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Modeling Mitigation Strategies to Reduce Opioid-Related Morbidity and Mortality in the US

IMPORTANCE: The US opioid epidemic is complex and dynamic, yet relatively little is known regarding its likely future impact and the potential mitigating impact of interventions to address it. OBJECTIVE: To estimate the future burden of the opioid epidemic and the potential of interventions to addre...

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Autores principales: Ballreich, Jeromie, Mansour, Omar, Hu, Ellen, Chingcuanco, Francine, Pollack, Harold A., Dowdy, David W., Alexander, G. Caleb
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643029/
https://www.ncbi.nlm.nih.gov/pubmed/33146732
http://dx.doi.org/10.1001/jamanetworkopen.2020.23677
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author Ballreich, Jeromie
Mansour, Omar
Hu, Ellen
Chingcuanco, Francine
Pollack, Harold A.
Dowdy, David W.
Alexander, G. Caleb
author_facet Ballreich, Jeromie
Mansour, Omar
Hu, Ellen
Chingcuanco, Francine
Pollack, Harold A.
Dowdy, David W.
Alexander, G. Caleb
author_sort Ballreich, Jeromie
collection PubMed
description IMPORTANCE: The US opioid epidemic is complex and dynamic, yet relatively little is known regarding its likely future impact and the potential mitigating impact of interventions to address it. OBJECTIVE: To estimate the future burden of the opioid epidemic and the potential of interventions to address the burden. DESIGN, SETTING, AND PARTICIPANTS: A decision analytic dynamic Markov model was calibrated using 2010-2018 data from the National Survey on Drug Use and Health, Centers for Disease Control and Prevention, National Health and Nutrition Examination Survey, the US Census, and National Epidemiologic Survey on Alcohol and Related Conditions–III. Data on individuals 12 years or older from the US general population or with prescription opioid medical use; prescription opioid nonmedical use; heroin use; prescription, heroin, or combined prescription and heroin opioid use disorder (OUD); 1 of 7 treatment categories; or nonfatal or fatal overdose were examined. The model was designed to project fatal opioid overdoses between 2020 and 2029. EXPOSURES: The model projected prescribing reductions (5% annually), naloxone distribution (assumed 5% reduction in case-fatality), and treatment expansion (assumed 35% increase in uptake annually for 4 years and 50% relapse reduction), with each compared vs status quo. MAIN OUTCOMES AND MEASURES: Projected 10-year overdose deaths and prevalence of OUD. RESULTS: Under status quo, 484 429 (95% confidence band, 390 543-576 631) individuals were projected to experience fatal opioid overdose between 2020 and 2029. Projected decreases in deaths were 0.3% with prescribing reductions, 15.4% with naloxone distribution, and 25.3% with treatment expansion; when combined, these interventions were associated with 179 151 fewer overdose deaths (37.0%) over 10 years. Interventions had a smaller association with the prevalence of OUD; for example, the combined intervention was estimated to reduce OUD prevalence by 27.5%, from 2.47 million in 2019 to 1.79 million in 2029. Model projections were most sensitive to assumptions regarding future rates of fatal and nonfatal overdose. CONCLUSIONS AND RELEVANCE: The findings of this study suggest that the opioid epidemic is likely to continue to cause tens of thousands of deaths annually over the next decade. Aggressive deployment of evidence-based interventions may reduce deaths by at least a third but will likely have less impact for the number of people with OUD.
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spelling pubmed-76430292020-11-10 Modeling Mitigation Strategies to Reduce Opioid-Related Morbidity and Mortality in the US Ballreich, Jeromie Mansour, Omar Hu, Ellen Chingcuanco, Francine Pollack, Harold A. Dowdy, David W. Alexander, G. Caleb JAMA Netw Open Original Investigation IMPORTANCE: The US opioid epidemic is complex and dynamic, yet relatively little is known regarding its likely future impact and the potential mitigating impact of interventions to address it. OBJECTIVE: To estimate the future burden of the opioid epidemic and the potential of interventions to address the burden. DESIGN, SETTING, AND PARTICIPANTS: A decision analytic dynamic Markov model was calibrated using 2010-2018 data from the National Survey on Drug Use and Health, Centers for Disease Control and Prevention, National Health and Nutrition Examination Survey, the US Census, and National Epidemiologic Survey on Alcohol and Related Conditions–III. Data on individuals 12 years or older from the US general population or with prescription opioid medical use; prescription opioid nonmedical use; heroin use; prescription, heroin, or combined prescription and heroin opioid use disorder (OUD); 1 of 7 treatment categories; or nonfatal or fatal overdose were examined. The model was designed to project fatal opioid overdoses between 2020 and 2029. EXPOSURES: The model projected prescribing reductions (5% annually), naloxone distribution (assumed 5% reduction in case-fatality), and treatment expansion (assumed 35% increase in uptake annually for 4 years and 50% relapse reduction), with each compared vs status quo. MAIN OUTCOMES AND MEASURES: Projected 10-year overdose deaths and prevalence of OUD. RESULTS: Under status quo, 484 429 (95% confidence band, 390 543-576 631) individuals were projected to experience fatal opioid overdose between 2020 and 2029. Projected decreases in deaths were 0.3% with prescribing reductions, 15.4% with naloxone distribution, and 25.3% with treatment expansion; when combined, these interventions were associated with 179 151 fewer overdose deaths (37.0%) over 10 years. Interventions had a smaller association with the prevalence of OUD; for example, the combined intervention was estimated to reduce OUD prevalence by 27.5%, from 2.47 million in 2019 to 1.79 million in 2029. Model projections were most sensitive to assumptions regarding future rates of fatal and nonfatal overdose. CONCLUSIONS AND RELEVANCE: The findings of this study suggest that the opioid epidemic is likely to continue to cause tens of thousands of deaths annually over the next decade. Aggressive deployment of evidence-based interventions may reduce deaths by at least a third but will likely have less impact for the number of people with OUD. American Medical Association 2020-11-04 /pmc/articles/PMC7643029/ /pubmed/33146732 http://dx.doi.org/10.1001/jamanetworkopen.2020.23677 Text en Copyright 2020 Ballreich J et al. JAMA Network Open. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the CC-BY-NC-ND License.
spellingShingle Original Investigation
Ballreich, Jeromie
Mansour, Omar
Hu, Ellen
Chingcuanco, Francine
Pollack, Harold A.
Dowdy, David W.
Alexander, G. Caleb
Modeling Mitigation Strategies to Reduce Opioid-Related Morbidity and Mortality in the US
title Modeling Mitigation Strategies to Reduce Opioid-Related Morbidity and Mortality in the US
title_full Modeling Mitigation Strategies to Reduce Opioid-Related Morbidity and Mortality in the US
title_fullStr Modeling Mitigation Strategies to Reduce Opioid-Related Morbidity and Mortality in the US
title_full_unstemmed Modeling Mitigation Strategies to Reduce Opioid-Related Morbidity and Mortality in the US
title_short Modeling Mitigation Strategies to Reduce Opioid-Related Morbidity and Mortality in the US
title_sort modeling mitigation strategies to reduce opioid-related morbidity and mortality in the us
topic Original Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643029/
https://www.ncbi.nlm.nih.gov/pubmed/33146732
http://dx.doi.org/10.1001/jamanetworkopen.2020.23677
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