When the facts are loud and clear: investing in women’s health could unlock $1 trillion in global gross domestic product annually by 2040, prevent 24 million female life years lost to disability, and yield exponential returns to economy for every investment across obstetrics and gynecology, female and maternal health, immunology, neurology, cardiology, and oncology.

Improving global health equity has been increasingly recognized as a strategic priority for different stakeholders in healthcare,¹ including policymakers, industry, governments, investors, and global health organizations. Beyond an ethical and human rights imperative, reducing health inequities and ensuring that everyone has a fair opportunity to achieve their full health potential — independent of socioeconomic status, sex, gender, geography, or race/ethnicity — leads to economic and societal benefits and resilient healthcare systems.² Although progress continues to be made toward improving general health outcomes, Cytel researchers have found that this has not translated equally for men and women.³

The gender health gap: A global health crisis

Attention to women’s health inequities and the potential economic impact from closing this health gap remain largely unnoticed. The gender health gap — the long-standing, unfair differences in health outcomes between women and men — has been only recently recognized as a medical and healthcare issue. The underinvestment in female health research, the absence of systematic data collection to understand and document the unique biological needs of females and assess disparities, as well as biases in male-dominant clinical trial programs have all contributed to the neglect of women’s health issues. The survival paradox is documented, with women outliving men but experiencing poorer general health, including mental health; recent data showing that women live approximately five years longer than men does not adequately categorize the fact that women spend more than one-quarter of their lives in poor health.^{4, 5} This health gap is a global health crisis that affects women of all ages to varying extents depending on geography and income levels.⁴

The gender health gap is generally defined by the conditions that affect women uniquely, differently, or disproportionately, and are not limited to those related to sexual and reproductive health.⁴ For example, women from the general population are at significantly greater risk of mental health disorders (e.g., depression, suicide) than men, and women with type 2 diabetes mellitus have a disproportionally higher risk of adverse cardiac events, including mortality.⁶ Men, on the other hand, are significantly more likely to have adverse events after specific types of surgeries and higher mortality after COVID-19.⁶ Despite the fact that cardiovascular disease is the top cause of death for women in the US, males outnumber females two to one in related clinical trials.⁷

Quantifying the economic benefit of closing the women’s health gap

Quantifying the economic benefit of closing the women’s health gap for societies and economies is important for several reasons and makes visible the “invisible” topic of women and their health. By attaching a measurable economic gain — such as productivity gains, increased workforce participation, reduced healthcare spending — policymakers and investors can grasp the tangible impact on global economies and growth. As financial pressures are restraining healthcare spending, prioritization of resource allocation where interventions yield the greatest returns to economies, such as women’s health, may be placed higher in the list of investment priorities.⁷ Therefore, systematic efforts to quantify the economic value when bridging the gender health gap will push reframing health equity as a driver for inclusive and sustainable growth, making it a strategic imperative for governments and businesses and overturn the negligible investment in women’s health (only 5% in 2020).⁸

Our findings: The value of investment to improve women’s health

We conducted a comprehensive literature review that aimed to systematically investigate and summarize quantitative evidence on the economic impact of investments to close the women’s health gap globally. We identified robust evidence to demonstrate that when investment is made to improve women’s health, there is return to this investment by bringing back higher value to economies.

A recent report jointly published by the World Economic Forum and the McKinsey Health Institute, for example, highlights that investments in addressing the women’s health gap could not only extend life years and healthy life years, but also have the potential to boost the global economy by $1 trillion annually by 2040.⁴ These findings were supported by an additional impact analysis conducted by Women’s Health Access Matters across three indications: rheumatoid arthritis, coronary artery disease, and Alzheimer’s disease. Key findings indicated that an investment of $300 million in women’s health research across these three diseases would conservatively result in a $13 billion return to the US economy.⁷

Over the past 70 years, the influx of women into the workforce has been closely linked to economic growth.⁴ Since nearly half of the health burden affects women in their working years, this can have serious consequences for the income-earning potential of women, causing a ripple effect on society.⁴ Economic benefits in the same direction were also documented by simulation studies in other countries such as the United Kingdom whereas limited data were identified for low- and middle-income countries.

We are committed to standing at the forefront of assessing public policy trends and critical policy matters that highlight emerging challenges and seizing opportunities for improving public health. Some examples include our environmental scan of publicly available data repositories to address disparities in healthcare decision-making,⁹ an umbrella review of the impacts of climate change on maternal health and birth outcomes,¹⁰ and blueprints for collective action to close the women health gap.

Interested in learning more?

