A Matched Analysis of the Association Between Federally Mandated Smoke-Free Housing Policies and Health Outcomes Among Medicaid-Enrolled Children in Subsidized Housing, New York City, 2015–2019
Bibliographic record
Abstract
Smoke-free housing policies are intended to reduce the deleterious health effects of secondhand smoke exposure, but there is limited evidence regarding their health impacts. We examined associations between implementation of a federal smoke-free housing rule by the New York City Housing Authority (NYCHA) and pediatric Medicaid claims for asthma, lower respiratory tract infections, and upper respiratory tract infections in the early post-policy intervention period. We used geocoded address data to match children living in tax lots with NYCHA buildings (exposed to the policy) to children living in lots with other subsidized housing (unexposed to the policy). We constructed longitudinal difference-in-differences models to assess relative changes in monthly rates of claims between November 1, 2015, and December 31, 2019 (the policy was introduced on July 30, 2018). We also examined effect modification by baseline age group (≤2, 3-6, or 7-15 years). In New York City, introduction of a smoke-free policy was not associated with lower rates of Medicaid claims for any outcomes in the early postpolicy period. Exposure to the smoke-free policy was associated with slightly higher than expected rates of outpatient upper respiratory tract infection claims (incidence rate ratio = 1.05, 95% confidence interval: 1.01, 1.08), a result most pronounced among children aged 3-6 years. Ongoing monitoring is essential to understanding long-term health impacts of smoke-free housing policies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".