Population-based prevalence of self-reported pediatric diabetes and screening for undiagnosed type 2 diabetes in Chinese children in years 2017–2019, a cross-sectional study
Bibliographic record
Abstract
Background: The worldwide geographical and temporal variation in the prevalence of diabetes represents a challenge, but also an opportunity for gaining etiological insights. Encompassing the bulk of East Asians, a large and distinct proportion of the world population, China can be a source of valuable epidemiological insights for diabetes, especially in early life, when pathophysiology begins. We carried out a nationwide, epidemiological survey of Prevalence and Risk of Obesity and Diabetes in Youth (PRODY) in China, from 2017 to 2019, to estimate the population-based prevalence of diagnosed pediatric diabetes and screen for undiagnosed pediatric type 2 diabetes (T2D). Methods: PRODY was a nation-wide, school population-based, cross-sectional, multicenter survey by questionnaire, fasting urine glucose test and simple oral glucose tolerance test (s-OGTT), among a total number of 193,801 general-population children and adolescents (covered a pediatric population of more than 96.8 million), aged 3-18, from twelve provinces across China. The prevalence of the self-reported pediatric diabetes, the proportion of subtypes, the crude prevalence of undiagnosed T2D and prediabetes in general juvenile population and the main risk factors of type 1 (T1D) and type 2 (T2D) diabetes had been analyzed in the study. Findings: The prevalence of all self-reported pediatric diabetes was estimated at 0.62/1000 (95% CI: 0.51-0.74), with T1D at 0.44/1000 (95% CI: 0.35-0.54) and T2D at 0.18/1000 (95% CI: 0.13-0.25). For undiagnosed T2D, the crude prevalence was almost ten-fold higher, at 1.59/1000, with an estimated extra 28.45/1000 of undiagnosed impaired glucose tolerance (IGT) and 53.74/1000 of undiagnosed impaired fasting glucose (IFG) by s-OGTT screening. Maternal diabetes history is the major risk factors for all subtypes of pediatric diabetes in China. Interpretation: The PRODY study provides the first population-based estimate of the prevalence of pediatric diabetes China and reveals a magnitude of the problem of undiagnosed pediatric T2D. We propose a practical screening strategy by s-OGTT to address this serious gap. Funding: The National Key Research and Development Programme of China, Key R&D Program of Zhejiang, the National Natural Science Foundation of China and the Zhejiang Provincial Key Disciplines of Medicine, Key R&D Program Projects in Zhejiang Province.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".