1. Exposure to food outlets and obesity
Matthew Hobbs PHD Student and Graduate Teaching Assistant Leeds Metropolitan

2. Obesity Impacts Health and Quality of Life
Consequences
Rationale
Obesity Impacts Health and Quality of Life
Wider impacts
Short term
Long term
Physical Wellbeing
Type 2 Diabetes
Coronary Heart Disease
Breast and Bowel Cancer
Mental Wellbeing
Depression
Self-esteem
Social Wellbeing
Stereotyped
Least desired friends
Economic Consequences
Direct costs – 2.5% of NHS expenditure treating obesity
Indirect costs – £56 billion by 2050
Sustainability
Will healthcare systems be able to cope in the future?

3. What has changed? Prevalence Rationale
Source: Health Survey for England Health and Social Care Information Centre

4. The Obesogenic Environment
Rationale
An environment that promotes gaining weight and one that is not conducive to weight loss
Ding and Gebel (2012) Built environment, physical activity, and obesity: what have we learned from reviewing the literature? Health and Place

5. The Built Environment
Rationale

6. The Current Evidence Weight Fast-food Proximity Convenience Density
Rationale
Fast-food
Convenience
Supermarkets
Proximity
Density
Weight
“There is a plausible link …..”
“Research into the link between food availability and obesity is still relatively underdeveloped…..”
“There is an unavoidable lack of evidence………”

7. Policy
Rationale

8. Data Methods Participants
Cross sectional data on 15,422 adults (54% female) that included BMI and area of residence data were obtained from Leeds Local Authority, England.
Definition of neighbourhood
MSOA’s are geographical areas that have an average size of 7,500 residents and 3,000 households.
Exposure to food outlets
Food outlets were defined as fast-food, supermarkets and retail.
Exposure to food outlets was defined as the simplistic count of food outlets in the mid-super output area (MSOA).
Risk of Obesity
Binary logistic regression models calculated whether participants surrounded by a greater number of food outlets were at greater risk of obesity.

9. Key Message
Results
This study provides little support for the notion that exposure to any type of food outlet in the home neighbourhood increases the risk of obesity in adults.
Only 3 of 12 potential associations were significant.
Associations were in fact weak or contrary to our hypotheses protective of risk of obesity.
No association between exposure to fast-food outlets and increases in BMI in adults.

10. Results
In reality?

11. Logistical Regression
Results
Logistic Regression
OR = 1
Outcome is equally likely for both groups
OR < 1
As the predictor > odds of the outcome occurring <
OR > 1
As the predictor > odds of the outcome occurring >
Increase in a monotonic (linear) way i.e. 1 extra shop = 1 increase in log odds of being obese
A confidence interval gives an indication of the likely error around an estimate that has been calculated from measurements based on a sample of the population. It indicates the range within which the true value for the population as a whole can be expected to lie, taking natural random variation into account.
Throughout this report, 95% confidence intervals are used. These are known as such because if it were possible to repeat the same programme under the same conditions a number of times, we would expect 95% of the confidence intervals calculated in this way to contain the true population value for that estimate.
Larger sample sizes lead to narrower confidence intervals, since there is less natural random variation in the results when more individuals are measured. The NCMP has relatively narrow confidence limits because of the large size of the sample.
All models are fixed effects
Obese or not as the outcome (did do all models with obese and overweight or not and sBMI as the outcome)
95% Confidence intervals = indication of the likely error around the sample estimate. It indicates the range within which the true value of the population as a whole can be expected to lie.

12. Exposure increases risk?
Results
Takeaway
Q1 (≤3)
Q2 (4-6)
Q3 (7-10)
Q4 (11+)
REF
0.95 [0.86:1.05]
1.07 [0.96:1.18]
1.00 [0.90:1.11]
Other Retail
Q1 (≤4)
Q2 (5-5)
Q3 (6-9)
Q4 (10+)
REF
0.96 [0.86:1.06]
1.02 [0.93:1.13]
0.88 [0.79:0.99]*
Supermarkets
Q1 (≤0)
Q2 (1-1)
Q3 (2-2)
Q4 (≥3)
REF
0.95 [0.87:1.04]
0.98 [0.87:1.10]
0.91 [0.80:1.04]
Total Outlets
Q1 (≤8)
Q2 (9-13)
Q3 (14-19)
Q4 (20+)
REF
0.88 [0.79:0.98]*
1.10 [1.00:1.21]*
0.96 [0.87:1.07]
Values = odds ratios [95% confidence intervals],
= significance p<0.05; All models: r2=0.01:0.02
All models adjusted for; gender, area level deprivation (IMD) and age

13. Considerations One of the largest sample sizes in England to date
Limitations
One of the largest sample sizes in England to date
Local Authority food outlet data
Objective assessment of obesity
No account for multi-level nature of the data
A persons neighbourhood is defined by an arbitrary MSOA

14. At present no or little evidence is not evidence of no effect…..
Take Home Message
Key messages
Given the lack of evidence on the built obesogenic environment policy makers should approach policies designed to limit food outlets with caution. For example the current fast food zoning laws being proposed.
At present no or little evidence is not evidence of no effect…..

15. Future Research and PhD…
South Yorkshire Cohort Study (n=20,000+)
Collaboration - Sheffield University, Leeds Metropolitan University, Local Councils Sheffield, Doncaster, Rotherham, Barnsley
Better definition of exposure – home and work postcode and journey
Weighted regression – more accurate models of exposure
Contrasting areas – rural/urban, increase covariates, commercial density
Behavioural data – self-reported physical activity, dietary intake
Park quality data – its not just about availability

16. Acknowledgements Key messages
Leeds Metropolitan University – PhD Bursary
Dr. Claire Griffiths – Senior Lecturer, Physical Activity Exercise and Health
Prof. Jim McKenna – Professor Physical Activity and Health
Prof. Paul Gately – Professor of Exercise and Obesity
Leeds City Council / Leeds Local Authority
Anna Frearson – Consultant in Public Health
Adam Taylor – Senior Information Analyst
Emma Strachan - Health Improvement Specialist (Food)

17. Any questions?
@hobbs_PA

18. The Current Evidence