Validation of Claims Algorithms to Identify Alzheimer's Disease and Related Dementias.

BACKGROUND: Using billing data generated through healthcare delivery to identify individuals with dementia has become important in research. To inform tradeoffs between approaches, we tested the validity of different Medicare claims-based algorithms.

METHODS: We included 5,784 Medicare-enrolled, Health and Retirement Study participants aged >65 years in 2012 clinically assessed for cognitive status over multiple waves and determined performance characteristics of different claims-based algorithms.

RESULTS: Positive predictive value (PPV) of claims ranged from 53.8-70.3% and was highest using a revised algorithm and 1-year of observation. The trade-off of greater PPV was lower sensitivity; sensitivity could be maximized using 3-years of observation. All algorithms had low sensitivity (31.3-56.8%) and high specificity (92.3-98.0%). Algorithm test performance varied by participant characteristics, including age and race.

CONCLUSIONS: Revised algorithms for dementia diagnosis using Medicare administrative data have reasonable accuracy for research purposes, but investigators should be cognizant of the trade-offs in accuracy among the approaches they consider.