COVID-19 and Subjective Cognitive Decline-Related Surveillance Indicators in U.S. Older Adults: A Time-Aware Machine Learning Benchmark Using the CDC Alzheimer’s Disease and Healthy Aging Data Portal

The COVID-19 pandemic disrupted health and social care systems and may have altered population-level patterns in cognitive health among older adults. Using aggregated U.S. surveillance data from the CDC Alzheimer’s Disease and Healthy Aging Data Portal, we examined four subjective cognitive decline (SCD) and memory-loss indicators across pre-pandemic (2015–2019), COVID-19 (2020–2021), and post-period (2022) windows. From 284,142 surveillance records, 11,444 annual aggregated estimates corresponding to the four prespecified indicators were retained for the final analysis. Period differences were assessed using Welch’s t-test and the Mann–Whitney U test, complemented by profile-matched analyses, effect sizes, and multiplicity adjustment. Five regression and machine-learning approaches were benchmarked using conventional random-split and time-aware validation. Period differences were generally small, with negligible standardized effect sizes. Q30 showed the clearest temporal variation, including a decrease in 2020 that should not be interpreted as improved cognitive health because the estimates may also reflect reporting, participation, and compositional changes. Temporal validation revealed heterogeneous and generally reduced out-of-period generalization, with model rankings varying across indicators and evaluation periods. Performance deterioration is interpreted as temporal instability, not as confirmatory evidence of a pandemic effect. These findings highlight the importance of time-aware validation and periodic model reassessment when machine-learning methods are applied to aggregated public-health surveillance data.