H-J Chen1, H Xue2, S Liu3, T T K Huang4, Y C Wang5, Y Wang6. 1. Institute of Public Health, School of Medicine, National Yang-Ming University, Taipei, Taiwan; Johns Hopkins Global Center on Childhood Obesity, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 2. Systems-Oriented Global Childhood Obesity Intervention Program, Fisher Institute of Health and Well-Being, College of Health, Ball State University, Muncie, IN, USA; Department of Health Behavior and Policy, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA. 3. Research Institute of Economics and Management, Southwestern University of Finance and Economics, #55 Guanghua Ave, Chengdu, Sichuan 610074, China. 4. CUNY School of Public Health, New York, NY, USA; Department of Health Promotion, Social and Behavioral Health, University of Nebraska Medical Center, Omaha, NE, USA. 5. Department of Health Policy and Management, Mailman School of Public Health, Columbia University, New York, NY, USA. 6. Systems-Oriented Global Childhood Obesity Intervention Program, Fisher Institute of Health and Well-Being, College of Health, Ball State University, Muncie, IN, USA; Department of Nutrition and Health Sciences, College of Health, Ball State University, Muncie, IN, USA. Electronic address: ywang26@bsu.edu.
Abstract
OBJECTIVES: To study the country-level dynamics and influences between population weight status and socio-economic distribution (employment status and family income) in the US and to project the potential impacts of socio-economic-based intervention options on obesity prevalence. STUDY DESIGN: Ecological study and simulation. METHODS: Using the longitudinal data from the 2001-2011 Medical Expenditure Panel Survey (N = 88,453 adults), we built and calibrated a system dynamics model (SDM) capturing the feedback loops between body weight status and socio-economic status distribution and simulated the effects of employment- and income-based intervention options. RESULTS: The SDM-based simulation projected rising overweight/obesity prevalence in the US in the future. Improving people's income from lower to middle-income group would help control the rising prevalence, while only creating jobs for the unemployed did not show such effect. CONCLUSIONS: Improving people from low- to middle-income levels may be effective, instead of solely improving reemployment rate, in curbing the rising obesity trend in the US adult population. This study indicates the value of the SDM as a virtual laboratory to evaluate complex distributive phenomena of the interplay between population health and economy.
OBJECTIVES: To study the country-level dynamics and influences between population weight status and socio-economic distribution (employment status and family income) in the US and to project the potential impacts of socio-economic-based intervention options on obesity prevalence. STUDY DESIGN: Ecological study and simulation. METHODS: Using the longitudinal data from the 2001-2011 Medical Expenditure Panel Survey (N = 88,453 adults), we built and calibrated a system dynamics model (SDM) capturing the feedback loops between body weight status and socio-economic status distribution and simulated the effects of employment- and income-based intervention options. RESULTS: The SDM-based simulation projected rising overweight/obesity prevalence in the US in the future. Improving people's income from lower to middle-income group would help control the rising prevalence, while only creating jobs for the unemployed did not show such effect. CONCLUSIONS: Improving people from low- to middle-income levels may be effective, instead of solely improving reemployment rate, in curbing the rising obesity trend in the US adult population. This study indicates the value of the SDM as a virtual laboratory to evaluate complex distributive phenomena of the interplay between population health and economy.
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