Tracking gender progress in health systems

Gender equity in health systems is not a parallel objective—it is the essential pathway to improving health outcomes across populations. This comprehensive toolkit, developed by Jhpiego with support from USAID, transforms gender analysis from a theoretical exercise into a practical, system-wide approach that guides how projects are designed, monitored, and evaluated. Far beyond a checklist, it provides health implementers and M&E professionals with a clear methodology to identify, examine, and address gender-based constraints that impact access to care, decision-making, and health equity. – It introduces foundational frameworks: USAID’s five-domain model (laws and policies, norms, roles, access to resources, and power dynamics), the Gender Analysis Framework (GAF), and a structured seven-step gender analysis process – It equips users with research tools: over 100 domain-specific questions across individual, community, facility, district, and national levels, combining quantitative and qualitative approaches tailored to health system assessments – It embeds gender in health system layers: offering practical applications for HIV, family planning, adolescent health, RMNCH, and workforce equality through facility and governance-level interrogation – It links analysis to programmatic design: emphasizing integration of findings into baseline studies, midline reviews, indicators, and adaptive strategies across the health project cycle – It provides a library of adaptable instruments: annotated data collection tools, participatory methodologies, illustrative questions, case studies, and sector-specific references This is not a theoretical briefing—it is a tactical guide for embedding gender equity into the structure and function of health systems. Whether conducting a baseline study, informing policy reform, or training facility managers, this toolkit equips development actors to move from diagnosing inequality to institutionalizing change.

Where do we stand in the effort to close the women’s health gap? The new Women’s Health Impact Tracking (WHIT) Platform, developed by the World Economic Forum and the McKinsey & Company Health Institute, is an important way to track that. It brings together a clear set of indicators to help researchers, clinicians, policy leaders, and advocates understand where we are and what still needs to be done. The platform looks at three critical areas: 💊 The #efficacy gap, which reflects how well treatments work for women compared to men. Today, only 12 percent of clinical trials publish sex-disaggregated data, making it harder to evaluate effectiveness of drugs and treatments in women. 🏥 The #care #delivery gap, which measures how consistently women receive timely, quality care. For example, currently, only 37 percent of women on average are screened for cervical cancer. 📉 The #data gap, which highlights where we lack reliable information altogether. The estimated prevalence of endometriosis and menopause symptoms is undercounted by up to 90 percent, meaning we may be missing the true scope of these burdens. They reveal where progress is still needed and highlight the structural gaps that continue to shape women’s health outcomes. This kind of shared measurement is also essential if we are serious about realizing the full potential of women’s health, both in terms of better lives and stronger economies. By closing these gaps, we are not just addressing long-standing inequities. We are also opening the door to significant health gains and substantial global economic growth. At GSD Health Research, we are working with partners to advance women’s health through flexible trial design, better data, and scalable research tools—the kinds of approaches that help accelerate progress like what this platform is tracking. Grateful to the teams behind this platform. This is the kind of resource that can help turn momentum into measurable progress. Explore the platform: https://lnkd.in/dAsr65Q4 ( 🔗 in comments) #WomensHealth #HealthEquity #WomensHealthResearch #WHIT #ClinicalTrials #DataGaps