Journal article
Bayesian Spatial-temporal Model for Cardiac Congenital Anomalies and Ambient Air Pollution Risk Assessment
Environmetrics (London, Ont.), Vol.23(8), pp.673-684
12/01/2012
DOI: 10.1002/env.2174
PMCID: PMC3589577
PMID: 23482298
Abstract
We introduce a Bayesian spatial-temporal hierarchical multivariate probit regression model that identifies weeks during the first trimester of pregnancy which are impactful in terms of cardiac congenital anomaly development. The model is able to consider multiple pollutants and a multivariate cardiac anomaly grouping outcome jointly while allowing the critical windows to vary in a continuous manner across time and space. We utilize a dataset of numerical chemical model output which contains information regarding multiple species of PM
2.5
. Our introduction of an innovative spatial-temporal semiparametric prior distribution for the pollution risk effects allows for greater flexibility to identify critical weeks during pregnancy which are missed when more standard models are applied. The multivariate kernel stick-breaking prior is extended to include space and time simultaneously in both the locations and the masses in order to accommodate complex data settings. Simulation study results suggest that our prior distribution has the flexibility to outperform competitor models in a number of data settings. When applied to the geo-coded Texas birth data, weeks 3, 7 and 8 of the pregnancy are identified as being impactful in terms of cardiac defect development for multiple pollutants across the spatial domain.
Details
- Title: Subtitle
- Bayesian Spatial-temporal Model for Cardiac Congenital Anomalies and Ambient Air Pollution Risk Assessment
- Creators
- Joshua Warren - Department of Biostatistics, University of North Carolina at Chapel Hill, U.S.AMontserrat Fuentes - Department of Statistics, North Carolina State University, U.S.AAmy Herring - Department of Biostatistics, University of North Carolina at Chapel Hill, U.S.APeter Langlois - Texas Department of State Health Services, U.S.A
- Resource Type
- Journal article
- Publication Details
- Environmetrics (London, Ont.), Vol.23(8), pp.673-684
- DOI
- 10.1002/env.2174
- PMID
- 23482298
- PMCID
- PMC3589577
- NLM abbreviation
- Environmetrics
- ISSN
- 1180-4009
- eISSN
- 1099-095X
- Grant note
- R01 ES014843 || ES / National Institute of Environmental Health Sciences : NIEHS
- Language
- English
- Date published
- 12/01/2012
- Academic Unit
- Statistics and Actuarial Science; President; Biostatistics
- Record Identifier
- 9984065767702771
Metrics
32 Record Views