The data consists of tables extracted from the National Health and Aging Trends Study (NHATS). The data contains residential history, environmental exposures, disease diagnoses, demographics, and income. This dataset is not publicly accessible because: This data is not owned by the EPA and cannot be posted publicly as it contains PII. It can be accessed through the following means: The data can be accessed by contacting the corresponding author for the study (Dr. Aisha Dickerson). Format: The data consists of tables extracted from the National Health and Aging Trends Study (NHATS). The data contains residential history, environmental exposures, disease diagnoses, demographics, and income.

IntroductionThe Clock-Drawing Test (CDT) is a simple cognitive tool to examine multiple domains of cognition including executive function. We aimed to build a CDT-based deep neural network (DNN) model using data from a large cohort of older adults, to automatically detect cognitive decline, and explore its potential as a mass screening tool.MethodsOver 40,000 CDT images were obtained from the National Health and Aging Trends Study (NHATS) database, which collects the annual surveys of nationally representative community-dwelling older adults in the United States. A convolutional neural network was utilized in deep learning architecture to predict the cognitive status of participants based on drawn clock images.ResultsThe trained DNN model achieved balanced accuracy of 90.1 ± 0.6% in identifying those with a decline in executive function compared to those without [positive likelihood ratio (PLH) = 16.3 ± 6.8, negative likelihood ratio (NLH) = 0.14 ± 0.03], and 77.2 ± 2.7 % balanced accuracy for identifying those with probable dementia from those without (PLH = 5.1 ± 0.5, NLH = 0.37 ± 0.07).ConclusionsThis study demonstrated the feasibility of implementing conventional CDT to be automatically evaluated by DNN with a fair performance in a larger scale than ever, suggesting its potential as a mass screening test for ruling-in or ruling-out those with executive dysfunction or with probable dementia.