@article {12060, title = {Validation of Claims Algorithms to Identify Alzheimer{\textquoteright}s Disease and Related Dementias.}, journal = {The Journals of Gerontology, Series A }, volume = {77}, year = {2022}, pages = {1261-1271}, abstract = {

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.

}, keywords = {Accuracy, algorithm, Dementia, Diagnosis, Medicare}, issn = {1758-535X}, doi = {10.1093/gerona/glab373}, author = {Ellen P McCarthy and Chang, Chiang-Hua and Tilton, Nicholas and Mohammed U Kabeto and Kenneth M. Langa and Julie P W Bynum} }