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Screening high-risk clusters for developing birth defects in mothers in Shanxi Province, China: application of latent class cluster analysis
BACKGROUND: Few studies on cluster-based synthetic effects of multiple risk factors for birth defects have been reported. The present study aimed to identify maternal exposure clusters, explore the association between clusters of risk factors and birth defects, and further screen women with high ris...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687365/ https://www.ncbi.nlm.nih.gov/pubmed/26694165 http://dx.doi.org/10.1186/s12884-015-0783-x |
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author | Cao, Hongyan Wei, Xiaoyuan Guo, Xingping Song, Chunying Luo, Yanhong Cui, Yuehua Hu, Xianming Zhang, Yanbo |
author_facet | Cao, Hongyan Wei, Xiaoyuan Guo, Xingping Song, Chunying Luo, Yanhong Cui, Yuehua Hu, Xianming Zhang, Yanbo |
author_sort | Cao, Hongyan |
collection | PubMed |
description | BACKGROUND: Few studies on cluster-based synthetic effects of multiple risk factors for birth defects have been reported. The present study aimed to identify maternal exposure clusters, explore the association between clusters of risk factors and birth defects, and further screen women with high risk for birth defects among expectant mothers. METHODS: Data were drawn from a large-scale, retrospective epidemiological survey of birth defects from 2006 to 2008 in six counties of Shanxi Province, China, using a three-level stratified random cluster sampling technique. Overall risk factors were extracted using eight synthetic variables summed and examined as a total risk factor score: maternal delivery age, genetic factors, medical history, nutrition and folic acid deficiency, maternal illness in pregnancy, drug use in pregnancy, environmental risk factors in pregnancy, and unhealthy maternal lifestyle in pregnancy. Latent class cluster analysis was used to identify maternal exposure clusters based on these synthetic variables. Adjusted odds ratios (AOR) were used to explore associations between clusters and birth defects, after adjusting for confounding variables using logistic regression. RESULTS: Three latent maternal exposure clusters were identified: a high-risk (6.15 %), a moderate-risk (22.39 %), and a low-risk (71.46 %) cluster. The prevalence of birth defects was 14.08 %, 0.85 %, and 0.52 % for the high-, middle- and low-risk clusters respectively. After adjusting for maternal demographic variables, women in the high-risk cluster were nearly 31 times (AOR: 30.61, 95 % CI: [24.87, 37.67]) more likely to have an infant with birth defects than low-risk women. CONCLUSIONS: A high-risk group of mothers in an area with a high risk for birth defects were screened in our study. Targeted interventions should be conducted with women of reproductive age to improve neonatal birth outcomes in areas with a high risk of birth defects. |
format | Online Article Text |
id | pubmed-4687365 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-46873652015-12-23 Screening high-risk clusters for developing birth defects in mothers in Shanxi Province, China: application of latent class cluster analysis Cao, Hongyan Wei, Xiaoyuan Guo, Xingping Song, Chunying Luo, Yanhong Cui, Yuehua Hu, Xianming Zhang, Yanbo BMC Pregnancy Childbirth Research Article BACKGROUND: Few studies on cluster-based synthetic effects of multiple risk factors for birth defects have been reported. The present study aimed to identify maternal exposure clusters, explore the association between clusters of risk factors and birth defects, and further screen women with high risk for birth defects among expectant mothers. METHODS: Data were drawn from a large-scale, retrospective epidemiological survey of birth defects from 2006 to 2008 in six counties of Shanxi Province, China, using a three-level stratified random cluster sampling technique. Overall risk factors were extracted using eight synthetic variables summed and examined as a total risk factor score: maternal delivery age, genetic factors, medical history, nutrition and folic acid deficiency, maternal illness in pregnancy, drug use in pregnancy, environmental risk factors in pregnancy, and unhealthy maternal lifestyle in pregnancy. Latent class cluster analysis was used to identify maternal exposure clusters based on these synthetic variables. Adjusted odds ratios (AOR) were used to explore associations between clusters and birth defects, after adjusting for confounding variables using logistic regression. RESULTS: Three latent maternal exposure clusters were identified: a high-risk (6.15 %), a moderate-risk (22.39 %), and a low-risk (71.46 %) cluster. The prevalence of birth defects was 14.08 %, 0.85 %, and 0.52 % for the high-, middle- and low-risk clusters respectively. After adjusting for maternal demographic variables, women in the high-risk cluster were nearly 31 times (AOR: 30.61, 95 % CI: [24.87, 37.67]) more likely to have an infant with birth defects than low-risk women. CONCLUSIONS: A high-risk group of mothers in an area with a high risk for birth defects were screened in our study. Targeted interventions should be conducted with women of reproductive age to improve neonatal birth outcomes in areas with a high risk of birth defects. BioMed Central 2015-12-22 /pmc/articles/PMC4687365/ /pubmed/26694165 http://dx.doi.org/10.1186/s12884-015-0783-x Text en © Cao et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Cao, Hongyan Wei, Xiaoyuan Guo, Xingping Song, Chunying Luo, Yanhong Cui, Yuehua Hu, Xianming Zhang, Yanbo Screening high-risk clusters for developing birth defects in mothers in Shanxi Province, China: application of latent class cluster analysis |
title | Screening high-risk clusters for developing birth defects in mothers in Shanxi Province, China: application of latent class cluster analysis |
title_full | Screening high-risk clusters for developing birth defects in mothers in Shanxi Province, China: application of latent class cluster analysis |
title_fullStr | Screening high-risk clusters for developing birth defects in mothers in Shanxi Province, China: application of latent class cluster analysis |
title_full_unstemmed | Screening high-risk clusters for developing birth defects in mothers in Shanxi Province, China: application of latent class cluster analysis |
title_short | Screening high-risk clusters for developing birth defects in mothers in Shanxi Province, China: application of latent class cluster analysis |
title_sort | screening high-risk clusters for developing birth defects in mothers in shanxi province, china: application of latent class cluster analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687365/ https://www.ncbi.nlm.nih.gov/pubmed/26694165 http://dx.doi.org/10.1186/s12884-015-0783-x |
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