TY - JOUR
T1 - Do COVID-19 Infectious Disease Models Incorporate the Social Determinants of Health? A Systematic Review
AU - John-Baptiste, Ava A.
AU - Moulin, Marc
AU - Li, Zhe
AU - Hamilton, Darren
AU - Crichlow, Gabrielle
AU - Klein, Daniel Eisenkraft
AU - Alemu, Feben W.
AU - Ghattas, Lina
AU - McDonald, Kathryn
AU - Asaria, Miqdad
AU - Sharpe, Cameron
AU - Pandya, Ekta
AU - Moqueet, Nasheed
AU - Champredon, David
AU - Moghadas, Seyed M.
AU - Cooper, Lisa A.
AU - Pinto, Andrew
AU - Stranges, Saverio
AU - Haworth-Brockman, Margaret J.
AU - Galvani, Alison
AU - Ali, Shehzad
N1 - Publisher Copyright:
Copyright © 2024 John-Baptiste, Moulin, Li, Hamilton, Crichlow, Klein, Alemu, Ghattas, McDonald, Asaria, Sharpe, Pandya, Moqueet, Champredon, Moghadas, Cooper, Pinto, Stranges, Haworth-Brockman, Galvani and Ali.
PY - 2024
Y1 - 2024
N2 - 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. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020207706], PROSPERO, CRD42020207706.
AB - 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. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020207706], PROSPERO, CRD42020207706.
KW - COVID-19
KW - infectious disease models
KW - model validity
KW - public health
KW - social determinants of health
UR - https://www.scopus.com/pages/publications/85207505978
U2 - 10.3389/phrs.2024.1607057
DO - 10.3389/phrs.2024.1607057
M3 - Review article
AN - SCOPUS:85207505978
SN - 0301-0422
VL - 45
JO - Public Health Reviews
JF - Public Health Reviews
M1 - 1607057
ER -