The Creation of a Multidomain Neighborhood Environmental Vulnerability Index Across an Urban Center
Bibliographic record
Abstract
ABSTRACT Compared to previous studies that have typically used a single summary score, we aimed to construct a multidomain neighborhood environmental vulnerability index (NEVI) to characterize the magnitude and variability of area-level factors with the potential to modify the health effects of environmental pollutants. Using the Toxicological Prioritization Index framework and data from the 2015-2019 U.S. Census American Community Survey and the 2020 CDC PLACES Project, we quantified census tract-level vulnerability overall and in 4 primary domains (demographic, economic, residential, and health status), 24 subdomains, and 54 distinct area-level features for New York City (NYC). Overall and domain-specific indices were calculated by summing standardized feature values within the subdomains and then aggregating and weighting subdomains within equally-weighted primary domains. In citywide comparisons, NEVI was correlated with both the Neighborhood Deprivation Index (r = 0.91) and the Social Vulnerability Index (r = 0.87) but provided additional information on features contributing to vulnerability. Vulnerability varied spatially across NYC, and hierarchical cluster analysis using subdomain scores revealed six patterns of vulnerability across domains: 1) low in all, 2) primarily low except residential, 3) medium in all, 4) high demographic, economic, and residential 5) high economic, residential, and health status, and 6) high demographic, economic and health status. Created using a tool that offers flexibility for theory-based construction, NEVI provided detailed metrics of vulnerability across domains that can inform targeted research and public health interventions aimed at reducing the health impacts from environmental exposures across an urban center.
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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 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".