1450-P: Associations between Marginalization and Insulin Pump Use among Adults with Type 1 Diabetes in Ontario
Bibliographic record
Abstract
Introduction & Objective: We evaluated associations between marginalization and pump use in the context of a public funding program, and temporal trends in marginalization among new insulin pump program applicants. Methods: We conducted population-based studies of adults with type 1 diabetes using administrative data. Unadjusted and adjusted logistic regression was used to assess associations between marginalization [determined by postal code using the Ontario Marginalization Index (ON-MARG)] and insulin pump use on March 31, 2021. Unadjusted ordinal logistic regression was used to evaluate association between insulin pump application year (2007-2022) and odds of being in a higher marginalization quintile. Results: 16,471 (60%) of 27,453 adults used insulin pumps. Higher ON-MARG quintile was associated with lower odds of insulin pump use [adjusted OR 0.44 (0.39-0.48) for lowest vs. highest quintile]. The most marginalized individuals were the smallest proportion of applicants to the insulin pump program between 2007 and 2022 (Figure 1). While narrowing of marginalization distribution occurred from 2007-2010, widening occurred from 2020-2022. Conclusion: Disparities in pump use persistent even in the context of public funding, and the COVID-19 pandemic may have disproportionately prevented more marginalized individuals initiating pump therapy. Residual barriers to pump use must be addressed. Figure: Distribution of material resources quintile for all new applicants to ADP by fiscal year for all ages (n=21,002) Disclosure Y. Soliman: None. K. Everett: None. R. Shulman: Advisory Panel; Dexcom Canada. Speaker's Bureau; Dexcom Canada. P. Austin: None. L. Lipscombe: Other Relationship; Novo Nordisk Canada Inc. G.L. Booth: None. A. Weisman: None. Funding Banting Research Foundation & Canadian Statistical Sciences Institute (CANSSI) Ontario Discovery Award
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".