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

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.

Investigating the transmission risk of infectious disease outbreaks through the Aotearoa Co-incidence Network (ACN): a population-based study

“…15 Moreover, the rapid digitalisation of health, education and broader social systems and significant improvements in data processing and storage capabilities have allowed researchers to access several sources of novel data not previously available, such as linked nationwide data on households, education, and employment. 16 Efforts continue to be made to model infectious disease transmission using complex systems approaches. 14 With advances in computing power, the utility of networks to analyse numerous sources of data have increased dramatically, 17,18 and studies employing network analysis to understand disease transmission patterns are increasingly popular.…”

“… 15 Moreover, the rapid digitalisation of health, education and broader social systems and significant improvements in data processing and storage capabilities have allowed researchers to access several sources of novel data not previously available, such as linked nationwide data on households, education, and employment. 16 …”

Evaluation of Survey Nonresponse in Measuring Cardiometabolic Health Risk Factors and Outcomes among Sexual Minority Populations: A National Data Linkage Analysis

“…The use of record linkages across different types of datasets is becoming increasingly important in advancing the measurement and understanding of socio-spatial inequalities in health, especially among minority or hard-to-sample populations [ 1 , 2 , 3 , 4 ]. In several high-income countries, studies using representative surveys or administrative data alone (unlinked) have demonstrated disparities in mental health, psychiatric morbidity, and problematic substance use among sexual minorities, including lesbian, gay, and bisexual (LGB) adults [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ].…”