Assessment of Micronutrient Status of Electronic Waste (E-waste) Recyclers at Agbogbloshie (Ghana) Using Dietary Information and Biomarker Data
Bibliographic record
Abstract
<title>Abstract</title> <bold>Background: </bold>Growing evidence suggests that heavy metals (e.g., cadmium, lead and arsenic) negatively influence micronutrient status. Electronic waste (e-waste) recyclers are amongst the highest metals-exposed groups worldwide, though their micronutrient status is yet to be explored. This study therefore assessed the micronutrient status of e-waste recyclers using dietary information and biomarker data. <bold>Methods:</bold> Micronutrient status of 151 participants (100 e-waste recyclers and 51 controls from the Accra region, Ghana) was assessed in March 2017 using a 2-day 24-hour recall survey and biomarker (blood and urine) levels. Blood and urine levels of iron [Fe], calcium [Ca], magnesium [Mg], selenium [Se], zinc [Zn] and copper [Cu] were analyzed by ICP-MS. Linear regression models were used to assess associations between work-related factors and sociodemographic characteristics with micronutrient intake, blood and urine micronutrient levels. <bold>Results: </bold>Dietary Fe and Zn were adequately consumed among the e-waste recyclers. Meanwhile, micronutrients—such as Ca, Se, Mg and Cu—were inadequately consumed by e-waste recyclers and controls. Except for the low levels of Mg and Fe detected in blood of e-waste recyclers, all other micronutrients measured in both blood and urine of both groups fell within their reference range. Exposure to biomass burning was associated with lower blood levels of Fe, Mg and Zn among the e‑waste recyclers. Further, among e-waste recyclers, significant relationships were found between the number of years of spent recycling e-waste and urinary Ca and Cu excretion.<bold>Conclusion: </bold>Apart from Fe and Zn<bold>, </bold>e-waste recyclers at Agbogbloshie did not meet the day to day dietary requirements for Ca, Cu, Se, and Mg intake. In addition, exposure to biomass burning could lead to reductions in Fe, Mg, and Zn uptake in blood. Given that, blood levels of micronutrients such as Mg and Fe were below their reference ranges, the implementation of evidence-based nutrition strategies remains necessary among e-waste recyclers to reduce their risk of becoming malnourished.<bold>Trial Registration:</bold><bold> </bold>Not applicable
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".