The study forecasts a 33% increase in adult obesity and a 130% increase in severe obesity in the U.S. by 2030, with significant healthcare cost implications.
(Generated with the help of GPT-4)
The researchers used data from the 1990-2008 Behavioral Risk Factor Surveillance System, adjusting for self-reporting error. They employed logistic regressions with individual and state-level variables to predict future obesity and severe obesity prevalence. The study also considered potential savings from obesity prevention efforts.
(Generated with the help of GPT-4)
The research predicts a significant rise in obesity and severe obesity in the U.S. by 2030, using nonlinear regression models based on data from the Behavioral Risk Factor Surveillance System. It suggests that if obesity rates remain at 2010 levels, there could be substantial savings in medical expenditures. The study also simulates the potential economic benefits of modest obesity prevention efforts.
(Generated with the help of GPT-4)
Categories: 2030 time horizon | 2030s time horizon | English publication language | Global geographic scope | United States geographic scope | adult obesity | behavioral risk factor surveillance system | food | forecasting | forecasts | healthcare costs | logistic regression | medical expenditures | nonlinear regression | nonlinear regression models | obesity | obesity prevalence | prevention efforts | severe obesity | state-level variables