Examining the effects of low back pain and mental health symptoms on healthcare utilisation and costs: a protocol for a population-based cohort study
Bibliographic record
Abstract
INTRODUCTION: Low back pain (LBP) is a leading cause of disability associated with high healthcare utilisation and costs. Mental health symptoms are negative prognostic factors for LBP recovery; however, no population-based studies have assessed the joint effects of LBP and mental health symptoms on healthcare utilisation. This proposed study will characterise the health system burden of LBP and help identify priority groups to inform resource allocation and public health strategies. Among community-dwelling adult respondents of five cycles of the Canadian Community Health Survey (CCHS) in Ontario, we aim to assess the effect of self-reported LBP on healthcare utilisation and costs and assess whether this effect differs between those with and without self-reported mental health symptoms. METHODS AND ANALYSIS: We designed a dynamic population-based cohort study using linkages of survey and administrative data housed at ICES. The Ontario sample of CCHS (2003-2004, 2005-2006, 2007/2008, 2009/2010, 2011/2012; total of ~1 30 000 eligible respondents) will be used to define the cohort of adults with self-reported LBP with and without mental health symptoms. Healthcare utilisation and costs will be assessed by linking health administrative databases. Follow-up ranges from 6 to 15 years (until 31 March 2018). Sociodemographic (eg, age, sex, education) and health behaviour (eg, comorbidities, physical activity) factors will be considered as potential confounders. Poisson and linear (log-transformed) regression models will be used to assess the association between LBP and healthcare utilisation and costs. We will assess effect modification with mental health symptoms on the additive and multiplicative scales and conduct sensitivity analyses to assess the impact of misclassification and residual confounding. ETHICS AND DISSEMINATION: This study is approved by the University of Toronto Research Ethics Board. We will disseminate findings using a multifaceted knowledge translation strategy, including scientific conference presentations, publications in peer-reviewed journals and workshops with key knowledge users.
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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 | 0.051 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
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".