Impact of social disadvantage on clinical and health care use indicators in adults with type 1 diabetes using insulin pumps in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Social disadvantage is associated with worse diabetes outcomes among individuals with type 1 diabetes (T1D). Insulin pump therapy in the context of a publicly funded programme may mitigate the effects of social disadvantage on outcomes of diabetes. We investigated the effects of social disadvantage on health care use indicators and outcomes among pump users. METHODS: We conducted a population-based retrospective cohort study using administrative health data in Ontario, Canada. Adults with T1D who initiated pump therapy between 1 April 2012 and 30 March 2020 were included. Multivariable Poisson and linear regressions were used to evaluate associations between social disadvantage (defined by the material resources quintile of the Ontario marginalization index) and the following outcomes: health care use indicators (number of HbA1c tests and endocrinologist outpatient visits per year), and clinical outcomes (HbA1c and hospitalization/emergency department visits for hyperglycaemia and hypoglycaemia). All models were adjusted for age, sex, diabetes duration, baseline HbA1c, immigrant status, rural residence, diabetes physician specialty, clinic type and were estimated using generalized estimating equations (GEE) models to account for clustering by region. RESULTS: Among 15 755 adults with T1D who initiated pump therapy, 14% were from the most socially disadvantaged quintile. There were no associations between social disadvantage and health care use indicators. Individuals with greater disadvantage had poorer diabetes outcomes, including 0.12% higher HbA1c (95% CI 0.06-0.17) and a higher rate for hospitalization/emergency department visits for hyperglycaemia [adjusted rate ratio (aRR) 2.07 (95% CI 1.64-2.62)] and hypoglycaemia [aRR 1.76 (95% CI 1.41-2.19)] comparing the most versus least socially disadvantaged quintiles. CONCLUSIONS: Social disadvantage was associated with worse clinical outcomes but not health care use indicators among pump users with T1D in Ontario. Social disadvantage remains a risk factor for poorer clinical outcomes among pump users, but pump use may sustain greater engagement with the diabetes care team.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".