T125. AN ASSOCIATION BETWEEN OZONE, BUT NOT OTHER AIR POLLUTANTS, AND DAILY NUMBER OF VISITS TO PSYCHIATRIC EMERGENCY SERVICES
Bibliographic record
Abstract
Abstract Background Air pollution is a major environmental risk to health. Exposure to xenobiotic heavy metals such as lead and cadmium—constituents of air pollution such as particulate matter and nitrogen and sulfur oxides, organic solvents, and other environmental pollutants—could be component causes for schizophrenia and other psychotic disorders. Long-term exposure to ambient air pollution could be an independent risk factor for mental health disorders. Recent studies analysing the association between daily levels of air pollutants and hospital admissions for mental disorders showed significant results for different pollutants considered both for admissions for generic mental disorders and for specific diagnoses such as schizophrenia, depression and substance abuse. Aim of the present study is to investigate the associations between daily levels of air pollutants (particulate matter, ozone, carbon monoxide, nitrogen dioxide) and daily admissions for mental disorders to the emergency department of two general hospitals in Umbria region (Italy) Methods We collected data about daily admissions to psychiatric emergency services of two general hospitals, daily levels of respirable particular matter (PM10 and PM2.5), ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), and meteorological data (daily average temperature, pressure, humidity, and maximum and average wind speed) for the time period 1 January 2015 until 31 December 2016 (731 days). We used air monitoring data for the different types of air pollutants which were averaged across up to 16 monitor stations dispersed in the region. We assessed the impact of an increase in air pollutants on the number of daily admissions using a time-series econometric framework. Results A total of 1860 cases of emergency department admissions for mental disorders were recorded over the study period. We observe a statistically significant impact of O3 levels on daily admissions. The estimated coefficient of O3 is statistically significant at the 1 percent level. According to our results, an increase of one µg/ m3 of O3 concentration (relative to the average concentration of the last 20 days) results in 0.013 more admissions to the hospitals. All other pollutants are not significantly associated with the number of daily admissions. Discussion We observed a statistically significant association of daily ozone levels and daily number of admissions to psychiatric emergency services. Ozone is a component of photochemical smog and a powerful oxidant, and is considered as one of the most important air pollutants. Ozone may have a relevant interference with CNS physiology, and its exposure may be linked to brain disease and contribute to inflammation and oxidative stress. Ozone exposure may be considered a potential environmental risk factor for impaired mental health. Epidemiological studies found evidence that ozone exposure may affect autism spectrum disorders, lead to motor disorders and cognitive impairment, and have an influence on the incidence of depression and suicide. In recent studies ozone air levels showed an association with increased risk of an emergency department visit for depression. This is one of the first studies worldwide investigating the association between daily concentration of air pollutants and the daily number of visits to a psychiatric emergency unit. Our results add to previous literature on existing evidence for air pollution to have a role in the cause or worsening of mental distress and psychiatric disorders. Contrary to our hypothesis, we did not find a positive association between most of the air pollutants considered in our study. This is in contrast with previous literature on this subject. This could be explained by the methodological limitations of our study.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".