Neighborhood Effects on Health
Bibliographic record
Abstract
BACKGROUND: Studies of neighborhood effects on health that are based on cohort data are subject to bias induced by neighborhood-related selective study participation. METHODS: We used data from the RECORD Cohort Study (REsidential Environment and CORonary heart Disease) carried out in the Paris metropolitan area, France (n = 7233). We performed separate and joint modeling of neighborhood determinants of study participation and type-2 diabetes. We sought to identify selective participation related to neighborhood, and account for any biasing effect on the associations with diabetes. RESULTS: After controlling for individual characteristics, study participation was higher for people residing close to the health centers and in neighborhoods with high income, high property values, high proportion of the population looking for work, and low built surface and low building height (contextual effects adjusted for each other). After individual-level adjustment, the prevalence of diabetes was elevated in neighborhoods with the lowest levels of educational attainment (prevalence odds ratio = 1.56 [95% credible interval = 1.06-2.31]). Neighborhood effects on participation did not bias the association between neighborhood education and diabetes. However, residual geographic variations in participation weakly biased the neighborhood education-diabetes association. Bias correction through the joint modeling of neighborhood determinants of participation and diabetes resulted in an 18% decrease in the log prevalence odds ratio for low versus high neighborhood education. CONCLUSIONS: Researchers should develop a comprehensive, theory-based model of neighborhood determinants of participation in their study, investigate resulting biases for the environment-health associations, and check that unexplained geographic variations in participation do not bias these environment-health relationships.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".