Health care for children with diabetes mellitus from low-income families in Ontario and California: a population-based cohort study
Bibliographic record
Abstract
Background: Children with diabetes mellitus in low-income families have poor outcomes, but little is known as to how this relates to healthcare system structure. Our objective was to gain insight into how best to structure health systems to serve these children by describing their health care use in 2 health system models: a Canadian model, with an organized diabetes care network that includes generalists, and an American model, with targeted support services for children from low-income families. Methods: We performed a population-based retrospective cohort study involving children aged 1-17 years with type 1 diabetes mellitus. We used administrative data from between 2009 and 2012 from the California Children9s Services program and Ontario. We used Ontario Drug Benefit Program enrolment to identify children from low-income families. Proportions of children receiving 2 or more routine diabetes visits per year were compared using χ2 tests, and diabetes-complication hospital admission rates were compared using direct standardization. Results: More California children from low-income families (n = 4922) received routine care for diabetes from pediatric endocrinologists (63.9% v. 26.9%, p < 0.001) and used insulin pumps (22.8% v. 16.4%, p < 0.001) than Ontario children (n = 2050).California children from low-income families were less likely than Ontario children to receive 2 visits for routine diabetes care per year (64.7% v. 75.7%, p < 0.001), and had slightly higher per-patient year hospital admission rates for diabetes complications (absolute differences 0.02, 95% confidence interval [CI] 0.02-0.02, for boys; 0.03, 95% CI 0.03-0.03, for girls). Interpretation: Ontario children from low-income families received more routine diabetes care than did California children from low-income families. Both groups of children had clinically comparable rates of hospital admission for diabetes complications. Diabetes care networks that integrate generalists may play a role in improving access and outcomes for the growing population of children with diabetes.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".