By Terje Andreas Eikemo, Sara Martino and Andrea Riebler
Around the world, education and health shape each other in powerful ways. People with more years of schooling tend to live longer, earn more, and enjoy better overall well‑being. Yet the same relationship also produces deep inequalities: those with fewer educational opportunities often miss out on the conditions that make good health possible. These disparities translate into lost human potential, lower productivity, and uneven social development.
Despite major progress in global schooling, inequalities are actually widening—both between and within countries. The gaps are also increasingly gendered. In many low‑income regions, girls still face barriers to even basic education, while in several high‑income countries, boys are now the ones falling behind. The COVID‑19 pandemic only magnified these divides, disrupting learning for millions of children and accelerating long‑term inequalities in both education and health.
If these trends continue unchecked, the consequences will reach far beyond individual well‑being: societies may find it harder to respond to future crises, drive innovation, or achieve sustainable development.
This is the challenge that CHAIN — the Centre for Health Equity Analytics — aims to tackle.
A New Interdisciplinary Approach
Hosted by NTNU and rooted in Norway’s tradition of equity and welfare innovation, CHAIN establishes a critical link between social sciences and statistics – an integration that has been largely overlooked but is essential for advancing our understanding of health equity. The centre is led by Prof. Terje Andreas Eikemo from the Department of Sociology and Political Sciences and Prof. Andrea Riebler from the Department of Mathematical Sciences. It has recently received top research status within the Faculty of Information Technology and Electrical Engineering at NTNU and brings together a unique network of leading institutions: NIPH, IHME, the London School of Economics, EuroHealthNet, UNICEF, WHO, and development partners such as Norad.
The centre’s core idea is simple but ambitious:
to combine global datasets, advanced statistical modeling, and close collaboration with policymakers to understand and reduce global health inequalities through education policy.
To do this, CHAIN uses methods from statistics and data science including:
- spatial statistics to map inequalities across more than 600 regions worldwide,
- Bayesian modeling to forecast future trends in educational attainment and their effects on disease burden,
- causal inference to identify which education policies genuinely improve health outcomes,
- AI‑assisted simulation models to estimate the impact of policy choices up to the year 2050.
Together, these tools allow the centre to quantify something that has long been difficult to measure: how changes in education systems today influence population health decades into the future.
Data That Reveals the Social Determinants of Health
A key strength of CHAIN’s approach is its use of high‑quality international data. This includes large global databases, administrative records, and survey infrastructures such as the European Social Survey (ESS) — a bi‑annual, repeated cross‑sectional survey conducted across more than 40 European countries since 2002.
ESS provides exceptionally rigorous, comparable data on living conditions, education, employment, discrimination, trust, mental health, and a range of other social determinants that strongly shape population health.
CHAIN has not only used ESS data extensively, but has also contributed to developing survey modules that collect information on the social conditions that shape people’s health, including education, employment, discrimination, and material living conditions. This kind of information helps reveal the bigger picture: health is not only about hospitals and medicines, but also about the social environments people live in. By using these richer data, CHAIN can better identify the underlying forces that create health inequalities.
As part of its long‑term vision, CHAIN also aims to help integrate key social determinants directly into the Global Burden of Disease Study (GBD), the world’s most influential effort to quantify global morbidity and mortality. Embedding these determinants would mark a major advancement in how global health is measured and understood.
From Data to Actionable Policy
CHAIN’s research is organized into tightly connected modules. Among its goals are:
- projecting future educational attainment and linking it to global disease burden,
- uncovering causal relationships between schooling and health across diverse socioeconomic settings,
- evaluating how different education policies—and complementary social policies—affect long‑term health equity,
- and translating all of this into evidence-based recommendations for governments, international organizations, and donors.
In short, the centre aims not only to understand inequalities, but to provide concrete tools for reducing them.
Why Statistics Matters
Statistical modeling is one of the backbones of this work. Health and education systems are incredibly complex, driven by numerous interacting factors—from local policy decisions to demographic trends to global shocks like pandemics.
Statistics allows researchers to:
- integrate data from multiple sources,
- quantify uncertainty,
- simulate alternative futures,
- and evaluate the likely consequences of different policy choices.
This ability to forecast and compare scenarios is critical for decision-makers who must invest limited resources in the policies that will have the greatest, most equitable impact.
Building a Healthier, Fairer Future
By combining statistics, social sciences, public health, and policy design, CHAIN sets out to create a new international standard for health equity analytics. The centre’s long‑term vision is to help countries identify strategies that reduce inequality, improve population health, and accelerate progress toward the Sustainable Development Goals.
At a moment when global inequalities are widening, and societies face unprecedented interconnected risks, the ability to understand—and change—the relationship between education and health has never been more important. CHAIN offers a rigorous, innovative, and collaborative path forward.
References:
Balaj, M., Henson, C. A., Aronsson, A., Aravkin, A., Beck, K., Degail, C., … & Gakidou, E. (2024). Effects of education on adult mortality: a global systematic review and meta-analysis. The Lancet Public Health, 9(3), e155-e165.
Hoven, H., Eikemo, T.A., Backhaus, I., Riebler, A., Fitzgerald, R., Martino, S., Huijts, T., Heggebø, K., Vidaurre-Teixidó, P., Bambra, C. and Balaj, M. (2025). The second Health Inequalities Module in the European Social Survey (ESS): Methodology and research opportunities. Social Science & Medicine, p.118228.
Paige, J., Fuglstad, G. A., Riebler, A., & Wakefield, J. (2022). Design-and model-based approaches to small-area estimation in a low-and middle-income country context: comparisons and recommendations. Journal of Survey Statistics and Methodology, 10(1), 50-80.