The health care utilization of people in prison and after prison release: A population-based cohort study in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Many people experience imprisonment each year, and this population bears a disproportionate burden of morbidity and mortality. States have an obligation to provide equitable health care in prison and to attend to care on release. Our objective was to describe health care utilization in prison and post-release for persons released from provincial prison in Ontario, Canada in 2010, and to compare health care utilization with the general population. METHODS: We conducted a population-based retrospective cohort study. We included all persons released from provincial prison to the community in 2010, and age- and sex-matched general population controls. We linked identities for persons released from prison to administrative health data. We matched each person by age and sex with four general population controls. We examined ambulatory care and emergency department utilization and medical-surgical and psychiatric hospitalization, both in prison and in the three months after release to the community. We compared rates with those of the general population. RESULTS: The rates of all types of health care utilization were significantly higher in prison and on release for people released from prison (N = 48,861) compared to general population controls (N = 195,444). Comparing those released from prison to general population controls in prison and in the 3 months after release, respectively, utilization rates were 5.3 (95% CI 5.2, 5.4) and 2.4 (95% CI 2.4, 2.5) for ambulatory care, 3.5 (95% CI 3.3, 3.7) and 5.0 (95% CI 4.9, 5.3) for emergency department utilization, 2.3 (95% CI 2.0, 2.7) and 3.2 (95% CI 2.9, 3.5) for medical-surgical hospitalization, and 21.5 (95% CI 16.7, 27.7) and 17.5 (14.4, 21.2) for psychiatric hospitalization. Comparing the time in prison to the week after release, ambulatory care use decreased from 16.0 (95% CI 15.9,16.1) to 10.7 (95% CI 10.5, 10.9) visits/person-year, emergency department use increased from 0.7 (95% CI 0.6, 0.7) to 2.6 (95% CI 2.5, 2.7) visits/person-year, and hospitalization increased from 5.4 (95% CI 4.8, 5.9) to 12.3 (95% CI 10.1, 14.6) admissions/100 person-years for medical-surgical reasons and from 8.6 (95% CI 7.9, 9.3) to 17.3 (95% CI 14.6, 20.0) admissions/100 person-years for psychiatric reasons. CONCLUSIONS: Across care types, health care utilization in prison and on release is elevated for people who experience imprisonment in Ontario, Canada. This may reflect high morbidity and suboptimal access to quality health care. Future research should identify reasons for increased use and interventions to improve care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".