Uptake of Publicly Funded Flash Glucose Monitoring Systems: A Population-based Cohort Study in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: In this study we investigated uptake of flash glucose monitoring (FGM) among Ontario residents ≥66 years of age who require insulin and are eligible for Ontario's universal drug coverage program (the Ontario Drug Benefit [ODB]). Specifically, we assessed differences based on immigration status. METHODS: Using administrative data, we conducted a population-based, repeated cross-sectional study among Ontarians ≥66 years of age with insulin-requiring diabetes between September 1, 2019, and March 31, 2023. The primary outcome was the monthly rate of unique individuals receiving publicly funded FGM through the ODB program. We compared characteristics of FGM users based on immigration status by considering demographic, neighbourhood, and health-care use factors. RESULTS: We found a total of 14,151 immigrants and 85,710 long-term residents who had FGM over the study period. In the first month of funding, the rate of new users was lower among immigrants (37.1 per 1,000) compared with long-term residents (48.8 per 1,000). Rates peaked at 98.0 and 96.0 per 1,000, for immigrants and long-term residents respectively, in October 2019, declining thereafter and stabilizing in April 2020. Immigrants receiving FGM were younger, more likely to reside in neighbourhoods with greater racialized and newcomer populations, and more likely to have received noninsulin diabetes medications in the prior year, when compared with long-term residents receiving FGM. CONCLUSIONS: We observed significant FGM uptake in the first months after public funding among both immigrants and long-term residents. Although long-term residents showed slightly higher initial uptake, differences between groups were minimal after the first month of funding.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".