Evaluating causal relationships between urban built environment characteristics and obesity: a methodological review of observational studies.

BACKGROUND: Existing reviews identify numerous studies of the relationship between urban built environment characteristics and obesity. These reviews do not generally distinguish between cross-sectional observational studies using single equation analytical techniques and other studies that may support more robust causal inferences. More advanced analytical techniques, including the use of instrumental variables and regression discontinuity designs, can help mitigate biases that arise from differences in observable and unobservable characteristics between intervention and control groups, and may represent a realistic alternative to scarcely-used randomised experiments. This review sought first to identify, and second to compare the results of analyses from, studies using more advanced analytical techniques or study designs. METHODS: In March 2013, studies of the relationship between urban built environment characteristics and obesity were identified that incorporated (i) more advanced analytical techniques specified in recent UK Medical Research Council guidance on evaluating natural experiments, or (ii) other relevant methodological approaches including randomised experiments, structural equation modelling or fixed effects panel data analysis. RESULTS: Two randomised experimental studies and twelve observational studies were identified. Within-study comparisons of results, where authors had undertaken at least two analyses using different techniques, indicated that effect sizes were often critically affected by the method employed, and did not support the commonly held view that cross-sectional, single equation analyses systematically overestimate the strength of association. CONCLUSIONS: Overall, the use of more advanced methods of analysis does not appear necessarily to undermine the observed strength of association between urban built environment characteristics and obesity when compared to more commonly-used cross-sectional, single equation analyses. Given observed differences in the results of studies using different techniques, further consideration should be given to how evidence gathered from studies using different analytical approaches is appraised, compared and aggregated in evidence synthesis.The work was undertaken by the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence. Funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council (MRC), Medical Research Council, the National Institute for Health Research, and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. David Ogilvie is also supported by the MRC [Unit Programme number MC_UU_12015/6].This is the final published version. It originally appeared at http://www.ijbnpa.org/content/11/1/142/abstract