Disparities in Continuous Glucose Monitoring Use in Children with Type 1 Diabetes Across Canada
Bibliographic record
Abstract
Background: Socioeconomic status (SES) and ethnic disparities in diabetes technology use have not been comprehensively explored across Canada. We describe SES disparities in continuous glucose monitoring (CGM) use among children in three Canadian provinces with differing public funding structures. Methods: We conducted a case–control study of children aged 1–18 years with type 1 diabetes using clinical data from three diabetes centers in Ontario, Alberta, and Québec. We measured SES using validated national neighborhood-level dimensions (residential instability, economic dependency [including employment], ethnocultural composition, and situational vulnerability [including education]). Cases were those with first-time CGM use in 2017–2022; controls were those without such use as of their last visit in that period. We examined the association between SES and CGM use using multilevel logistic regression with random effects for province, adjusting for age, sex, diabetes duration, insulin pump use, and average hemoglobin A1c. Results: We identified 1770 children, 48.9% female, with median (interquartile range) age 11.8 (8.8–14.3) years and duration of diabetes 2.6 (0.7–5.9) years. Of the 1770 children, 1411 (79.7%) used CGM. We observed significant associations with CGM use for three of the four SES dimensions. Compared with the least deprived quintiles for economic dependency, those in the middle quintile had 17% higher odds (adjusted odds ratio [aOR] 1.17, 95% confidence interval [CI] 1.02, 1.34) of using CGM. The most versus least diverse ethnocultural composition quintiles had 37% lower odds (aOR 0.63, 95% CI 0.60, 0.67) of using CGM, and those in the most versus least deprived quintiles for situational vulnerability (including least well-educated) had 50% lower odds (aOR 0.50, 95% CI 0.40, 0.62) of using CGM. Conclusions and Relevance: We found significant associations of employment status, ethnocultural diversity, and education with CGM use across Canada. Future work should promote equitable technology use among all groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".