What qualitative research taught me about using data for health equity

by Tanjina Islam

Health inequities are often described through statistics: life expectancy gaps, service use differences and outcome variations. But behind those numbers are people whose lives rarely fit neatly into datasets. My journey from qualitative research to applied data science and PPIE has shaped how I think about what it means to use data for health equity and create sustainable, continuous change.  

During my Master’s research, I explored how sociocultural context shaped health and wellbeing among British Bangladeshis, and how far this was reflected in the NHS Long Term Plan’s goal to reduce health inequity. Qualitative methods allowed me to move beyond broad ethnic categories to see how place, income and context can influence outcomes. I found important differences between inner-city London and towns elsewhere, and between towns themselves, for example, between lower-income areas and more affluent ones with distinct demographics. These variations influenced access to services, social support, employment opportunities, and health behaviours in ways invisible in aggregated data.

This became especially visible during COVID19: although higher mortality among British Bangladeshis challenged the idea of “South Asian” communities as a single group, responses still tended to medicalise the findings and risk to ethnicity rather than the contextual factors – social, cultural, economic, environmental – through which inequity is produced and sustained. Which reinforced for me that ethnic labels often conceal more than they reveal.

Now, in my role within the Networked Data Lab programme at The Health Foundation, I work with linked and novel local datasets across a federation of analytical partners to generate insights that can inform decision-making. This work has shown me the immense potential of data science to identify patterns, highlight gaps, and support system-level change. Local data, in particular, offers an opportunity to move closer to people’s real experiences rather than relying only on national averages.

 For example, our NDL lab partner  in Wales linked routine secondary care data with Welsh Ambulance Services Trust callout data and found that only 21% of mental health related ambulance callouts for children and young people resulted in hospital admission, highlighting how national indicators such as A&E attendances capture only a fraction of crisis presentations. The linked data revealed marked inequalities, with adolescent girls and young people living in the most deprived areas experiencing much higher rates of crisis. These insights have already informed action, including targeted workforce recruitment within ambulance services and engagement with the Welsh Government to strengthen data sharing and prevention focused responses. For further information, please see this link. 

For me, health equity sits at the intersection of lived experience and analytical insight. Qualitative research grounds us in the realities of people’s lives; data science helps scale those insights to inform policy and practice. Without meaningful Patient and Public Involvement and Engagement (PPIE), data risks reproducing the same blind spots that qualitative research works to uncover. 

---

Want to write something for the DSxHE blog? We’re always keen to publish thoughtful, practical pieces from across the community, whether that’s a project you’re working on, a lesson you learned the hard way, a useful method, a commentary on equity in data, or a short reflection from the field.

If you’ve got an idea (even a rough one), email info@datascienceforhealthequity.com

and we’ll happily chat it through.