Association of chronic pain with comorbidities and health care utilization: a retrospective cohort study using health administrative data
Bibliographic record
Abstract
ABSTRACT: Health administrative data provide a potentially robust information source regarding the substantial burden chronic pain exerts on individuals and the health care system. This study aimed to use health administrative data to estimate comorbidity prevalence and annual health care utilization associated with chronic pain in Newfoundland and Labrador, Canada. Applying the validated Chronic Pain Algorithm to provincial Fee-for-Service Physician Claims File data (1999-2009) established the Chronic Pain (n = 184,580) and No Chronic Pain (n = 320,113) comparator groups. Applying the Canadian Chronic Disease Surveillance System coding algorithms to Claims File and Provincial Discharge Abstract Data (1999-2009) determined the prevalence of 16 comorbidities. The 2009/2010 risk and person-year rate of physician and diagnostic imaging visits and hospital admissions were calculated and adjusted using the robust Poisson model with log link function (risks) and negative binomial model (rates). Results indicated a significantly higher prevalence of all comorbidities and up to 4 times the odds of multimorbidity in the Chronic Pain Group (P-value < 0.001). Chronic Pain Group members accounted for 58.8% of all physician visits, 57.6% of all diagnostic imaging visits, and 54.2% of all hospital admissions in 2009/2010, but only 12% to 16% of these were for pain-related conditions as per recorded diagnostic codes. The Chronic Pain Group had significantly higher rates of physician visits and high-cost hospital admission/diagnostic imaging visits (P-value < 0.001) when adjusted for demographics and comorbidities. Observations made using this methodology supported that people identified as having chronic pain have higher prevalence of comorbidities and use significantly more publicly funded health services.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".