The prevalence of diabetes mellitus with chronic kidney disease in adults and associated factors in Songjiang District, Shanghai
Bibliographic record
Abstract
BACKGROUND: Diabetes mellitus (DM) complicated with chronic kidney disease (CKD) has been a severe public health issue in China. This study aimed to investigate the prevalence of adult DM complicated with CKD and its associated factors in Songjiang District, Shanghai. METHODS: A large-scale community-based cross-sectional study was conducted among 36,077 participants aged 20-74 years in Songjiang District, Shanghai between June 2016 and December 2017. Epidemiological data of the Shanghai Suburban Adult Cohort and Biobank (SSACB) and electronic health record (EHR) data based on the big data platform of Songjiang District were combined to estimate the prevalence of adult DM with CKD. Factors associated with DM complicated with CKD were analyzed by using univariate and multivariate logistic regression models with adjusted odds ratios (aOR) and their 95% confidence intervals (CI). RESULTS: The overall prevalence of adult DM with CKD among the community population in Songjiang District, Shanghai was 2.9% (95% CI: 2.8-3.1%). The prevalence significantly increased with age and the prevalence was significantly lower than in men than in women (2.4% vs. 3.3%, P<0.001). Factors were positively associated with adult DM with CKD including female (aOR: 1.40; 95% CI: 1.13-1.72), older age (aOR for 40-59 years age group: 3.85; 95% CI: 1.92-7.72; aOR for ≥60 years age group: 6.17; 95% CI: 3.05-12.47), urban community (aOR: 1.61; 95% CI: 1.38-1.87), HbAlc ≥6.5% (aOR: 24.01; 95% CI: 20.59-28.01), hypertension (aOR: 2.16; 95% CI: 1.77-2.64), dyslipidemia (aOR: 1.58; 95% CI: 1.35-1.84), coronary heart disease (CHD, aOR: 2.15; 95% CI: 1.84-2.52), hyperuricemia (aOR: 1.61; 95% CI: 1.36-1.90) and family history of DM (aOR: 2.73; 95% CI: 2.26-3.29). In sensitivity analysis, those factors except female and urban community were still positively associated with adult DM with CKD. CONCLUSIONS: DM with CKD was common among adults in Songjiang District, Shanghai. Effective public health programs should be developed to control DM with CKD for targeted 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".