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A multimorbidity model for estimating health outcomes from the syndemic of injection drug use and associated infections in the United States
BACKGROUND: Fatal drug overdoses and serious injection-related infections are rising in the US. Multiple concurrent infections in people who inject drugs (PWID) exacerbate poor health outcomes, but little is known about how the synergy among infections compounds clinical outcomes and costs. Injectio...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10353126/ https://www.ncbi.nlm.nih.gov/pubmed/37461007 http://dx.doi.org/10.1186/s12913-023-09773-1 |
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author | Chiosi, John J. Mueller, Peter P. Chhatwal, Jagpreet Ciaranello, Andrea L. |
author_facet | Chiosi, John J. Mueller, Peter P. Chhatwal, Jagpreet Ciaranello, Andrea L. |
author_sort | Chiosi, John J. |
collection | PubMed |
description | BACKGROUND: Fatal drug overdoses and serious injection-related infections are rising in the US. Multiple concurrent infections in people who inject drugs (PWID) exacerbate poor health outcomes, but little is known about how the synergy among infections compounds clinical outcomes and costs. Injection drug use (IDU) converges multiple epidemics into a syndemic in the US, including opioid use and HIV. Estimated rates of new injection-related infections in the US are limited due to widely varying estimates of the number of PWID in the US, and in the absence of clinical trials and nationally representative longitudinal observational studies of PWID, simulation models provide important insights to policymakers for informed decisions. METHODS: We developed and validated a MultimorbiditY model to Reduce Infections Associated with Drug use (MYRIAD). This microsimulation model of drug use and associated infections (HIV, hepatitis C virus [HCV], and severe bacterial infections) uses inputs derived from published data to estimate national level trends in the US. We used Latin hypercube sampling to calibrate model output against published data from 2015 to 2019 for fatal opioid overdose rates. We internally validated the model for HIV and HCV incidence and bacterial infection hospitalization rates among PWID. We identified best fitting parameter sets that met pre-established goodness-of-fit targets using the Pearson’s chi-square test. We externally validated the model by comparing model output to published fatal opioid overdose rates from 2020. RESULTS: Out of 100 sample parameter sets for opioid use, the model produced 3 sets with well-fitting results to key calibration targets for fatal opioid overdose rates with Pearson’s chi-square test ranging from 1.56E-5 to 2.65E-5, and 2 sets that met validation targets. The model produced well-fitting results within validation targets for HIV and HCV incidence and serious bacterial infection hospitalization rates. From 2015 to 2019, the model estimated 120,000 injection-related overdose deaths, 17,000 new HIV infections, and 144,000 new HCV infections among PWID. CONCLUSIONS: This multimorbidity microsimulation model, populated with data from national surveillance data and published literature, accurately replicated fatal opioid overdose, incidence of HIV and HCV, and serious bacterial infections hospitalization rates. The MYRIAD model of IDU could be an important tool to assess clinical and economic outcomes related to IDU behavior and infections with serious morbidity and mortality for PWID. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-023-09773-1. |
format | Online Article Text |
id | pubmed-10353126 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-103531262023-07-19 A multimorbidity model for estimating health outcomes from the syndemic of injection drug use and associated infections in the United States Chiosi, John J. Mueller, Peter P. Chhatwal, Jagpreet Ciaranello, Andrea L. BMC Health Serv Res Research BACKGROUND: Fatal drug overdoses and serious injection-related infections are rising in the US. Multiple concurrent infections in people who inject drugs (PWID) exacerbate poor health outcomes, but little is known about how the synergy among infections compounds clinical outcomes and costs. Injection drug use (IDU) converges multiple epidemics into a syndemic in the US, including opioid use and HIV. Estimated rates of new injection-related infections in the US are limited due to widely varying estimates of the number of PWID in the US, and in the absence of clinical trials and nationally representative longitudinal observational studies of PWID, simulation models provide important insights to policymakers for informed decisions. METHODS: We developed and validated a MultimorbiditY model to Reduce Infections Associated with Drug use (MYRIAD). This microsimulation model of drug use and associated infections (HIV, hepatitis C virus [HCV], and severe bacterial infections) uses inputs derived from published data to estimate national level trends in the US. We used Latin hypercube sampling to calibrate model output against published data from 2015 to 2019 for fatal opioid overdose rates. We internally validated the model for HIV and HCV incidence and bacterial infection hospitalization rates among PWID. We identified best fitting parameter sets that met pre-established goodness-of-fit targets using the Pearson’s chi-square test. We externally validated the model by comparing model output to published fatal opioid overdose rates from 2020. RESULTS: Out of 100 sample parameter sets for opioid use, the model produced 3 sets with well-fitting results to key calibration targets for fatal opioid overdose rates with Pearson’s chi-square test ranging from 1.56E-5 to 2.65E-5, and 2 sets that met validation targets. The model produced well-fitting results within validation targets for HIV and HCV incidence and serious bacterial infection hospitalization rates. From 2015 to 2019, the model estimated 120,000 injection-related overdose deaths, 17,000 new HIV infections, and 144,000 new HCV infections among PWID. CONCLUSIONS: This multimorbidity microsimulation model, populated with data from national surveillance data and published literature, accurately replicated fatal opioid overdose, incidence of HIV and HCV, and serious bacterial infections hospitalization rates. The MYRIAD model of IDU could be an important tool to assess clinical and economic outcomes related to IDU behavior and infections with serious morbidity and mortality for PWID. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-023-09773-1. BioMed Central 2023-07-17 /pmc/articles/PMC10353126/ /pubmed/37461007 http://dx.doi.org/10.1186/s12913-023-09773-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Chiosi, John J. Mueller, Peter P. Chhatwal, Jagpreet Ciaranello, Andrea L. A multimorbidity model for estimating health outcomes from the syndemic of injection drug use and associated infections in the United States |
title | A multimorbidity model for estimating health outcomes from the syndemic of injection drug use and associated infections in the United States |
title_full | A multimorbidity model for estimating health outcomes from the syndemic of injection drug use and associated infections in the United States |
title_fullStr | A multimorbidity model for estimating health outcomes from the syndemic of injection drug use and associated infections in the United States |
title_full_unstemmed | A multimorbidity model for estimating health outcomes from the syndemic of injection drug use and associated infections in the United States |
title_short | A multimorbidity model for estimating health outcomes from the syndemic of injection drug use and associated infections in the United States |
title_sort | multimorbidity model for estimating health outcomes from the syndemic of injection drug use and associated infections in the united states |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10353126/ https://www.ncbi.nlm.nih.gov/pubmed/37461007 http://dx.doi.org/10.1186/s12913-023-09773-1 |
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