Catherine M Bulka1, Molly Scannell Bryan2, Melissa A Lombard3, Scott M Bartell4, Daniel K Jones5, Paul M Bradley6, Veronica M Vieira7, Debra T Silverman8, Michael Focazio9, Patricia L Toccalino10, Johnni Daniel11, Lorraine C Backer12, Joseph D Ayotte13, Matthew O Gribble14, Maria Argos15. 1. Department of Environmental Sciences and Engineering, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC 27599, USA. Electronic address: cbulka@email.unc.edu. 2. Institute for Minority Health Research, University of Illinois at Chicago, 1819 W. Polk Street, Chicago, IL 60612, USA. Electronic address: scannemo@uic.edu. 3. U.S. Geological Survey, New England Water Science Center, 331 Commerce Way, Pembroke, NH 03275, USA. Electronic address: mlombard@usgs.gov. 4. Department of Environmental and Occupational Health, University of California, 653 E. Peltason Drive, Irvine, CA 92697, USA; Department of Statistics, University of California, Bren Hall 2019, Irvine, CA 92697, USA. Electronic address: sbartell@uci.edu. 5. U.S. Geological Survey, Utah Water Science Center, 2329 West Orton Circle, West Valley City, UT 84119, USA. Electronic address: dkjones@usgs.gov. 6. U.S. Geological Survey, South Atlantic Water Science Center, 720 Gracern Rd, Columbia, SC 29210, USA. Electronic address: pbradley@usgs.gov. 7. Department of Environmental and Occupational Health, University of California, 653 E. Peltason Drive, Irvine, CA 92697, USA. Electronic address: vvieira@uci.edu. 8. Occupational and Environmental Epidemiology Branch, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20850, USA. Electronic address: silvermd@mail.nih.gov. 9. U.S. Geological Survey, National Center, 12201 Sunrise Valley Dr, Reston, VA 20192, USA. Electronic address: mfocazio@usgs.gov. 10. U.S. Geological Survey, Northwest-Pacific Region, 2130 SW 5th Ave, Portland, OR 97201, USA. Electronic address: ptocca@usgs.gov. 11. National Center for Environmental Health, Centers for Disease Control and Prevention, 4770 Buford Highway NE, Atlanta, GA 30341, USA. Electronic address: bez2@cdc.gov. 12. National Center for Environmental Health, Centers for Disease Control and Prevention, 4770 Buford Highway NE, Atlanta, GA 30341, USA. Electronic address: lfb9@cdc.gov. 13. U.S. Geological Survey, New England Water Science Center, 331 Commerce Way, Pembroke, NH 03275, USA. Electronic address: jayotte@usgs.gov. 14. Department of Epidemiology, University of Alabama at Birmingham, 217G Ryals Public Health Building, 1665 University Boulevard, Birmingham AL 35294, USA. Electronic address: mgribble@uab.edu. 15. Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Office 878A, Chicago, IL 60612, USA. Electronic address: argos@uic.edu.
Abstract
BACKGROUND: Prenatal exposure to drinking water with arsenic concentrations >50 μg/L is associated with adverse birth outcomes, with inconclusive evidence for concentrations ≤50 μg/L. In a collaborative effort by public health experts, hydrologists, and geologists, we used published machine learning model estimates to characterize arsenic concentrations in private wells-federally unregulated for drinking water contaminants-and evaluated associations with birth outcomes throughout the conterminous U.S. METHODS: Using several machine learning models, including boosted regression trees (BRT) and random forest classification (RFC), developed from measured groundwater arsenic concentrations of ∼20,000 private wells, we characterized the probability that arsenic concentrations occurred within specific ranges in groundwater. Probabilistic model estimates and private well usage data were linked by county to all live birth certificates from 2016 (n = 3.6 million). We evaluated associations with gestational age and term birth weight using mixed-effects models, adjusted for potential confounders and incorporated random intercepts for spatial clustering. RESULTS: We generally observed inverse associations with term birth weight. For instance, when using BRT estimates, a 10-percentage point increase in the probability that private well arsenic concentrations exceeded 5 μg/L was associated with a -1.83 g (95% CI: -3.30, -0.38) lower term birth weight after adjusting for covariates. Similarly, a 10-percentage point increase in the probability that private well arsenic concentrations exceeded 10 μg/L was associated with a -2.79 g (95% CI: -4.99, -0.58) lower term birth weight. Associations with gestational age were null. CONCLUSION: In this largest epidemiologic study of arsenic and birth outcomes to date, we did not observe associations of modeled arsenic estimates in private wells with gestational age and found modest inverse associations with term birth weight. Study limitations may have obscured true associations, including measurement error stemming from a lack of individual-level information on primary water sources, water arsenic concentrations, and water consumption patterns.
BACKGROUND: Prenatal exposure to drinking water with arsenic concentrations >50 μg/L is associated with adverse birth outcomes, with inconclusive evidence for concentrations ≤50 μg/L. In a collaborative effort by public health experts, hydrologists, and geologists, we used published machine learning model estimates to characterize arsenic concentrations in private wells-federally unregulated for drinking water contaminants-and evaluated associations with birth outcomes throughout the conterminous U.S. METHODS: Using several machine learning models, including boosted regression trees (BRT) and random forest classification (RFC), developed from measured groundwater arsenic concentrations of ∼20,000 private wells, we characterized the probability that arsenic concentrations occurred within specific ranges in groundwater. Probabilistic model estimates and private well usage data were linked by county to all live birth certificates from 2016 (n = 3.6 million). We evaluated associations with gestational age and term birth weight using mixed-effects models, adjusted for potential confounders and incorporated random intercepts for spatial clustering. RESULTS: We generally observed inverse associations with term birth weight. For instance, when using BRT estimates, a 10-percentage point increase in the probability that private well arsenic concentrations exceeded 5 μg/L was associated with a -1.83 g (95% CI: -3.30, -0.38) lower term birth weight after adjusting for covariates. Similarly, a 10-percentage point increase in the probability that private well arsenic concentrations exceeded 10 μg/L was associated with a -2.79 g (95% CI: -4.99, -0.58) lower term birth weight. Associations with gestational age were null. CONCLUSION: In this largest epidemiologic study of arsenic and birth outcomes to date, we did not observe associations of modeled arsenic estimates in private wells with gestational age and found modest inverse associations with term birth weight. Study limitations may have obscured true associations, including measurement error stemming from a lack of individual-level information on primary water sources, water arsenic concentrations, and water consumption patterns.
Authors: Kirsten S Almberg; Mary E Turyk; Rachael M Jones; Kristin Rankin; Sally Freels; Judith M Graber; Leslie T Stayner Journal: Environ Res Date: 2017-05-16 Impact factor: 6.498
Authors: Anne E Nigra; Tiffany R Sanchez; Keeve E Nachman; David Harvey; Steven N Chillrud; Joseph H Graziano; Ana Navas-Acien Journal: Lancet Public Health Date: 2017-11
Authors: Melissa A Lombard; Molly Scannell Bryan; Daniel K Jones; Catherine Bulka; Paul M Bradley; Lorraine C Backer; Michael J Focazio; Debra T Silverman; Patricia Toccalino; Maria Argos; Matthew O Gribble; Joseph D Ayotte Journal: Environ Sci Technol Date: 2021-03-17 Impact factor: 9.028