A multi-level analysis of the association between built environmental factors and childhood overweight in the city of Hannover, Germany
Bibliographic record
Abstract
Abstract Introduction In recent years, built environmental characteristics have been linked to childhood overweight, but the results remain inconsistent across studies. The present study examines associations between several built environmental features and body weight status (BMI) among a large sample of preschool children in the city of Hannover, Germany.Methods Walkability (index), green space availability and playground availability related to preschool children’s home environments was measured using the Geographic Information Systems (GIS) and data from OpenstreetMap (OSM). These built environment characteristics were linked to the data from the 2010-2014 school entry examinations in the Hannover city (n = 22,678), and analysed using multi-level linear regression models to examine associations between the built environment features and the BMI percentiles of these children (4-8 years old). Several socio-demographic factors were included in the analysis, including migration background, family structure and parental educational level. Results No significant associations of built environmental factors on children’s BMI were detected, but the effect between green space availability and BMI was modified by the parental educational level. In children with lower compared to higher educated parents, a higher spatial availability of greenspace was significantly associated with reduced body weight. Conclusion The finding of no overall association between the built environment and body weight could be due to misclassification in the presence of suboptimal variable reliability. However, the environmental effects may also be restricted to the less privileged children, which may indicate that built environmental features might have the potential to reduce health inequalities. Future research should continue to monitor the disparities in diverse built environment features and how these are related to children’s health.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".