Objective: The aims of this study were to identify groups of women in the late menopausal transition stage who experienced the same cluster of symptoms and to identify indicators that predicted membership in these distinct groups.
Methods: The sample consisted of a subset of Seattle Midlife Women's Health Study participants who were in the late menopausal transition stage and provided self-report data on symptoms experienced between 1990 and 2005. Latent class analysis (LCA) was used to identify groups of women who experienced similar clusters of the following five symptoms: problem concentrating, hot flashes, joint ache, mood changes, and awakening at night. LCA with multivariate logistic regression was used to identify covariates that predicted membership in each group.
Results: Four groups of women were identified: (1) low severity for all symptoms except for joint ache, which was moderate (65%); (2) high severity for all symptoms except for hot flashes, which was moderate (13%); (3) high severity for hot flashes, joint ache, and awakening at night (12%); and (4) high severity for problem concentrating and joint ache (10%). A clear delineation between groups based on individual characteristics was not fully elucidated.
Conclusions: This analysis demonstrates that LCA may be useful to identify women who may experience poorer outcomes related to a higher propensity for severe symptoms. Shifting the focus from single symptoms to symptom clusters will aid in the identification of phenotypic profiles, thus facilitating symptom management strategies that can be tailored to meet the needs of individual women.