Explaining variations in obesity and inactivity between US metropolitan areas
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This paper discusses measurement of the main dimensions of the urban environment that have been proposed as relevant to explaining geographic variations in obesity and inactivity. It considers urban sprawl, food access and exercise access as latent constructs, defined by sets of observed indicators for areas. In an application to 993 US metropolitan counties, the paper shows how these latent constructs may be incorporated in an ecological (area-scale) model, which recognizes spatial aspects in the patterning of both outcomes and environmental factors. Urban sprawl and area socioeconomic status emerge from regression modelling as leading influences on obesity and inactivity.
KeywordsObesity Inactivity Sprawl Food access Income Spatial correlation
The author acknowledges comments on an earlier draft by Stephanie Jilcott Pitts.
- Atkinson, A. (1985). Plots, transformations, and regression (p. 5). New York: Oxford University Press.Google Scholar
- Barker, L., Thompson, T., Kirtland, K., Boyle, J., Geiss, L., McCauley, M., & Albright, A. (2013). Bayesian small area estimates of diabetes incidence by United States county. Journal of Data Science, 11, 249–269.Google Scholar
- Block, J., Scribner, R., & DeSalvo, K. (2004). Fast-food, race/ethnicity, and income: A geographic analysis. American Journal of Preventive Medicine, 27(3), 211–217.Google Scholar
- Brooks, S., & Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7(4), 434–445.Google Scholar
- Brown, T. (2006). Confirmatory factor analysis for applied research. New York: Guilford Press.Google Scholar
- Centers for Disease Control and Prevention (CDC). (2014). Healthier food retail: Beginning the assessment process in your state or community. Atlanta: U.S. Department of Health and Human Services.Google Scholar
- Cho, S., Chen, Z., Yen, S., & Eastwood, D. (2006). The effects of urban sprawl on body mass index: Where people live does matter? Consumer Interests Annual, 52(1), 159–169.Google Scholar
- Cole, C. (2012). Access to healthy and affordable food is critical to good nutrition. Austin, Texas: Center for Public Policy Priorities.Google Scholar
- Drewnowski, A., & Specter, S. (2004). Poverty and obesity: The role of energy density and energy costs. American Journal Clinical Nutrition, 79(1), 6–16.Google Scholar
- Edwards, M., Jilcott, S., Floyd, M., & Moore, J. (2011). County-level disparities in access to recreational resources and associations with adult obesity. Journal of Park and Recreation Administration, 29(2), 39–54.Google Scholar
- Ewing, R., & Hamidi, S. (2010). Measuring urban sprawl and validating sprawl measures. Metropolitan Research Center, University of Utah.Google Scholar
- Ewing, R., & Hamidi, S. (2014) Measuring sprawl 2014. Smart Growth America and Metropolitan Research Center, University of Utah.Google Scholar
- Health Canada. (2013). Measuring the food environment in Canada. Ottawa: Health Canada.Google Scholar
- Ingram, D., & Franco, S. (2012). 2013 NCHS urban–rural classification scheme for counties. Vital Health Statistics, 2(154), 1–73.Google Scholar
- Jilcott, Pitts S., Edwards, M., Moore, J., Shores, K., DuBose, K., & McGranahan, D. (2013). Obesity is inversely associated with natural amenities and recreation facilities per capita. Journal of Physical Activity and Health, 10(7), 1032–1038.Google Scholar
- Jilcott, S., Liu, H., Moore, J., Bethel, J., Wilson, J., & Ammerman, A. (2010a). Commute times, food retail gaps, and body mass index in North Carolina counties. Preventing Chronic Disease, 7(5), A107.Google Scholar
- Koh, K. (2011). A spatial analysis of county level obesity prevalence in Michigan. Presented to 2011 mid-continent Regional Science Association conference. http://www.mcrsa.org/Assets/Documents/Proceedings/A-SpatialAnalysisofCounty-levelObesityPrevalenceinMichigan.pdf.
- Leroux, B., Lei, X., & Breslow, N. (1999). Estimation of disease rates in small areas: A new mixed model for spatial dependence. In M. Halloran & D. Berry (Eds.), Statistical models in epidemiology, the environment and clinical trials (pp. 135–178). New York: Springer.Google Scholar
- Lopez, R. (2014). Urban sprawl in the United States: 1970–2010. Cities and the Environment, 7(1). http://digitalcommons.lmu.edu/cate/.
- Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., & Giovannini, E. (2008). Handbook on constructing composite indicators: Methodology and user guide. Paris: OECD.Google Scholar
- Neckerman, K., Bader, M., Purciel M., & Yousefzadeh, P. (2009) Measuring food access in urban areas. National Poverty Center, University of Michigan.Google Scholar
- Petrella, R., Kennedy, E., & Overend, T. (2008). Geographic determinants of healthy lifestyle change in a community-based exercise prescription delivered in family practice. Environmental Health Insights, 1, 51–62.Google Scholar
- Robert Wood Johnson Foundation. (2014). 2014 County health rankings key findings. http://www.countyhealthrankings.org/file/2014-county-health-rankings-key-findingspdf.
- Schirm, A., Czajka, J., & Zaslavsky, A. (1999). Large numbers of estimates for small areas. Federal Committee on Statistical Methodology Research Conference, 1999 (pp. 77–83). https://fcsm.sites.usa.gov/files/2014/05/IV-A_Schirm_FCSM1999.pdf.
- Sexton, K. (2008). Modifiable areal unit problem (MAUP). In E. Melnick & B. Everitt (Eds.), Encyclopedia of quantitative risk analysis and assessment (Vol. 3). Chichester: Wiley.Google Scholar
- Swinburn, B., Caterson, I., Seidell, J., & James, W. (2004). Diet, nutrition and the prevention of excess weight gain and obesity. Public Health Nutrition, 7(1A), 123–146.Google Scholar
- U.S. Census Bureau. (2014). Small area income and poverty estimates. http://www.census.gov/did/www/saipe/about/index.html.
- U.S. Department of Agriculture (USDA). (2014a). Food environment atlas. http://www.ers.usda.gov/data-products/food-environment-atlas.aspx. Economic Research Service, USDA.
- U.S. Department of Agriculture (USDA). (2014b). Natural amenities scale. http://www.ers.usda.gov/data-products/natural-amenities-scale.aspx. Economic Research Service, USDA.
- U.S. Department of Agriculture (USDA). (2014c). Food Environment Atlas Data Documentation. http://ers.usda.gov/datafiles/Food_Environment_Atlas/Data_Access_and_Documentation_Downloads/Current_Version/documentation.pdf. Economic Research Service, USDA.
- Ver Ploeg, M. (2010). Access to affordable, nutritious food is limited in “food deserts”. Amber Waves, 8(1), 20–27.Google Scholar
- Ver Ploeg, M., Breneman, V., Dutko, P., Williams R., Snyder S., Dicken C., et al. (2012) Access to affordable and nutritious food: Updated estimates of distance to supermarkets using 2010 data. US Department of Agriculture, Economic Research Report No. (ERR-143).Google Scholar
- Wakefield, J., Best, N., & Waller, L. (2000). Bayesian approaches to disease mapping. In P. Elliott, J. Wakefield, N. Best, & D. Briggs (Eds.), Spatial epidemiology: Methods and applications (pp. 104–127). Oxford: Oxford University Press.Google Scholar