Effect of socioeconomic characteristics and lifestyle on BMI distribution in the Chinese population: a population-based cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Body mass index (BMI) is an accepted measurement that is widely used to quantify overweight and obesity at the population level. Previous studies have described the distribution variation of BMI through applying common statistical approaches, such as multiple linear or logistic regression analyses. This study proposed that associations between BMI and socioeconomic characteristics, diet, and lifestyle factors varied across the conditional BMI distribution. METHODS: This study was based on a sample of 10,023 Chinese adults who participated in the monitoring of chronic diseases and associated risk factors in Shaanxi Province, Northwest China, in 2013. Cross-quantile factors were observed in the relationships between major risk factors and BMI through quantile regression (QR) and ordinary least squares (OLS) regression. RESULTS: ). The QR results showed that living in urban areas was associated with BMI in the low and central quantiles (10th-60th). Participants with 6-9 years of education were 0.23-0.38 BMI units higher in the first half of the BMI quantiles compared with those with ≤6 years of education. There was a positive association between consumption of red meat and BMI; however, the association diminished from the 10th to the 50th quantile. Intake of oil and alcohol were positively associated with all BMI quantiles. Cigarette smoking per day was negatively associated with BMI, which showed a U-shaped distribution. The above results were also observed in the OLS. CONCLUSION: This study implies that in addition to socioeconomic characteristics, limiting oil and alcohol intake may decrease BMI score. Consuming more red meat could be a strategy to increase BMI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".