Impact of neighbourhood‐level inequity on paediatric diabetes care
Bibliographic record
Abstract
Abstract Aims To evaluate the association between neighbourhood‐level inequity and glycaemic control in paediatric participants with Type 1 diabetes using the Neighbourhood Equity Index (NEI). Methods The NEI was linked to the clinical data of 519 children with diabetes followed at the Hospital for Sick Children (Toronto, Canada). The NEI is a composite measure of inequity developed using the World Health Organization's Urban Health Equity Assessment and Response Tool (HEART), which encompasses 15 weighted indicators evaluating economic, social, environmental and lifestyle factors. The geographic distribution of participants was determined using postal codes, and the relationship between HbA1c and NEI was evaluated using regression and spatial analysis techniques. Results Participants’ mean HbA1c was significantly correlated with NEI (R = −0.24, P < 0.0001). Regression analysis demonstrated that NEI was a strong predictor of mean HbA1c (P < 0.0001), accounting for differences in HbA1c as large as 1.0% (11 mmol/mol) when controlled for age, sex, diabetes duration, insulin pump therapy and number of annual clinic visits. Geo‐mapping using spatial scan testing revealed the presence of two clusters of low‐equity neighbourhoods containing 3.22 (P = 0.001) and 2.83 (P = 0.02) times more participants with HbA1c ≥ 9.5% (80 mmol/mol) than expected. Conclusions Our findings demonstrated that NEI was a significant predictor of HbA1c in our clinic population and a useful tool for investigating spatial trends related to inequities in health, providing evidence that a composite, area‐based measure of overall inequity is well suited to the study of glycaemic control in urban paediatric Type 1 diabetes 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".