Iron- and protein rich diets may boost hemoglobin levels among informal electronic waste recyclers exposed to metals at Agbogbloshie, Ghana
Bibliographic record
Abstract
While human exposure to metals may play a role in the pathogenesis of anemia, consumption of balanced diets may boost blood hemoglobin (Hb) levels in humans. Although informal electronic waste (e-waste) recycling processes have recently drawn attention as an important source of pollution, there is almost no empirical evidence on the relationship between diet, metals exposure and anemia among e-waste recyclers. Therefore, we evaluated possible ameliorating effects of diet on metal exposure related anemia, as measured by Hb levels of e-waste recyclers and a reference population in Ghana. This repeated measure study used data collected from e-waste recyclers (n=142) and a reference population (n=65) between March 2017 and October 2018. Stored whole blood samples were analyzed for the following metals; Cd, Pb, Rb, Tb, Tl, and Eu. Next, Hb levels were analysed using the URIT-810® semi-automatic biochemistry analyzer. Furthermore, a 48-hour dietary recall questionnaire was administered to assess dietary intake parameters such as protein, folate, carbohydrates, Fe, Ca, Mg, Se, Zn, and Cu. Ordinary regression models were used to estimate joint effects of metals and nutrients on Hb levels. At baseline, the mean Hb was lower among recyclers (12.99 ± 3.17g/dL) than the reference group (13.02±2.37g/dL). Blood Pb, Cd, Rb, Eu and Tb were associated with significant decreases in Hb levels of e-waste recyclers. Dietary intake of proteins and Fe was associated with concomitant increase in Hb levels of both groups as well as when analysis was restricted to recyclers. Despite the high exposure of e-waste recyclers to a myriad of metals, consumption of Fe-rich diets appears to ameliorate anaemia and improved Hb levels (β=0.229; 95% CI: 0.013, 0.445; p=0.04). Therefore, the consumption of Fe and protein-rich foods may boost blood Hb levels in e-waste recyclers, even though exposure to high levels of metals is a predictor of anemia among this worker-group.
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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 | 0.000 |
| 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) | 0.001 | 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".