Disparities in diabetes prevalence and preventable hospitalizations in people with intellectual and developmental disability: a population‐based study
Bibliographic record
Abstract
AIMS: To describe and compare population-level aspects of diabetes and diabetes primary care among people with and without intellectual and developmental disabilities. METHODS: Administrative health data accessed from the Institute for Clinical Evaluative Sciences was used to identify a cohort of Ontarians with and without intellectual and developmental disabilities between the ages of 30 and 69 years (n = 28 567). These people were compared with a random sample of people without intellectual and developmental disabilities (n = 2 261 919) according to diabetes prevalence, incidence, age, sex, rurality, neighbourhood income and morbidity. To measure diabetes primary care, we also studied hospitalizations for diabetes-related ambulatory care-sensitive conditions. RESULTS: Adults with intellectual and developmental disabilities had a consistently higher prevalence and incidence of diabetes than those without intellectual and developmental disabilities. Disparities in prevalence between those with and without intellectual and developmental disabilities were most notable among women, younger adults and those residing in rural or high income neighbourhoods. In terms of hospitalizations for diabetes-related ambulatory care-sensitive conditions, people with intellectual and developmental disabilities were 2.6 times more likely to be hospitalized. CONCLUSIONS: Adults with intellectual and developmental disabilities are at high risk of developing and being hospitalized for diabetes. The findings of the present study have a number of important implications related to the early detection, prevention and proper management of diabetes among adults with intellectual and developmental disabilities.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".