Reducing case ascertainment costs in U.S. population studies of Alzheimer's disease, dementia, and cognitive impairment-Part 1

Abstract

Establishing methods for ascertainment of dementia and cognitive impairment that are accurate and also cost-effective is a challenging enterprise. Large population-based studies often using administrative data sets offer relatively inexpensive and reliable estimates of severe conditions including moderate to advanced dementia that are useful for public health planning, but they can miss less severe cognitive impairment which may be the most effective point for intervention. Clinical and epidemiological cohorts, intensively assessed, provide more sensitive detection of less severe cognitive impairment but are often costly. In this article, several approaches to ascertainment are evaluated for validity, reliability, and cost. In particular, the methods of ascertainment from the Health and Retirement Study are described briefly, along with those of the Aging, Demographics, and Memory Study (ADAMS). ADAMS, a resource-intense sub-study of the Health and Retirement Study, was designed to provide diagnostic accuracy among persons with more advanced dementia. A proposal to streamline future ADAMS assessments is offered. Also considered are algorithmic and Web-based approaches to diagnosis that can reduce the expense of clinical expertise and, in some contexts, can reduce the extent of data collection. These approaches are intended for intensively assessed epidemiological cohorts where goal is valid and reliable case detection with efficient and cost-effective tools.