The use of administrative health data for research, health monitoring, and quality improvement purposes has proliferated in recent decades. Administrative health data include records of contact with hospitals, emergency departments, primary care, pharmacies, psychiatric services, and more. These data provide a powerful basis to generate information on the health of populations, permitting a detailed understanding of health disparities, the resources required to improve them, and the consequences of inaction.

However, not all populations have equally benefitted from the use of these data to understand and improve health outcomes.

Some such populations are people that experience disadvantage and subsequent social exclusion. The homeless, people with substance dependence, people involved in sex work, migrants or asylum seekers, and people involved in the criminal justice system are examples of socially excluded populations that experience woeful health inequities relative to the general population. Despite their poor health and mortality outcomes, they remain under-represented in research using administrative health data. As a result, we know very little about their health needs and what needs to be done to address them.

A core issue preventing this needed research is that administrative health data do not routinely capture reliable information on markers of disadvantage and social exclusion. These populations are often ‘invisible’ in these data sources. A long-standing and well recognised solution to this problem has been to use cross-sectoral data linkage. This means linking administrative health data with data from other sectors and services that are more likely to encounter social excluded populations. However, despite widespread consensus on the need for cross-sectoral data linkage and its demonstrated utility in existing research, this method remains underutilised globally.

In this article, researchers Lindsay Pearce, Rohan Borschmann, Jesse Young, and Stuart Kinner summarise the key benefits, challenges, and potential solutions to expediting the application of cross-sectoral data linkage to understand and address the health inequities associated with social exclusion. Using international examples that consider a broad range of political and data linkage infrastructure contexts, both high and low/middle income countries, and socially excluded populations, they highlight what is currently being done and what more can be done to advance this field and realise the potentially widespread improvements in health inequity.

Lindsay Pearce adds: “The exclusion of socially excluded populations from health research using administrative data reinforces their health inequities and represents a missed opportunity for public health. Fortunately, there is a lot of momentum towards cross-sectoral data linkage for inclusion health in many jurisdictions such as the UK, Australia, and Canada who have emerged as global leaders; but even in these contexts it remains fraught with challenges and inefficiency. And then there’s countries with far less developed or centralised data collection and linkage infrastructure – including low and middle-income countries. We wrote this paper out of a recognised need to establish a cohesive and collective summary on where we are at – and where we need to go – not only to resonate with researchers, but to stimulate joint dialogue between researchers, data custodians, governments, linkage administrators, and the populations we intend to study.”


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Lindsay Pearce, Rohan Borschmann, Jesse Young and Stuart Kinner

Pearce, L., Borschmann, R., Young, J. and Kinner, S. (2023) “Advancing cross-sectoral data linkage to understand and address the health impacts of social exclusion: Challenges and potential solutions”, International Journal of Population Data Science, 8(1). doi: 10.23889/ijpds.v8i1.2116.