Joint effects of back pain and mental health conditions on healthcare utilization and costs in Ontario, Canada: a population-based cohort study
Bibliographic record
Abstract
ABSTRACT: We assessed the joint effects of back pain and mental health conditions on healthcare utilization and costs in a population-based sample of adults in Ontario. We included Ontario adult respondents of the Canadian Community Health Survey between 2003 and 2012, followed up to 2018 by linking survey data to administrative databases. Joint exposures were self-reported back pain and mental health conditions (fair/poor mental health, mood, and anxiety disorder). We built negative binomial, modified Poisson and linear (log-transformed) models to assess joint effects (effects of 2 exposures in combination) of comorbid back pain and mental health condition on healthcare utilization, opioid prescription, and costs. The models were adjusted for sociodemographic, health-related, and behavioural factors. We evaluated positive additive and multiplicative interaction (synergism) between back pain and mental health conditions with relative excess risk due to interaction (RERI) and ratio of rate ratios (RRs). The cohort (n = 147,486) had a mean age of 46 years (SD = 17), and 51% were female. We found positive additive and multiplicative interaction between back pain and fair/poor mental health (RERI = 0.40; ratio of RR = 1.12) and mood disorder (RERI = 0.41; ratio of RR = 1.04) but not anxiety for back pain-specific utilization. For opioid prescription, we found positive additive and multiplicative interaction between back pain and fair/poor mental health (RERI = 2.71; ratio of risk ratio = 3.20) and anxiety (RERI = 1.60; ratio of risk ratio = 1.80) and positive additive interaction with mood disorder (RERI = 0.74). There was no evidence of synergism for all-cause utilization or costs. Combined effects of back pain and mental health conditions on back pain-specific utilization or opioid prescription were greater than expected, with evidence of synergism. Health services targeting back pain and mental health conditions together may provide greater improvements in outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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".