Presenter:

Albert Okunade

Authors:

Albert Okunade, Rose Rubin

Chair: Jeffrey Pyne; Discussant: Nazmi Sari Mon June 5, 2006 17:15-18:45 Room 309

Medical care and the social science disciplines, together with global bodies (e.g., The UN Food and Agriculture Organization, and The World Health Organization) concerned with human health conditions, agree that the rising obesity epidemic (the US, worldwide) and related ill-health outcomes (e.g., discounted quality of life, atrophied labor productivity, preventable mortality and morbidity) are increasingly problematic. The resource costs of unhealthy Body Mass Index (BMI) increasingly individuals and the society (US DHHS, Healthy People 2010. Washington, DC: USGPO, November 2000). The BMI, a number adjusting adult body weight (in lbs. or kgs.) for height (in ft. or m.), classifies individuals as ‘not overweight/obese’ (BMI< 24.9), ‘overweight’ (25 30). The wider human health and medical literature increasingly links specific-cause and all-cause mortality to unhealthy BMI values. During 1990 and 2000, poor diet and physical inactivity ranked second, next to tobacco, as the second leading actual causes of death in the US (National Center for Chronic Disease Prevention and Health Promotion, 2000). Heart disease, cancer, and stroke, the three leading causes of US deaths, are positively correlated with the conditions of being overweight and obese. Obesity has been linked to almost all causes of death and morbidity, including chronic respiratory diseases and diabetes (respectively, also the 4th and 6th leading cause of deaths in the US in 2000).

Therefore, the goal of this study is to construct an econometric model linking all-cause mortality to the status of ‘overweight’ and ‘obese’ separately, as measured by BMI. Data are taken from the Behavioral Risk Factor Surveillance System, for the four years 2000, 2001, 2002, and 2003 (for which BMI data are consistently reported). Data of the 50 US states and the District of Columbia are modeled. Separate regression models linking all-cause mortality to ‘not overweight/ obese’, ‘overweight’, and ‘obese’ adult BMI, controlling for a set of independent other effects (e.g., per capita income, race/ethnic classification, number of toxic waste sites on location, others). Annual and panel regression model estimation first tested for, confirmed, and adjusted the model for the skewed data distribution of all-cause mortality per 100,000 people, and then modeled it as a Box-Cox power-transformed response to symmetry. Separate regression models of the BMI effects of mortality are reported for each year, and for the panel of years. Specific intervention policies are linked with the effect of BMI on mortality to assess their effectiveness. This paper concludes on the effectiveness of the various health promotion and disease prevention programs in specific US states, and reflects on the differences in health and economic consequences of unhealthy BMI for the ‘overweight’ and ‘obese’ in the US, from 2000 to 2003.