Pain and self-rated health among middle-aged and older Canadians: an analysis of the Canadian community health survey―healthy aging
Bibliographic record
Abstract
BACKGROUND: Pain is an important health problem adversely affecting functionality and quality of life. Though self- rated health (SRH) is a major predictor of mortality, its relationship with pain is not well understood. We explore 1) how pain and age interact to influence SRH, and 2) provincial variations in SRH across Canada. METHODS: We analyzed cross-sectional data from Statistics Canada's Canadian Community Health Survey-Healthy Aging (n = 30,685), which targeted those 45 years and older and was conducted from 2008 to 12-01 to 2009-11-30. The response rate was 74.4%.The topics covered included socio-demographics, well-being and chronic diseases. We performed both bivariate and multivariate analyses between each predictor and SRH; unadjusted and adjusted odds ratios and 95% confidence intervals are reported. Two-level logistic regression mixed model was used to account for provincial differences. An intraclass correlation coefficient was also computed. RESULTS: Slightly more than half of respondents (56.40%) were female. In bivariate analyses, those experiencing pain had an odds ratio of 0.20. Which means that the odds of reporting good self-rated health are 4 to 5 times lower for those with pain, compare to the odds of reporting good self-rated health among those without pain (p < 0.001). In multivariate analyses the highly educated, female gender, the never married or single and households with high yearly income were predictors of good health (p < 0.001). Those who reported depressive symptoms, the lonely, the obese, daily smokers and/or the stressed were less likely to rate their health as good (p < 0.001). The influence of pain on SRH was stronger among younger age groups (45-54 years) compared to older age groups (75-84 years, with an odds ratio of 3.53 [p < 0.001] versus 3.14 [p < 0.001]). CONCLUSIONS: Pain, among other determinants, is associated with SRH. Individuals in rating their health may consider a variety of factors, some of which may not be apparent to health providers. We found that those who reported depressive symptoms, were daily smokers, the obese, the lonely, and/or having a stressful life were less likely to rate their health as good. No significant provincial variations in SRH in Canada was observed in this study.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".