Identification of cognitive impairment using the Lancet Commission's risk factors and Medicare administrative data.

INTRODUCTION: Prediction models based on administrative data may present a scalable opportunity to identify risk of cognitive impairment, but their accuracy relative to models using richer information is uncertain.

METHODS: We developed and validated models to identify the likelihood of mild cognitive impairment (MCI) and dementia using the Health and Retirement Study linked to Medicare data from 2000 to 2016 ( = 63,740). Predictors covered most risk factors identified by the 2024 Lancet Commission. Model performance was assessed using multiple metrics, including the area under the receiver operating characteristic curve (AUC).

RESULTS: Probit models with demographics and chronic conditions yielded high AUCs of 71.3% (MCI) and 82.1% (dementia). Adding individual level education provided the largest improvement in AUCs, whereas dual eligibility status offered smaller gains (  <  0.001). Air pollution exposure, obesity, and interaction terms did not enhance prediction.

DISCUSSION: Predictors in administrative data can be used to generate reasonably accurate, well calibrated models predicting likelihood of cognitive impairment.