Evaluation of the diabetes care cascade and compliance with WHO global coverage targets in Iran based on STEPS survey 2021
Bibliographic record
Abstract
This study aimed to investigate the diabetes mellitus (DM) and prediabetes epidemiology, care cascade, and compliance with global coverage targets. We recruited the results of the nationally representative Iran STEPS Survey 2021. Diabetes and prediabetes were two main outcomes. Diabetes awareness, treatment coverage, and glycemic control were calculated for all population with diabetes to investigate the care cascade. Four global coverage targets for diabetes developed by the World Health Organization were adopted to assess the DM diagnosis and control status. Among 18,119 participants, the national prevalence of DM and prediabetes were 14.2% (95% confidence interval 13.4-14.9) and 24.8% (23.9-25.7), respectively. The prevalence of DM treatment coverage was 65.0% (62.4-67.7), while the prevalence of good (HbA1C < 7%) glycemic control was 28.0% (25.0-31.0) among all individuals with diabetes. DM diagnosis and statin use statics were close to global targets (73.3% vs 80%, and 50.1% vs 60%); however, good glycemic control and strict blood pressure control statistics, were much way behind the goals (36.7% vs 80%, and 28.5% vs 80%). A major proportion of the Iranian population are affected by DM and prediabetes, and glycemic control is poorly achieved, indicating a sub-optimal care for diabetes and comorbidities like hypertension.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".