Health Heterogeneity in Older Adults: Exploration in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: A widely held dictum in aging research is that heterogeneity in health increases with age, but the basis for this claim has not been fully investigated. We examined heterogeneity at different ages across health characteristics to describe variation and trends; we investigated the comparative importance of between-age versus within-age heterogeneity. DESIGN: This was a cohort study. SETTING: Community-dwelling older adults. PARTICIPANTS: A total of 30,097 adults aged 45 to 86 years, from the Canadian Longitudinal Study on Aging, were included. MEASUREMENTS: Thirty-four health characteristics in eight domains (physical measures, vital signs, physiological measures, physical performance, function/disability, chronic conditions, frailty, laboratory values) were assessed cross-sectionally. We used regression models to examine heterogeneity in health characteristics (using absolute deviation) and domains (using effective variance) in relation to age. Comparison between between-age and within-age heterogeneity was quantified by estimating the age threshold at which the former exceeds the latter. RESULTS: Of the 34 health characteristics, 17 showed increased heterogeneity, 8 decreased, and 9 no association with age. The associations between heterogeneity and age increased generally but were nonlinear for most domains and nonmonotonic for some. We observed peak heterogeneity at approximately 70 years. Between-age heterogeneity, compared with within-age heterogeneity, was most important for forced expiratory volume in 1 second and grip strength but varied across characteristics. CONCLUSION: Overall health heterogeneity increases with age but does not uniformly increase across all variables and domains. Heterogeneity in aging reinforces the need for geriatric assessment and personalized care, depending on which health characteristics are assessed, their measurement properties, and their referent group. Our findings suggest further research to develop improved single-dimension and multidimensional instruments, as well as specific vital and laboratory reference ranges for older adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".