Using big data to tackle inequalities in society

This event took place on Thursday 28 June 2018.

Nichola Shackleton hosted three visiting speakers through a Faculty Research & Development Fund grant, and we secured several local researchers to fill a day with discussion of the uses of big data in the health and the social sciences. In the order they presented:

Using the National Pupil Database to assess inequalities in access to preschools and the transition to primary schools in the UK

Dr Tammy Campbell is a Research Fellow in the Centre for Analysis of Social Exclusion at the London School of Economics. She completed her PhD at the UCL Institute of Education analysing large-scale cohort study and administrative data to investigate structural and psychological factors creating differences among primary school pupils. She was previously a Government Social Researcher in the UK Department for Education, and before that worked with children and young people in London, Japan, and Norway.

Tammy uses big data, including England’s census of schools and pupils – the National Pupil Database (NPD) – to explore inequalities in childhood experiences and trajectories. Current research interests include: inequalities in preschooling and in transitions to primary school, month of birth / relative age effects within education, stereotyping and bias in perceptions of pupils, ‘ability’-grouping, and factors influencing breastfeeding. Tammy’s latest paper using the NPD to unpick inequalities – ‘“Universal” early education: who benefits? Patterns in take-up of the entitlement to free early education among three-year-olds in England’ – can be found in the British Educational Research Journal (Campbell, Gambaro, Stewart 2018).

Using big data to tackle inequities in CVD

Professor Rod Jackson is in epidemiology in the Section of Epidemiology & Biostatistics at the School of Population Health, Faculty of Medical and Health Sciences, the University of Auckland. He is also the director of EPIQ (, an in-house group undertaking teaching and research in Evidence-based Practice (EP), health Informatics (I) and Quality improvement (Q), for healthcare services.

Rod is medically trained, has a PhD in Epidemiology, and is a fellow of the New Zealand College of Public Health Medicine. He has published over 295 peer-reviewed papers. His main research interest for the last 35 years has been the epidemiology of chronic diseases, particularly cardiovascular diseases. Rod is one of the architects of New Zealand risk-based clinical guidelines for managing CVD risk and leads the HRC-funded VIEW2020 (Vascular Informatics using Epidemiology & the Web 2020) research programme.

Big data, small populations: unpacking inequalities using linked data

Professor Louisa Jorm is the Foundation Director of the Centre for Big Data Research in Health at the University of New South Wales (UNSW), Sydney, Australia. She has spent equal periods (more than 10 years each) in senior leadership roles in government and academia, giving her unique opportunities for translational research impacts.

Professor Jorm is an international leader in health “big data” research and specifically in applying advanced analytic methods to large-scale routinely collected data and linked data, including hospital inpatient, mortality, perinatal, and medical and pharmaceutical claims data. She has made major scientific contributions to research in the areas of health system performance, health surveillance, data linkage and Aboriginal health.

Professor Jorm has played a leading role in the establishment of major infrastructure and capacity for “big data” health research in Australia, including the NSW/ACT Centre for Health Record Linkage, the 45 and Up Study, the NSW Biostatistical Officer Training Programme, and the SURE and ERICA secure data analysis environments. She led the development of the new UNSW Master of Science in Health Data Science, the first such programme in the southern hemisphere.

Professor Jorm has published >160 scientific papers and been awarded >$30 million in research grants. She is a high-profile advocate for more and better use of routinely collected health data for research.

Big data and health: from New Zealand to the UK

Dr Fran Darlington-Pollock is a lecturer in Population Geography and Co-Director of the Centre for Spatial Demographics Research at the University of Liverpool.

Fran is also Membership Secretary for the Population Geography Research Group (with the Royal Geographical Society) and Council Member for the British Society for Population Studies. After completing her PhD at the University of Leeds (December 2015), Fran worked as a lecturer in Health Geography at Queen Mary University of London before moving to Liverpool.

As a population and health geographer, Fran is interested in health inequality; ethnicity; place and health; migration, residential mobility, and health. Fran’s PhD explored the nature of ethnic inequalities in health, and explanations for changing health gradients rooted in migration, deprivation change, and social mobility. The primary focus of this research was census microdata in the form of cross-sectional anonymised records, and the Office for National Statistics Longitudinal Study.

