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Incorporating Social Determinants of Health in Infectious Disease Models: A Systematic Review of Guidelines

Author

Listed:
  • Shehzad Ali

    (Department of Epidemiology & Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
    Centre for Medical Evidence, Decision Integrity & Clinical Impact (MEDICI), Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
    Schulich Interfaculty Program in Public Health, Western University, London, ON, Canada)

  • Zhe Li

    (Department of Epidemiology & Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
    University of Ottawa Heart Institute, Ottawa, ON, Canada)

  • Nasheed Moqueet

    (Public Health Agency of Canada, Ottawa, ON, Canada)

  • Seyed M. Moghadas

    (Agent-Based Modelling Laboratory, York University, Toronto, ON, Canada)

  • Alison P. Galvani

    (Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA)

  • Lisa A. Cooper

    (Department of Medicine, Johns Hopkins University School of Medicine, USA
    Department of Health, Behavior, and Society, Johns Hopkins Bloomberg School of Public Health, USA)

  • Saverio Stranges

    (Department of Epidemiology & Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
    Department of Clinical Medicine and Surgery, University of Naples Federico II, Italy)

  • Margaret Haworth-Brockman

    (Department of Sociology, University of Winnipeg, MB, Canada and National Collaborating Centre for Infectious Diseases, Winnipeg, MB, Canada)

  • Andrew D. Pinto

    (Division of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada and Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada)

  • Miqdad Asaria

    (Department of Health Policy, London School of Economics and Political Science, UK)

  • David Champredon

    (Public Health Agency of Canada, National Microbiological Laboratory, Guelph, ON, Canada)

  • Darren Hamilton

    (London Health Sciences Centre, London, ON, Canada)

  • Marc Moulin

    (London Health Sciences Centre, London, ON, Canada
    Department of Anesthesia & Perioperative Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada)

  • Ava A. John-Baptiste

    (Department of Epidemiology & Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
    Centre for Medical Evidence, Decision Integrity & Clinical Impact (MEDICI), Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
    Schulich Interfaculty Program in Public Health, Western University, London, ON, Canada
    Department of Anesthesia & Perioperative Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada)

Abstract
Background Infectious disease (ID) models have been the backbone of policy decisions during the COVID-19 pandemic. However, models often overlook variation in disease risk, health burden, and policy impact across social groups. Nonetheless, social determinants are becoming increasingly recognized as fundamental to the success of control strategies overall and to the mitigation of disparities. Methods To underscore the importance of considering social heterogeneity in epidemiological modeling, we systematically reviewed ID modeling guidelines to identify reasons and recommendations for incorporating social determinants of health into models in relation to the conceptualization, implementation, and interpretations of models. Results After identifying 1,372 citations, we found 19 guidelines, of which 14 directly referenced at least 1 social determinant. Age ( n  = 11), sex and gender ( n  = 5), and socioeconomic status ( n  = 5) were the most commonly discussed social determinants. Specific recommendations were identified to consider social determinants to 1) improve the predictive accuracy of models, 2) understand heterogeneity of disease burden and policy impact, 3) contextualize decision making, 4) address inequalities, and 5) assess implementation challenges. Conclusion This study can support modelers and policy makers in taking into account social heterogeneity, to consider the distributional impact of infectious disease outbreaks across social groups as well as to tailor approaches to improve equitable access to prevention, diagnostics, and therapeutics. Highlights Infectious disease (ID) models often overlook the role of social determinants of health (SDH) in understanding variation in disease risk, health burden, and policy impact across social groups. In this study, we systematically review ID guidelines and identify key areas to consider SDH in relation to the conceptualization, implementation, and interpretations of models. We identify specific recommendations to consider SDH to improve model accuracy, understand heterogeneity, estimate policy impact, address inequalities, and assess implementation challenges.

Suggested Citation

  • Shehzad Ali & Zhe Li & Nasheed Moqueet & Seyed M. Moghadas & Alison P. Galvani & Lisa A. Cooper & Saverio Stranges & Margaret Haworth-Brockman & Andrew D. Pinto & Miqdad Asaria & David Champredon & Da, 2024. "Incorporating Social Determinants of Health in Infectious Disease Models: A Systematic Review of Guidelines," Medical Decision Making, , vol. 44(7), pages 742-755, October.
    • Handle: RePEc:sae:medema:v:44:y:2024:i:7:p:742-755
    DOI: 10.1177/0272989X241280611
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