Characterization of menopause onset and associated disease risks using large-scale electronic health records

Menopause affects over one billion women worldwide, yet remains poorly characterized at scale. We apply an ICD-10-based phenotyping algorithm to electronic health records (EHR) from an academic medical center (n=33,444 women aged 35-64) and a safety-net hospital system (n=7,041), yielding one of the most racially and socioeconomically diverse menopause cohorts in the literature. Structured EHR fields underrepresent symptom burden: only 38.8% of patients had any documented symptom via natural language processing, despite an estimated prevalence of 90%. Adverse pregnancy outcomes were associated with earlier menopause onset after adjustment (beta=-1.12 years, p=8.7x10^-45). Menopausal women showed elevated risk for osteoporosis (hazard ratio of 12.40), rheumatoid arthritis (HR of 2.43), and mental and behavioral disorders (HR of 2.38) relative to age-matched men, with divergence at menopause onset. We show that large-scale EHR can characterize menopause at a scale and diversity that pros