Association Between Water Pollution and Other Environmental Factor Exposures with Preterm Births and Potential Subsequent Birth Defects

Presentation Type

Poster

Student

Yes

Track

Epidemiology

Abstract

Key words: truncated Poisson regression, spatial-temporal modeling, Bayesian setting, environment, preterm birth.

According to World Health Organization (WHO), 25% of the children with health problems under age five are related to environmental risk factors. The percentage for preterm birth is considered to be even higher. This project studies the association between the number of preterm births at the county level in South Dakota with water and air pollution variables building on studies that have researched the association between them and preterm births individually. The preterm birth and birth defect occurrences of less than three are removed from data due to privacy concerns which lead to employing a truncated Poisson regression model. Furthermore, the Bayesian approach has been used for parameter estimation to allow for appropriate uncertainty characterization.

Start Date

2-7-2025 1:00 PM

End Date

2-7-2025 2:30 PM

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Feb 7th, 1:00 PM Feb 7th, 2:30 PM

Association Between Water Pollution and Other Environmental Factor Exposures with Preterm Births and Potential Subsequent Birth Defects

Volstorff A

Key words: truncated Poisson regression, spatial-temporal modeling, Bayesian setting, environment, preterm birth.

According to World Health Organization (WHO), 25% of the children with health problems under age five are related to environmental risk factors. The percentage for preterm birth is considered to be even higher. This project studies the association between the number of preterm births at the county level in South Dakota with water and air pollution variables building on studies that have researched the association between them and preterm births individually. The preterm birth and birth defect occurrences of less than three are removed from data due to privacy concerns which lead to employing a truncated Poisson regression model. Furthermore, the Bayesian approach has been used for parameter estimation to allow for appropriate uncertainty characterization.