ArticleInnovation in aging2026
Simulation modeling of neighborhood-level differences in life course health outcomes: a proof of concept.
Article in Innovation in aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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9 authors.
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Abstract
Background and Objectives: Adults in low-resource neighborhoods face greater burden of preventable diseases than the general population. Methodologies to measure the impact of population-level efforts to reduce neighborhood-level disparities in health outcomes are lacking. We introduce a simulation modeling approach for incident disease and mortality risk over the life course and apply the model to assess the impact of a hypothetical population health initiative to reduce neighborhood-level disparities. Research Design and Methods: Using electronic health records, we constructed a probabilistic dynamic systems model that simulated the life course of each patient in a large primary care population, taking into account specific relationships by sex and Area Deprivation Index (ADI) quintile. The model predicted long-term incidence of 10 chronic conditions and all-cause mortality. Results: The model was reliable, with strong discrimination (C-statistic for 10-year mortality: 0.871) and calibration. Predicted median life expectancy was nearly a decade lower for patients ages 40-45 who resided in the highest (ADI quintile 5) vs the lowest (quintile 1) deprivation neighborhoods (difference [95% CI]; women, 8.6 [8.2, 9.0] years; men, 10.0 [9.6, 10.3]. A hypothetical initiative to reduce the number of smokers by 10% had minimal effect on disparities in life expectancy when implemented in the general patient population (women, -0.4 years; men, -0.2), but meaningfully narrowed disparities when focused on high-deprivation neighborhoods (women, -0.8 years; men, -0.6). Discussion and Implications: In a broad patient population, it was feasible to measure socioeconomic disparities in health outcomes and life expectancy and evaluate interventions to improve health equity.
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