Economic burden of chronic pain in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Although chronic pain (CP) is common, little is known about its economic burden in Alberta, Canada. AIMS: To estimate incremental (as compared to the general population or people without CP) societal (healthcare and lost productivity) costs of CP in Alberta. METHODS: We applied the prevalence estimated from the Canadian Community Health Survey data to the population retrieved from the Statistics Canada to estimate the number of people with CP in Alberta in 2019. We analyzed the Alberta Health administrative databases to estimate the healthcare costs of person with CP. Finally, we multiplied the number of people with the cost per person. RESULTS: The prevalence of any CP was 20.1% and of activity-preventing CP was 14.5% among people aged > = 12 years. Incremental cost per person with CP per year was CA$2,217 for healthcare services (among people aged > = 12 years) and CA$8,412 for productivity losses (among people aged 18-64 years). Of the healthcare cost, prescription drugs accounted for the largest share (32.8%), followed by inpatient services (31.0%), outpatient services (13.1%), physician services (9.8%), other services (7.4%), and diagnostic imaging (5.8%). Provincially, total incremental cost of CP ranges from CA$1.2 to 1.7 billion for healthcare services (6% to 8% of total provincial health expenditure); and CA$3.4 to 4.7 billion for productivity losses. Considering costs for long-term care services, the total societal cost of CP in Alberta was CA$6.3 to 8.3 billion per year, reflecting 2.0% to 2.7% of Alberta's GDP. CONCLUSIONS: Interventions improving CP prevention and management to reduce this substantial economic burden are urgently needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".