The Effect of Back Pain on Health Care Utilization and Costs
Bibliographic record
Abstract
Introduction: We assessed the effect of self-reported back pain on health care utilization and costs in a population-based sample of Ontario adults. Methods: We conducted a population-based matched cohort study of Ontarian respondents aged ≥18 years of Canadian Community Health Survey (CCHS) from 2003-2012. CCHS data were individually linked to health administrative data to measure health care utilization and costs up to 2018. We propensity-score matched (hard-matched on sex) adults with self-reported back pain to those without back pain, accounting for sociodemographic, health-related, and behavioural factors. We evaluated back pain-specific and all-cause health care utilization and costs from healthcare payer perspective adjusted to 2018 Canadian dollars. Poisson and linear (log-transformed) models were used to assess healthcare utilization rates and costs. Results: After propensity-score matching, we identified 36,806 pairs (21,054 for women, 15,752 for men) of CCHS respondents with and without back pain (mean age 51 years; SD=18). Compared to propensity-score matched adults without back pain, adults with back pain had two times the rate of back pain-specific visits (women: rate ratio [RR] 2.06, 95% CI 1.88-2.25; men: RR 2.32, 95% CI 2.04-2.64), 1.1 times the rate of all-cause physician visits (women: RR 1.12, 95% CI 1.09-1.16; men: RR 1.10, 95% CI 1.05-1.14), and 1.2 times the costs (women: 1.21, 95% CI 1.16-1.27; men: 1.16, 95% CI 1.09-1.23). Incremental annual per-person costs were higher in adults with back pain versus those without (women: $395, 95% CI $281-$509; men: $196, 95% CI $94-$300), corresponding to $532 million for women and $227 million CAD for men annually in Ontario. Conclusions: Adults with back pain had considerably higher health care utilization and costs compared to adults without back pain. These findings provide the most recent, comprehensive, and high-quality estimates of the health system burden of back pain to inform healthcare policy and decision-making. New strategies to reduce the substantial burden of back pain are warranted.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".