An Epidemiologic Study of Arsenic-Related Skin Disorders and Skin Cancer and the Consumption of Arsenic-Contaminated Well Waters in Huhhot, Inner Mongolia, China
Bibliographic record
Abstract
ABSTRACT Well-use histories were obtained and dermatological examinations were conducted for 3,179 of the 3,228 (98.5%) residents of 3 villages in Inner Mongolia with well water arsenic levels as high as 2,000 ppb (ug/L). Eight persons were found to have skin cancer, 172 had hyperkeratoses, 121 had dyspigmentation, 94 had both hyperkeratoses and dyspigmentation, and, strikingly, none had Blackfoot disease. All 8 subjects with skin cancer also had both hyperkeratoses and dyspigmentation. Arsenic levels were measured for 184 wells and individual well-use histories were obtained. Arsenic exposure histories were summarized as both highest arsenic concentration (highest exposure level for at least 1-year duration) and cumulative arsenic exposure (ppb-years). Sixty-nine percent of the participants had highest arsenic concentrations below 100 ppb; 71% had cumulative arsenic exposures below 2,000 ppb-years. Exposure-response analyses included frequency-weighted, simple linear regression, and most-likely estimate (hockey-stick) models. Skin cancer cases were only found for those with a highest arsenic concentration greater than 150 ppb, and those with exposure less than 150 ppb had a statistically significant deficit. A frequency-weighted model showed a threshold at 150 ppb, and a hockey-stick model showed a threshold at 122 ppb. Considerations of duration, age, latency, and misclassification did not appear to markedly affect the analysis. The non-malignant skin findings showed thresholds of 40–50 ppb in the hockey-stick models. Application of these analytic models to the data from other epidemiological studies of arsenic ingestion and malignant and non-malignant skin disorders can be used to examine patterns of arsenic carcinogenicity. Key Words: arsenic-related skin effectsskin cancer riskInner Mongoliathreshold (hockey-stick) modelView correction statement:Erratum ACKNOWLEDGMENTS This analysis was funded in part by a grant [# H75/ATH682885] to the University of Texas—Houston Medical School (Department of Dermatology) from the Agency for Toxic Substances and Disease Registry [ATSDR]. We thank the colleagues of the Huhhot Center for Disease Control and Prevention, Inner Mongolia, China [formerly, the Huhhot Sanitation and Anti-Epidemic Station] for their diligence and maintenance of the study and their follow-through on the care of the patients. We thank the residents of the three villages for providing the information upon which this study is based and the acceptance of the investigators. We thank Katharine Shelley for assistance in development of this article. This article was presented in part at the American Association for Cancer Research meeting (2006) section on chemical carcinogenesis. We wish particularly to thank Sharon S. Campolucci, project director of the ASTDR grant, whose personal encouragement, interest, and support has been greatly appreciated. The findings and conclusions in this report are those of the author(s) and do not necessarily represent the views of the Agency for Toxic Substances and Disease Registry. Notes *Well-use data missing for 45 and dermatological findings missing for 4 participants. *Age missing on four participants (three from Hei He and one from Tie Men Geng). *A-mean = arithmetic mean; A-std = arithmetic standard deviation; Min = minimum; P25 = 25th percentile value; Med = Median; P75 = 75th percentile value; Max = maximum. *A-mean = arithmetic mean; A-std = arithmetic standard deviation; G-mean = geometric mean; G-std = geometric standard deviation; Min = minimum; P25 = 25th percentile value; Med = Median; P75 = 75th percentile value; Max = maximum. *Percent of total population, i.e., those examined and with well-use history. **Skin cancer cases as a percent of subjects with listed skin disorder. +Unit risk per ppb. * p < 0.05. *Threshold significantly different from zero at p < 0.05. *Significantly different from zero at p < 0.05 (two-tail). +Significantly different from zero at p < 0.05 (one-tail). *Time interval in years from beginning of highest exposure until examination date (1992). *Double numbers indicate that two threshold points satisfactorily fit the model.
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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 | 0.002 | 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 | 0.000 | 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 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".