A Decade of Decline in Serious Cognitive Problems Among Older Americans: A Population-Based Study of 5.4 Million Respondents
Bibliographic record
Abstract
BACKGROUND: Numerous studies suggest the prevalence of dementia has decreased over the past several decades in Western countries. Less is known about whether these trends differ by gender or age cohort, and if generational differences in educational attainment explain these trajectories. OBJECTIVE: 1) To detect temporal trends in the age-sex-race adjusted prevalence of serious cognitive problems among Americans aged 65+; 2) To establish if these temporal trends differ by gender and age cohort; 3) To examine if these temporal trends are attenuated by generational differences in educational attainment. METHODS: Secondary analysis of 10 years of annual nationally representative data from the American Community Survey with 5.4 million community-dwelling and institutionalized older adults aged 65+. The question on serious cognitive problems was, "Because of a physical, mental, or emotional condition, does this person have serious difficulty concentrating, remembering, or making decisions?" RESULTS: The prevalence of serious cognitive problems in the US population aged 65 and older declined from 12.2% to 10.0% between 2008 and 2017. Had the prevalence remained at the 2008 levels, there would have been an additional 1.13 million older Americans with serious cognitive problems in 2017. The decline in memory problems across the decade was higher for women (23%) than for men (13%). Adjusting for education substantially attenuated the decline. CONCLUSION: Between 2008 and 2017, the prevalence of serious cognitive impairment among older Americans declined significantly, although these declines were partially attributable to generational differences in educational attainment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".