Canadian Veteran chronic disease prevalence and health services use in the five years following release: a matched retrospective cohort study using routinely collected data
Bibliographic record
Abstract
BACKGROUND: Occupational exposures may result in Canadian military Veterans having poorer health and higher use of health services after transitioning to civilian life compared to the general population. However, few studies have documented the physical health and health services use of Veterans in Canada, and thus there is limited evidence to inform public health policy and resource allocation. METHODS: In a retrospective, matched cohort of Veterans and the Ontario general population between 1990-2019, we used routinely collected provincial administrative health data to examine chronic disease prevalence and health service use. Veterans were defined as former members of the Canadian Armed Forces or RCMP. Crude and adjusted effect estimates, and 95% confidence limits were calculated using logistic regression (asthma, COPD, diabetes, myocardial infarction, rheumatoid arthritis, family physician, specialist, emergency department, and home care visits, as well as hospitalizations). Modified Poisson was used to estimate relative differences in the prevalence of hypertension. Poisson regression compares rates of health services use between the two groups. RESULTS: The study included 30,576 Veterans and 122,293 matched civilians. In the first five years after transition to civilian life, Veterans were less likely than the general population to experience asthma (RR 0.50, 95% CI 0.48-0.53), COPD (RR 0.32, 95% CI 0.29-0.36), hypertension (RR 0.74, 95% CI 0.71-0.76), diabetes (RR 0.71, 95% CI 0.67-0.76), myocardial infarction (RR 0.76, 95% CI 0.63-0.92), and rheumatoid arthritis (RR 0.74, 95% CI 0.60-0.92). Compared to the general population, Veterans had greater odds of visiting a primary care physician (OR 1.76, 95% CI 1.70-1.83) or specialist physician (OR 1.39, 95% CI 1.35-1.42) at least once in the five-year period and lower odds of visiting the emergency department (OR 0.95, 95% CI 0.92-0.97). Risks of hospitalization and of receiving home care services were similar in both groups. CONCLUSIONS: Despite a lower burden of comorbidities, Veterans had slightly higher physician visit rates. While these visits may reflect an underlying need for services, our findings suggest that Canadian Veterans have good access to primary and specialty health care. But in light of contradictory findings in other jurisdictions, the underlying reasons for our findings warrant further study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".