Burden and prevalence, life-course impact, research and investment priorities
Many people say women’s health is underfunded. This is often framed in moral or political terms linked to patriarchy, historical exclusion or bias within medicine. These forces are real and well documented. But if the goal is to understand how health priorities are actually set, they might not be the most useful place to begin.
A more productive starting point is measurement.
The Bath | Mary Cassatt | Wikimedia Commons | Metropolitan Museum of Art. https://commons.wikimedia.org/w/index.php?curid=60858766
The IQVIA Report
A recent report from the IQVIA Institute, Quantifying Differences in Female and Male Healthcare, compares women and men across 182 diseases, examining prevalence, disease burden, clinical trial participation, drug development, medicine use and investment patterns.
Are women disadvantaged?
There’s no single answer to that question. The report shows that patterns differ depending on which metric is examined and what aspect of healthcare it reflects.
Here, I draw on these findings for a brief exploration of a broader question: how different health metrics influence or drive prioritisation decisions, and why women’s health can appear well served by some measures and less so by others.
Burden and prevalence
Prevalence answers a simple question: how many people are living with a condition? Disease burden addresses a different one: how much healthy life does that condition remove?
Burden is most commonly measured using disability-adjusted life years (DALYs), which combine years of life lost due to premature death with years lived with disability. DALYs are widely used in global health to compare the relative impact of diseases across populations.
The IQVIA report compares each disease’s share of global prevalence with its share of global disease burden. Across 182 diseases analysed, the share of male disease burden exceeds the share of male prevalence in four times as many conditions than is the case for women. IQVIA links this pattern largely to higher mortality and acute severity in several male-predominant diseases.
This is a descriptive epidemiological finding. It does not imply that one metric is more legitimate than another, or that burden should always necessarily outweigh prevalence in priority-setting.
The Gleaners | Jean-François Millet | Public Domain. Labour-intensive tasks performed by women often fall outside standard economic measures, much like high-morbidity conditions in burden of disease metrics.
Long-duration disease in burden metrics
Burden-based and prevalence-based measures emphasise different characteristics of disease that are important for how conditions are perceived.
DALYs place their weight on premature death and severe disability. Conditions that shorten life abruptly accumulate burden quickly. Conditions that begin early, persist for decades, and impair daily functioning without causing early death accumulate burden more gradually, even when they affect large numbers of people.
Many female-predominant conditions follow the latter pattern. Autoimmune diseases, migraine, anxiety and depressive disorders, endometriosis, and polycystic ovary syndrome (PCOS) often begin in adolescence or early adulthood, persist for many years, and affect quality of life rather than survival. Prevalence captures their scale. Burden captures an important part of their impact, but not its full life-course accumulation. This reflects what each metric is designed to measure, rather than a shortcoming of either approach.
Prioritisation is not a single decision
Discussions about women’s health often dwell on a single metric that it is argued should determine funding and attention. In practice, healthcare and life sciences operate through a series of distinct decisions: research funding, trial design, regulatory approval, product development, service planning and clinical use.
Each of these responds to different signals or measures. When considered in isolation, conditions that are common, long-lasting, and resource-intensive but not fatal may receive relatively less attention. Many women’s health conditions fall into this category.
The IQVIA data – multiple frames of reference
A key feature of the IQVIA report is its use of multiple frames of reference – prevalence, disease burden, clinical trials, drug innovation, real-world medicine use, and investment patterns, each highlighting a different aspect of how diseases are recognised, studied, and treated. The table below summarises selected findings across these frames and puts them in context. The findings vary by measure rather than pointing in a single direction toward men or women.
| Area of analysis | Key IQVIA finding |
| Disease burden vs prevalence | In 50 diseases, the share of disease burden differs by more than 5% from the share of prevalence (40 higher for men; 10 higher for women), largely reflecting higher male mortality |
| Clinical trials | Women are under-represented relative to prevalence in 43% of trials, compared with 33% for men |
| Oncology | 39% of cancer trials under-enrol women; innovation is skewed toward male-aligned tumours |
| Drug innovation | Female-focused new drug launches exceed male-focused launches overall, but female-specific diseases account for only 3% of U.S. launches |
| Medicine use | Female-focused diseases account for 40.9% of dispensed prescriptions compared with 5.4% for male-focused conditions |
| Leadership | Women are founders or co-founders of life sciences companies at 2.2 times the rate seen in venture-backed companies overall |
Taken together, these findings show that patterns differ based on severity, scale, evidence generation, innovation, or real-world use.