Fran has also undertaken research at the University of Auckland, applying the analytical and theoretical frameworks of her PhD to a different context and different data sets. Collaborating with colleagues in New Zealand, this body of research made use of the VIEW data set hosted by the University of Auckland's School of Population Health. Fran’s current research reflects sustained interest in selection effects, migration, and residential mobility, and extends to ageing populations and education transitions. 

Data, difference, and the possibility of an indigenous development dividend to big data health analytics

Andrew Sporle is a social epidemiologist based part-time in the Department of Statistics at the University of Auckland. He works primarily as a research developer using cross-sectoral and linked data resources, including Statistics New Zealand’s Integrated Data Infrastructure. He has worked on improving the Māori responsiveness of the data ecosystem, creating longitudinal studies with existing data, and developing public domain tools to improve accessibility and utility of official statistics.

Andrew has been involved with official data innovations since 1998, including PRIMHD, the Family, Whānau and Wellbeing Project, the NZ Census Mortality Study, the Māori Mortality study, the Virtual Health Information Network (VHIN), Statistics NZ's legislative review, and applications of the NZ Longitudinal Census. He currently serves on the executive of the VHIN and is a founding member of Te Mana Rauranga – the Māori data sovereignty network.

Development and implementation of the Index of Multiple Deprivation in New Zealand

Associate Professor Dan Exeter works in Epidemiology and Biostatistics at the School of Population Health, the University of Auckland. He is a quantitative health geographer and has a background in Geographical Information Systems and spatial analysis. Dan’s research uses large data sets such as the census or routine health databases to identify occurrence of, or potential solutions to, inequalities in health. Dan's current research interests focus on the use of data zones and the Index of Multiple Deprivation to explore health and social outcomes. He leads a Marsden-funded project investigating measures of socioeconomic position among older people, and is a co-investigator on the VIEW2020 HRC programme, investigating inequities in CVD treatment, management, and outcomes using big data. 

Big data for a better start in life: examples from A Better Start National Science Challenge

Dr Sheree Gibb is a Senior Research Fellow in the Department of Public Health at the University of Otago Wellington. She has a background in longitudinal social and health research. Her current interest is in the use of administrative data for health research. Sheree is one of New Zealand’s leading experts on the Integrated Data Infrastructure (IDI), and is involved in a number of research projects and groups using IDI data, including the National Science Challenge "A Better Start", and the Virtual Health Information Network.

Sheree was unable to join us on the day, but Dr Barry Milne gave the presentation in her stead!

A small segment of the New Zealand population with a high concentration of service use

Dr Barry Milne is Director of the Centre of Methods and Policy Application in the Social Sciences (COMPASS) at the University of Auckland. His research focuses on socioeconomic inequalities in relation to human health and development, and how childhood experiences shape outcomes in both childhood and adulthood. Barry leads several projects using data from Statistics New Zealand’s Integrated Data Infrastructure to investigate inequalities in health and development outcomes, and has previously developed simulation models to investigate factors with the largest impacts on different domains of functioning.

We have taken down the presentation files for this talk for the moment to avoid any conflict with publishing elsewhere.

Segmentation Towards Enabling Pathways (STEP): an approach to integrate health and support delivery

Avinesh Pillai is a Senior Research Fellow in the Department of Statistics, and the Data Analytics Manager for the Growing Up in New Zealand study at the University of Auckland. His research focuses on challenges with analysing longitudinal data, statistical computing, and data governance. 

“Necessary but not sufficient”: adding voice and choice to big data

Associate Professor Susan Morton is an expert in life-course epidemiology and a specialist in Public Health Medicine. She is the Director of the University of Auckland cross-faculty Centre for Longitudinal Research – He Ara ki Mua, and has been the Director (PI) of the contemporary longitudinal study of NZ children and families (Growing Up in New Zealand) since its inception in 2005. The study follows 6,853 children (born in 2009–2010) in the context of their families and the NZ environment. Her team engages with 16 government agencies to provide evidence to inform cross-sectoral policies to improve population wellbeing and solutions to reduce inequities in life course outcomes.