Mcdonalds Case Study On Obesity

Mcdonalds Case Study On Obesity-50
In this article we use a novel measure – the number of per capita fast food transactions (local and transnational) – to test the hypothesis that rising fast food consumption has been a major determinant of population increases in body mass index (BMI) among high-income countries belonging to the Organisation for Economic Co-operation and Development (OECD).

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Division of Epidemiology, Human Genetics and Environmental Health Sciences, The University of Texas Health Science Center at Houston, Houston, USA. showed that participants who visited fast food restaurants more than twice a week at baseline and were still doing so at a follow-up 15 years later had gained an average of 4.5 kg.

However, in a recent ecological analysis, the density of Subway outlets, used as a marker of fast food penetration, was positively associated with the prevalence of obesity across 26 advanced economies.

The first two values were obtained from the Statistics Division of the Food and Agriculture Organization; Our regression models included corrections for fixed aspects of initial country conditions and other characteristics that could influence the level of fast food consumption – and hence average BMI – in a given country.

By assessing within-country annual variations in fast food and obesity over time and adjusting for fixed, country-level characteristics, these conservative models effectively address the problem of confounding of study results.

Data on per capita fast food transactions were taken from Euromonitor’s Passport Global Market Information Database (GMID), 2012 edition.

The data comprise industry records of annual sales of meals and refreshments delivered in local and transnational fast food outlets, including chain restaurants, independent eateries and convenience stores (Appendix A, available at:

This measure is the most comprehensive indicator of fast food consumption for comparisons across nations.

Appendix B (available at: Thi G5) shows the scatterplot and strong correlation coefficient (), was obtained from the Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group, which produced comparative estimates of cross-country differences and changes over time in BMI for adults aged 20 years or older.

All these measures were taken from the World Bank’s World Development Indicators database.

We also included as confounders time-invariant measures (2008) of the percentage of the population doing insufficient physical activity (i.e.

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