Socioeconomic status and prevalence of type 2 diabetes in mainland China, Hong Kong and Taiwan: a systematic review
Bibliographic record
Abstract
BACKGROUND: China is estimated to have had the largest number of people with diabetes in the world in 2015, with extrapolation of existing data suggesting that this situation will continue until at least 2030. Type 2 diabetes has been reported to be more prevalent among people with low socioeconomic status (SES) in high-income countries, whereas the opposite pattern has been found in studies from low- and middle-income countries. We conducted a systematic review to describe the cross-sectional association between SES and prevalence of type 2 diabetes in Chinese in mainland China, Hong Kong and Taiwan. METHODS: We conducted a systematic literature search in Medline, Embase and Global Health electronic databases for English language studies reporting prevalence or odds ratio for type 2 diabetes in a Chinese population for different SES groups measured by education, income and occupation. We appraised the quality of included studies using a modified Newcastle-Ottawa Scale. Heterogeneity of studies precluded meta-analyses, therefore we summarized study results using a narrative synthesis. RESULTS: Thirty-three studies met the inclusion criteria and were included in the systematic review. The association between education, income and occupation and type 2 diabetes was reported by 27, 19 and 12 studies, respectively. Most, but not all, studies reported an inverse association between education and type 2 diabetes, with odds ratios (OR) and 95% confidence interval (CI) ranging from 0.39 (CI not reported) to 1.52 (95% CI 0.91 - 2.54) for the highest compared to the lowest education level. The association between income and type 2 diabetes was inconsistent between studies. Only a small number of studies identified a significant association between occupation and type 2 diabetes. Retired people and people working in white collar jobs were reported to have a higher risk of type 2 diabetes than other occupational groups even after adjusting for age. CONCLUSIONS: This first systematic review of the association between individual SES and prevalence of type 2 diabetes in China found that low education is probably associated with an increased prevalence of type 2 diabetes, while the association between income and occupation and type 2 diabetes is unclear.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".