Evidence map›Paper›PMID 39709408›Full record

ArticleBMC women's health2024

Clustering Iranian women according to their menopausal severity symptoms and exploring the factors associated with severe categories, using baseline category logit model.

Fahimeh Hoseinzadeh, Habibollah Esmaily, Sedigheh Ayatiafin, Azadeh Saki

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Article in BMC women's health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Fahimeh HoseinzadehDepartment of Epidemiology and Biostatistics, School of Health, Mashhad University of Medical sciences, Mashhad, Iran.
Habibollah EsmailyDepartment of Epidemiology and Biostatistics, School of Health, Mashhad University of Medical sciences, Mashhad, Iran. esmailyh@mums.ac.ir.
Sedigheh AyatiafinDepartment of Obstetrics and Gynecology, School of Medicine, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran.
Azadeh SakiDepartment of Epidemiology and Biostatistics, School of Health, Mashhad University of Medical sciences, Mashhad, Iran. sakia@mums.ac.ir.

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6 · The paper itself

Abstract

introductionMany 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.

methodIn 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.

resultK-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.

conclusionBased 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.

Indexed as

MenopauseAdultCluster AnalysisCross-Sectional StudiesFemaleHot FlashesHumansIranLogistic ModelsMiddle AgedRisk FactorsSeverity of Illness IndexBaseline category logit modelCluster analysisK-Means algorithmMenopausal Severity Symptoms (MSSI-38)

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PMID39709408
PMCPMC11662853

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