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April 3, 2020

Addressing Social Determinants of Health: Time for a Polysocial Risk Score

Author Affiliations
  • 1Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
  • 2Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
  • 3Boston VA Healthcare System, Boston, Massachusetts
  • 4Boston University School of Public Health, Boston, Massachusetts
  • 5Harvard Global Health Institute, Harvard University, Cambridge, Massachusetts
JAMA. 2020;323(16):1553-1554. doi:10.1001/jama.2020.2436

What determines health? For decades, researchers have attempted to quantify the exact contribution of social factors, such as income, education, race/ethnicity, and the community environment, to health. The motivation is simple: understanding which social factors most affect health would help prioritize societal investments in those areas. However, most efforts to precisely quantify the influence of individual social determinants of health have failed, largely because the causal pathways are numerous, interconnected, and complex. Most empirical evidence has found that social factors matter in aggregate, but quantifying their individual contributions is difficult. After decades of efforts in this area, it may be time for a new approach.

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    2 Comments for this article
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    Polysocial Risk Score: Validation of a Systems Science Approach to Population Health and Social Determinants
    Saroj Jayasinghe, MBBS, MD, PhD | Faculty of Medicine, University of Colombo, Sri Lanka
    This description of the potential utility of a polysocial risk score is timely. However, its theoretical basis needs further elaboration.

    We need to appreciate that such scoring methods and modeling are based on a systems science approach to population or individual health (1, 2). In this approach, the health status of an individual or health outcomes of a population is viewed as emergent properties of underlying dynamic complex systems. A complex system has several elements that are interacting with each other in a non-linear and dynamic manner, while being 'open' to interactions with the wider 'external' system.


    The given health status or outcome can, therefore, be predicted by looking at a series of parameters from several dimensions.

    Such methods are used in ICUs where multiple parameters are fed to algorithms that predict clusters of parameters that predict prognosis (3). This same methodology could be adopted using multiple -omics (i.e. proteomics, metaboolomics etc.) that will predict future disease states at a very early asymptomatic stage (4). It has also been applied to describe inequalities in populations (5).

    The polysocial risk score is, therefore, an application of the systems approach to social parameters.

    REFERENCES

    (1) Jayasinghe, S. Complexity Science to Conceptualize Health and Disease: Is It Relevant to Clinical Medicine? Mayo Clinic Proceedings. 2012;87 (4):314–319
    https://www.mayoclinicproceedings.org/article/S0025-6196(12)00211-X/fulltext

    (2) Jayasinghe, S. Conceptualising population health: from mechanistic thinking to complexity science. Emerg Themes Epidemiol. 2011; 8: 2https://ete-online.biomedcentral.com/track/pdf/10.1186/1742-7622-8-2.

    (3) Cohen, M.J., Grossman, A.D., Morabito, D., Knudson, M.M., Butte, A.J., and Manley, G.T. Identification of complex metabolic states in critically injured patients using bioinformatics cluster analysis. Crit Care. 2010; 14: R10

    (4) Jayasinghe S. 'Prognostic -Omic Clusters' (POCs): a novel approach to health and disease.
    Medical Hypotheses, 2014, 82(6):703-705

    (5) Tebani A, Afonso C, Marret S, Bekri S. Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations. Int J Mol Sci. 2016;17(9):1555.


    Saroj Jayasinghe
    Professor of Medicine
    Faculty of Medicine
    University of Colombo
    Sri Lanka
    CONFLICT OF INTEREST: None Reported
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    The truth may be somewhere nearby. On the cooperation of sociologists and doctors.
    Anatoly Zhirkov, Professor | Saint Petersburg State University
    I read this article (1) with great interest. The authors turn their attention to the role of social factors in the development of diseases. This approach is traditional in medicine. Unfortunately, every new discovery of the biological causes of the disease in medicine temporarily obscures the role of social factors in the development of diseases. The authors rightly consider in this regard the latest research in the field of the genetic causes of diseases using the example of cardiovascular diseases (2). They note an obvious social vector in the development of this pathology. The authors quite correctly raise the question of the need to improve approaches in assessing the role of social factors in the development of diseases. Groups of signs are proposed that can be used in the development of integrated prognostic indicators. Attention is drawn to the difficulties in creating social risk scales.

    But I would like to draw attention to the need to study the importance of social factors in the development of diseases in dynamics. Here, the work of prominent American sociologists can provide significant assistance (3). Using their experience in building dynamic models of social processes can be useful both at the stage of collecting material and evaluating the results. This shows the possibility of cooperation between sociologists and doctors in creating integrated models of disease risk.

    REFERENCES

    1. Figueroa JF, Frakt AB, Jha AK. Addressing Social Determinants of Health: Time for a Polysocial Risk Score. JAMA. Published online April 03, 2020. doi:10.1001/jama.2020.2436
    2. .Justina Motiejūnaitė, Eiichi Akiyama, Alain Cohen-Solal, Aldo Pietro Maggioni, Christian Mueller, Dong-Ju Choi, Aušra Kavoliūnienė, Jelena Čelutkienė, Jiri Parenica, Johan Lassus, Katsuya Kajimoto, Naoki Sato, Òscar Miró, W Frank Peacock, Yuya Matsue, Adriaan A Voors, Carolyn S P Lam, Justin A Ezekowitz, Ali Ahmed, Gregg C Fonarow, Etienne Gayat, Vera Regitz-Zagrosek, Alexandre Mebazaa, on behalf of the GREAT (Global Research on Acute Conditions Team) Network, The association of long-term outcome and biological sex in patients with acute heart failure from different geographic regions, European Heart Journal, , ehaa071, https://doi.org/10.1093/eurheartj/ehaa071
    3. Ingelhart R F. Cultural evolution. People's Motivations are Changing, and Reshaping the World. Cambridge University Press. https://doi.org/10.1017/9781108613880
    CONFLICT OF INTEREST: None Reported
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