Obesity is an epidemic in the U.S. with significant health, economic and social costs. Elucidation of obesity risk factors can potentially aid in alleviating the pandemic. In this study, we aim to focus on investigating behavioral factors that are fairly easily controlled with the ultimate goal of calling for action to change the identified behaviors. Using the NHANES 2013-2014 data, the main factors we studied are related to alcohol use, TV consumption and selected aspects of eating habits. Using multiple linear regression to control for 12 potential confounders including race, age, marital status, smoking status and sleep hours, we found that the frequency of alcohol consumption (measured by number of days in a year a person consumes alcoholic beverage) has a slight but statistically significant negative association with obesity. Furthermore, in general, more TV consumption, more milk consumption, and more money spent on eating out are associated with higher BMI. In addition, Mexican Americans and the female gender are more prone to increases in BMI for every additional fast food meal compared to certain other races and the male gender, respectively. We also used multinomial logistic regression to build a best predictive model for predicting obesity levels (“underweight”: BMI<18.5, “healthy”: 18.5<BMI<25, “overweight”: 25<BMI<30, “obese”: BMI>30) with a cross-validated classification accuracy of 0.4904.
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