PCOS as a case study
Polycystic Ovary Syndrome (PCOS), a female-only condition, exemplifies a disease that is common and clinically consequential, with substantial health-system impact but a low profile in mortality-weighted measures.
PCOS affects an estimated 6-13% of women worldwide. It influences fertility, metabolic and mental health and long-term cardiovascular risk. Its impact accumulates over decades through repeated care for infertility, diabetes, obesity, cardiovascular risk and mental health, as well as through productivity loss. Although PCOS may contribute indirectly to mortality, it rarely appears in mortality statistics.
PCOS is also, in an important sense, an invisible disease. Up to 70% of affected women remain undiagnosed. It has no single defining complication and no clear medical home, with care fragmented across gynaecology, endocrinology, primary care, mental health, and cardiometabolic services. Its burden is therefore dispersed, reducing its visibility in research and prioritisation frameworks. And, there is currently no medication approved specifically to treat PCOS itself. Available therapies address individual symptoms or comorbidities rather than the syndrome as a whole.
The economic impact of PCOS is substantial. Direct costs in the United States exceed $15 billion per year (2021 USD) but research investment has been modest: between 2006 and 2015, total NIH funding for PCOS was approximately $215 million, compared with $454 million for rheumatoid arthritis and $610 million for systemic lupus erythematosus. PCOS funding accounted for as little as 0.1% of the total NIH budget over this period. In Europe, no EU-funded PCOS research projects were active between 2020 and September 2024.
PCOS illustrates how a condition can be common, chronic, and impactful, but under-recognised when prioritisation frameworks focus on mortality, single-organ outcomes, or narrowly defined disease categories.
Beyond the report: system-level implications
The IQVIA report documents sex-based differences in epidemiology, trials, innovation, and medicine use. It does not assess the broader social roles of women in health and healthcare systems, including on service demand, care pathways, workforce participation and long-term system costs. In this context, women’s health has additional systemic implications because of the scale and chronicity of conditions affecting women.
For example, women do the largest share of caregiving, tending to play the major role in managing household health decisions and in supporting partners and children during illness. Chronic illness in women therefore affects not only individual outcomes but outcomes in family members, through patterns of healthcare use, continuity of care, and labour-force participation.
Image: Nano banana. The lioness, as both hunter and carer, is a biological multiplier for the pride’s wellbeing.
Beyond the healthcare system, there is extensive evidence, particularly from lower-income settings, that investments in women’s health are multipliers for household stability, and economic productivity. These broader societal effects aren’t captured by the usual measures, but they help explain why conditions that appear modest on standard health metrics may still justify higher prioritisation.
From moral claims to structural understanding
Structural bias and historical exclusion in medicine are real and important. There is an indisputable moral case but a structural perspective and evidence are needed.
When prioritisation frameworks rely heavily on mortality-weighted measures, conditions characterised by long duration, early onset and functional impairment risk being undervalued. Many such conditions disproportionately affect women.
The limits of existing metrics should be recognised when they are used to guide priorities.
Conclusion
The IQVIA report does not offer a single headline conclusion about disadvantage.
Its central message is that assessments of parity and representation depend on the frame of reference applied. Distinct frames need to be integrated in decision-making.
When prevalence, burden, clinical trials, innovation, and real-world use are considered together, it’s a mixed picture. Within it, it’s clear that women carry a large share of chronic, disabling disease that accounts for a substantial and likely undervalued proportion of ongoing healthcare demand. Taken together with the broader societal benefits that are possible when women’s health is preserved or improved, they suggest these conditions deserve more sustained attention and investment.
AI infographic – Google Gemini / Nano banana