The Epidemiology of Chronic Pain in Canadian Men and Women between 1994 and 2007: Results from the Longitudinal Component of the National Population Health Survey
Bibliographic record
Abstract
BACKGROUND: The epidemiology of chronic pain is poorly understood due to a paucity of longitudinal studies limiting the ability to develop prevention strategies for a condition resistant to many current therapies. OBJECTIVES: To identify the incidence of and sociodemographic risk factors for chronic pain in Canadian women and men over a 12-year period. METHODS: Using data from the National Population Health Survey, individuals who developed chronic pain, defined as the presence of "usual pain" were identified. The cumulative incidence of chronic pain was calculated separately for men and women followed from 1994 to 2007. Biannual incidence and prevalence estimates of chronic pain were calculated during the same time period. Logistic regression analysis was used to examine predictors of chronic pain in men and women. RESULTS: The cumulative incidence over the 12-year period was 35.6% (women 39.0%; men 32.2%). Women had a higher biannual prevalence, but not incidence, of chronic pain compared with men. In women, being older, having lower education and being widowed, separated or divorced, increased the risk of chronic pain. There were no sociodemographic risk factors for chronic pain in men. CONCLUSION: Women had a higher prevalence - but not incidence - of chronic pain compared with men, indicative of longer duration of illness in women. Risk factors also differed according to sex, supporting current literature reporting potentially different mechanisms for men and women. A better understanding of risk factors is necessary to develop population-based preventive interventions. The former can only be achieved with population-based, longitudinal studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".