Quantifying geographic regions of excess stillbirth risk in the presence of spatial and spatio-temporal heterogeneity

David Zahrieh; Jacob J Oleson; Paul A Romitti

doi:10.1016/j.sste.2019.01.002

Back

Quantifying geographic regions of excess stillbirth risk in the presence of spatial and spatio-temporal heterogeneity

Journal article

Peer reviewed

Quantifying geographic regions of excess stillbirth risk in the presence of spatial and spatio-temporal heterogeneity

David Zahrieh, Jacob J Oleson and Paul A Romitti

Spatial and spatio-temporal epidemiology, Vol.29, pp.97-109

06/2019

DOI: 10.1016/j.sste.2019.01.002

PMCID: PMC7156247

PMID: 31128635

View Online

Abstract

Motivated by population-based geocoded data for Iowa stillbirths and live births delivered during 2005–2011, we sought to identify spatio-temporal variation of stillbirth risk. Our high-quality data consisting of point locations of these delivery events allows use of a Bayesian Poisson point process approach to evaluate the spatial pattern of events. With this large epidemiologic dataset, we implemented the integrated nested Laplace approximation (INLA) to fit the conditional formulation of the point process via a Bayesian hierarchical model and empirically showed that INLA, compared to Markov chain Monte Carlo (MCMC) sampling, is an attractive approach. Furthermore, we modeled the temporal variability in stillbirth to better understand how stillbirths are geographically linked over the seven-year study period and demonstrate the similarity between the conditional formulation of the spatio-temporal model and a log Gaussian Cox process governed by discrete space-time random fields. After controlling for important features of the data, the Bayesian temporal relative risk maps identified areas of increasing and decreasing stillbirth risk over the birth period, which may warrant further public health investigation in the regions identified.

Point process

Spatio-temporal heterogeneity

Bayesian

Stillbirth

Details

Title: Subtitle: Quantifying geographic regions of excess stillbirth risk in the presence of spatial and spatio-temporal heterogeneity
Creators: David Zahrieh - Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
Jacob J Oleson - Department of Biostatistics, The University of Iowa, Iowa City, IA 52242, USA
Paul A Romitti - Department of Epidemiology, The University of Iowa, Iowa City, IA 52242, USA
Resource Type: Journal article
Publication Details: Spatial and spatio-temporal epidemiology, Vol.29, pp.97-109
DOI: 10.1016/j.sste.2019.01.002
PMID: 31128635
PMCID: PMC7156247
NLM abbreviation: Spat Spatiotemporal Epidemiol
ISSN: 1877-5845
eISSN: 1877-5853
Publisher: Elsevier Ltd
Language: English
Date published: 06/2019
Academic Unit: Epidemiology; Biostatistics
Record Identifier: 9983997493002771

Metrics

15 Record Views