Impact of aeroplane noise on mental and physical health: a quasi-experimental analysis
Bibliographic record
Abstract
OBJECTIVES: Historically, departures at New York City's LaGuardia airport flew over a large sports complex within a park. During the US Open tennis games, flights were diverted to fly over a heavily populated foreign-born neighbourhood for roughly 2 weeks out of the year so that the tennis match was not disturbed (the 'TNNIS' departure). In 2012, the use of the TNNIS departure became year-round to better optimise flight patterns around the metropolitan area. METHODS: We exploited exogenously induced spatial and temporal variation in flight patterns to examine difference-in-difference effects of this new exposure to aircraft noise on the health of individual residents in the community relative to individuals residing within a demographically similar community that was not impacted. We used individual-level Medicaid records, focusing on conditions associated with noise: sleep disturbance, psychological stress, mental illness, substance use, and cardiovascular disease. RESULTS: We found that increased exposure to aeroplane noise was associated with a significant increase in insomnia across all age groups, but particularly in children ages 5-17 (OR=1.64, 95% CI=1.12 to 2.39). Cardiovascular disease increased significantly both among 18-44-year-old (OR=1.45, 95% CI=1.41 to 1.49) and 45-64-year-old Medicaid recipients (OR=1.15, 95% CI=1.07 to 1.25). Substance use and mental health-related emergency department visits also increased. For ages 5-17,rate ratio (RR) was 4.11 (95% CI=3.28 to 5.16); for ages 18-44, RR was 2.46 (95% CI=2.20 to 2.76); and for ages 45-64, RR was 1.48 (95% CI=1.31 to 1.67). CONCLUSION: We find that increased exposure to aeroplane noise was associated with an increase in diagnosis of cardiovascular disease, substance use/mental health emergencies and insomnia among local residents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".