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Background Public Health England recently published a tool to help local authorities monitor the density of fast food outlets, with restrictions on access to these ‘unhealthy’ outlets being promoted in the fight against obesity. Secondary data containing the locations of food outlets present valuable resources to guide and evaluate these interventions. However, evidence appraising these data sources is limited. This study therefore seeks to validate two sources of secondary food environment data (SFED): Ordnance Survey Points of Interest (POI) data and food hygiene data from the Food Standards Agency (FSA), against street audits. These data sources are commonly used in research, with the former also being used in the Public Health England tool. Methods Audits were conducted across 54 Lower Super Output Areas in England. All streets within each Lower Super Output Area were covered to identify the name and street address of all food outlets therein. Audit identified outlets were matched to outlets in the SFED to identify true positives (TP: outlets in both the SFED and the audits), false positives (FP: outlets in the SFED only) and false negatives (FN: outlets in the audits only). Agreement was assessed using positive predictive values (PPV: TP/(TP +FP)) and sensitivities (TP/(TP +FN)). Confidence intervals were calculated in Excel using the Agresti-Coull method. Results Overall, the street audits identified 1188 food outlets, compared to 1102 and 1098 for the POI and FSA data respectively. Sensitivity and PPV were significantly higher for FSA data (sensitivity: 0.80, CI: 0.77–0.82; PPV: 0.86, CI: 0.84–0.88) than for the POI data (sensitivity: 0.73, CI: 0.71–0.76; PPV: 0.79, CI: 0.77–0.81). Both datasets had ‘good’ agreement with street audits according to the Paquet classifications (‘good’ defined as PPV and sensitivities between 0.71 and 0.90). Conclusion This study provides new evidence for the validity of SFED commonly used in research and emergently used by policymakers. Agreement between the SFED and street audits is sufficiently good to provide local authorities with confidence in using tools and research based on these SFED. Whilst FSA data has statistically significantly higher agreement with street audits than POI, the magnitude of the difference is relatively small. POI also has other advantages (e.g. more detailed outlet classifications and better spatial accuracy). Thus, POI is still a useful and recommended source of food environment data.

Background Recent research has demonstrated that neighbourhood features such as fast-food outlets and supermarkets may co-occur. However, little research has investigated the combined influences of both the built food and physical activity (PA) environments and associations with body mass index and obesity. This study aims to use latent class analysis within a large UK adult population to investigate associations between the combined environment and obesity. Methods Cross-sectional, individual-level data (n=22,889) from Wave 1 of The Yorkshire Health Study (2010–2012) were used. Body mass index (BMI) was calculated using self-reported height and weight; obesity=BMI≥30. Neighbourhood was defined as a 2 km radial buffer; food outlets and physical activity facilities were sourced (2012) from Ordnance Survey Points of Interest (PoI) and categorised into ‘fast-food’, ‘large supermarkets’, ‘convenience and other food retail outlets’ and ‘physical activity facilities’. Parks were sourced from Open Street Map. Latent class analysis (LCA) was conducted on these five environmental variables. Logistic regression was then conducted to predict obesity based on the five neighbourhood types identified within LCA. Models adjusted for age, gender, ethnicity, area-level deprivation and rural or urban classification of the neighbourhood. Results A five-class solution fitted the dataset best and was interpretable. Neighbourhood typologies were labelled as; “low exposure” (19.0% of study population); “moderate exposure” (33.3%); “moderate PA, limited food” (12.2%); “saturated” (13.6%); “moderate PA, ample food” (21.2%). For associations with obesity, those within the low exposure typology were chosen as the exposure because low exposure to physical activity environments have the potential to reduce physical activity behaviours and although more debatable poorer access to the food environment may result in poorer dietary intake. Compared to the low exposure, one typology showed lower odds of obesity (“saturated”, OR=0.86 [0.75,0.99]) and one showed increased odds of obesity (“moderate exposure, OR=1.18 [1.05,1.32]. Discussion Meaningful neighbourhood typologies were derived from a range of food and physical activity measures using latent class analysis which explained differences in obesity in large UK based sample of adults. This study suggests that neighbourhoods were not wholly unhealthy or healthy, they were characterised by neighbourhood features that are both health-promoting and health-constraining and this resulted in complex associations with obesity.