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Activity Number:
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360
- Contributed Poster Presentations: Section on Bayesian Statistical Science
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #309600
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Title:
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Bayesian Estimates of Low Birth Weight Risk by U.S. County
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Author(s):
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Pallavi Dwivedi* and Quynh Nguyen
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Companies:
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University of Maryland College Park and University of Maryland College Park
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Keywords:
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low birth weight;
Bayesian;
Poisson regression;
prevention
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Abstract:
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Several studies have shown that infants with low birth weight (< 2500 grams) have greater risk of mortality and chronic diseases as compared to infants with normal birth weight. County-level estimates of low birth weight rate can be used to identify areas of the U.S. where prevention programs might have the greatest impact toward reducing the rate of low birth weight. However, estimates of low birth weight rate are available primarily at the state level. The aim of the present study was to explore the spatial pattern of low birth weight risk at the county level in the U.S. Data on birth weight was obtained from the National Association for Public Health Statistics and Information Systems for the duration 2006-2016. The raw Standardized Incidence Ratio (SIR) of low birth weight for each county was estimated by comparing observed cases relative to expected cases. The estimated raw SIRs were smoothed by log linear Bayesian Poisson regression model. Higher than expected risk of low birth weight was found in several counties in southern states. Concentrating intervention efforts on these high burden counties can be an effective strategy for preventing future cases of low birth weight.
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Authors who are presenting talks have a * after their name.
