Kirsten S Almberg1, Mary Turyk2, Rachael M Jones3, Robert Anderson4, Judith Graber5, Elizabeth Banda6, Lance A Waller7, Roger Gibson8, Leslie T Stayner9. 1. University of Illinois at Chicago, School of Public Health, Epidemiology and Biostatistics Division, 1603W. Taylor Street, Chicago, IL 60607, United States of America. Electronic address: almberg@uic.edu. 2. University of Illinois at Chicago, School of Public Health, Epidemiology and Biostatistics Division, 1603W. Taylor Street, Chicago, IL 60607, United States of America. Electronic address: mturyk1@uic.edu. 3. University of Illinois at Chicago, School of Public Health, Environmental and Occupational Health Sciences Division, 2121W. Taylor Street, Chicago, IL 60612, United States of America. Electronic address: rjones25@uic.edu. 4. University of Illinois at Chicago, School of Public Health, Epidemiology and Biostatistics Division, 1603W. Taylor Street, Chicago, IL 60607, United States of America. Electronic address: tallbob@uic.edu. 5. University of Illinois at Chicago, School of Public Health, Epidemiology and Biostatistics Division, 1603W. Taylor Street, Chicago, IL 60607, United States of America; Environmental and Occupational Health Sciences Institute, Rutgers University, 170 Frelinghuysen Road, Piscataway, NJ 08854, United States of America. Electronic address: jmg502@eohsi.rutgers.edu. 6. University of Illinois at Chicago, School of Public Health, Epidemiology and Biostatistics Division, 1603W. Taylor Street, Chicago, IL 60607, United States of America. Electronic address: ebanda2@uic.edu. 7. Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, United States of America. Electronic address: lwaller@emory.edu. 8. Missouri Department of Health and Senior Services, Division of Community and Public Health, Bureau of Environmental Health, P.O. Box 570, Jefferson City, MO 65109, United States of America. Electronic address: roger.gibson@health.mo.gov. 9. University of Illinois at Chicago, School of Public Health, Epidemiology and Biostatistics Division, 1603W. Taylor Street, Chicago, IL 60607, United States of America.
Abstract
BACKGROUND: Missouri is an agriculturally intensive state, primarily growing corn and soybeans with additional rice and cotton farming in some southeastern counties. Communities located in close proximity to pesticide-treated fields are known to have increased exposure to pesticides and may be at increased risk of adverse birth outcomes. The study aims were to assess the relationship between county-level measures of crop-specific agricultural production and adverse birth outcomes in Missouri and to evaluate the most appropriate statistical methodologies for doing so. METHODS: Potential associations between county level data on the densities of particular crops and low birth weight and preterm births were examined in Missouri between 2004-2006. Covariates considered as potential confounders and effect modifiers included gender, maternal race/ethnicity, maternal age at delivery, maternal smoking, access to prenatal care, quarter of birth, county median household income, and population density. These data were analyzed using both standard Poisson regression models as well as models allowing for temporal and spatial correlation of the data. RESULTS: There was no evidence of an association between corn, soybean, or wheat densities with low birth weight or preterm births. Significant positive associations between both rice and cotton density were observed with both low birth weight and preterm births. Model results were consistent using Poisson and alternative models accounting for spatial and temporal variability. CONCLUSIONS: The associations of rice and cotton with low birth weight and preterm births warrant further investigation. Study limitations include the ecological study design and limited available covariate information.
BACKGROUND: Missouri is an agriculturally intensive state, primarily growing corn and soybeans with additional rice and cotton farming in some southeastern counties. Communities located in close proximity to pesticide-treated fields are known to have increased exposure to pesticides and may be at increased risk of adverse birth outcomes. The study aims were to assess the relationship between county-level measures of crop-specific agricultural production and adverse birth outcomes in Missouri and to evaluate the most appropriate statistical methodologies for doing so. METHODS: Potential associations between county level data on the densities of particular crops and low birth weight and preterm births were examined in Missouri between 2004-2006. Covariates considered as potential confounders and effect modifiers included gender, maternal race/ethnicity, maternal age at delivery, maternal smoking, access to prenatal care, quarter of birth, county median household income, and population density. These data were analyzed using both standard Poisson regression models as well as models allowing for temporal and spatial correlation of the data. RESULTS: There was no evidence of an association between corn, soybean, or wheat densities with low birth weight or preterm births. Significant positive associations between both rice and cotton density were observed with both low birth weight and preterm births. Model results were consistent using Poisson and alternative models accounting for spatial and temporal variability. CONCLUSIONS: The associations of rice and cotton with low birth weight and preterm births warrant further investigation. Study limitations include the ecological study design and limited available covariate information.
Authors: Jesse J Plascak; William C Griffith; Tomomi Workman; Marissa N Smith; Eric Vigoren; Elaine M Faustman; Beti Thompson Journal: J Expo Sci Environ Epidemiol Date: 2019-04 Impact factor: 5.563