Characterizing high-cost healthcare users among adults with back pain in Ontario, Canada: a population-based cohort study
Bibliographic record
Abstract
ABSTRACT: Some patients with back pain contribute disproportionately to high healthcare costs; however, characteristics of high-cost users with back pain are not well defined. We described high-cost healthcare users based on total costs among a population-based cohort of adults with back pain within the Ontario government's single-payer health system across sociodemographic, health, and behavioural characteristics. We conducted a population-based cohort study of Ontario adult (aged 18 years or older) respondents of the Canadian Community Health Survey (CCHS) with back pain (2003-2012), linked to administrative data (n = 36,605; weighted n = 2,076,937, representative of Ontario). Respondents were ranked based on gradients of total healthcare costs (top 1%, top 2%-5%, top 6%-50%, and bottom 50%) for 1 year following the CCHS survey, with high-cost users as top 5%. We used multinomial logistic regression to investigate characteristics associated with the 4 cost groups. Top 5% of cost users accounted for 49% ($4 billion CAD) of total healthcare spending, with inpatient hospital care as the largest contributing service type (approximately 40% of costs). Top 5% high-cost users were more likely aged 65 years or older (OR top1% = 16.6; OR top2-5% = 44.2), with lower income (OR top1% = 3.6; OR top 2-5% = 1.8), chronic disease(s) (OR top1% = 3.8; OR top2-5% = 1.6), Aggregated Diagnosis Groups measuring comorbidities (OR top1% = 25.4; OR top2-5% = 13.9), and fair/poor self-rated general health (OR top1% = 6.7; OR top2-5% = 4.6) compared with bottom 50% users. High-cost users tended to be current/former smokers, obese, and report fair/poor mental health. High-cost users (based on total costs) among adults with back pain account for nearly half of all healthcare spending over a 1-year period and are associated with older age, lower income, comorbidities, and fair/poor general health. Findings identify characteristics associated with a high-risk group for back pain to inform healthcare and public health strategies that target upstream determinants.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".