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

Area of Science:

• Health Policy Modeling
• Health Equity Research
• Decision Analysis

Background:

• Health policy simulation models often overlook social determinants of health, potentially leading to flawed policy recommendations.
• Existing models typically focus on disease progression, neglecting how social factors influence health outcomes and healthcare utilization.

Purpose of the Study:

• To develop and evaluate a novel decision-analytic modeling framework that integrates social processes into health policy simulations.
• To assess the impact of incorporating social factors, such as health insurance disparities, on policy recommendations and health equity.

Main Methods:

• A social factors framework was developed and integrated into a decision-analytic model.
• Two models were compared: a standard model and one incorporating the social factors framework.
• The models simulated a hypothetical treatment's impact on life expectancy, sickness duration, and healthcare utilization in a diverse cohort.

Main Results:

• The standard model showed a new treatment increased life expectancy equally across racial groups, masking disparities.
• The social factors framework revealed the treatment yielded smaller life expectancy gains for non-Hispanic Black adults, exacerbating racial disparities.
• Incorporating social factors highlighted how seemingly neutral policies can disproportionately affect certain populations.

Conclusions:

• Excluding social processes from health policy models can lead to unrealistic projections and biased policy outcomes.
• The proposed social factors framework enhances the ability of simulation models to evaluate interventions for health equity.
• Integrating social factors is crucial for developing effective and equitable health policies.