Exposure to Major Air Pollutants and Predictive Value of Pollutant Levels on Increased Completed Suicide rate in OECD Countries (Preprint)
Bibliographic record
Abstract
BACKGROUND Many studies have investigated the link between exposure to several air pollutants such as carbon dioxide (CO2), carbon monoxide (CO), and ozone (O3) and how they impact the overall completed suicide rate. However, It is unclear how air pollution may affect the increased suicide rate. Furthermore, it is unclear which pollutants may lead to an increased completed suicide rate. OBJECTIVE Therefore, our study aimed to predict the rise in suicide rates using publicly available open air pollution data. METHODS Public Organization for Economic Co-operation and Development （OECD）data was used to extract relevant air pollutants data. Our study included countries of Turkey (2019), Greece (2019), Slovak Republic (2019), United Kingdom (2019), Canada (2019), Luxembourg (2019), Poland (2019), Japan (2019), Hungary (2019), Lithuania (2020), Italy (2017) Spain (2020) Portugal (2018), Ireland (2018)，Denmark (2018), Germany (2020), Chile (2018), Netherlands (2020), Canada (2019), Austria (2020) Czech Republic (2020), Switzerland (2018), Australia (2020), Sweden (2018), Iceland (2020), United States (2020), Finland (2018), Latvia (2020) Belgium (2018), Estonia (2020), Slovenia (2020), and Korea (2019). Air pollutants of CO2, CO, greenhouse gas (GHG), Nitrogen oxides (NOX), Sulfur dioxide (SOX), and Volatile organic compounds (VOC) were used to assess the air quality of the countries mentioned above. We have divided countries into high and low-suicide-risk countries according to the mean completed suicide rate of the measured countries. RESULTS According to our main study results, the mean suicide rate among countries was 11.36 per 100.000 people. A significant correlation was not found between the suicide rates and levels of measured air pollution parameters. The correlation was negative only between SOX and the suicide rate. Random Forest (RF) Machine Learning (ML) model showed that we predicted a low or high suicide rate status with an accuracy of 90% (area under curve (AUC): 94%). The level of CO2 was the most predictive factor according to the RF model. Furthermore, the Neural Networks (NN) ML model predicted a low or high suicide rate with an accuracy of 90% (AUC: 75%). The level of CO was the most predictive factor according to the NN model. CONCLUSIONS In conclusion, certain levels of air pollutants may be used to predict the completed high or low suicide rate status. CLINICALTRIAL -
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".