Micronutrient Status of Electronic Waste Recyclers at Agbogbloshie, Ghana
Bibliographic record
Abstract
Growing evidence suggests that micronutrient status is adversely impacted by toxic metals (e.g., cadmium, lead, and arsenic) exposures; however, the micronutrient status of e-waste recyclers who are amongst the highest metal-exposed groups is not known. This study, therefore, assessed the micronutrient status of e-waste recyclers using dietary information (2-day 24-h recall survey) and biomarker data (whole blood and urine) among 151 participants (100 e-waste recyclers at Agbogbloshie and 51 controls at Madina Zongo from the Accra region, Ghana) in March 2017. Biomarker levels of iron (Fe), calcium (Ca), magnesium (Mg), selenium (Se), zinc (Zn) and copper (Cu) were analyzed by the ICP-MS. Linear regression models were used to assess associations ofwork-related factors and sociodemographic characteristics with micronutrient intake, blood, and urine micronutrient levels. The results showed that apart from Fe and Zn, e-waste recyclers at Agbogbloshie did not meet the day-to-day dietary requirements for Ca, Cu, Se, and Mg intake. 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 spent recycling e-waste and urinary Ca and Cu excretion. Given that, some dietary and blood levels of micronutrients 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.
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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 | 0.001 | 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 | 0.000 |
| 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".