Hypotheses and Objectives: This two part study will (a) use refined air pollution data to examine the influence of air pollution on adverse birth outcomes such as low birth weight and premature birth, and (b) evaluate the feasibility of assembling a birth-cohort to provide saliva samples for the analysis of genetic susceptibility to the effects of air pollution on birth outcomes. The study will make use of several existing data sources from Fresno and Bakersfield, CA, two areas with some of the highest ozone and particulate concentrations in the U.S. This project will lay the groundwork for the analysis of genetic polymorphisms that may modify the influence of air pollution on adverse birth outcomes, and ultimately, influence the development of asthma. Methods: Part A involves linking exposure data from the California Regional PM10/PM2.5 Air Quality Study (CRPAQS) to California birth certificate data to evaluate the impact on adverse birth outcomes such as premature birth and low birth weight. The data will be limited to Fresno and Bakersfield, California, for the years 2000-2004, providing an estimated 68,000 births. We will also examine the role of traffic exposure, as measured by the California Department of Transportation on birth outcomes. Part B involves a case-control design where 500 low birth weight and/or preterm infants will be counter-matched with 500 normal infants. The mothers of these 1000 infants will be sent a short health survey and be asked to provide a maternal and child saliva sample via the mail. The saliva samples will be used for future analysis of gene-environment interactions. Additional survey data will be collected to address confounding and effect-modification of the pollution/birth outcomes association. Expected Benefits: This proposal directly addresses NIEHS's research priorities regarding early life exposures and subsequent environmentally induced health effects such as adverse birth outcomes. It will make use of the most detailed and highly refined air pollution data ever collected. It will lay the groundwork to examine the impact of genetic polymorphisms that may identify susceptible children and pregnant women. It will allow for the extrapolation of small data sets to obtain population based estimates of disease burden, while establishing a birth cohort and specimen bank for future analyses of both maternal and child gene-environment interactions. Given that the children who we will target are now up to age 8 years, this may be a last chance opportunity to establish a birth cohort. Study designs that allow for cost-efficient participant recruitment and sample collection would guide future cohort studies of children's environmental health. Project Narrative--Expected Benefits: These research questions directly address NIEHS's research priorities regarding early life exposures and subsequent environmentally induced health effects such as adverse birth outcomes. It will make use of the most detailed and highly refined air pollution data ever collected. It will lay the groundwork to examine the impact of genetic polymorphisms that may identify susceptible children and pregnant women. It will allow for the extrapolation of small data sets to obtain population based estimates of disease burden, while establishing a birth cohort and specimen bank for future analyses of both maternal and child gene-environment interactions. Given that the children who we will target are now up to age 8 years, this may be a last chance opportunity to establish a birth cohort. Study designs that allow for cost-efficient participant recruitment and sample collection would guide future cohort studies of children's environmental health. ? ? ?
| Padula, Amy M; Mortimer, Kathleen M; Tager, Ira B et al. (2014) Traffic-related air pollution and risk of preterm birth in the San Joaquin Valley of California. Ann Epidemiol 24:888-95e4 |
| Padula, Amy M; Mortimer, Kathleen; Hubbard, Alan et al. (2012) Exposure to traffic-related air pollution during pregnancy and term low birth weight: estimation of causal associations in a semiparametric model. Am J Epidemiol 176:815-24 |
