Ava A John-Baptiste, Marc Moulin, Zhe Li, Darren Hamilton, Gabrielle Crichlow, Daniel Eisenkraft Klein, Feben W Alemu, Lina Ghattas, Kathryn McDonald, Miqdad Asaria, Cameron Sharpe, Ekta Pandya, Nasheed Moqueet, David Champredon, Seyed M Moghadas, Lisa A Cooper, Andrew Pinto, Saverio Stranges, Margaret J Haworth-Brockman, Alison Galvani, Shehzad Ali
{"title":"COVID-19 传染病模型是否纳入了健康的社会决定因素？系统回顾。","authors":"Ava A John-Baptiste, Marc Moulin, Zhe Li, Darren Hamilton, Gabrielle Crichlow, Daniel Eisenkraft Klein, Feben W Alemu, Lina Ghattas, Kathryn McDonald, Miqdad Asaria, Cameron Sharpe, Ekta Pandya, Nasheed Moqueet, David Champredon, Seyed M Moghadas, Lisa A Cooper, Andrew Pinto, Saverio Stranges, Margaret J Haworth-Brockman, Alison Galvani, Shehzad Ali","doi":"10.3389/phrs.2024.1607057","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>To identify COVID-19 infectious disease models that accounted for social determinants of health (SDH).</p><p><strong>Methods: </strong>We searched MEDLINE, EMBASE, Cochrane Library, medRxiv, and the Web of Science from December 2019 to August 2020. We included mathematical modelling studies focused on humans investigating COVID-19 impact and including at least one SDH. We abstracted study characteristics (e.g., country, model type, social determinants of health) and appraised study quality using best practices guidelines.</p><p><strong>Results: </strong>83 studies were included. Most pertained to multiple countries (n = 15), the United States (n = 12), or China (n = 7). Most models were compartmental (n = 45) and agent-based (n = 7). Age was the most incorporated SDH (n = 74), followed by gender (n = 15), race/ethnicity (n = 7) and remote/rural location (n = 6). Most models reflected the dynamic nature of infectious disease spread (n = 51, 61%) but few reported on internal (n = 10, 12%) or external (n = 31, 37%) model validation.</p><p><strong>Conclusion: </strong>Few models published early in the pandemic accounted for SDH other than age. Neglect of SDH in mathematical models of disease spread may result in foregone opportunities to understand differential impacts of the pandemic and to assess targeted interventions.</p><p><strong>Systematic review registration: </strong>[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020207706], PROSPERO, CRD42020207706.</p>","PeriodicalId":35944,"journal":{"name":"PUBLIC HEALTH REVIEWS","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11499127/pdf/","citationCount":"0","resultStr":"{\"title\":\"Do COVID-19 Infectious Disease Models Incorporate the Social Determinants of Health? A Systematic Review.\",\"authors\":\"Ava A John-Baptiste, Marc Moulin, Zhe Li, Darren Hamilton, Gabrielle Crichlow, Daniel Eisenkraft Klein, Feben W Alemu, Lina Ghattas, Kathryn McDonald, Miqdad Asaria, Cameron Sharpe, Ekta Pandya, Nasheed Moqueet, David Champredon, Seyed M Moghadas, Lisa A Cooper, Andrew Pinto, Saverio Stranges, Margaret J Haworth-Brockman, Alison Galvani, Shehzad Ali\",\"doi\":\"10.3389/phrs.2024.1607057\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>To identify COVID-19 infectious disease models that accounted for social determinants of health (SDH).</p><p><strong>Methods: </strong>We searched MEDLINE, EMBASE, Cochrane Library, medRxiv, and the Web of Science from December 2019 to August 2020. We included mathematical modelling studies focused on humans investigating COVID-19 impact and including at least one SDH. We abstracted study characteristics (e.g., country, model type, social determinants of health) and appraised study quality using best practices guidelines.</p><p><strong>Results: </strong>83 studies were included. Most pertained to multiple countries (n = 15), the United States (n = 12), or China (n = 7). Most models were compartmental (n = 45) and agent-based (n = 7). Age was the most incorporated SDH (n = 74), followed by gender (n = 15), race/ethnicity (n = 7) and remote/rural location (n = 6). Most models reflected the dynamic nature of infectious disease spread (n = 51, 61%) but few reported on internal (n = 10, 12%) or external (n = 31, 37%) model validation.</p><p><strong>Conclusion: </strong>Few models published early in the pandemic accounted for SDH other than age. Neglect of SDH in mathematical models of disease spread may result in foregone opportunities to understand differential impacts of the pandemic and to assess targeted interventions.</p><p><strong>Systematic review registration: </strong>[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020207706], PROSPERO, CRD42020207706.</p>\",\"PeriodicalId\":35944,\"journal\":{\"name\":\"PUBLIC HEALTH REVIEWS\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11499127/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PUBLIC HEALTH REVIEWS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/phrs.2024.1607057\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PUBLIC HEALTH REVIEWS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/phrs.2024.1607057","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
Do COVID-19 Infectious Disease Models Incorporate the Social Determinants of Health? A Systematic Review.
Objectives: To identify COVID-19 infectious disease models that accounted for social determinants of health (SDH).
Methods: We searched MEDLINE, EMBASE, Cochrane Library, medRxiv, and the Web of Science from December 2019 to August 2020. We included mathematical modelling studies focused on humans investigating COVID-19 impact and including at least one SDH. We abstracted study characteristics (e.g., country, model type, social determinants of health) and appraised study quality using best practices guidelines.
Results: 83 studies were included. Most pertained to multiple countries (n = 15), the United States (n = 12), or China (n = 7). Most models were compartmental (n = 45) and agent-based (n = 7). Age was the most incorporated SDH (n = 74), followed by gender (n = 15), race/ethnicity (n = 7) and remote/rural location (n = 6). Most models reflected the dynamic nature of infectious disease spread (n = 51, 61%) but few reported on internal (n = 10, 12%) or external (n = 31, 37%) model validation.
Conclusion: Few models published early in the pandemic accounted for SDH other than age. Neglect of SDH in mathematical models of disease spread may result in foregone opportunities to understand differential impacts of the pandemic and to assess targeted interventions.
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