Rural-urban residence and life expectancies with and without pain
Bibliographic record
Abstract
This paper analyzes rural-urban disparities in life expectancy with and without pain among upper-middle age and older adults. Data are from the nationally representative Health and Retirement Study, 2000–2018, N = 18,160, age 53+. Interpolated Markov Chain software, based on the multistate life tables, is used to calculate absolute and relative pain expectancies by age, sex, rural-suburban-urban residence and U.S. regions. Results show significant rural disadvantages versus those in urban and often suburban areas. Example: males at 55 in rural areas can expect to live 15.1 years, or 65.2 percent pain-free life, while those in suburban areas expect to live 1.7 more years, or 2.6 percentage points more, pain-free life and urban residents expect to live 2.4 more year, or 4.7 percentage points more. The rural disadvantage persists for females, with differences being a little less prominent. At very old age (85+), rural-urban differences diminish or reverse. Rural-urban pain disparities are most pronounced in the Northeast and South regions, and least in the Midwest and West. The findings highlight that rural-urban is an important dimension shaping the geography of pain. More research is needed to disentangle the mechanisms through which residential environments impact people's pain experiences. • This study is among very few examining rural-urban disparities in pain outcomes. • Those in rural areas expect fewer pain-free years and a lower percentage of pain-free life compared to others. • Rural-urban pain disparities are most pronounced in the Northeast and South regions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".