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Predicting the impact of placing an overdose prevention site in Philadelphia: a mathematical modeling approach

BACKGROUND: Fatal overdoses from opioid use and substance disorders are increasing at an alarming rate. One proposed harm reduction strategy for reducing overdose fatalities is to place overdose prevention sites—commonly known as safe injection facilities—in proximity of locations with the highest r...

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Autores principales: Wares, Joanna R., Dong, Jing, Gevertz, Jana L., Radunskaya, Ami, Vine, Kendra, Wiebe, Doug, Solomon, Sara
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8556858/
https://www.ncbi.nlm.nih.gov/pubmed/34717657
http://dx.doi.org/10.1186/s12954-021-00559-4
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author Wares, Joanna R.
Dong, Jing
Gevertz, Jana L.
Radunskaya, Ami
Vine, Kendra
Wiebe, Doug
Solomon, Sara
author_facet Wares, Joanna R.
Dong, Jing
Gevertz, Jana L.
Radunskaya, Ami
Vine, Kendra
Wiebe, Doug
Solomon, Sara
author_sort Wares, Joanna R.
collection PubMed
description BACKGROUND: Fatal overdoses from opioid use and substance disorders are increasing at an alarming rate. One proposed harm reduction strategy for reducing overdose fatalities is to place overdose prevention sites—commonly known as safe injection facilities—in proximity of locations with the highest rates of overdose. As urban centers in the USA are tackling legal hurdles and community skepticism around the introduction and location of these sites, it becomes increasingly important to assess the magnitude of the effect that these services might have on public health. METHODS: We developed a mathematical model to describe the movement of people who used opioids to an overdose prevention site in order to understand the impact that the facility would have on overdoses, fatalities, and user education and treatment/recovery. The discrete-time, stochastic model is able to describe a range of user behaviors, including the effects from how far they need to travel to the site. We calibrated the model to overdose data from Philadelphia and ran simulations to describe the effect of placing a site in the Kensington neighborhood. RESULTS: In Philadelphia, which has a non-uniform racial population distribution, choice of site placement can determine which demographic groups are most helped. In our simulations, placement of the site in the Kensington neighborhood resulted in White opioid users being more likely to benefit from the site’s services. Overdoses that occur onsite can be reversed. Our results predict that for every 30 stations in the overdose prevention site, 6 per year of these would have resulted in fatalities if they had occurred outside of the overdose prevention site. Additionally, we estimate that fatalities will decrease further when referrals from the OPS to treatment are considered. CONCLUSIONS: Mathematical modeling was used to predict the impact of placing an overdose prevention site in the Kensington neighborhood of Philadelphia. To fully understand the impact of site placement, both direct and indirect effects must be included in the analysis. Introducing more than one site and distributing sites equally across neighborhoods with different racial and demographic characteristics would have the broadest public health impact. Cities and locales can use mathematical modeling to help quantify the predicted impact of placing an overdose prevention site in a particular location.
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spelling pubmed-85568582021-11-01 Predicting the impact of placing an overdose prevention site in Philadelphia: a mathematical modeling approach Wares, Joanna R. Dong, Jing Gevertz, Jana L. Radunskaya, Ami Vine, Kendra Wiebe, Doug Solomon, Sara Harm Reduct J Research BACKGROUND: Fatal overdoses from opioid use and substance disorders are increasing at an alarming rate. One proposed harm reduction strategy for reducing overdose fatalities is to place overdose prevention sites—commonly known as safe injection facilities—in proximity of locations with the highest rates of overdose. As urban centers in the USA are tackling legal hurdles and community skepticism around the introduction and location of these sites, it becomes increasingly important to assess the magnitude of the effect that these services might have on public health. METHODS: We developed a mathematical model to describe the movement of people who used opioids to an overdose prevention site in order to understand the impact that the facility would have on overdoses, fatalities, and user education and treatment/recovery. The discrete-time, stochastic model is able to describe a range of user behaviors, including the effects from how far they need to travel to the site. We calibrated the model to overdose data from Philadelphia and ran simulations to describe the effect of placing a site in the Kensington neighborhood. RESULTS: In Philadelphia, which has a non-uniform racial population distribution, choice of site placement can determine which demographic groups are most helped. In our simulations, placement of the site in the Kensington neighborhood resulted in White opioid users being more likely to benefit from the site’s services. Overdoses that occur onsite can be reversed. Our results predict that for every 30 stations in the overdose prevention site, 6 per year of these would have resulted in fatalities if they had occurred outside of the overdose prevention site. Additionally, we estimate that fatalities will decrease further when referrals from the OPS to treatment are considered. CONCLUSIONS: Mathematical modeling was used to predict the impact of placing an overdose prevention site in the Kensington neighborhood of Philadelphia. To fully understand the impact of site placement, both direct and indirect effects must be included in the analysis. Introducing more than one site and distributing sites equally across neighborhoods with different racial and demographic characteristics would have the broadest public health impact. Cities and locales can use mathematical modeling to help quantify the predicted impact of placing an overdose prevention site in a particular location. BioMed Central 2021-10-30 /pmc/articles/PMC8556858/ /pubmed/34717657 http://dx.doi.org/10.1186/s12954-021-00559-4 Text en © The Author(s) 2021 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
Wares, Joanna R.
Dong, Jing
Gevertz, Jana L.
Radunskaya, Ami
Vine, Kendra
Wiebe, Doug
Solomon, Sara
Predicting the impact of placing an overdose prevention site in Philadelphia: a mathematical modeling approach
title Predicting the impact of placing an overdose prevention site in Philadelphia: a mathematical modeling approach
title_full Predicting the impact of placing an overdose prevention site in Philadelphia: a mathematical modeling approach
title_fullStr Predicting the impact of placing an overdose prevention site in Philadelphia: a mathematical modeling approach
title_full_unstemmed Predicting the impact of placing an overdose prevention site in Philadelphia: a mathematical modeling approach
title_short Predicting the impact of placing an overdose prevention site in Philadelphia: a mathematical modeling approach
title_sort predicting the impact of placing an overdose prevention site in philadelphia: a mathematical modeling approach
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8556858/
https://www.ncbi.nlm.nih.gov/pubmed/34717657
http://dx.doi.org/10.1186/s12954-021-00559-4
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