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Explaining obesity with urban form: a cautionary tale

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Abstract

In recent years, there has been a dramatic increase in studies exploring associations between the built environment and obesity. Many studies have found that built environment characteristics, such as high-density land developments, mixed-land uses, and connected street networks, are associated with lower rates of obesity. However, depending on the research field and the researcher, how one specifies the experimental model and how sociodemographic characteristics of the population are defined and included in the model has led to different policy conclusions and implications. This is not a surprising observation; however, it is one that does seem to have been lost in current discussions. This article highlights several data-processing, model-specification, and model-estimation factors that should be comprehensively considered in studies of the built environment and obesity. Empirical results based on data from Atlanta, GA, USA, illustrate that the association between the built environment and obesity is sensitive to how age, income, and educational attainment are included in the model. Also, a detailed examination of land-use-mix measures shows that it is difficult to create this measure and that results are sensitive to the treatment of missing values. Models that distinguish between overweight and obese individuals are shown to provide richer insights into the associations among obesity, built environment, and sociodemographic characteristics for the Atlanta area. The article concludes by offering modeling recommendations for future studies.

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Notes

  1. Land-use mix represents the evenness of distribution of square footage of development across four types (i.e., residential, commercial, office, and institutional) of land use within a 1-km distance from each participant’s household (excerpt from Frank et al. (2004, p. 90)).

  2. Street connectivity as reflected by the number of connecting streets in a given area helps reduce the volume of traffic and traffic delays on major streets and improves livability in surrounding communities. By increasing the number of street connections, bicycle and pedestrian travel is theoretically enhanced, as these modes of travel are local in nature and involve shorter trips (excerpt from Metro Council (2004, p. 4)).

  3. We empirically verified that the use of imputed income (versus using a missing income dummy variable or excluding these observations) did not influence the interpretation of results reported in this study.

  4. The OR for age in Model 2 are computed using the reference age of 30. \( {\text{OR}} = {\text{exp}}{\left( {{\text{B}}_{{{\text{Age}}}} \cdot \Updelta {\text{Age}} + 2 \cdot {\text{B}}_{{{\text{Age}}^{{\text{2}}} }} \cdot {\text{RA}} \cdot \Updelta {\text{Age}} + {\text{B}}_{{{\text{Age}}^{{\text{2}}} }} \cdot {\left( {\Updelta {\text{Age}}} \right)}^{2} } \right)}. \)

  5. Assumptions were used that resulted in the most conservative bounds for OR, e.g., all women who did not respond were assumed to be obese and all men who did not response were assumed to be nonobese/nonoverweight.

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Acknowledgments

The authors are grateful to Jason Barringer and Ying Zhu who assisted in creating built-environment and travel-time variables and to Marco Castillo who provided helpful suggestions and guidance for testing for the presence of bias in parameter estimates. In addition, the authors are grateful for the helpful comments and suggestions of reviewers.

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Correspondence to Laurie A. Garrow.

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Bodea, T.D., Garrow, L.A., Meyer, M.D. et al. Explaining obesity with urban form: a cautionary tale. Transportation 35, 179–199 (2008). https://doi.org/10.1007/s11116-007-9148-2

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