264249 Investigating Risk Factors Affecting Obesity Rates in the United States based on Artificial Neural Network and Partial Least Squares Regression Models

Wednesday, October 31, 2012 : 10:30 AM - 10:50 AM

Chau-Kuang Chen, EDD , Institutional Research, Meharry Medical College, Nashville, TN
Tifini Batts, BA, MSPH Student , School of Graduate Studies, Meharry Medical College, Nashville, TN
Aiping Yang, PhD , Industrial Engineering, Beijing Union University, Beijing, China
Rachel Cooper, BA, MSPH Student , Statistics, Meharry Medical College, Nashville, TN
Background/Purpose: Recently, obesity rates across the United States have been on a continuous incline. The morbid chronic disorders associated with this disease correlate to those of the leading causes of death. With younger generations engaging in this epidemic, the factors affecting obesity should be unveiled in order to interrupt this trend. This study attempts to identify obesity- related risk factors as well as determine the strength and direction of these associations across the United States using the Artificial Neural Network (ANN) and Partial Least Squares (PLS) Regression models. Method: ANN model tested and trained data was collected by the U.S. Department of Labor for years 2000- 2006. This model was used to rank the relative importance of suggested risk factors. PLS regression validated study results. Risk factors analyzed for possible correlation with obesity rate included: per capita income, unemployment rate, poverty rate, lack/ possession of high school diploma, alcohol consumption, smoking habits, physical exercise, violent crime rate, etc. Results: Of the 14 variables analyzed, 10 were statistically associated (α= 0.01). The factors that had the highest impact on the obesity trends were: per capita income, cost of physician care, alcohol consumption, year, cost of hospital care, and cigarette smoking habits. Implications: With obesity increasing among adults and children, determining the factors associated with obesity rates will allow for the development of programs/ interventions that can stall or decrease this trend. As a result, solutions to this problem will impact the chronic disorders that are acquired due to weight.

Learning Areas:
Implementation of health education strategies, interventions and programs
Planning of health education strategies, interventions, and programs
Program planning
Public health or related organizational policy, standards, or other guidelines
Public health or related public policy

Learning Objectives:
(1) Identify significant risk factors contributing to obesity rates within all 50 states (2) Determine the strength and direction of their association with U.S. obesity rates

Keywords: Obesity, Risk Factors

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have been a part of the analysis and interpretation of obesity on a national level by both regression models (Artificial Neural Network and Partial Least Squares).
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.