Effects of chiropractic use on medical healthcare utilization and costs in adults with back pain in Ontario, Canada from 2003 to 2018: a population-based cohort study
Bibliographic record
Abstract
Abstract Background Adults with back pain commonly consult chiropractors, but the impact of chiropractic use on medical utilization and costs within the Canadian health system is unclear. We assessed the association between chiropractic utilization and subsequent medical healthcare utilization and costs in a population-based cohort of Ontario adults with back pain. Methods We conducted a population-based cohort study that included Ontario adult respondents of the Canadian Community Health Survey (CCHS) with back pain from 2003 to 2010 (n = 29,475), followed up to 2018. The CCHS data were individually-linked to individual-level health administrative data up to 2018. Chiropractic utilization was self-reported consultation with a chiropractor in the past 12 months. We propensity score-matched adults with and without chiropractic utilization, accounting for confounders. We evaluated back pain-specific and all-cause medical utilization and costs at 1- and 5-year follow-up using negative binomial and linear (log-transformed) regression, respectively. We assessed whether sex and prior specialist consultation in the past 12 months were effect modifiers of the association. Results There were 6972 matched pairs of CCHS respondents with and without chiropractic utilization. Women with chiropractic utilization had 0.8 times lower rate of cause-specific medical visits at follow-up than those without chiropractic utilization (RR5years = 0.82, 95% CI 0.68-1.00); this association was not found in men (RR5years = 0.96, 95% CI 0.73–1.24). There were no associations between chiropractic utilization and all-cause physician visits, all-cause emergency department visits, all-cause hospitalizations, or costs. Effect modification of the association between chiropractic utilization and cause-specific utilization by prior specialist consultation was found at 1-year but not 5-year follow-up; cause-specific utilization at 1 year was lower in adults without prior specialist consultation only (RR1year = 0.74, 95% CI 0.57–0.97). Conclusions Among adults with back pain, chiropractic use is associated with lower rates of back pain-specific utilization in women but not men over a 5-year follow-up period. Findings have implications for guiding allied healthcare delivery in the Ontario health system.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".