Personal Exposure To Particulate Matter And Heart Rate Variability Among Informal Electronic Waste Workers At Agbogbloshie: A Longitudinal Study
Bibliographic record
Abstract
Abstract Background: Informal electronic waste recycling activities are major contributors to ambient air pollution, yet studies assessing the effects or relationship between direct/continuous exposure of informal e-waste workers to particulate matter and cardiovascular function are rare. Methods: Repeated measurements of fractions of PM 2.5 , PM 10-2.5 , and PM 10 in personal air of informal e-waste workers, (n=142) and a comparable group (n=65) were taken over a period of 20 months (March 2017 to November, 2018). Concurrently, 5-minute resting electrocardiogram was performed on each participant to assess resting heart rate variability indices. Linear mixed-effects models were used to assess the association between PM fractions and cardiac function. Results : SDNN, RMSSD, LF, HF and LH/HF ratio were all associated with PM. Significant associations were observed for PM 2.5 and MEANNN ( p = 0.039), PM10 and SDNN ( p = 0.035) and PM 10-2.5 and LH/HF ( p = 0.039). A 10µg/m 3 increase in the concentrations of PM 2.5 , PM 10-2.5 , and PM 10 in personal air was associated with reduced HRV indices and increased resting HR. A 10µg/m 3 per interquartile (IQR) increase in PM 10-2.5 and PM 10 , decreased SDNN by 11% [(95% CI: -0.002- 0.000); ( p = 0.187)] and 34% [(95% CI:-0.002-0.001); ( p = 0.035)] respectively. However, PM 2.5 increased SDNN by 34 % (95% CI: -1.32-0.64); ( p = 0.493). Also, 10µg/m 3 increase in PM 2.5 , PM 10-2.5 and PM 10 decreased RMSSD by 27% [(-1.34-0.79); ( p = 0.620)], 11% [(-1.73, 0.95); ( p = 0.846)] and 0.57% [(-1.56-0.46); ( p = 0.255%)]. Conclusion : Informal e-waste workers are at increased risk of developing cardiovascular disease from cardiac autonomic dysfunction as seen in reduced HRV and increased heart rate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".