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

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.

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 (N = 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).

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 (p  <  0.001). Air pollution exposure, obesity, and interaction terms did not enhance prediction.

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