In the 15-year span between 1993 and 2008, the prevalence of obesity rose by 89.9 percent. And according to a study published in the September edition of the American Journal of Preventive Medicine, between the same periods, the burden of obesity resulted in a hefty loss of quality-adjusted life years (QALYs) that nearly doubled.
Haomia Jia, PhD, of Columbia University in New York City, and Erica I. Lubetkin, MD, of the City College of New York City, used survey data from the 1993-2008 Behavioral Risk Factor Surveillance System to assess the trends and patterns of obesity in U.S. adults by estimating the QALYs lost from obesity-related mortality.
"Although the prevalence of obesity has been well documented in the general population, less is known about the impact on QALYs both in the general population and at the state and local levels.… Our analysis enables the impact of obesity on morbidity and mortality to be examined using a single value to measure the Healthy People 2020 objectives and goals at the national, state and local levels and for population subgroups," said Lubetkin.
Jia and Lubetkin found that the prevalence of obesity grew from 14.1 percent to 26.7 percent between 1993 and 2008. Additionally, after studying subsets of patients, the researchers found that black women lost the most QALYs—0.068 per person in 2008. This was 31 percent higher than the QALYs lost by black men and 50 percent higher than those lost by white women and white men.
Researchers noted a direct correlation between the obesity-related QALYs lost and the U.S. adults who reported having no physical activity.
And while the researchers found that overall obesity levels increased for all 50 states, the disparities among the states over time lessened, “with less obese states catching up to more obese states and producing a greater percentage change of QALYs lost.”
In a press release, Jia suggested that national, state and local initiatives should be put in place to curb obesity and reduce its high prevalence.
“The availability of timely data would enable the impact of evidence-based interventions to be assessed on targeted populations and subgroups, promote continuous quality improvement through monitoring trends, and facilitate head-to-head comparisons with other modifiable health behaviors/risk factors and diseases,” concluded Jia.