Temporal Dynamics of Obesity: A Multivariate Regression Analysis of Caloric Supply, Population Growth, and Autocorrelated Prevalence Trends

The global increase in obesity prevalence is a significant public health issue in the 21st century. In recent years, over two billion people were overweight or obese. Existing research has limitations in understanding obesity trends. This study focuses on the autoregressive nature of obesity, the interaction between caloric supply and population demographics, and improving analytical methods. This paper employ three advanced statistical methods using comprehensive, nationally representative data from the NCD Risk Factor Collaboration (NCD-RisC) 2017. These salient results evince that: past adiposity metrics constitute a notable determinant of present-day incidence; the interplay linking energy availability and corpulence is observably modulated by discrete population-based factors; and the integration of autoregressive elements into predictive algorithms markedly refines their prognostic accuracy. By integrating these crucial temporal, environmental, and demographic factors, this research provides a significantly refined and more holistic understanding of global obesity dynamics. This integrated framework offers valuable contributions to epidemiological theory concerning the multifactorial aetiology of obesity and informs the design of more effective, evidence-based policy interventions.