Exposure to food outlets and obesity Matthew Hobbs PHD Student and Graduate Teaching Assistant Leeds Metropolitan University m.hobbs@leedsmet.ac.uk @hobbs_PA

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?

What has changed? Prevalence Rationale Source: Health Survey for England 2011. Health and Social Care Information Centre

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

The Built Environment Rationale

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………”

Policy Rationale

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.

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.

Results In reality?

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.

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

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

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…..

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

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)

Any questions? m.hobbs@leedsmet.ac.uk @hobbs_PA

The Current Evidence