The Impact of Public Policy on Equitable Access to Technology for Children and Youth Living with Type 1 Diabetes in British Columbia, Canada
Bibliographic record
Abstract
Objective:Structural inequities impede technology uptake in marginalized populations living with type 1 diabetes (T1D). Our objective was to describe hemoglobin A1c (HbA1c), time in range (TIR), and pump use to evaluate the impact of a universal funding policy for continuous glucose monitoring (CGM) across levels of deprivation in children with T1D in the Canadian province of British Columbia (BC). Methods:Patients with T1D and at least one outpatient visit after June 10, 2020 (1-year before universal CGM funding) who were enrolled in the BC Pediatric Diabetes Registry were included (n = 477). The Canadian Index of Multiple Deprivation (quintile 1 = least deprived; quintile 5 = most deprived) was determined using postal code. Mixed effects models were used to describe HbA1c, TIR, and pump use, and an interrupted time series generalized additive model estimated the change in CGM use pre- and postintroduction of universal coverage. Results:No differences were observed among the five levels of deprivation for HbA1c and TIR; however, for residential instability, those with the highest level of deprivation had a lower probability of pump use (−18.9%, 95% confidence interval [CI] = −26.1% to −11.7% for quintile 5 vs. 1). There was an increase in CGM uptake across all levels of deprivation 1-year after introduction of universal CGM funding. For example, the difference in sensor use from the most to least deprived situational group was −21.0% (−35.4%, −6.6%) at the time of universal coverage and shrank to −4.6% (−21.6%, 12.4%) after 12 months of coverage. However, an equity gap in CGM use persisted between the least and most deprived groups (−21.9, 95% CI = −34.5 to −9.4 for quintile 5 vs. 1 in economic dependency). Conclusions:Universal coverage of CGM improved uptake; however, equity gaps persisted. More research is needed to explore nonfinancial barriers to diabetes technology use in marginalized populations.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".