Effects of Back Pain and Mental Health Conditions on Health Care Utilization and Costs: A Population-based Perspective using a Novel Data Platform in Back Pain Research
Bibliographic record
Abstract
Back pain is the leading cause of disability globally, and a driver of health care utilization across health systems. Back pain is a complex condition with multiple contributors to disability, including biopsychosocial factors and comorbidities. Notably, this includes mental health conditions as common comorbidities that can negatively impact back pain outcomes. Given the growing burden of back pain and its relationship with mental health, this dissertation aims to comprehensively examine the effects of back pain and mental health conditions at the population level. The first objective assessed the effects of back pain on health care utilization and costs among Ontario adults in a single-payer health system. In this population-based cohort study, adults with back pain had substantially higher rates of health care utilization and costs than those without back pain. Incremental costs corresponded to an annual burden of $759 million in Ontario. The second objective assessed the association between depressive symptoms/depression and outcomes in persons with back pain. This systematic review and meta-analysis found that depressive symptoms may be associated with disability and worse recovery for acute and chronic back pain, and greater primary health care utilization for acute back pain. The third objective assessed the joint effects of back pain and mental health conditions on health care utilization and costs among Ontario adults in a single-payer health system. In this population-based cohort study, the joint effects of back pain and mental health conditions on back pain-specific utilization and opioid prescription were greater than expected, with evidence of synergism. Study findings quantify the substantial burden of back pain on the Ontario health system, and highlight adults with back pain and mental health conditions as a priority group with worse outcomes and greater health care needs. This dissertation offers unique contributions to musculoskeletal epidemiologic research through several methodological approaches, including novel linkages between survey and administrative data for Ontario, propensity-score matching, prognostic review methodology, and analyses of joint effects. Overall, this dissertation provides the evidentiary basis to inform health programs and resources planning tailored to back pain and mental health conditions, to improve population health and health care sustainability in Ontario.
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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 | 0.027 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".