Association of Particulate Matter Exposure and Depression: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BackgroundImpact of particulate matter (PM) air pollution exposure and mental health conditions is generating lot of curiosity among public health practitioners and policy makers. We aimed to generate evidence on daily PM2.5 and PM10 exposure and depression.MethodsA systematic search was conducted for published studies in English till December 2017. Independent two electronic searches were carried out in Medline, Web of Science and Cochrane Library using key words “Airborne Particulate Matter” OR “Air Pollutants” OR “Air Quality”, “Outdoor Air Pollution” “PM2.5” “PM10” “ Depression” “Depressive symptoms” “Mood disorder.” We used Medical Subject Headings (MeSH) terms: “Particulate matter” and “Depression” in Medline and Cochrane Search. We have included articles reporting adjusted relative risk (RR) or odds ratio (OR) PM exposure and depression. References of the selected articles were also traced. Meta-analysis was performed to calculate pooled estimate using fixed effect model. Quality of studies was assessed using Newcastle-Ottawa Scale for observational studies. Protocol of the systematic review was registered in PROSPERO.ResultsWe have got 938 records from all searched databases. Of which, 14 records were included for full text review after screening abstract and removing duplicates. Finally, eight articles were included for meta-analysis. Most of studies were longitudinal studies and spread out geographically across the regions. Daily PM2.5 exposure was found to be associated with depression (RR=1.08, 95 CI: 1.02, 1.14) with acceptable heterogeneity (I2: 56%). Daily PM10 exposure was not found to be associated with depression (RR = 1.01, 95% CI: 0.97,1.04). Age, sex, household income, education, co-morbidity and road traffic noise were adjusted as confounders.ConclusionWe have found modest strength of association for daily PM2.5 exposure and depression. Unknown confounders might have affected the pooled estimate from longitudinal studies in our analysis.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".