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

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,...

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Autores principales: Wu, Jilei, Wang, Jinfeng, Meng, Bin, Chen, Gong, Pang, Lihua, Song, Xinming, Zhang, Keli, Zhang, Ting, Zheng, Xiaoying
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2004
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC441386/
https://www.ncbi.nlm.nih.gov/pubmed/15202947
http://dx.doi.org/10.1186/1471-2458-4-23
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author Wu, Jilei
Wang, Jinfeng
Meng, Bin
Chen, Gong
Pang, Lihua
Song, Xinming
Zhang, Keli
Zhang, Ting
Zheng, Xiaoying
author_facet Wu, Jilei
Wang, Jinfeng
Meng, Bin
Chen, Gong
Pang, Lihua
Song, Xinming
Zhang, Keli
Zhang, Ting
Zheng, Xiaoying
author_sort Wu, Jilei
collection PubMed
description 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 [Image: see text] [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 [Image: see text] 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.
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spelling pubmed-4413862004-07-02 Exploratory spatial data analysis for the identification of risk factors to birth defects Wu, Jilei Wang, Jinfeng Meng, Bin Chen, Gong Pang, Lihua Song, Xinming Zhang, Keli Zhang, Ting Zheng, Xiaoying BMC Public Health Research Article 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 [Image: see text] [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 [Image: see text] 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. BioMed Central 2004-06-18 /pmc/articles/PMC441386/ /pubmed/15202947 http://dx.doi.org/10.1186/1471-2458-4-23 Text en Copyright © 2004 Wu et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.
spellingShingle Research Article
Wu, Jilei
Wang, Jinfeng
Meng, Bin
Chen, Gong
Pang, Lihua
Song, Xinming
Zhang, Keli
Zhang, Ting
Zheng, Xiaoying
Exploratory spatial data analysis for the identification of risk factors to birth defects
title Exploratory spatial data analysis for the identification of risk factors to birth defects
title_full Exploratory spatial data analysis for the identification of risk factors to birth defects
title_fullStr Exploratory spatial data analysis for the identification of risk factors to birth defects
title_full_unstemmed Exploratory spatial data analysis for the identification of risk factors to birth defects
title_short Exploratory spatial data analysis for the identification of risk factors to birth defects
title_sort exploratory spatial data analysis for the identification of risk factors to birth defects
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC441386/
https://www.ncbi.nlm.nih.gov/pubmed/15202947
http://dx.doi.org/10.1186/1471-2458-4-23
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