The influence of socioeconomic status on the association between residential greenness and gestational diabetes mellitus in an urban setting: a multicenter study
Bibliographic record
Abstract
Inconsistencies were found between residential greenness and the risk of gestational diabetes mellitus (GDM), possibly due to variations in susceptibility among populations with different characteristics. However, little is known about whether this association could be modified by population characteristics like socioeconomic status (SES). This multicenter study conducted in a dense megacity aims to clarify these associations and explore the modification effects of demographic and socioeconomic factors. The study included 19,618 pregnant women in 20 hospitals throughout Shanghai, China, between 2015 and 2017. Multivariable logistic regression models were utilized to assess the associations of satellite-based greenness indicators [normalized difference vegetation index (NDVI) within 500 m- and 1000 m buffers] with GDM and whether demographic and socioeconomic factors modified the associations. Potential mediation effects of fine particulate matter (PM 2.5 ) on the associations between greenness and GDM were also explored. During the first two trimesters of pregnancy, an increase in NDVI-500 m or NDVI-1000 m interquartile range was consistently associated with lower GDM risks, with adjusted odds ratios (aORs) and 95% confidence interval (CI) ranging from 0.82 (0.76, 0.88) to 0.90 (0.85, 0.96). Stratified analyses revealed that the health benefits of residential greenness are more pronounced during the first two trimesters among unemployed women (aOR = 0.70; 95%CI: 0.60, 0.82), those with lower education levels (aOR = 0.72; 95%CI: 0.63, 0.82), and those without medical insurance (aOR = 0.76; 95%CI: 0.69, 0.84). Mediation analysis shows that PM 2.5 reduction by greenness may explain 16.4% of the inverse association between the NDVI-500 m during early pregnancy and the risk of GDM. Our research indicates that elevated residential greenness was associated with reduced GDM risks, partly attributed to decreased PM 2.5 levels. Women with lower SES experience amplified benefits from greenness. These findings highlight the significance of bolstering urban green infrastructure, particularly in communities confronting socioeconomic challenges and areas with high levels of air pollution.
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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 | 0.000 |
| 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".