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

Autor:	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., 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., Moqueet N; Public Health Agency of Canada, Ottawa, ON, Canada., Moghadas SM; Agent-Based Modelling Laboratory, York University, Toronto, ON, Canada., Galvani AP; Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA., 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., 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., Haworth-Brockman M; Department of Sociology, University of Winnipeg, MB, Canada and National Collaborating Centre for Infectious Diseases, Winnipeg, MB, Canada., 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., Asaria M; Department of Health Policy, London School of Economics and Political Science, UK., Champredon D; Public Health Agency of Canada, National Microbiological Laboratory, Guelph, ON, Canada., Hamilton D; London Health Sciences Centre, London, ON, Canada., 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., 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.
Jazyk:	angličtina
Zdroj:	Medical decision making : an international journal of the Society for Medical Decision Making [Med Decis Making] 2024 Oct; Vol. 44 (7), pp. 742-755. Date of Electronic Publication: 2024 Sep 21.
DOI:	10.1177/0272989X241280611
Abstrakt:	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. Competing Interests: 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.
Databáze:	MEDLINE
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