Assessing the 10/66 dementia classification algorithm for international comparative analyses with the United States

Cross-national comparisons of dementia prevalence are essential for identifying unique determinants and cultural-specific risk factors, but methodological differences in dementia classification across countries hinder global comparisons. This study maps the 10/66 algorithm for dementia classification, widely used and validated in low- and middle-income countries (LMICs), to the US Aging, Demographics, and Memory Study (ADAMS), the dementia sub-study of the Health and Retirement Study, and assesses its performance in ADAMS. We identified the subset of 10/66 algorithm items comparably measured in ADAMS, then used these items to retrain the 10/66 algorithm against the ADAMS clinical dementia diagnosis, using k-fold cross-validation to assess performance. We compared the modified 10/66 algorithm to 4 other dementia classification algorithms previously validated in ADAMS, both for overall dementia estimation as well as for estimating education gradients. The modified 10/66 algorithm had higher sensitivity (87%) and specificity (93%) than the comparison algorithms. All the algorithms overestimated the education gradient in dementia, although the modest ADAMS sample size precludes precise comparisons of education gradient accuracy. Overall, we found that the modified 10/66 algorithm performs well in classifying dementia status in the United States. Our results support the validity of risk factor comparisons between US and 10/66 LMIC dementia data sets. This article is part of a Special Collection on Cross-National Gerontology.