Health service use in major trauma survivors: A population-based cohort study from Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Little is known about how major trauma survivors access health services in the years following their injury. Our study sought to characterize patterns of health services use in trauma survivors following discharge from a provincial trauma center and to identify sociodemographic factors associated with service utilization. METHODS: We conducted a population-based retrospective case-control study using linked administrative data on trauma survivors' population-based controls between April 1, 2011, and March 31, 2021. For each major trauma survivor, we matched four cases based on age and sex. The primary outcome was the composite rate (sum) of health service use episodes including outpatient visits to family physicians and specialists, emergency department visits, and acute care hospital admissions during the 5-year period following discharge from the trauma center. We used multivariate regression to compute rate ratios comparing the rates of health service use in trauma survivors versus controls and to assess for associations between sociodemographic variables and health services use. RESULTS: The study cohort consisted of a total of 273,406 individuals: 55,060 trauma survivors and 218,346 controls. Trauma survivors were predominately males (71%) with a median age of 46 years (interquartile range, 26-65 years). Health service use in trauma survivors peaked within a year of hospital discharge but remained increased throughout the follow-up period. Trauma survivorship was associated with a 56% increase in overall health services use (adjusted rate ratio, 1.56; 95% confidence interval, 1.55-1.57), including an 88% increase in hospital admissions (adjusted rate ratio, 1.88; 95% confidence interval, 1.85-1.92). Male sex and rural residence were associated with a reduced overall use of health services but greater use of emergency department services. CONCLUSION: Major trauma survivors have long-term health services needs that persist for years after discharge from the trauma center. Future research should focus on the understanding why trauma survivors have prolonged health services requirements and ensure that care needs are aligned with service delivery. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level IV.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".