A novel decision modeling framework for health policy analyses when outcomes are influenced by social and disease processes
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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.
Methods
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 cohort of 100,000 healthy, non-Hispanic Black and non-Hispanic white 40-year-old adults. Main outcomes included life expectancy, cumulative incidence and duration of sickness, and health system utilization over the lifetime. Secondary outcomes included costs, quality-adjusted life years, and incremental cost-effectiveness ratios.
Results
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.
Limitations
The availability of social factors and complexity of causal pathways between factors may pose challenges in applying our social factors framework.
Conclusions
Excluding social processes from health policy modeling can result in unrealistic projections and biased policy recommendations. Incorporating a social factors framework enhances simulation models’ effectiveness in evaluating interventions with health equity implications.
