Exploring the gender data gap

By Gayatri Koshy, Head of mHealth

Historically, most information collected about people is on men. This is true all over the world, whether in medical research, economics or urban planning. As a result, key aspects of our lives are skewed towards the male experience. This includes the way health conditions are diagnosed and treated, and how cities and built environments are designed. This phenomenon is called the gender data gap.

It’s gaining increased attention, particularly after the publication of Invisible Women by Caroline Criado-Perez and Doing Harm by Maya Dusenbury. Both authors argue that a world built and designed with men in mind leaves women disadvantaged and unable to realise their full potential.

Why do we have a gender data gap?

There’s a vicious cycle at play. Structural inequalities mean fewer women are in education, the workforce and positions of power around the world. These inequalities have led to imbalanced data collection, which in turn, has only served to exacerbate existing inequalities. And so the pattern continues.

For example, it is because of these structural inequalities that areas of health research that affect female bodies are underfunded, such as menopause.

Women are also typically underrepresented in clinical trials. As a result, we don’t know enough about how women experience conditions and how this differs to men. Women’s differing hormonal states and cycles are rarely considered in medical research, so there are gaping holes in our knowledge base.

There’s a danger that substandard data is also being fed into artificial intelligence (AI) systems used in healthcare, finance, education, and judicial and policing practices, compounding inequalities further. This could distort the algorithms that steer decisions as crucial as which university you might get into, or whether you are eligible for a mortgage.

The real-life consequences of the gender data gap are significant

There are countless daily ways that male-focused research impacts women’s health.

For example, insufficient data on women in pain research means we lack insights into how and why pain medication works differently for them. This has also meant women’s self-reported pain is not taken seriously by healthcare professionals. It’s even harder to get a diagnosis if you’re a Black, Asian or mixed heritage woman (RCOG 2020). There is also evidence to suggest that women are more likely than men to be prescribed sedatives to deal with pain rather than painkillers (Kiesel 2017). This is known as the gender pain gap.

But the impact of the gender data gap extends far wider than health services and care.

Take vehicle safety, for example. Research shows that women are nearly 50% more likely than men to be seriously injured when wearing a car seatbelt and 17% more likely to die than a man in the same car crash. This is because crash-test dummies were based on the dimensions of an average man (ITU 2020). Female dummies are nothing more than scaled down versions of male dummies which don’t consider female physiology (Criado-Perez 2019).

The very way we live is influenced by male bias. For example, because our urban spaces have been designed by men, they are not mindful of women’s myriad needs and responsibilities, including childcare. The fundamental principle on which they are planned is that work takes place in factories and offices, while home is a place of recreation and rest. Criado-Perez calls for gender-balanced city design including public transport systems that connect workplaces with nurseries and clinics.

Our towns and cities are also designed without considering the daily threat of violence women face (Cogley 2020). A survey led by UN Women in the UK found that 80% of women of all ages have experienced sexual harassment in public spaces (UN Women UK 2021). Women typically report that they feel more fearful than men in transit environments, such as train stations (Ceccato 2013, Yavuz and Welch 2010) and avoid what they see as unsafe spaces. Activists call for more walkable streets, open gathering spaces and well-lit pathways so that women feel visible and safe.

Due to a lack of gender-specific data sets collected, we are unable to monitor the progress made by women and girls towards the UN’s sustainable development goals (SDGs). These are a set of global goals said by the UN to be “blueprint to achieve a better and more sustainable future for all”. But often the questions asked in national surveys are biased. For example, labour force surveys often fail to capture women who perceive paid work as secondary to their unpaid care and domestic work (UN 2018). This can lead to policy-level decisions that ignore the contributions made by women.

How to close the data gap

  • Acknowledge

The first step for all decision-makers is to acknowledge that the data we base our decisions on is heavily skewed against women. Ask ‘what are we missing?’ and ‘what don’t we know?’ then go about trying to fill these knowledge gaps.

  • Include

Gender diverse teams can shed light on what’s missing and provide varied perspectives. We must also appreciate the role narratives and storytelling play in understanding lived experiences. Using these techniques in research provides valuable insight into the complexity of lives, cultures and behaviours of groups typically excluded or hidden.

  • Change

There’s a clear need to alter the way we collect data. Criado-Perez believes we need more sex-disaggregated data. In other words, data collected and analysed separately on males and females. The World Health Organization first used this approach for its global health statistics in 2019. UN Women (2018) also calls for mainstreaming gender into national statistical strategies and ensuring data represents the “lived reality of women and girls in all their diversity”.

  • Innovate

Tech innovation and AI can help to bridge the data gap, particularly in health research. Users of apps and wearables can make health choices based on the data they input. The information gathered by consumers can potentially form a useful anonymised data bank for health researchers. Making these innovations available and accessible to everyone means we can gather data that is also more representative.

Gayatri is the Head of mHealth at Thrive. With a Master of Public Health (MPH) degree, she has extensive experience working in global health projects as well as national level programmes such as India’s National AIDS Control Programme.

Here at Thrive, we work with brands, partner agencies, governments and charities who want to transform lives and societies for good.

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Cogley B (2020) Urban planning is “really very biased against women” says Caroline Criado Perez www.dezeen.com/2020/03/10/caroline-criado-perez-urban-planning-is-really-very-biased-against-women

Criado-Perez C (2019), Invisible Women: Exposing Data Bias in a World Designed for Men. Chatto.

Dusenbury M (2019), Doing Harm: The Truth About How Bad Medicine and Lazy Science Leave Women Dismissed, Misdiagnosed, and Sick. HarperCollins.

Kiesel L (2017), Women and pain: Disparities in experience and treatment. Harvard Health Blog. www.health.harvard.edu/blog/women-and-pain-disparities-in-experience-and-treatment-2017100912562

ITU (2020), Bridging the AI gender gap: Why we need better data for an equal world. www.itu.int/hub/2020/09/bridging-the-ai-gender-gap-why-we-need-better-data-for-an-equal-world

RCOG (2020), Position Statement: Racial disparities in women’s healthcare. www.rcog.org.uk/media/qbtblxrx/racial-disparities-womens-healthcare-march-2020.pdf

UN Women (2018), Making women and girls visible: Gender data gaps and why they matter. www.unwomen.org/sites/default/files/Headquarters/Attachments/Sections/Library/Publications/2018/Issue-brief-Making-women-and-girls-visible-en.pdf

UN Women UK (2021), Prevalence and reporting of sexual harassment in UK public spaces.  www.unwomenuk.org/site/wp-content/uploads/2021/03/APPG-UN-Women-Sexual-Harassment-Report_Updated.pdf

Yavuz N and Welch EW (2010), Addressing Fear of Crime in Public Space: Gender Differences in Reaction to Safety Measures in Train Transit journals.sagepub.com/doi/pdf/10.1177/0042098009359033

Ceccato V (2013), Moving safely: Crime and perceived safety in Stockholm’s subway stations. Lexington.

WHO, Closing data gaps in gender. www.who.int/activities/closing-data-gaps-in-gender

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