Diabetes prevalence and complication rates
Bibliographic record
Abstract
Abstract Objective To test the feasibility of reporting diabetes indicators at a regional and community level in order to provide feedback to local leaders on health system performance. Design Analysis of administrative data from hospital discharges and physician billings. Setting Sioux Lookout region of Ontario. Participants Residents from 30 remote communities served by the Sioux Lookout First Nations Health Authority. Main outcome measures Incidence and prevalence of diabetes and incidence of diabetes complications, including heart attack, stroke, retinopathy, amputations, end-stage kidney disease, diabetes-related hospitalizations, and death. Results Data were available for 18 542 residents from the 30 remote communities. Residents were almost entirely of First Nations descent. The prevalence of diabetes was 12.9%, the annual incidence was 1.0%, and the annual rate of complications was 5.4% in 2015-2016. Prevalence increased slightly over time. We had sufficient data to report prevalence in 25 of 30 communities (average population 738; range 234 to 2626). We reported statistically significant differences in prevalence by community; 8 were above average and 2 were below average. For diabetes complications, data were pooled over 5 years, and while community-level results could be reported, the variance was too high to allow detection of significant differences. Using 2-tailed t tests for difference of proportions, we determined that grouping communities into subregions of approximately 2000 persons would permit the detection of differences of 30% from the average 5-year complication rate. Conclusion This study demonstrates the possibility of reporting diabetes prevalence by individual First Nations reserve communities. Complication rates can be reported by individual community, but estimates are more useful for comparison if the smallest communities are grouped together. Such studies could be replicated across Canada to promote local use of these data for resource planning and monitoring long-term progress of diabetes programs and services.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".