Educational Differences in Life Expectancies With and Without Pain
Bibliographic record
Abstract
OBJECTIVES: This study computes years and proportion of life that older adults living in the United States can expect to live pain-free and in different pain states, by age, sex, and level of education. The analysis addresses challenges related to dynamics and mortality selection when studying associations between education and pain in older populations. METHODS: Data are from National Health and Aging Trends Study, 2011-2020. The sample contains 10,180 respondents who are age 65 and older. Pain expectancy estimates are computed using the Interpolated Markov Chain software that applies probability transitions to multistate life tables. RESULTS: Those with higher educational levels expect not only a longer life but also a higher proportion of life without pain. For example, a 65-year-old female with less than high school education expects 18.1 years in total and 5.8 years, or 32% of life, without pain compared with 23.7 years in total with 10.7 years, or 45% of life without pain if she completed college. The education gradient in pain expectancies is more salient for females than males and narrows at the oldest ages. There is no educational disparity in the percent of life with nonlimiting pain. DISCUSSION: Education promotes longer life and more pain-free years, but the specific degree of improvement by education varies across demographic groups. More research is needed to explain associations between education and more and less severe and limiting aspects of pain.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".