Are We Adding Pain-Free Years to Life? A Test of Compression Versus Expansion of Morbidity
Bibliographic record
Abstract
BACKGROUND: There has been debate regarding whether increases in longevity result in longer and healthier lives or more disease and suffering. To address the issue, this study uses health expectancy methods and tests an expansion versus compression of morbidity with respect to pain. METHODS: Data are from 1993 to 2018 Health and Retirement Study. Pain is categorized as no pain, nonlimiting, and limiting pain. Multistate life tables examine 77 996 wave-to-wave transitions across pain states or death using the Stochastic Population Analysis for Complex Events program. Results are presented as expected absolute and relative years of life for 70-, 80-, and 90-year-old men and women. Confidence intervals assess significance of differences over time. Population- and status-based results are presented. RESULTS: For those 70 and 80 years old, relative and absolute life with nonlimiting and limiting pain increased substantially for men and women, and despite variability on a wave-to-wave basis, results generally confirm an expanding pain morbidity trend. Results do not vary by baseline status, indicating those already in pain are just as likely to experience expansion of morbidity as those pain-free at baseline. Results are different for 90-year-olds who have not experienced expanding pain morbidity and do not show an increase in life expectancy. CONCLUSIONS: Findings are consistent with extant literature indicating increasing pain prevalence among older Americans and portend a need for attention to pain-coping resources, therapies, and prevention strategies.
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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 | 0.019 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".