Accurate categorisation of menopausal status for research studies: a step-by-step guide and detailed algorithm considering age, self-reported menopause and factors potentially masking the occurrence of menopause

Abstract Objective Menopausal status impacts risk for many health outcomes. However, factors including hysterectomy without oophorectomy and Menopausal Hormone Therapy (MHT) can mask menopause, affecting reliability of self-reported menopausal status in surveys. We describe a step-by-step algorithm for classifying menopausal status using: directly self-reported menopausal status; MHT use; hysterectomy; oophorectomy; intervention timing; and attained age. We illustrate this approach using the Australian 45 and Up Study cohort (142,973 women aged ≥ 45 years). Results We derived a detailed seven-category menopausal status, able to be further consolidated into four categories (“pre-menopause”/“peri-menopause”/“post-menopause”/“unknown”) accounting for participants’ ages. 48.3% of women had potentially menopause-masking interventions. Overall, 93,107 (65.1%), 9076 (6.4%), 17,930 (12.5%) and 22,860 (16.0%) women had a directly self-reported “post-menopause”, “peri-menopause”, “pre-menopause” and “not sure”/missing status, respectively. 61,464 women with directly self-reported “post-menopause” status were assigned a “natural menopause” detailed derived status (menopause without MHT use/hysterectomy/oophorectomy). By accounting for participants’ ages, 105,817 (74.0%) women were assigned a “post-menopause” consolidated derived status, including 15,009 of 22,860 women with “not sure”/missing directly self-reported status. Conversely, 3178 of women with directly self-reported “post-menopause” status were assigned “unknown” consolidated derived status. This algorithm is likely to improve the accuracy and reliability of studies examining outcomes impacted by menopausal status.