Effects of Medicare predictors in health disparities in the risk of Alzheimer's disease.

Disparities in Alzheimer's disease (AD) and related dementias (ADRD) persist across race/ethnicity, sex, and US geographic regions, but limited quantitative information exists to explain how specific predictors contribute to these disparities. Many traditional methods lack precision in addressing both exposure (higher prevalence of a predictor) and vulnerability (higher risk associated with a predictor) effects. This study introduces an approach that leverages population attributable fraction (PAF) to analyze and explain AD/ADRD disparities using Medicare data.We applied our method to Medicare claims data from a nationally representative sample of the US adults aged 70, 75, 80, and 85. The analysis focused on six types of disparities: Black-White, Hispanic-White, Native American-White, Asian-White, female-male, and stroke-belt versus non-stroke-belt states. Predictors included Medicare/Medicaid dual eligibility as an indicator of low income and 10 AD/ADRD-related diseases. The method quantified the exposure and vulnerability effects of each predictor on the observed disparities.Low income and vulnerability to arterial hypertension were the primary contributors to AD/ADRD disparities, with cerebrovascular diseases and depression as notable secondary predictors. The exposure effect dominated for income-related disparities, while hypertension's effect was largely driven by increased vulnerability. Racial disparities (Black-White, Hispanic-White) were most affected by income and hypertension, while female-male and stroke-belt disparities were less influenced by the examined predictors.Our findings indicate that different intervention strategies are needed to address AD/ADRD disparities. Income-related disparities require targeting exposure (e.g., socioeconomic improvements), while hypertension-related disparities suggest a focus on managing vulnerability (e.g., better control of hypertension). The developed approach offers a robust framework for explaining disparities and designing targeted interventions. Further application to other datasets and exploration of additional predictors could enhance understanding and lead to more effective prevention strategies for AD/ADRD disparities.Our new approach addresses disparities leveraging the concept of population attributable fraction for Cox models.Exposure and vulnerability mechanisms of health disparity generation are evaluated.Vulnerability to hypertension is a consistent dominant factor in Alzheimer's disease (AD) risk disparities.Predictors explain AD disparities better in Black and Hispanic populations.Disparities in AD are driven by exposure to socioeconomic status suggesting targeted interventions.

Duke Scholars

Author Arseniy Yashkin Social Science Research Institute