Linking environment and health data to investigate the association between access to unhealthy food and child BMI

Main Article Content

Amy Mizen
Sarah Rodgers
Richard Fry
Ronan Lyons


Modelling the daily exposure environment provides evidence for policy and practice. However, the dose-response relationship between exposure to food environments and obesity has not been widely investigated. This study investigated whether increased retail food environment (RFE) exposure in children was associated with a larger body mass index (BMI).

Objectives and Approach
Individually tailored environmental exposures were calculated in a GIS for home and school locations, and modelled walking routes to and from school. Exposures were linked to individual level health data in the SAIL databank for a cohort of individuals aged 11-13 years from south Wales who had BMI measurements. A fully adjusted multilevel regression model was fitted to investigate the association of RFE exposure with BMI. Based on the distance individuals lived from school, we investigated differences between children who have the potential to walk to school (“walkers” lived 4.8km).

Home exposure and exposure along the walk to school was significantly greater for children living in deprived catchments, compared with children living in affluent school catchments (t = -5.25, p

Increased BMI was associated with greater RFE exposure along the walk home from school. The findings suggest that the walk home from school should be the focus for developing interventions and policies to discourage unhealthy eating. Research should be undertaken to better understand child purchasing habits.


Welsh Government invests over £120m annually in housing related support to help prevent and tackle homelessness under the ‘Supporting People Programme’.

A 2016 data-linkage Feasibility Study indicated health-service utilisation reductions post-intervention, and led to a four year project to create a national, all-Wales dataset to provide robust statistical results.

Objectives and Approach

Establish data sharing agreements, acquire and import anonymised individual-level data into the SAIL Databank. Create a research ready dataset, designed to permit annual administrative data updates to form dynamic cohort and control groups.

Create several control group methods: 1) Internal Programme Data; 2) Matched controls; 3) Healthcare-Utilisation Patterns; 4) External Data Sources.

Link to routine health data, obtain and link to other public service data to gain a deeper understanding of the Programme; how it affects use of other public services, and whether it helps people live independently.

Complete statistical analysis using a Generalised Linear Mixed Modelling approach.


Data sharing agreements, data acquisition and standardisation complete for nineteen of twenty-two Unitary Authorities in Wales. Temporal coverage varies by Unitary Authority (2003-2017). 2016 data measures: match rates >85%; 57% female; lead reason for support (top 5) : ‘General’ 20%, ‘Mental Health’ 15%, ‘Older People’ 14%, ‘Domestic Abuse’ 9%, ‘Young People’ 7%.

Various control group methods employed: 1) Internal ‘Programme’ Data – no support taken up; 2) Matched controls; 3) Healthcare-Utilisation Patterns – rejected due to sparse outcome data; 4) External Data Sources being further explored.

Health data-linkage (emergency admissions, emergency department attendance and primary care events) complete. Ongoing discussions to obtain sample social care, and police call data during 2018.

Statistical analysis underway with results planned to be published during the summer of 2018.


Despite many challenges, creation of a national linked dataset for people at risk of homelessness is possible with collaborative working between central government, academic and local government bodies. This ‘Administrative Data Research Centre Wales’ project has created a rich research resource enabling statistical analysis to answer research questions around homelessness.

Article Details

How to Cite
Mizen, A., Rodgers, S., Fry, R. and Lyons, R. (2018) “Linking environment and health data to investigate the association between access to unhealthy food and child BMI”, International Journal of Population Data Science, 3(4). doi: 10.23889/ijpds.v3i4.906.

Most read articles by the same author(s)

<< < 4 5 6 7 8 9