Unadjusted Prevalence Rates: Why they Still Matter for Older Adults' Disability Rates
Bibliographic record
Abstract
We thank Martin and colleagues for their thoughtful and important comments. We very much appreciate their supplementary age-standardized analysis of the public use American Community Survey (ACS) data. Their findings give support to our suggestion (1) that the rise in prevalence of activities of daily living (ADL) disabilities between 2000 and 2005 among those aged 65 and older was largely driven by the disproportionate growth in the oldest age category. Their findings also have important ramifications in the projection of the burden of disabilities in the American population aged 65 and older. Projections suggest that the percentage of senior citizens who are older than age 85 will increase from 12.2% in the year 2000 to 24.1% in the year 2050 (2). We deliberately chose to report the crude (unadjusted) trend in prevalence for those aged 65 and older rather than age-adjusted figures for two reasons. Most importantly, the government is currently mandated to provide assistance in many programs (eg, Medicare) to those aged 65 and older. Therefore, it is the unadjusted prevalence rate of disability that is a measure of the burden of care and is of immediate salience to policy makers. Secondly, our report is in keeping with a National Institute of Aging funded technical working group that chose to compare unadjusted estimates of disability across five national data sets (3). This group justified its decision to use crude rates because “conclusions about standardized rates are sensitive to the choice of age distribution” and “stratification by age, gender and other subgroups of interest leads to an unmanageable number of comparisons.” (3, p. 421).
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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 | 0.151 | 0.631 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".