{"title":"根据伊朗妇女的更年期严重症状进行聚类,并使用基线类别logit模型探索与严重类别相关的因素。","authors":"Fahimeh Hoseinzadeh, Habibollah Esmaily, Sedigheh Ayatiafin, Azadeh Saki","doi":"10.1186/s12905-024-03511-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Many studies reported that the factors associated with the intensity of menopausal symptoms vary according to race, culture, and ethnicity. Different instruments, measure severe menopausal symptoms. The present study aims to classify Iranian women between 42 and 60 years according to the similarity of menopausal severity symptoms and then find the risk factors related to allocating in severe symptoms groups.</p><p><strong>Method: </strong>In this cross-sectional study, 664 women aged 42-60, living in Mashhad, Iran were collected. The Menopause Severity Symptoms Inventory (MSSI-38) was used to collect information about menopausal symptoms. K-Means clustering algorithm was applied to classify women with different menopausal symptoms in separate groups. The baseline category logit model and ANOVA were used to find the associated factors and covariates with clusters.</p><p><strong>Result: </strong>K-Means clustering algorithm, extracted three major clusters based on different menopausal symptoms. The first cluster involved 301 (45%) women with mild symptoms, the second was a cluster of moderate symptoms women with size 131 (20%). The remaining 232 (35%) of women were placed in the third cluster. The baseline category logit model showed that Compared to Cluster 1, Cluster 2 is associated with a higher underlying diseases (OR = 1.51, P-value = 0.03), lack of physical activity (OR = 1.79, P-value = 0.003), having more than five pregnancies (OR = 2.11, P-value = 0.017), and being peri menopause (OR = 1.71, P-value = 0.03). In contrast, Cluster 3 shows an even stronger association with underlying diseases (OR = 3.71, P-value < 0.001), physical activity (OR = 2.46, P-value = 0.001), insufficient income (OR = 3.43, P-value < 0.001, and being peri menopause (OR = 2.09, P-value = 0.029) or post menopause (OR = 2.02, P-value = 0.044) when compared to Cluster 1.</p><p><strong>Conclusion: </strong>Based on these findings, women with underlying diseases, varying levels of physical activity, different income levels, different number of pregnancies, and menopause status in the moderate (Cluster 2) and severe symptomatic groups (Cluster 3) exhibited significant differences compared to those in the mild symptomatic group (Cluster 1). These results underscore the necessity for targeted interventions, such as promoting physical activity and providing mental health support, to alleviate menopausal symptoms. Additionally, further research is essential to identify the causal factors contributing to these symptoms, which could lead to improved care and health policies for women experiencing menopause.</p>","PeriodicalId":9204,"journal":{"name":"BMC Women's Health","volume":"24 1","pages":"653"},"PeriodicalIF":2.4000,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11662853/pdf/","citationCount":"0","resultStr":"{\"title\":\"Clustering Iranian women according to their menopausal severity symptoms and exploring the factors associated with severe categories, using baseline category logit model.\",\"authors\":\"Fahimeh Hoseinzadeh, Habibollah Esmaily, Sedigheh Ayatiafin, Azadeh Saki\",\"doi\":\"10.1186/s12905-024-03511-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Many studies reported that the factors associated with the intensity of menopausal symptoms vary according to race, culture, and ethnicity. Different instruments, measure severe menopausal symptoms. The present study aims to classify Iranian women between 42 and 60 years according to the similarity of menopausal severity symptoms and then find the risk factors related to allocating in severe symptoms groups.</p><p><strong>Method: </strong>In this cross-sectional study, 664 women aged 42-60, living in Mashhad, Iran were collected. The Menopause Severity Symptoms Inventory (MSSI-38) was used to collect information about menopausal symptoms. K-Means clustering algorithm was applied to classify women with different menopausal symptoms in separate groups. The baseline category logit model and ANOVA were used to find the associated factors and covariates with clusters.</p><p><strong>Result: </strong>K-Means clustering algorithm, extracted three major clusters based on different menopausal symptoms. The first cluster involved 301 (45%) women with mild symptoms, the second was a cluster of moderate symptoms women with size 131 (20%). The remaining 232 (35%) of women were placed in the third cluster. The baseline category logit model showed that Compared to Cluster 1, Cluster 2 is associated with a higher underlying diseases (OR = 1.51, P-value = 0.03), lack of physical activity (OR = 1.79, P-value = 0.003), having more than five pregnancies (OR = 2.11, P-value = 0.017), and being peri menopause (OR = 1.71, P-value = 0.03). In contrast, Cluster 3 shows an even stronger association with underlying diseases (OR = 3.71, P-value < 0.001), physical activity (OR = 2.46, P-value = 0.001), insufficient income (OR = 3.43, P-value < 0.001, and being peri menopause (OR = 2.09, P-value = 0.029) or post menopause (OR = 2.02, P-value = 0.044) when compared to Cluster 1.