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Open Access Research article

A spatial model to predict the incidence of neural tube defects

Lianfa Li12*, Jinfeng Wang1 and Jun Wu23

Author Affiliations

1 State Key Lab of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 1305, No. A11, Rd. Datun, Anwai, Beijing, 100101, China

2 Program in Public Health, College of Health Sciences, University of California, Irvine, USA

3 Department OF Epidemiology, School of Medicine, University of California, Irvine, USA

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BMC Public Health 2012, 12:951  doi:10.1186/1471-2458-12-951

Published: 7 November 2012

Abstract

Background

Environmental exposure may play an important role in the incidences of neural tube defects (NTD) of birth defects. Their influence on NTD may likely be non-linear; few studies have considered spatial autocorrelation of residuals in the estimation of NTD risk. We aimed to develop a spatial model based on generalized additive model (GAM) plus cokriging to examine and model the expected incidences of NTD and make the inference of the incidence risk.

Methods

We developed a spatial model to predict the expected incidences of NTD at village level in Heshun County, Shanxi Province, China, a region with high NTD cases. GAM was used to establish linear and non-linear relationships between local covariates and the expected NTD incidences. We examined the following village-level covariates in the model: projected coordinates, soil types, lithodological classes, distance to watershed, rivers, faults and major roads, annual average fertilizer uses, fruit and vegetable production, gross domestic product, and the number of doctors. The residuals from GAM were assumed to be spatially auto-correlative and cokriged with regional residuals to improve the prediction. Our approach was compared with three other models, universal kriging, generalized linear regression and GAM. Cross validation was conducted for validation.

Results

Our model predicted the expected incidences of NTD well, with a good CV R2 of 0.80. Important predictive factors included the fertilizer uses, locations of the centroid of each village, the shortest distance to rivers and faults and lithological classes with significant spatial autocorrelation of residuals. Our model out-performed the other three methods by 16% or more in term of R2.

Conclusions

The variance explained by our model was approximately 80%. This modeling approach is useful for NTD epidemiological studies and intervention planning.

Keywords:
NTD; Birth defects; Residual; Spatial model; GAM