Mercury exposure and health risks associated with use of skin-lightening products: A systematic review
Bibliographic record
Abstract
Abstract Background The Minamata Convention on Mercury (Article 4) prohibits the manufacture, import or export of skin-lightening products containing mercury concentrations above 1 μg/g. However, there is a lack of knowledge surrounding the global prevalence of mercury-added skin-lightening products. Objective The objective of this study was to increase our understanding of worldwide human mercury exposure and associated health risks from the use of skin-lightening products. Methods A systematic search of peer-reviewed scientific literature was performed in four databases (PubMed, Web of Science Core Collection, Scopus, and Toxline). The initial search in July of 2018 identified 1,711 unique scientific articles, of which 34 were ultimately deemed eligible for inclusion after iterative screens at the title, abstract, and whole text levels. A second search was performed in November of 2020 using the same methods, of which another 7 scientific articles were included. All papers were organized according to four data groups 1) “Mercury in products”, 2) “Usage of products”, 3) “Human biomarkers of exposure”; and 4) “Health impacts”, prior to data extraction and synthesis. Results This review was based on data contained within 41 peer-reviewed scientific papers from 22 countries worldwide published between 2000 and 2020. In total, we captured mercury concentration values from 787 skin-lightening product samples (overall pooled central median mercury level was 0.49 μg/g, IQR: 0.02 – 5.9) and 1,042 human biomarker measurements from 863 individuals. We also synthesized usage information from 3,898 individuals, and self-reported health impacts associated with using mercury-added products from 832 individuals. Discussion This review suggests that mercury widely exists as an active ingredient in many skin-lightening products worldwide, and that users are at risk of variable, and often high exposures. These synthesized findings help increase our understanding of the health risks associated with the use of these products.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".