Statistical methods for body mass index: a selective review of the literature

TL;DR

This paper reviews classical and modern statistical methods for analyzing body mass index (BMI), emphasizing the importance of considering data complexity for effective public health insights.

Contribution

It provides a comparative overview of statistical techniques for BMI analysis, highlighting the limitations of classical methods and the potential of modern approaches with illustrative case studies.

Findings

Classical methods are simple but overlook data complexity.

Modern methods account for complexity but are harder to implement.

Case studies demonstrate the application of various statistical techniques.

Abstract

Obesity rates have been increasing over recent decades, causing significant concern among policy makers. Excess body fat, commonly measured by body mass index (BMI), is a major risk factor for several common disorders including diabetes and cardiovascular disease, placing a substantial burden on health care systems. % Body mass index (BMI) is one indicator for excess body fat. To guide effective public health action, we need to understand the complex system of intercorrelated influences on BMI. This paper will review both classical and modern statistical methods for BMI analysis, highlighting that most of the classical methods are simple and easy to implement but ignore the complexity of data and structure, whereas modern methods do take complexity into consideration but can be difficult to implement. A series of case studies are presented to illustrate these methods and some potentially useful new models are suggested.