2.3.4 Metric 7: Sex-disaggregated data Definition: Proportion of trials publishing sex- disaggregated data Source: clinicaltrials.gov Period: January 2022–December 2022 Sex-disaggregated research data is critical in the interpretation of research findings.193 Research has shown that this can improve the understanding of treatment effectiveness and potential sex-related differences in the effects of those treatments (e.g. side effects, efficacy, dosage, etc.).194,195 To better understand the current state of sex disaggregation of data in the selected conditions, a comprehensive assessment identified relevant and published clinical trials completed from 1 January 2022 to 31 December 2022, captured the available published results and assessed these results to determine whether published data was disaggregated by sex. The clinicaltrials.gov database was used, with the search terms specified under migraine and ischaemic heart disease (IHD). This assessment was conducted only for migraine and for IHD, whereas the other seven conditions were excluded given they mostly or exclusively affect women. This metric was applicable to only two conditions, and the analysis was done manually. As clinical trial databases do not report in this level of detail, each trial examined was explored manually and one at a time as part of the assessment. It may be the case that some pharmaceutical companies do collect and review sex-disaggregated data and simply do not publish it. However, it is argued that the publication of data may be beneficial to advance scientific knowledge overall196 – for instance, sex- disaggregated data published today may inform future publications or meta-analyses in the future and further the understanding of sex-based differences. Condition-level metrics3 Definition: Estimate of real-world prevalence of endometriosis and menopause compared to global databases Source: IHME, WHO, academic literature Period: IHME 2019 Global Burden of Disease (GBD) dataset (the most recent academic study available) Undercounting the prevalence of women’s health conditions exacerbates the data gap and contributes to a more limited understanding of how burdensome these conditions are. Addressing prevalence can provide a more accurate picture of the number of women with a disease and the disease severity, contributing to increased awareness, better access to care and the possibility of enhanced treatments. This analysis focused solely on endometriosis and menopause; however, it is likely there are other gaps that are women-specific given current research. More analysis is needed.In the McKinsey Health Institute’s analysis with the Forum, a data gap was uncovered for the undercounting of endometriosis and menopause prevalence globally. For these two conditions, there is an opportunity to address an estimated 6 million DALYs if the data gap was closed by 2040. Undercounting of prevalence was analysed by comparing the endometriosis disease burden based on the WHO prevalence and IHME disease weight and the IHME-reported endometriosis burden. Similarly for menopause, the difference was looked at between real disease burden based on prevalence sizing from academic literature and the IHME prevalence, for which menopause falls in the “other gynaecological conditions” bucket. Given the inclusion of menopause in this broad category of “other gynaecological conditions”, it is not possible to clearly identify the underlying prevalence, or the symptom severity associated with menopause in the data. As all women transition to menopause at some point, lack of complete data further compounds the limited understanding of the condition more broadly.3.1 Metric 1: Undercounting of prevalence Blueprint to Close the Women’s Health Gap: How to Improve Lives and Economies for All 43

Blueprint to Close the Women%E2%80%99s Health Gap 2025