics of neighborhoods with high heroin overdose rates, such as a higher crime rate, a high percentage of the male, a high poverty level, a lower education level, and a lower income level. The hot spots in the Southwest areas of Cincinnati had longer distances to opioid treatment programs and buprenorphine prescribing physicians than the median, while the hot spots in the South-Central areas of the city had shorter distances to those health resources. CONCLUSIONS: Our study showed that the opioid epidemic disproportionately affected Cincinnati. Multi-phased spatial clustering models based on various data sources can be useful to identify areas that require more policy attention and targeted interventions to alleviate high heroin-related overdose rates. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-13557-3. |
format | Online Article Text |
id | pubmed-9233379 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-92333792022-06-26 Spatial clustering of heroin-related overdose incidents: a case study in Cincinnati, Ohio Choi, Jung Im Lee, Jinha Yeh, Arthur B. Lan, Qizhen Kang, Hyojung BMC Public Health Research BACKGROUND: Drug overdose is one of the top leading causes of accidental death in the U.S., largely due to the opioid epidemic. Although the opioid epidemic is a nationwide issue, it has not affected the nation uniformly. METHODS: We combined multiple data sources, including emergency medical service response, American Community Survey data, and health facilities datasets to analyze distributions of heroin-related overdose incidents in Cincinnati, Ohio at the census block group level. The Ripley’s K function and the local Moran’s I statistics were performed to examine geographic variation patterns in heroin-related overdose incidents within the study area. Then, conditional cluster maps were plotted to examine a relationship between heroin-related incident rates and sociodemographic characteristics of areas as well as the resources for opioid use disorder treatment. RESULTS: The global spatial analysis indicated that there was a clustered pattern of heroin-related overdose incident rates at every distance across the study area. The univariate local spatial analysis identified 7 hot spot clusters, 27 cold spot clusters, and 1 outlier cluster. Conditional cluster maps showed characteristics of neighborhoods with high heroin overdose rates, such as a higher crime rate, a high percentage of the male, a high poverty level, a lower education level, and a lower income level. The hot spots in the Southwest areas of Cincinnati had longer distances to opioid treatment programs and buprenorphine prescribing physicians than the median, while the hot spots in the South-Central areas of the city had shorter distances to those health resources. CONCLUSIONS: Our study showed that the opioid epidemic disproportionately affected Cincinnati. Multi-phased spatial clustering models based on various data sources can be useful to identify areas that require more policy attention and targeted interventions to alleviate high heroin-related overdose rates. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-13557-3. BioMed Central 2022-06-25 /pmc/articles/PMC9233379/ /pubmed/35752791 http://dx.doi.org/10.1186/s12889-022-13557-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Choi, Jung Im Lee, Jinha Yeh, Arthur B. Lan, Qizhen Kang, Hyojung Spatial clustering of heroin-related overdose incidents: a case study in Cincinnati, Ohio |
title | Spatial clustering of heroin-related overdose incidents: a case study in Cincinnati, Ohio |
title_full | Spatial clustering of heroin-related overdose incidents: a case study in Cincinnati, Ohio |
title_fullStr | Spatial clustering of heroin-related overdose incidents: a case study in Cincinnati, Ohio |
title_full_unstemmed | Spatial clustering of heroin-related overdose incidents: a case study in Cincinnati, Ohio |
title_short | Spatial clustering of heroin-related overdose incidents: a case study in Cincinnati, Ohio |
title_sort | spatial clustering of heroin-related overdose incidents: a case study in cincinnati, ohio |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9233379/ https://www.ncbi.nlm.nih.gov/pubmed/35752791 http://dx.doi.org/10.1186/s12889-022-13557-3 |
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