Geophagy practices and the content of chemical elements in the soil eaten by pregnant women in artisanal and small scale gold mining communities in Tanzania
Bibliographic record
Abstract
BACKGROUND: Geophagy, a form of pica, is the deliberate consumption of soil and is relatively common across Sub-Saharan Africa. In Tanzania, pregnant women commonly eat soil sticks sold in the market (pemba), soil from walls of houses, termite mounds, and ground soil (kichuguu). The present study examined geophagy practices of pregnant women in a gold mining area of Geita District in northwestern Tanzania, and also examined the potential for exposure to chemical elements by testing soil samples. METHOD: We conducted a cross sectional study using a convenience sample of 340 pregnant women, ranging in age from 15-49 years, who attended six government antenatal clinics in the Geita District, Tanzania. Structured interviews were conducted in June-August, 2012, to understand geophagy practices. In addition, soil samples taken from sources identified by pregnant women practicing geophagy were analysed for mineral element content. RESULTS: Geophagy was reported by 155 (45.6%) pregnant women with 85 (54.8%) initiating the practice in the first trimester. A total of 101 (65%) pregnant women reported eating soil 2 to 3 times per day while 20 (13%) ate soil more than 3 times per day. Of 155 pregnant women 107 (69%) bought pemba from local shops, while 48 (31%) consumed ground soil kichuguu. The estimated mean quantity of soil consumed from pemba was 62.5 grams/day. Arsenic, chromium, copper, iron, manganese, nickel and zinc levels were found in both pemba and kichuguu samples. Cadmium and mercury were found only in the kichuguu samples. Based on daily intake estimates, arsenic, copper and manganese for kichuguu and copper and manganese for pemba samples exceed the oral Minimum Risk Levels designated by the U.S. Agency for Toxic Substance and Disease Registry. CONCLUSION: Almost 50% of participants practiced geophagy in Geita District consistent with other reports from Africa. Both pemba and kichuguu contained chemical elements at varying concentration, mostly above MRLs. As such, pregnant women who eat soil in Geita District are exposed to potentially high levels of chemical elements, depending upon frequency of consumption, daily amount consumed and the source location of soil eaten.
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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 | 0.001 | 0.001 |
| 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".