Health Care Use and System Costs Among Pediatric Refugees in Canada
Bibliographic record
Abstract
BACKGROUND: Resettled refugees land in Canada through 3 sponsorship models with similar health insurance and financial supports but differences in how resettlement is facilitated. We examined whether health system utilization, costs, and aggregate 1-year morbidity differed by resettlement model. METHODS: Population-based matched cohort study in Ontario, 2008 to 2018, including pediatric (0-17 years) resettled refugees and matched Ontario-born peers and categorized refugees by resettlement model: (1) private sponsorship (PSRs), (2) Blended Visa Office-Referred program (BVORs), and (3) government-assisted refugee (GAR). Primary outcomes were health system utilization and costs in year 1 in Canada. Multivariable logistic regression was used to test the associations between sponsorship model and major illnesses. RESULTS: We included 23 287 resettled refugees (13 360 GARs, 1544 BVORs, 8383 PSRs) and 93 148 matched Ontario-born. Primary care visits were highest among GARs and lowest in PSRs (median visits [interquartile range], GARs 4[2-6]; BVORs 3[2-5]; PSRs 3[2-5]; P <.001). Emergency department visits and hospitalizations were more common among GARs and BVORs versus PSRs (emergency department: GARs 19.2%; BVORs 23.4%; PSRs 13.8%; hospitalizations: GARs 2.5%; BVORs 3.2%; PSRs 1.1%, P <.001). Mean 1-year health system costs were highest among GARs (mean [standard deviation] $1278 [$7475]) and lowest among PSRs ($555 [$2799]; Ontario-born $851 [9226]). Compared with PSRs, GARs (adjusted odds ratio 1.63, 95% confidence interval 1.47-1.81) and BVORs (adjusted odds ratio 1.52, 95% confidence interval 1.26-1.84) were more likely to have major illnesses. CONCLUSIONS: Health care use and morbidity of PSRs suggests they are healthier and less costly than GARs and BVOR model refugees. Despite a greater intensity of health care utilization than Ontario-born, overall excess demand on the health system for all resettled refugee children is low.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".