1
Data and Modeling
Data on women’s health is underfunded, incomplete, and unrepresentative—lacking sex/gender distinctions, diverse populations, lifespan coverage, and accurate morbidity measures. This critical gap skews research, funding, and care. Innovating data types, collection methods, metrics, and analysis methods will improve understanding, decisions, investment, and outcomes for women globally.
To ensure that women receive evidence-based, tailored healthcare, the evidence base itself must be complete, rigorous, and representative.
Accurate and accessible data are essential to understanding health conditions, informing diagnoses and treatment plans, and driving investment decisions and innovations. Women’s health, however, is not accurately measured nor consistently understood.
Women’s health research and data collection are underfunded and exclude the experiences of diverse populations based on race, ethnicity, age, gender, sexual orientation, geography, and socio-economic status, among other factors.
Insufficient recognition and recording of gender as a socio-cultural variable distinct from biological sex inhibits researchers’ understanding of gendered differences in disease burden, prevention, and treatment. Crucial data gaps also exist across the lifespan. The key metric used for global health funding and prioritization—the disability-adjusted life year (DALY)—suffers from gaps in data on morbidity, particularly regarding sex- and gender-related differences in care-seeking behavior, access to quality care, and social restrictions and stigma that affect participation in surveys. Society has normalized intrusive symptoms—like those associated with premenstrual syndrome and menopause—as inevitable aspects of female biology, which discourages measurement of their frequency and impact. These and other data disparities across the R&D spectrum lead to surveillance, modeling, market sizing, product design, policy and investment decisions, and outcome measurements that are not appropriately tailored to women’s true health needs.
Innovation is greatly needed in the types of data that are captured (quantitative and qualitative), methods for data collection (technological and human approaches), and applications (in analysis and modeling and for decision-making). Advancing a clearer understanding of, and metrics for, sex- and gender-based women’s health will catalyze more significant funding, collection, reporting, and usage of data disaggregated by sex and gender. Strengthening a sex- and gender-informed data value chain across the lifespan will enhance understanding of women’s health needs, policy and programmatic decision-making, and, ultimately, healthcare and outcomes for all.
Overview Data and Modeling
1.1 Granular data
Researchers face limitations in understanding the diseases and disease determinants that have the greatest impact on the health of women across the life course. The limitations stem from inadequate measurement of indicators in an age-sex-specific fashion or aggregation of data that obscures age, life stage, and sex. Additionally, disparities in women’s health outcomes that are associated with various facets of identity—such as race and ethnicity, socio-economic status, or gender identity—are also obscured when data are measured and reported in aggregate. Furthermore, existing data sets’ applicability to health equity is limited by a historical lack of standard operational definitions for measuring these dimensions. For example, most data sets only reflect sex assigned at birth, leading to potential misclassification and an inability to characterize all groups regarding sexual orientation and gender identity (SOGI). Achieving an international, harmonized standard for sex and SOGI data collection will require collaboration among diverse stakeholders to ensure cultural acceptability and widespread support.
Progress Assessment
Progress made against Opportunities, from the 2024 Progress Report
Status Moderate Progress
0 % Achievement
Solution Strategies
-
Establish an international body of stakeholders to develop policies that articulate minimal data elements that should be collected across different data sources, as well as requirements and incentives for their inclusion. These stakeholders should include multilaterals (including the WHO, which sets reporting standards), regulators, foundations, funders, governments, and health coordinating bodies.
- Minimum core data elements that should be collected and reported across different types of data collection include sex, gender, age, life stage (e.g. pre-menarche, reproductive, peri-menopause, post-menopause) race, ethnicity, income, country, time use, history of trauma and gender-based violence, and disability.
- These minimum elements should be complemented with qualitative data on ageism, gender discrimination, influences of racism/colonialism, and lived experiences, including economic, political, social, and geographical.
- Guidance tools should be developed in collaboration with relevant stakeholders for core minimum data collection—and complementary qualitative data collection—highlighting the purpose, definition, and reporting channels of these data across various types of data sources.
-
Create study networks for research and data harmonization to ensure adequate and appropriate data collection for women across the life course—from pre-puberty through post-menopause—and across social and structural determinants of health.
-
Create an international data and modeling community of practice across sex, gender, and social determinants of health stakeholders to establish recommendations for standardized methods of collecting, reporting, analyzing, and disseminating health data in a sex- and gender-specific way; to ensure implementation; and to engage in continuous learning.
- The community of practice’s recommendations should target critical data collection gaps, e.g., standardizing electronic platforms of clinical data from low-resource settings and rural areas, improving the representation of populations historically lacking access to health services and facilities, etc.
- The community of practice’s recommendations may include developing a checklist to assess for gender intentionality of data collection activities, including and beyond the minimal data elements described above.
1.2 Capacity to collect and utilize data
To reap the potential benefits of more granular data to improve decision-making on women’s health innovation, technical capacity is needed for appropriate data collection, extraction, analysis, and reporting. As comprehensive sex- and gender-intentional data collection is not consistently practiced across different regions and institutions, this shift necessitates training for researchers, local health workers, community leaders, and women.
Progress Assessment
Progress made against Opportunities, from the 2024 Progress Report
Status Unchanged Progress
0 % Achievement
Solution Strategies
-
Emphasize and request plans for training researchers, health workers, community leaders, women, and others involved in data collection on how to collect minimum data elements—including but not limited to sex and gender—that will advance sex- and gender-sensitive analysis. Stakeholders, including funders and regulators, should support these plans.
-
Monitor implementation and incentivize such training by establishing requirements from research accreditation bodies, honor rolls, rewards, etc.
