Introduction of publicly funded pharmacare and socioeconomic disparities in glycemic management in children and youth with type 1 diabetes in Ontario, Canada: a population-based trend analysis
Bibliographic record
Abstract
Background: We evaluated the impact of publicly funded pharmacare (Ontario Health Insurance Plan [OHIP]+), which was introduced in Ontario on Jan. 1, 2018, for youth less than 25 years of age, on temporal trends in hemoglobin A1c (HbA1c, a measure of glycemic management) and the differential effect on the change in temporal trends in HbA1c according to socioeconomic status (SES). Methods: We conducted a trend analysis using administrative data sets. We included youth aged 21 years, 9 months or younger, residing in Ontario on Jan. 1, 2016, with diabetes diagnosed before age 15 years and before Jan. 1, 2015. We used claims for insulin to measure pharmacare use. We evaluated the change in HbA1c (%) per 90 days before (Jan. 1, 2016, to Dec. 31, 2017) the introduction of and during (Apr. 1, 2018, to Mar. 31, 2019) OHIP+ coverage, and the difference in the change in HbA1c according to SES, using segmented regression analysis. Results: Of 9641 patients, 7041 (73.0%) made an insulin claim. We found a negligible difference in the temporal change in HbA1c during compared with before OHIP+ coverage that was not statistically significant (β estimate −0.0002, 95% confidence interval [CI] −0.0004 to 0.0000). The size of the effect was slightly greater in those individuals with the lowest SES than in those with the highest SES (β estimate −0.0008, 95% CI −0.0015 to −0.0001). Interpretation: We found that the effect of OHIP+ on the change in HbA1c was slightly greater for youth in the lowest SES than for those in the highest SES. Our findings suggest that publicly funded pharmacare may be an effective policy tool to combat worsening socioeconomic disparities in diabetes care and outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".