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Using latent class cluster analysis to screen high risk clusters of birth defects between 2009 and 2013 in Northwest China
In the study, we aimed to explore the synergistic effects of multiple risk factors on birth defects, and examine temporal trend of the synergistic effects over time. Two cross-sectional surveys conducted in 2009 and 2013 were merged and then latent class cluster analysis and generalized linear Poiss...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5537369/ https://www.ncbi.nlm.nih.gov/pubmed/28761054 http://dx.doi.org/10.1038/s41598-017-07076-0 |
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author | Pei, Leilei Zeng, Lingxia Zhao, Yaling Wang, Duolao Yan, Hong |
author_facet | Pei, Leilei Zeng, Lingxia Zhao, Yaling Wang, Duolao Yan, Hong |
author_sort | Pei, Leilei |
collection | PubMed |
description | In the study, we aimed to explore the synergistic effects of multiple risk factors on birth defects, and examine temporal trend of the synergistic effects over time. Two cross-sectional surveys conducted in 2009 and 2013 were merged and then latent class cluster analysis and generalized linear Poisson model were used. A total of 9085 and 29094 young children born within the last three years and their mothers were enrolled in 2009 and 2013 respectively. Three latent maternal exposure clusters were determined: a high-risk, a moderate-risk, and a low-risk cluster (88.97%, 1.49%, 9.54% in 2009 and 82.42%, 3.39%, 14.19% in 2013). The synthetic effects of maternal exposure to multiple risk factors could increase the risk of overall birth defects and cardiovascular system malformation among live births, and this risk is significantly higher in high-risk cluster than that in low-risk cluster. After adjusting for confounding factors using a generalized linear Poisson model, in high-risk cluster the prevalence of nervous system malformation decreased by approximately 2.71%, and the proportion of cardiovascular system malformation rose by 0.92% from 2009 to 2013. The Chinese government should make great efforts to provide primary prevention for those on high-risk cluster as a priority target population. |
format | Online Article Text |
id | pubmed-5537369 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-55373692017-08-03 Using latent class cluster analysis to screen high risk clusters of birth defects between 2009 and 2013 in Northwest China Pei, Leilei Zeng, Lingxia Zhao, Yaling Wang, Duolao Yan, Hong Sci Rep Article In the study, we aimed to explore the synergistic effects of multiple risk factors on birth defects, and examine temporal trend of the synergistic effects over time. Two cross-sectional surveys conducted in 2009 and 2013 were merged and then latent class cluster analysis and generalized linear Poisson model were used. A total of 9085 and 29094 young children born within the last three years and their mothers were enrolled in 2009 and 2013 respectively. Three latent maternal exposure clusters were determined: a high-risk, a moderate-risk, and a low-risk cluster (88.97%, 1.49%, 9.54% in 2009 and 82.42%, 3.39%, 14.19% in 2013). The synthetic effects of maternal exposure to multiple risk factors could increase the risk of overall birth defects and cardiovascular system malformation among live births, and this risk is significantly higher in high-risk cluster than that in low-risk cluster. After adjusting for confounding factors using a generalized linear Poisson model, in high-risk cluster the prevalence of nervous system malformation decreased by approximately 2.71%, and the proportion of cardiovascular system malformation rose by 0.92% from 2009 to 2013. The Chinese government should make great efforts to provide primary prevention for those on high-risk cluster as a priority target population. Nature Publishing Group UK 2017-07-31 /pmc/articles/PMC5537369/ /pubmed/28761054 http://dx.doi.org/10.1038/s41598-017-07076-0 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Pei, Leilei Zeng, Lingxia Zhao, Yaling Wang, Duolao Yan, Hong Using latent class cluster analysis to screen high risk clusters of birth defects between 2009 and 2013 in Northwest China |
title | Using latent class cluster analysis to screen high risk clusters of birth defects between 2009 and 2013 in Northwest China |
title_full | Using latent class cluster analysis to screen high risk clusters of birth defects between 2009 and 2013 in Northwest China |
title_fullStr | Using latent class cluster analysis to screen high risk clusters of birth defects between 2009 and 2013 in Northwest China |
title_full_unstemmed | Using latent class cluster analysis to screen high risk clusters of birth defects between 2009 and 2013 in Northwest China |
title_short | Using latent class cluster analysis to screen high risk clusters of birth defects between 2009 and 2013 in Northwest China |
title_sort | using latent class cluster analysis to screen high risk clusters of birth defects between 2009 and 2013 in northwest china |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5537369/ https://www.ncbi.nlm.nih.gov/pubmed/28761054 http://dx.doi.org/10.1038/s41598-017-07076-0 |
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