Steven H Lamm1, Shayhan Robbins2, Rusan Chen3, Jun Lu4, Brian Goodrich2, Manning Feinleib5. 1. Center for Epidemiology and Global Health, CEOH, LLC., Washington, D.C., USA; Johns Hopkins University-Bloomberg School of Public Health, Baltimore, MD, USA; Georgetown University School of Medicine, Washington, D.C., USA. Electronic address: Steve@ceoh.com. 2. Center for Epidemiology and Global Health, CEOH, LLC., Washington, D.C., USA. 3. Georgetown University Graduate School, Washington, D.C., USA. 4. American University Department of Mathematics, Washington, D.C., USA. 5. Johns Hopkins University-Bloomberg School of Public Health, Baltimore, MD, USA.
Abstract
BACKGROUND: The ingestion of inorganic arsenic causes bladder and lung cancers demonstrably at >400-500ug/L but questionably below 100-200ug/L. Using the standard 42-village cancer mortality dataset from the Blackfoot-disease (BFD) endemic area of southwest Taiwan (Wu et al., 1989), we examined the risk from low exposures by excluding the high exposures. METHOD: Poisson regression analyses with the sequential removal of the highest exposure village have been performed using the median, mean, or maximum village well water arsenic level and demonstrated graphically. RESULTS: Risk estimates are positive when villages with exposures of 200-400ug/L are included and significantly so when villages with >400ug/L are included. Risk estimates for exposures below 100ug/L are negative but rarely significantly so. The inflection point where the slope is no longer positive occurs in the range of 100-200ug/L, depending upon whether the exposure metric used is the median, the mean or the maximum. CONCLUSION: There is a discontinuity in the cancer slope factor or risk from arsenic exposure that occurs in the range of 100-200ug/L. Above these levels, there are significantly positive risks, while below these levels there are not. The analysis reveals within this dataset an intrinsic non-linearity in the cancer risk. The literature speaks to this discontinuity, but this is the first demonstration within a single dataset that shows the discontinuity across the full exposure range and where the low-dose data are not compromised with high-dose data.
BACKGROUND: The ingestion of inorganic arsenic causes bladder and lung cancers demonstrably at >400-500ug/L but questionably below 100-200ug/L. Using the standard 42-village cancer mortality dataset from the Blackfoot-disease (BFD) endemic area of southwest Taiwan (Wu et al., 1989), we examined the risk from low exposures by excluding the high exposures. METHOD: Poisson regression analyses with the sequential removal of the highest exposure village have been performed using the median, mean, or maximum village well waterarsenic level and demonstrated graphically. RESULTS: Risk estimates are positive when villages with exposures of 200-400ug/L are included and significantly so when villages with >400ug/L are included. Risk estimates for exposures below 100ug/L are negative but rarely significantly so. The inflection point where the slope is no longer positive occurs in the range of 100-200ug/L, depending upon whether the exposure metric used is the median, the mean or the maximum. CONCLUSION: There is a discontinuity in the cancer slope factor or risk from arsenic exposure that occurs in the range of 100-200ug/L. Above these levels, there are significantly positive risks, while below these levels there are not. The analysis reveals within this dataset an intrinsic non-linearity in the cancer risk. The literature speaks to this discontinuity, but this is the first demonstration within a single dataset that shows the discontinuity across the full exposure range and where the low-dose data are not compromised with high-dose data.
Authors: Hamid Ferdosi; Elisabeth K Dissen; Nana Ama Afari-Dwamena; Ji Li; Rusan Chen; Manning Feinleib; Steven H Lamm Journal: J Environ Public Health Date: 2016-06-13
Authors: Alesia C Ferguson; Jennifer C Black; Isaac B Sims; Jennifer N Welday; Samir M Elmir; Kendra F Goff; J Mark Higginbotham; Helena M Solo-Gabriele Journal: Int J Environ Res Public Health Date: 2018-01-04 Impact factor: 3.390
Authors: Noelle Liao; Edmund Seto; Brenda Eskenazi; May Wang; Yan Li; Jenna Hua Journal: Int J Environ Res Public Health Date: 2018-10-08 Impact factor: 3.390