</p><p><strong>Conclusion: </strong>Based on these findings, women with underlying diseases, varying levels of physical activity, different income levels, different number of pregnancies, and menopause status in the moderate (Cluster 2) and severe symptomatic groups (Cluster 3) exhibited significant differences compared to those in the mild symptomatic group (Cluster 1). These results underscore the necessity for targeted interventions, such as promoting physical activity and providing mental health support, to alleviate menopausal symptoms. Additionally, further research is essential to identify the causal factors contributing to these symptoms, which could lead to improved care and health policies for women experiencing menopause.</p>\",\"PeriodicalId\":9204,\"journal\":{\"name\":\"BMC Women's Health\",\"volume\":\"24 1\",\"pages\":\"653\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11662853/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Women's Health\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12905-024-03511-3\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OBSTETRICS & GYNECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Women's Health","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12905-024-03511-3","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
Clustering Iranian women according to their menopausal severity symptoms and exploring the factors associated with severe categories, using baseline category logit model.
Introduction: Many studies reported that the factors associated with the intensity of menopausal symptoms vary according to race, culture, and ethnicity. Different instruments, measure severe menopausal symptoms. The present study aims to classify Iranian women between 42 and 60 years according to the similarity of menopausal severity symptoms and then find the risk factors related to allocating in severe symptoms groups.
Method: In this cross-sectional study, 664 women aged 42-60, living in Mashhad, Iran were collected. The Menopause Severity Symptoms Inventory (MSSI-38) was used to collect information about menopausal symptoms. K-Means clustering algorithm was applied to classify women with different menopausal symptoms in separate groups. The baseline category logit model and ANOVA were used to find the associated factors and covariates with clusters.
Result: K-Means clustering algorithm, extracted three major clusters based on different menopausal symptoms. The first cluster involved 301 (45%) women with mild symptoms, the second was a cluster of moderate symptoms women with size 131 (20%). The remaining 232 (35%) of women were placed in the third cluster. The baseline category logit model showed that Compared to Cluster 1, Cluster 2 is associated with a higher underlying diseases (OR = 1.51, P-value = 0.03), lack of physical activity (OR = 1.79, P-value = 0.003), having more than five pregnancies (OR = 2.11, P-value = 0.017), and being peri menopause (OR = 1.71, P-value = 0.03). In contrast, Cluster 3 shows an even stronger association with underlying diseases (OR = 3.71, P-value < 0.001), physical activity (OR = 2.46, P-value = 0.001), insufficient income (OR = 3.43, P-value < 0.001, and being peri menopause (OR = 2.09, P-value = 0.029) or post menopause (OR = 2.02, P-value = 0.044) when compared to Cluster 1.
Conclusion: Based on these findings, women with underlying diseases, varying levels of physical activity, different income levels, different number of pregnancies, and menopause status in the moderate (Cluster 2) and severe symptomatic groups (Cluster 3) exhibited significant differences compared to those in the mild symptomatic group (Cluster 1). These results underscore the necessity for targeted interventions, such as promoting physical activity and providing mental health support, to alleviate menopausal symptoms. Additionally, further research is essential to identify the causal factors contributing to these symptoms, which could lead to improved care and health policies for women experiencing menopause.
期刊介绍:
BMC Women''s Health is an open access, peer-reviewed journal that considers articles on all aspects of the health and wellbeing of adolescent girls and women, with a particular focus on the physical, mental, and emotional health of women in developed and developing nations. The journal welcomes submissions on women''s public health issues, health behaviours, breast cancer, gynecological diseases, mental health and health promotion.