A pragmatic approach to the classification of menopausal status for community-based research

Abstract

In Brief Objective: The aim of this article was to describe a pragmatic approach to the menopausal status classification of clinical research study participants that allows for women who have gynecological circumstances that mask their natural menstrual pattern. Design: We demonstrate the application of an algorithm for the Health and Wellbeing After Breast Cancer study based on self-reported menstrual cycle pattern, gynecological history, presence or absence of vasomotor symptoms, and systemic hormone use to classify women with newly diagnosed breast cancer as premenopausal, perimenopausal, or postmenopausal for research purposes. Results: Within 12 months of their breast cancer diagnosis, 1,684 participants, mean ± SD age 57.4 ± 11.9 years, completed a comprehensive women's health questionnaire. Menopausal status in 71.8% of the women was classified by reported bilateral oophorectomy, age, greater than 12 months of amenorrhea, or regular menstrual cycles and absence of symptoms. Status in the remainder was classified by progression through the decision tree. Conclusions: The Health and Wellbeing After Breast Cancer study menopausal classification algorithm is a useful tool for research involving female participants that allows for the classification of women who have had a hysterectomy and/or use systemic hormonal contraception or hormone therapy. The classification of menopausal status in clinical research often presents a challenge. This study describes an algorithm that has been developed as a research tool which enables the menopausal status classification of women, including those who have had a hysterectomy and/or use systematic hormonal contraception or hormone therapy.