Abstract
BackgroundData sparsity is a major limitation to estimating national and global dementia burden. Surveys with full diagnostic evaluations of dementia prevalence are prohibitively resource-intensive in many settings. However, validation samples from nationally representative surveys allow for the development of algorithms for the prediction of dementia prevalence nationally.MethodsUsing cognitive testing data and data on functional limitations from Wave A (2001–2003) of the ADAMS study (n = 744) and the 2000 wave of the HRS study (n = 6358) we estimated a two-dimensional item response theory model to calculate cognition and function scores for all individuals over 70. Based on diagnostic information from the formal clinical adjudication in ADAMS, we fit a logistic regression model for the classification of dementia status using cognition and function scores and applied this algorithm to the full HRS sample to calculate dementia prevalence by age and sex.ResultsOur algorithm had a cross-validated predictive accuracy of 88% (86–90), and an area under the curve of 0.97 (0.97–0.98) in ADAMS. Prevalence was higher in females than males and increased over age, with a prevalence of 4% (3–4) in individuals 70–79, 11% (9–12) in individuals 80–89 years old, and 28% (22–35) in those 90 and older.ConclusionsOur model had similar or better accuracy as compared to previously reviewed algorithms for the prediction of dementia prevalence in HRS, while utilizing more flexible methods. These methods could be more easily generalized and utilized to estimate dementia prevalence in other national surveys.
Highlights
Data sparsity is a major limitation to estimating national and global dementia burden
A number of algorithms have been developed for the estimation of dementia prevalence in Health and Retirement Survey (HRS) based on cut-points or regression-based methods using the ADAMS subsample and the questions on demographic information, cognitive status, and daily functional limitations that are included in both surveys [8,9,10,11,12]
ADAMS oversampled individuals with higher levels of cognitive impairment, and this is reflected in the lower scores on the Telephone Interview for Cognitive Status (TICS) cognitive assessment, higher mean number of Activities of daily living (ADL) limitations, and lower levels of education as compared to the HRS sample (Table 1)
Summary
Data sparsity is a major limitation to estimating national and global dementia burden. One of the major limitations in the estimation of dementia both nationally and globally is GBD 2019 Dementia Collaborators BMC Med Inform Decis Mak (2021) 21:241 the lack of large, nationally representative surveys with valid data on dementia prevalence using the Diagnostic and Statistical Manual (DSM) definition [2, 3]. Many large-scale surveys, such as the Health and Retirement Survey (HRS), a nationally representative sample of older adults in the United States, do not include dementia diagnoses. A number of algorithms have been developed for the estimation of dementia prevalence in HRS based on cut-points or regression-based methods using the ADAMS subsample and the questions on demographic information, cognitive status, and daily functional limitations that are included in both surveys [8,9,10,11,12]. This study aimed to improve on these methods by using multidimensional item response theory (IRT) methods to more flexibly characterize cognitive status and functional limitations, potentially facilitating the use of similar strategies in other samples
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