A novel decision modeling framework for health policy analyses when outcomes are influenced by social and disease processes

Purpose Health policy simulation models incorporate disease processes but often ignore social processes that influence health outcomes, potentially leading to suboptimal policy recommendations. To address this gap, we developed a novel decision-analytic modeling framework to integrate social processes.

We evaluated a simplified decision problem using two models: a standard decision-analytic model and a model incorporating our social factors framework. The standard model simulated individuals transitioning through three disease natural history states–healthy, sick, and dead–without accounting for differential health system utilization. Our social factors framework incorporated heterogeneous health insurance coverage, which influenced disease progression and health system utilization. We assessed the impact of a new treatment on a hypothetical cohort of 100,000 healthy, non-Hispanic Black and non-Hispanic white 40-year-old adults. Primary outcomes included life expectancy, cumulative incidence and duration of sickness, and health system utilization throughout a person’s lifetime. Secondary outcomes included costs, quality-adjusted life years, and incremental cost-effectiveness ratios.

In the standard model, the new treatment increased life expectancy by 2.7 years for both non-Hispanic Black and non-Hispanic white adults, without affecting racial/ethnic gaps in life expectancy. However, incorporating known racial/ethnic disparities in health insurance coverage with the social factors framework led to smaller life expectancy gains for non-Hispanic Black adults (2.0 years) compared to non-Hispanic white adults (2.2 years), increasing racial/ethnic disparities in life expectancy.

The availability of social factors data and complexity of causal pathways between factors may pose challenges in applying our social factors framework.

Excluding social processes from health policy modeling can result in unrealistic projections and biased policy recommendations. Incorporating the social factors framework enhances simulation models’ effectiveness in evaluating interventions with health equity implications. Highlights Health policy simulation models that ignore social processes may be biased and lead to suboptimal policy recommendations. To address this, we proposed a novel social factors framework to integrate social factors into decision-analytic models for health policy. Applying our social factors framework to a simplified example highlighted the potential bias that results from ignoring social factors. In a standard model, a hypothetical new treatment appeared to have no effect on health disparities. However, incorporating our social factors framework demonstrated that this treatment would exacerbate disparities. Incorporating a social factors framework into health policy simulation models has particular relevance for evaluating health interventions with equity implications. Competing Interest Statement The authors have declared no competing interest. Funding Statement Financial support for this study was provided by the National Institutes of Health and a Stanford Interdisciplinary Graduate Fellowship. Marika Cusick, Fernando Alarid-Escudero, Jeremy D. Goldhaber-Fiebert and Sherri Rose were supported by NIH Director's Pioneer Award DP1LM014278. Marika Cusick was additionally supported by the Stanford Interdisciplinary Graduate Fellowship. The funding agencies had no role in the design of the study, interpretation of results, or writing of the manuscript. The funding agreement ensured the authors' independence in designing the study, interpreting the data, writing, and publishing the report. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes Minor changes to text; update to Figure 2; updates to Tables 1 and 2 1 Individuals in the health system are undetected and untreated for the sickness. 2 In the standard model, all individuals are initiated within the health system. 4 We estimated the proportion of our cohort in the health system state using the National Health and Nutrition Examination Survey question for routine place to go for healthcare. ↵4 Parameters were modified for both standard model and model with social factors framework ↵5 Parameters were only modified for the model with the social factors framework Data availability The Python code to reproduce our results is available at: https://github.com/StanfordHPDS/social_factors_microsim.