Association Between Neighborhood Deprivation and Gestational Diabetes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Gestational diabetes mellitus (GDM) is a major global health concern, affecting maternal and child health. Although genetic predispositions and individual medical histories are well-recognized risk factors, emerging research suggests a significant impact of external factors like neighborhood socioeconomic characteristics. This study systematically reviews and meta-analyzes the association between neighborhood deprivation and GDM incidence. We searched multiple databases up to January 10, 2024, for studies linking neighborhood deprivation with GDM. Eligible studies were selected based on predefined criteria, with the Nested Knowledge software assisting in screening and data extraction. Quality assessment utilized the Newcastle-Ottawa Scale, and a random-effects model computed the pooled relative risk (RR) using R software, version 4.3. The review included six studies varying significantly in design, sample sizes, and deprivation assessment methods. The meta-analysis combined data from five studies totaling 15 827 participants from the least deprived and 18 147 from the most deprived neighborhoods, yielding an RR of 0.909, indicating a non-significant lower risk of GDM in more deprived groups. A substantial heterogeneity (I 2 = 70%) was observed, and sensitivity analysis confirmed the robustness of these findings. This analysis suggests that living in a deprived neighborhood does not significantly alter GDM risk, underscoring the necessity for further research to refine public health strategies and interventions. The variability in neighborhood deprivation definitions and potential unaccounted confounding factors highlight the need for comprehensive studies, especially from low-income and middle-income countries, to elucidate the intricate links between socioeconomic factors and GDM.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".