1.3 Burden of disease metrics
The disability-adjusted life year (DALY) is one of the key metrics used to prioritize products and assess impact in global health. DALYs are a summary measure of population health that accounts for mortality and non-fatal health consequences by summing years of life lost due to premature mortality (YLLs) and years lived with disability (YLDs). The quantity and quality of data available to estimate mortality are generally better than for measuring morbidity. Morbidity data are also subject to differences in care-seeking behavior, access to quality care, and social restrictions and stigma that affect participation in surveys—all of which may be exacerbated by disparities across gender, age, race, ethnicity, and socio-economic status. The data inputs and modeling framework for the widely-used DALY measure should be strengthened to more comprehensively reflect the full impact of health conditions affecting women.
Furthermore, the DALY estimation framework exclusively measures health loss and does not account for downstream effects that matter to women, such as impacts on educational attainment or career advancement, social standing, agency, well-being, and relationships. This gap highlights the need to identify—or develop—and disseminate complementary metrics that move beyond health loss and account for a broader concept of well-being.
These updated and expanded metrics—which should reflect the input of diverse stakeholders—will illuminate the actual burden of conditions affecting women. The metrics will also help to demonstrate the true market size for new products to prevent and treat conditions affecting women and would allow for funders to appropriately direct R&D resources and funding toward conditions with the greatest burden.
Progress Assessment
Progress made against Opportunities, from the 2024 Progress Report
Status Moderate Progress
0 % Achievement
Solution Strategies
-
Improve data inputs for estimating YLDs beyond only incidence and prevalence: measure the nature, severity, timing, and duration of non-fatal sequelae of diseases and injuries, and ensure that disability weight surveys quantify all health states relevant to women’s health (for example, disability weights for pelvic pain exist but not for vaginal bleeding).
- Philanthropy and other financial avenues can support the work of academics surveying women (quantitatively and qualitatively), leveraging methods co-created and co-identified by women, and engaging policymakers as consumers of information.
-
Identify measures that complement DALYs with other biopsychosocial factors and their interactions (such as relationship status, emotional status, social stigma, integration, educational attainment, earning power, agency) and disseminate their use when discussing the full impact of a disease or condition. Develop novel measures to fill this role if satisfactory measures are not identified.
-
Capture and establish relationship of early life events to later life sequelae to guide the development of upstream interventions.
1.4 Data for ROI on WH innovation
Business cases are typically necessary to define a potential return on investment. Calculation, measurement, and maximization of ROI in women’s health—including economic models and ROI for specific disease areas—face limitations due to insufficient data and operational definitions. Historically, women’s health has been narrowly focused on reproductive and maternal health. As the definition expands to include conditions that impact women uniquely, differently, or disproportionately compared to men, gaps remain in understanding of ROI. ROI is more often measured for curative interventions; while preventive interventions (such as prevention of physical, sexual, or psychological abuse) can yield substantial returns, they require better estimation. Robust economic models are needed to link women’s treatment preferences in different settings, the costs and quality of public health and healthcare interventions, and impacts on robust, sex-and-gender-informed measures of disability and well-being (as addressed in Opportunity 1.3). Identifying and filling these data gaps will strengthen the case for investment in women’s health innovations.
Progress Assessment
Progress made against Opportunities, from the 2024 Progress Report
Status Moderate Progress
0 % Achievement
Solution Strategies
-
Identify data gaps related to women’s and girls’ preferences, agency, spending, and decision-making across conditions affecting them; social and structural determinants of health; access and barriers to interventions (both public health and healthcare) over the life course; and the efficacy of those interventions. Academics, non-governmental organizations (NGOs), and health agencies can lead this work through a literature review of existing studies.
-
Conduct longitudinal mixed-methods studies and modeling to generate and synthesize evidence on financial and opportunity costs of health events and experiences, including evidence on the links between health and economic outcomes. Data and models—collection and creation of which should be led by academics, NGOs, and regional partners—should take a life course perspective and quantify differences in preferences for services by gender in estimating the impact and efficiency of programs and products.
-
Invest in research on effective implementation of interventions (e.g., screening) that generate better returns for women and girls—including their cost-effectiveness, coverage, and quality.
1.5 Qualitative info in models
Although most research is based in the derivation and synthesis of quantitative information, more data may be needed to capture the complexity or diversity of the phenomena under study. Qualitative data can provide context and meaning to quantitative data by explaining the reasons, motivations, or mechanisms behind the quantitative findings, while also potentially reducing bias by capturing different perspectives, experiences, or values. Increasing the use of qualitative data across women’s health R&D could improve study reliability and serve as a time- and resource-saving strategy by identifying highly valued variables, enabling flexibility to respond to emerging themes, and validating, complementing, or triangulating the quantitative results.
Progress Assessment
Progress made against Opportunities, from the 2024 Progress Report
Status Unchanged Progress
0 % Achievement
Solution Strategies
-
Bring together an international committee of institutions, governments, funding companies, academic organizations, high-tech and statistical modelers, and social scientists to develop an action plan for developing methods and approaches to leverage qualitative data. This action plan may include the development of use cases; best practice guidelines; templates with clear recommendations, trainings, and short courses to educate and equip stakeholders; and other materials that communities can use to advance and implement qualitative methods, including in models. This process should be inclusive, allowing for different scenarios across communities.
-
Develop artificial intelligence (AI) tools to incorporate qualitative information into models of diseases, including techniques able to capture linguistics and non-linguistics cues and sources from representative groups of women that can inform and refine unmet needs for designing and developing innovative products and interventions. The development of bioethics standards must accompany the use of these AI tools.
