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Open Access Research article

Exploratory spatial data analysis for the identification of risk factors to birth defects

Jilei Wu12, Jinfeng Wang1, Bin Meng1, Gong Chen2, Lihua Pang2, Xinming Song2, Keli Zhang3, Ting Zhang4 and Xiaoying Zheng2*

Author Affiliations

1 Institute of Geographical Sciences and Nature Resources Research, CAS, Beijing, 100101, P. R. China

2 Institute of Population Research, Peking University, Beijing, 100871, P. R. China

3 Department of Resources and Environment, Peking Normal University, Beijing, 100875, P. R. China

4 Capital Institute of Pediatrics, Beijing, 100020, P. R. China

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BMC Public Health 2004, 4:23  doi:10.1186/1471-2458-4-23

Published: 18 June 2004

Abstract

Background

Birth defects, which are the major cause of infant mortality and a leading cause of disability, refer to "Any anomaly, functional or structural, that presents in infancy or later in life and is caused by events preceding birth, whether inherited, or acquired (ICBDMS)". However, the risk factors associated with heredity and/or environment are very difficult to filter out accurately. This study selected an area with the highest ratio of neural-tube birth defect (NTBD) occurrences worldwide to identify the scale of environmental risk factors for birth defects using exploratory spatial data analysis methods.

Methods

By birth defect registers based on hospital records and investigation in villages, the number of birth defects cases within a four-year period was acquired and classified by organ system. The neural-tube birth defect ratio was calculated according to the number of births planned for each village in the study area, as the family planning policy is strictly adhered to in China. The Bayesian modeling method was used to estimate the ratio in order to remove the dependence of variance caused by different populations in each village. A recently developed statistical spatial method for detecting hotspots, Getis's [7], was used to detect the high-risk regions for neural-tube birth defects in the study area.

Results

After the Bayesian modeling method was used to calculate the ratio of neural-tube birth defects occurrences, Getis's statistics method was used in different distance scales. Two typical clustering phenomena were present in the study area. One was related to socioeconomic activities, and the other was related to soil type distributions.

Conclusion

The fact that there were two typical hotspot clustering phenomena provides evidence that the risk for neural-tube birth defect exists on two different scales (a socioeconomic scale at 6.84 km and a soil type scale at 22.8 km) for the area studied. Although our study has limited spatial exploratory data for the analysis of the neural-tube birth defect occurrence ratio and for finding clues to risk factors, this result provides effective clues for further physical, chemical and even more molecular laboratory testing according to these two spatial scales.