Grammati Sarri, Lilia Leisle, and Jeffrey M. Muir will be at the upcoming ISPOR Europe conference in Glasgow, Scotland, where they will present “The Economic Case for Gender Equity: How Closing the Women’s Health Gap Benefits Healthcare Systems and Economies” on Wednesday, November 12, 2025, from 9 to 11:30 a.m. Register below to book a meeting or visit us at Booth #1024 to connect with our experts:

By Hoora Moradian, Victor Laliman-Khara, Peter Wigfield, Michael Dolph, and Michael Groff

Global health technology assessment (HTA) bodies are setting higher standards for rigorous evidence to support access decisions. In this evolving landscape, generating meaningful health economic (HE) models and indirect treatment comparison (ITC) analysis is critical — particularly in rare and chronic disease settings. However, traditional modeling techniques often fall short, prompting the need for more advanced and adaptable approaches.

Here, we discuss finding the right method for various market access scenarios, given your indication, patient population characteristics, and data gaps.

Addressing heterogeneity across multiple studies

Victor Laliman-Khara

Problem: A sponsor has an asset that is entering a crowded market (3rd or 4th entrant), with a significant number of comparators and a related shift in standard of care. While network meta-analyses (NMA) are broadly accepted, one of the challenges is the limited possibility to adjust for between-study heterogeneity. While Matching-Adjusted Indirect Comparison (MAIC) and Simulated Treatment Comparison (STC) exist, they only allow for pairwise comparison, limiting comparative effectiveness to two treatments.

Solution: Population-adjusted indirect comparisons (PAICs) at the aggregate level — such as Multilevel Network Meta-Regression (ML-NMR) and Network Meta-Interpolation (NMI) — are increasingly used to address heterogeneity across multiple studies. These methods are particularly well-suited for situations where population adjustments are necessary across a network of trials, and where standard NMA falls short in accounting for treatment effect modifiers and between-study differences. Unlike traditional approaches such as MAIC or STC, which are limited to pairwise comparisons, ML-NMR and NMI extend the capability to more complex networks, enabling more robust and generalizable estimates.

Accurately reflecting treatment sequencing

Michael Groff

Problem: Many clients struggle to accurately reflect treatment sequencing in their decision problem modeling — especially in indications such as chronic inflammation with multiple therapeutic options. Capturing sequences can make it difficult to fully exploit the value proposition of a product, and poor structuring often leads to models that oversimplify the clinical pathway, become overly complex, or drive runaway costs that make the intervention appear less favorable.

Solution: Apply a structured, evidence-based methodology to transparently map and model clinical sequences, ensuring all clinically relevant transitions, such as disease progression, treatment pathways, and health state changes, are incorporated without compromising credibility, usability, or cost realism.

Handling pairwise comparisons in rare disease indications

Hoora Moradian

Problem: Assets in an indication where the referent trial and comparator trial have sparse data and/or heterogeneous populations present a challenge, especially in pairwise comparisons. This is common in rare disease indications, where standard PAIC approaches often struggle to produce reliable insights.

Solution: Apply innovative strategies for pairwise comparisons when traditional methods fall short:

• G-Computation, a flexible and well-established method, can be applied when there is poor covariate overlap between study populations.
• MAIC with random forest-based weighting can be used when the overlap is poor and the sample size is very small, delivering improved robustness over traditional methods.

Accounting for health equity

Mike Dolph

Problem: Accounting for health inequity is growing in importance in certain areas with a public health focus such as vaccination programs.

Solution: As health systems increasingly prioritize equity-informed decision-making, distributional cost-effectiveness analysis (DCEA) has emerged as an important extension of the traditional cost-effectiveness analysis, reflecting a growing recognition that efficiency alone is not enough to guide healthcare decisions. This approach allows decision-makers to quantify and weigh the trade-offs between improving overall health and reducing health inequalities.

Managing immature survival data

Peter Wigfield

Problem: Survival data, particularly in early-stage diseases, are often immature, and the emergence of more effective treatments is likely to prolong this trend.

Solution: State transition models (especially multistate frameworks) offer benefits that are difficult to achieve with conventional partitioned survival methods alone. With real-world evidence evolving rapidly, the relative ease of incorporating external evidence sources is a significant advantage, enabling the real-world value of a product to be realized. However, multiple limitations (particularly related to indirect treatment comparisons and model fits) also need to be considered.

Final takeaways

At Cytel, we specialize in solving complex evidence challenges, whether it’s navigating complex markets, rare diseases, or evolving treatment pathways. Our advanced methodologies, including ML-NMR, NMI, structured sequencing frameworks, equity-informed modeling (DCEA), and multistate survival models, empower sponsors to generate credible, realistic, and actionable insights. These tools go beyond traditional approaches to support better decisions and stronger value demonstration.

Interested in learning more?

Watch the authors’ recent webinar, “Leveraging the Right Advanced Quantitative Methods to Address Evidentiary Gaps,” on demand: