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Unsupervised clustering analysis of comprehensive health status and its influencing factors on women of childbearing age: a cross-sectional study from a province in central China

BACKGROUND: Most previous studies on women of childbearing age have focused on reproductive health and fertility intentions, and evidence regarding the comprehensive health status of women of childbearing age is limited. This study aimed to comprehensively examine the health status of women of child...

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Autores principales: He, Lu, Li, Si-Tian, Qin, Meng-Xia, Yan, Yan, La, Yuan-Yuan, Cao, Xi, Cai, Yu-Tong, Wang, Yu-Xiao, Liu, Jie, Wu, Da-Hong, Feng, Qilong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10634171/
https://www.ncbi.nlm.nih.gov/pubmed/37946124
http://dx.doi.org/10.1186/s12889-023-17096-3
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author He, Lu
Li, Si-Tian
Qin, Meng-Xia
Yan, Yan
La, Yuan-Yuan
Cao, Xi
Cai, Yu-Tong
Wang, Yu-Xiao
Liu, Jie
Wu, Da-Hong
Feng, Qilong
author_facet He, Lu
Li, Si-Tian
Qin, Meng-Xia
Yan, Yan
La, Yuan-Yuan
Cao, Xi
Cai, Yu-Tong
Wang, Yu-Xiao
Liu, Jie
Wu, Da-Hong
Feng, Qilong
author_sort He, Lu
collection PubMed
description BACKGROUND: Most previous studies on women of childbearing age have focused on reproductive health and fertility intentions, and evidence regarding the comprehensive health status of women of childbearing age is limited. This study aimed to comprehensively examine the health status of women of childbearing age through a multi-method and multi-indicator evaluation, analyze the factors that influence their overall health, and provide sound recommendations for the improvement and promotion of healthy behaviors. METHODS: Data on women of childbearing age living in Shanxi Province were collected between September 2021 and January 2022 through online and offline surveys. The k-means algorithm was used to assess health-related patterns in women, and multivariate nonconditional logistic regression was used to assess the influencing factors of women’s overall health. RESULTS: In total, 1,258 of 2,925 (43%) participants were classified as having a good health status in all five domains of the three health dimensions: quality of life, mental health, and illness. Multivariate logistic regression showed that education level, gynecological examination status, health status of family members, access to medical treatment, age, cooking preferences, diet, social support, hand washing habits, attitude toward breast cancer prevention, and awareness of reproductive health were significantly associated with different health patterns. CONCLUSIONS: The comprehensive health status of women of childbearing age in Shanxi Province is generally good; however, a large proportion of women with deficiencies in some dimensions remains. Since lifestyle greatly impacts women’s health, health education on lifestyle and health-related issues should be strengthened. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-023-17096-3.
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spelling pubmed-106341712023-11-10 Unsupervised clustering analysis of comprehensive health status and its influencing factors on women of childbearing age: a cross-sectional study from a province in central China He, Lu Li, Si-Tian Qin, Meng-Xia Yan, Yan La, Yuan-Yuan Cao, Xi Cai, Yu-Tong Wang, Yu-Xiao Liu, Jie Wu, Da-Hong Feng, Qilong BMC Public Health Research BACKGROUND: Most previous studies on women of childbearing age have focused on reproductive health and fertility intentions, and evidence regarding the comprehensive health status of women of childbearing age is limited. This study aimed to comprehensively examine the health status of women of childbearing age through a multi-method and multi-indicator evaluation, analyze the factors that influence their overall health, and provide sound recommendations for the improvement and promotion of healthy behaviors. METHODS: Data on women of childbearing age living in Shanxi Province were collected between September 2021 and January 2022 through online and offline surveys. The k-means algorithm was used to assess health-related patterns in women, and multivariate nonconditional logistic regression was used to assess the influencing factors of women’s overall health. RESULTS: In total, 1,258 of 2,925 (43%) participants were classified as having a good health status in all five domains of the three health dimensions: quality of life, mental health, and illness. Multivariate logistic regression showed that education level, gynecological examination status, health status of family members, access to medical treatment, age, cooking preferences, diet, social support, hand washing habits, attitude toward breast cancer prevention, and awareness of reproductive health were significantly associated with different health patterns. CONCLUSIONS: The comprehensive health status of women of childbearing age in Shanxi Province is generally good; however, a large proportion of women with deficiencies in some dimensions remains. Since lifestyle greatly impacts women’s health, health education on lifestyle and health-related issues should be strengthened. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-023-17096-3. BioMed Central 2023-11-09 /pmc/articles/PMC10634171/ /pubmed/37946124 http://dx.doi.org/10.1186/s12889-023-17096-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
He, Lu
Li, Si-Tian
Qin, Meng-Xia
Yan, Yan
La, Yuan-Yuan
Cao, Xi
Cai, Yu-Tong
Wang, Yu-Xiao
Liu, Jie
Wu, Da-Hong
Feng, Qilong
Unsupervised clustering analysis of comprehensive health status and its influencing factors on women of childbearing age: a cross-sectional study from a province in central China
title Unsupervised clustering analysis of comprehensive health status and its influencing factors on women of childbearing age: a cross-sectional study from a province in central China
title_full Unsupervised clustering analysis of comprehensive health status and its influencing factors on women of childbearing age: a cross-sectional study from a province in central China
title_fullStr Unsupervised clustering analysis of comprehensive health status and its influencing factors on women of childbearing age: a cross-sectional study from a province in central China
title_full_unstemmed Unsupervised clustering analysis of comprehensive health status and its influencing factors on women of childbearing age: a cross-sectional study from a province in central China
title_short Unsupervised clustering analysis of comprehensive health status and its influencing factors on women of childbearing age: a cross-sectional study from a province in central China
title_sort unsupervised clustering analysis of comprehensive health status and its influencing factors on women of childbearing age: a cross-sectional study from a province in central china
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10634171/
https://www.ncbi.nlm.nih.gov/pubmed/37946124
http://dx.doi.org/10.1186/s12889-023-17096-3
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