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

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.

Assessing diet in a university student population: a longitudinal food card transaction data approach.

Starting university is an important time with respect to dietary changes. This study reports a novel approach to assessing student diet by utilising student-level food transaction data to explore dietary patterns. First-year students living in catered accommodation at the University of Leeds (UK) received pre-credited food cards for use in university catering facilities. Food card transaction data were obtained for semester 1, 2016 and linked with student age and sex. k-Means cluster analysis was applied to the transaction data to identify clusters of food purchasing behaviours. Differences in demographic and behavioural characteristics across clusters were examined using χ2 tests. The semester was divided into three time periods to explore longitudinal changes in purchasing patterns. Seven dietary clusters were identified: 'Vegetarian', 'Omnivores', 'Dieters', 'Dish of the Day', 'Grab-and-Go', 'Carb Lovers' and 'Snackers'. There were statistically significant differences in sex (P < 0·001), with women dominating the Vegetarian and Dieters, age (P = 0·003), with over 20s representing a high proportion of the Omnivores and time of day of transactions (P < 0·001), with Dieters and Snackers purchasing least at breakfast. Many students (n 474, 60·4 %) changed dietary cluster across the semester. This study demonstrates that transactional data present a feasible method for dietary assessment, collecting detailed dietary information over time and at scale, while eliminating participant burden and possible bias from self-selection, observation and attrition. It revealed that student diets are complex and that simplistic measures of diet, focusing on narrow food groups in isolation, are unlikely to adequately capture dietary behaviours.

A comparison of small-area hospitalisation rates, estimated morbidity and hospital access.

Published data on hospitalisation rates tend to reveal marked spatial variations within a city or region. Such variations may simply reflect corresponding variations in need at the small-area level. However, they might also be a consequence of poorer accessibility to medical facilities for certain communities within the region. To help answer this question it is important to compare these variable hospitalisation rates with small-area estimates of need. This paper first maps hospitalisation rates at the small-area level across the region of Yorkshire in the UK to show the spatial variations present. Then the Health Survey of England is used to explore the characteristics of persons with heart disease, using chi-square and logistic regression analysis. Using the most significant variables from this analysis the authors build a spatial microsimulation model of morbidity for heart disease for the Yorkshire region. We then compare these estimates of need with the patterns of hospitalisation rates seen across the region.

The estimation of population microdata by using data from small area statistics and samples of anonymised records.

"Traditionally, estimates of the number of people in small areas (the smallest geographical units for which data are available) have been disaggregated only by age and sex. More recently, much research effort has been directed towards developing some form of enhanced small-area population estimation, in which the population in a small area is disaggregated not only by age and sex, but also by a wide range of additional economic and social characteristics. Solutions to this problem currently include account-based demographic models, often used by local authorities."

Indexing the census: a by-product of the simulation of whole populations by means of SAS and SAR data.

"Amongst the new output formats adopted for the 1991 [U.K.] census were the Small Area and Local Base Statistics tables and the Samples of Anonymised Records. During an attempt to combine these data sources to estimate whole populations, a need was recognised for a computer algorithm to aggregate SAR data flexibly into LBS and SAS table look-alikes. This paper is a report on progress in the development of such an algorithm, including the concomitant development of a meta-database of census tables and variables required as an input to the algorithm. Out of this work a user-friendly, freely disseminable version of the census meta-database has been created, of interest to all census users."

Census-based information systems for ethnic groups: a study of Leeds and Bradford.

"In this paper are explored the problems involved in using Census data as a guide to the evolving ethnic geography of British cities. The relationship between the concept of ethnicity and Census questions on place of birth is traced. The country-of-birth statistics for Small Areas from the 1971 and 1981 Censuses are employed in an attempt to ascertain the influence of technical specifications on the measurement of the residential segregation of ethnic groups in order to distinguish real differences from apparent ones. The attempt is not fully successful, but it does provide clear pointers on how to proceed further in research on the ethnic spatial mosaics that [British] cities now are."