ArticleSpatial and spatio-temporal epidemiology2011
Spatio-temporal patterning of small area low birth weight incidence and its correlates: a latent spatial structure approach.
Article in Spatial and spatio-temporal epidemiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Analysis of association between low birth weight and socioeconomic deprivation level in Japan: an ecological study using nationwide municipal data.Maternal health, neonatology and perinatology · 2022Article
- Spatial epidemiology of gestational age and birth weight in Switzerland: census-based linkage study.BMJ open · 2019Article
- Using an External Exposome Framework to Examine Pregnancy-Related Morbidities and Mortalities: Implications for Health Disparities Research.International journal of environmental research and public health · 2015Article
- Bayesian semiparametric model with spatially-temporally varying coefficients selection.Statistics in medicine · 2013Article
- A Bayesian latent model with spatio-temporally varying coefficients in low birth weight incidence data.Statistical methods in medical research · 2012Article
- Impact of Income on Small Area Low Birth Weight Incidence Using Multiscale Models.AIMS public healthArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Low birth weight (LBW) defined as infant weight at birth of less than 2500 g is a useful health outcome for exploring spatio-temporal variation and the role of covariates. LBW is a key measure of population health used by local, national and international health organizations. Yet its spatio-temporal patterns and their dependence structures are poorly understood. In this study we examine the use of flexible latent structure models for the analysis of spatio-temporal variation in LBW. Beyond the explanatory capabilities of well-known predictors, we observe spatio-temporal effects, which are not directly observable using conventional modeling approaches. Our analysis shows that for county-level counts of LBW in Georgia and South Carolina the proportion of black population is a positive risk factor while high-income is a negative risk factor. Two dominant residual temporal components are also estimated. Finally our proposed method provides a better goodness-of-fit to these data than the conventional space–time models.
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Registered trials
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