Progress Towards Using Linked Population-Based Data For Geohealth Research: Comparisons Of Aotearoa New Zealand And The United Kingdom

R A Oldroyd,C Sturley,S Kingham,M Hobbs,L Marek,J Wiki,F Pontin,M Campbell,M Birkin,M A Morris,M Tomintz,M Wilson,V Jenneson

doi:10.1007/s12061-021-09381-8

Abstract

Globally, geospatial concepts are becoming increasingly important in epidemiological and public health research. Individual level linked population-based data afford researchers with opportunities to undertake complex analyses unrivalled by other sources. However, there are significant challenges associated with using such data for impactful geohealth research. Issues range from extracting, linking and anonymising data, to the translation of findings into policy whilst working to often conflicting agendas of government and academia. Innovative organisational partnerships are therefore central to effective data use. To extend and develop existing collaborations between the institutions, in June 2019, authors from the Leeds Institute for Data Analytics and the Alan Turing Institute, London, visited the Geohealth Laboratory based at the University of Canterbury, New Zealand. This paper provides an overview of insight shared during a two-day workshop considering aspects of linked population-based data for impactful geohealth research. Specifically, we discuss both the collaborative partnership between New Zealand’s Ministry of Health (MoH) and the University of Canterbury’s GeoHealth Lab and novel infrastructure, and commercial partnerships enabled through the Leeds Institute for Data Analytics and the Alan Turing Institute in the UK. We consider the New Zealand Integrated Data Infrastructure as a case study approach to population-based linked health data and compare similar approaches taken by the UK towards integrated data infrastructures, including the ESRC Big Data Network centres, the UK Biobank, and longitudinal cohorts. We reflect on and compare the geohealth landscapes in New Zealand and the UK to set out recommendations and considerations for this rapidly evolving discipline.

Highlights

An individual’s health status is determined by a myriad of factors including personal physiological and genetic predispositions, cultural, social and economic contexts, and the wider environment within which they live and interact (Dahlgren & Whitehead, 1991)
This paper provides an overview of the lessons learned and insight shared during the workshop, including: the collaborative partnership between the Ministry of Health (MoH) and the University of Canterbury’s GeoHealth Lab (GHL) in New Zealand (NZ) which has enabled the successful translation of research to health policy, and the novel infrastructure and commercial partnerships enabled through Leeds Institute for Data Analytics (LIDA)
A higher proportion of Māori people, fewer people in the 20–39 age group compared to the national average, and a relatively high proportion of people living in the most deprived areas (Sheridan et al, 2011), were found within these transient groups. These findings enabled Lakes District Health Board (DHB) to better understand the characteristics of the affected population, use of primary health services, and their accessibility for vulnerable populations (Ministry of Health | Manatū Haoura, 2019)

Summary

Introduction

An individual’s health status is determined by a myriad of factors including personal physiological and genetic predispositions, cultural, social and economic contexts, and the wider environment within which they live and interact (Dahlgren & Whitehead, 1991). In high income countries such as New Zealand (NZ) and the United Kingdom (UK), spatial health research is reliant on surveillance data including, but not limited to, the prevalence and incidence of disease and its influencing factors, of variation across the population, and of changes over time.

Results

Conclusion