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Dr. Daniel Exeter
September 13, 2017 @ 4:00 pm - 5:00 pm
Using Big Data to understand the geography of health and deprivation in New Zealand
Dr Daniel Exeter, School of Population Health, The University of Auckland
There has been a considerable amount of research into socio-demographic patterns of health outcomes in New Zealand using record linkage of key routine health databases (e.g. primary care enrolments, hospitalisations, medication dispensing). These administrative data capture patient journeys through the publically-funded health system in New Zealand used by the majority of the population.
The government’s recent introduction of the Integrated Data Infrastructure (IDI), a large research database maintained by Statistics New Zealand containing de-identified microdata about people and households, has provided unprecedented opportunities for health and social research in New Zealand.
In this presentation, following a brief overview of the IDI and a discussion of its strengths and weaknesses, I will highlight some of the research my team has conducted using Big Data to explore the social, economic and geographic variations in area-level deprivation, cardiovascular disease and childhood obesity.
I’ll close this presentation with a discussion of the benefits and risks of using big data to inform health and social policy in New Zealand.
Dr Daniel Exeter is a Senior Lecturer in Epidemiology at the University of Auckland. He is a quantitative health geographer and has a background in Geographical Information Systems and spatial analysis. Using large datasets such as the census and the IDI, his research aims to identify, and provide solutions to inequities in health. He recently developed the New Zealand Index of Multiple Deprivation (IMD) and a geographic boundary file known as ‘data zones’. He is currently leading research to determine measures of socioeconomic position among the elderly, and is a co-investigator on a programme of cardiovascular disease (CVD) risk prediction research, where he uses big data to investigate the geographical variations in treatment, outcomes and CVD-related service utilisation.