A Novel Decision-Modeling Framework for Health Policy Analyses When Outcomes Are Influenced by Social and Disease Processes.

PurposeHealth 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.MethodsWe 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-y-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.ResultsIn the standard model, the new treatment increased life expectancy by 2.7 y 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 y) compared with non-Hispanic white adults (2.2 y), increasing racial/ethnic disparities in life expectancy.LimitationsThe availability of social factors data and complexity of causal pathways between factors may pose challenges in applying our social factors framework.ConclusionsExcluding 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.HighlightsHealth 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.