Obesity among adolescents is still on the rise and various reasons have been attributed to this increase. Obesity has been associated with many diseases, as well as, increase in healthcare costs. Concentration index and logistic regression have been extensively used to measure inequalities in health, including obesity, but these methods require each parameter to be calculated discretely. In this study, the logistic regression model is modified to predict the degree of obesity distribution that might be associated with multiple variables including income and race among adolescents in the United States. Unlike the methods currently used, the modified logistic model (MLM) can capture all variables at the same time in a single equation. The results produced by the model are comparable with those obtained when concentration index is used in a shorter time. It is hoped that this study will shorten the time to estimate or predict obesity rates among various races using existing Medical Expenditure Panel Survey (MEPS) data based on socioeconomic status. The ultimate goal is to develop targeted intervention strategies. Using existing data yields faster, reliable results since the sampling and collection utilize standard procedures. Results can easily be generalized due to random sampling and the MLM has the potential to predict the rate of obesity without performing further statistical analysis.

modified logistic regression, Income, obesity, concentration index, socioeconomic inequality, adolescents