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

equity infectious disease models modelling guidelines pandemic social determinants of health

Journal

Medical decision making : an international journal of the Society for Medical Decision Making
ISSN: 1552-681X
Titre abrégé: Med Decis Making
Pays: United States
ID NLM: 8109073

Informations de publication

Date de publication:
21 Sep 2024
Historique:
medline: 21 9 2024
pubmed: 21 9 2024
entrez: 21 9 2024
Statut: aheadofprint

Résumé

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. 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. After identifying 1,372 citations, we found 19 guidelines, of which 14 directly referenced at least 1 social determinant. Age ( 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. 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.

Sections du résumé

BACKGROUND 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 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 RESULTS
After identifying 1,372 citations, we found 19 guidelines, of which 14 directly referenced at least 1 social determinant. Age (
CONCLUSION CONCLUSIONS
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 CONCLUSIONS
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.

Identifiants

DOI: 10.1177/0272989X241280611 PMID: 39305116
pubmed: 39305116
doi: 10.1177/0272989X241280611
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

272989X241280611

Déclaration de conflit d'intérêts

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was funded by the Gordon and Betty Moore Foundation through Grant GBMF9634 to Johns Hopkins University to support the work of the Society for Medical Decision Making (SMDM) COVID-19 Decision Modeling Initiative (CDMI). The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report.

Auteurs

Shehzad Ali (S)

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.
ORCID: 0000-0002-8042-3630

Zhe Li (Z)

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 (N)

Public Health Agency of Canada, Ottawa, ON, Canada.

Seyed M Moghadas (SM)

Agent-Based Modelling Laboratory, York University, Toronto, ON, Canada.

Alison P Galvani (AP)

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

Lisa A Cooper (LA)

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 (S)

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 (M)

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

Andrew D Pinto (AD)

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 (M)

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

David Champredon (D)

Public Health Agency of Canada, National Microbiological Laboratory, Guelph, ON, Canada.
ORCID: 0000-0002-7090-8757

Darren Hamilton (D)

London Health Sciences Centre, London, ON, Canada.

Marc Moulin (M)

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 (AA)

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.

Classifications MeSH