Impact of government-funded insulin pump programs on insulin pump use in Canada: a cross-sectional study using the National Diabetes Repository
Bibliographic record
Abstract
INTRODUCTION: Insulin pump access in type 1 diabetes may be inequitable. We studied the association between government funding programs for insulin pumps and rates of insulin pump use and disparities between pump users and non-users. RESEARCH DESIGN AND METHODS: Adults with type 1 diabetes were identified in the National Diabetes Repository, a primary care electronic medical record database of individuals with diabetes from five Canadian provinces. Proportions of individuals using insulin pumps were compared between provinces with and without pump funding programs. Multivariable logistic regression models were used to estimate the odds of insulin pump use adjusting for confounders. Univariate logistic regression models were used to estimate the odds of insulin pump use according to each predictor, according to pump funding program status. RESULTS: Of 1559 adults with type 1 diabetes, proportions using insulin pumps were 47.8% (95% CI 45.1% to 50.5%) and 37.7% (95% CI 31.5% to 44.1%) in provinces with and without pump funding programs (p=0.0038). Adjusting for age, sex, HbA1c, income quintile, and rural/urban location, the OR for insulin pump use was 1.45 (1.08-1.94) for provinces with pump funding programs compared with provinces without. Higher income was associated with a greater odds of insulin pump use in provinces with pump funding programs, and rural/urban location was not associated with insulin pump use. CONCLUSIONS: Insulin pump use is more common in regions with government funding programs. Further research is required to best understand and comprehensively address persistent income disparities between pump users and non-users despite the availability of reimbursement programs.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".