Effect of back problems on healthcare utilization and costs in Ontario, Canada: a population-based matched cohort study
Bibliographic record
Abstract
ABSTRACT: We assessed the effect of back problems on healthcare utilization and costs in a population-based sample of adults from a single-payer health system in Ontario. We conducted a population-based cohort study of Ontario respondents aged ≥18 years of the Canadian Community Health Survey (CCHS) from 2003 to 2012. The CCHS data were individually linked to health administrative data to measure healthcare utilization and costs up to 2018. We propensity score-matched (hard matched on sex) adults with self-reported back problems to those without back problems, accounting for sociodemographic, health-related, and behavioural factors. We evaluated cause-specific and all-cause healthcare utilization and costs adjusted to 2018 Canadian dollars using negative binomial and linear (log transformed) regression models. After propensity score matching, we identified 36,806 pairs (women: 21,054 pairs; men: 15,752 pairs) of CCHS respondents with and without back problems (mean age 51 years, standard deviation = 18). Compared with propensity score matched adults without back problems, adults with back problems had 2 times the rate of cause-specific visits (rate ratio [RR]women 2.06, 95% confidence interval [CI] 1.88-2.25; RRmen 2.32, 95% CI 2.04-2.64), slightly more all-cause physician visits (RRwomen 1.12, 95% CI 1.09-1.16; RRmen 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 problems than those without back problems (women: $395, 95% CI $281-$509; men: $196, 95% CI $94-$300). This corresponded to $532 million for women and $227 million for men (adjusted to 2018 Canadian dollars) annually in Ontario given the high prevalence of back problems. Given the high health system burden, new strategies to effectively prevent and treat back problems and thus potentially reduce the long-term costs are warranted.
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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 | 0.003 | 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".