International Journal of Obesity (2017) 41, 352–353 © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved 0307-0565/17 www.nature.com/ijo
LETTERS TO THE EDITOR
GDP per capita and obesity prevalence worldwide: an ambiguity of effects modification International Journal of Obesity (2017) 41, 352; doi:10.1038/ijo.2016.215
We are very grateful for forwarding us the letter from Prof Torsten J Selck1 and for his interest in our research. However, after reading Prof Selck’s comments, we have several remarks. First and foremost, we should note that our paper, published in IJO in 2016,2 was explicitly focused on the association between country-level obesity rates and artificial light-at-night (ALAN) exposure. In the study, per capita income, that is, the variable, which is pivotal to Prof Selck’s remarks, was not investigated explicitly and was used as a control variable in the multivariate analysis only. As we explicitly stated in our paper,2 per capita income emerged as a negative predictor for both female and male overweight and obesity rates (see Tables 1 and 2 of the paper,2 variable—GDP per capita). This finding is fully in line with the results of previous studies, indicating that socioeconomic status is negatively associated with body mass gain (see inter aliarefs 3–7). In this regard, the whole argument by Prof Selck that we presumably found a positive association between GDPpc and obesity rates is counterfactual. It resulted, most likely, from the fact that Prof Selck apparently overlooked the sign of the regression coefficients of the GDP per capita variable reported in Tables 1 and 2,2 which are negative in all models, not positive, as he incorrectly assumed, and this misreading apparently led to the whole line of argument he developed. We greatly appreciate the time and efforts Prof Selck invested in creating the bivariate scatter plot linking the logarithm of per-capita income with adult obesity prevalence rates in different countries, which he attached to his letter. However, we should note that a simple bivariate correlation featured by this graph may be obscure and even misleading due to a potential effect modification. The matter is that obesity rates in individual countries may be affected by several interacting factors, not only by incomes. For instance, high per capita incomes may reduce obesity rates by enabling greater access to healthy food and nutrition education, while, on the other hand, high per capita incomes may increase the exposure to ALAN, both at work and homes, due to nighttime activities ALAN enables, and circadian
disruption associated herewith. As a result, only a multivariate analysis, such as that we performed in our paper, may minimize confounding and help to detect true associations. It also appears that Prof Selck overlooked that we included in our analysis a dummy variable for the outlying Pacific Island countries and territories (see Tables 1 and 2 of the paper),2 which helped us to account for regional differences in the overweight/ obesity prevalence rates and their potential geographically localized predictors. Lastly, we should note that at the time of the preparation of the article, 2016 data on obesity rates, which Prof Selck explicitly refers to, were unavailable. We are grateful for advising us about this new data source and we will be glad to use these data in our future studies. CONFLICT OF INTEREST The author declare no conflict of interest.
NA Rybnikova and BA Portnov Department of Natural Resources and Environmental Management, Faculty of Management, University of Haifa, Mt. Carmel, Israel E-mail:
[email protected] or
[email protected] REFERENCES 1 Selck TJ. Does income positively affect obesity at the macro level? Int J Obes 2016. (this issue). 2 Rybnikova NA, Haim A, Portnov BA. Does artificial light-at-night exposure contribute to the worldwide obesity pandemic? Int J Obes 2016; 40: 815–823. 3 Drewnowski A, Darmon N. The economics of obesity: dietary energy density and energy cost. Am J Clin Nutr 2005; 82: 265S–273S. 4 Jeffery RW, French SA. Socioeconomic status and weight control practices among 20-to 45-year-old women. Am J Public Health. 1996; 86: 1005–1010. 5 Kark M, Rasmussen F. Growing social inequalities in the occurrence of overweight and obesity among young men in Sweden. Scand J Public Health 2005; 33: 472–477. 6 Miech RA, Kumanyika SK, Settler N, Link BG, Phelan JC, Chang VW. Trends in the association of poverty with overweight among US adolescents, 1971–2004. JAMA 2006; 295: 2385–2393. 7 Meydan C, Afek A, Derazne E, Tzur D, Twig G, Gordon B et al. Population-based trends in overweight and obesity: a comparative study of 2148342 Israeli male and female adolescents born 1950–1993. Pediatr Obes 2012; 8: 98–111.
Accepted article preview online 25 November 2016; advance online publication, 20 December 2016
Does income positively affect obesity at the macro level? International Journal of Obesity (2017) 41, 352–353; doi:10.1038/ijo.2016.200
Studying obesity is first and foremost an individual-level research problem. We know the components affecting the condition:
calorie intake, calorie output and genetic predisposition. If sufficient income is available (sometimes, it is not, particularly in poor countries like Somalia), different dietary choice can be made. Many individual-level studies report a negative relationship between income and obesity, that is, the wealthier a person is, the less likely she is to become obese. However, many country-level studies like the recent one by Rybnikova et al.1 in IJO find that per
Accepted article preview online 4 November 2016; advance online publication, 20 December 2016
