Implications of the Use of Algorithmic Diagnoses or Medicare Claims to Ascertain Dementia

Abstract

Introduction: Formal dementia ascertainment with research criteria is resource-intensive, prompting the growing use of alternative approaches. Our objective was to illustrate the potential bias and implications for study conclusions introduced through the use of alternate dementia ascertainment approaches.

Methods: We compared dementia prevalence and risk factor associations obtained using criterion-standard dementia diagnoses to those obtained using algorithmic or Medicare-based dementia ascertainment in participants of the baseline visit of the Aging, Demographics, and Memory Study (ADAMS), a Health and Retirement Study (HRS) sub-study.

Results: Estimates of dementia prevalence derived using algorithmic or Medicare-based ascertainment differ substantially from those obtained using criterion-standard ascertainment. Use of algorithmic or Medicare-based dementia ascertainment can, but does not always, lead to risk factor associations that substantially differ from those obtained using criterion-standard ascertainment.

Discussion/conclusions: Absolute estimates of dementia prevalence should rely on samples with formal dementia ascertainment. The use of multiple algorithms is recommended for risk factor studies when formal dementia ascertainment is not available.

Keywords: Algorithms; Dementia; Diagnosis; Medicare; Sensitivity and specificity.