SciCurious has written a review of an interesting paper suggesting a correlation between obesity and city vs. non-city life. As usual, the review by Sci is excellent, but I have a comment or two to add.

Having read the review and then the paper, I had to ask if it might be possible to conclude based on the data presentation that “race” (and thus “genetics”) underlies the observed effect. This is because of this graph:

The results as depicted here divides the population into black vs white, making it appear that skin color is a major factor. The paper does not make that specific argument. (I can think of a way that skin color could be an indirect factor but that has not been an overt part of the discussion in any of this literature.) In fact, it is clear that the authors are not pushing a racialized explanation. But they are, following the habit of human behavioral and medical researchers, keeping race on the table for pragmatic reasons, but ultimately in a manner that is commonly misunderstood by passers-by who look mainly at the pictures and don’t understand the research itself in detail.

The research considered a number of features that not only correlate to the binary skin color categories people were put in, but that also shift self identification in such a way that people on the dual line (e.g. 1/4 black and poor vs 1/4 black and rich, or whatever) will tend to self identify in a way predicted by the dominant social model at the time. This tends to make the variable (race) pre-fixed in a bad way.

(There could be an overall genetic factor. It is starting to look like Europeans might lack the “thrifty genotype” … a feature once thought to be present in “some populations” but probably a variable yet widespread set of traits that result in rapid obesity, higher rates of diabetes, and other effects in response to certain diets, etc. etc. … but a binary racialized category is hardly the best way to get at that relationship … I’d have rather seen some measurements on blood sugar, diabetes familial history, or in a big enough study, just count the limbs and divide by the number of subjects.)

Ethnicity is correlated to reduced activity levels in some studies (e.g. Crespo et al 2000) in a way that is not explained by SES, but that is correlated (in the same study) to country of origin and langauge spoken at home. This suggests a strong cultural effect. Pedestrian fatality risk vary by ethnicity, gender and age groups in a study carried out in Arizona in a way that suggests that cultural practices in alcohol consumption and other cultural factors are important (Campos-Outcault et. al 2002). This suggests a strong rural/urban difference in the basic context of transport and travel by individuals, which is part of the underlying context for the study at hand, although the two studies were done in very different regions, using different approaches, and can only be compared a very meta- level. Nonetheless, there are life and death factors that are related to learned practice and other cultural features that seem to have large impacts and that are not very likely to be linked in any meaningful way to population genetics. Indeed, simply being poor is a risk factor; Poverty correlates strongly with many condition-specific death rates (Jemal et al 2008).

In one study (Yancey et al. 2004), which looked at (and correlated) inactivity rates and obesity in Los Angeles, the range of variation based on race was about 37 to 41% (a range of 8.9%). In the same study, variation across gender ranged at 14.6%, education at 23.5%, region of birth at 11.7%, poverty level at 20.3% and as expected age at a whopping 27.6 percent. In other words, inactivity levels are correlated to lots of things, with race being a rather ho-hum feature in the mix at best. (Please understand: These numbers are ranges across the measured percentages of inactivity levels, not the percentages themselves.)

Regarding the overall pattern of city vs. country: Try driving the transect from Minneapolis (not the thinnest city because it is in the midwest/sub-arctic, but pretty thin because of the local obsession with bikes) down through rural southern Minnesota, Iowa and Nebraska (where things are so spread out you have to drive from the living room to the kitchen) and then on to Boulder Co, which is one of the Hip Cities where everyone is not only thin but sinewy. In the Great Rural In-between owners of diners and other eating establishments actually pay extra attention to their chairs, buying sturdier than average ones and maintaining them carefully. Well, I don’t really know that but so it seems.

Personally, I find that when I can walk five miles a day I’m in good shape. At the moment, I’m working on just walking at all, but I suppose I could aim for bathing suit season 2011.

BPRR Citation:

FRANK, L. (2004). Obesity relationships with community design, physical activity, and time spent in cars American Journal of Preventive Medicine, 27 (2), 87-96 DOI: 10.1016/j.amepre.2004.04.011

References:

Campos-Outcalt, D., Bay, C., Dellapenna, A., & Cota, M. K. (2002). Pedestrian fatalities by race/ethnicity in Arizona, 1990-1996. American journal of preventive medicine, 23(2), 129-35.

Crespo, C. J., Smit, E., Andersen, R. E., Carter-Pokras, O., & Ainsworth, B. E. (2000). Race/ethnicity, social class and their relation to physical inactivity during leisure time: results from the Third National Health and Nutrition Examination Survey, 1988-1994. American journal of preventive medicine, 18(1), 46-53.

Frank, L. D., Andresen, M. a., & Schmid, T. L. (2004). Obesity relationships with community design, physical activity, and time spent in cars. American journal of preventive medicine, 27(2), 87-96. doi: 10.1016/j.amepre.2004.04.011.

Jemal, A., Thun, M. J., Ward, E. E., Henley, S. J., Cokkinides, V. E., Murray, T. E., et al. (2008). Mortality from leading causes by education and race in the United States, 2001. American journal of preventive medicine, 34(1), 1-8. doi: 10.1016/j.amepre.2007.09.017.

Salsberry, P. J., Corwin, E., & Reagan, P. B. (2007). A complex web of risks for metabolic syndrome: race/ethnicity, economics, and gender. American journal of preventive medicine, 33(2), 114-20. doi: 10.1016/j.amepre.2007.03.017.

Yancey, A. K., Wold, C. M., McCarthy, W. J., Weber, M. D., Lee, B., Simon, P. a., et al. (2004). Physical inactivity and overweight among Los Angeles County adults. American journal of preventive medicine, 27(2), 146-52. doi: 10.1016/j.amepre.2004.03.012.

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