Exact Statistical Distribution of the Body Mass Index (BMI): Analysis and Experimental Confirmation (original) (raw)
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Statistical methods for body mass index: A selective review
Statistical methods in medical research, 2016
Obesity rates have been increasing over recent decades, causing significant concern among policy makers. Excess body fat, commonly measured by body mass index, is a major risk factor for several common disorders including diabetes and cardiovascular disease, placing a substantial burden on health care systems. To guide effective public health action, we need to understand the complex system of intercorrelated influences on body mass index. This paper, based on all eligible articles searched from Global health, Medline and Web of Science databases, reviews both classical and modern statistical methods for body mass index analysis. We give a description of each of these methods, exploring the classification, links and differences between them and the reasons for choosing one over the others in different settings. We aim to provide a key resource and statistical library for researchers in public health and medicine to deal with obesity and body mass index data analysis.
Statistical methods for body mass index: a selective review of the literature
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.
Preventing Chronic Disease, 2006
Possible changes over time in the population distribution of body mass index (BMI). Geoffrey Rose proposed that within a single population over time, an increase in the mean value of a risk factor and an increase in the corresponding prevalence of deviants would represent an upward shift, or a movement to the right (dashed curve) along the X-axis, in the entire population distribution of that risk factor (A) (7). We believe that the adult population distribution of BMI is more correctly described by a positively skewed distribution and that over time the degree of skewing has increased; that is, there is proportionately much more shifting of the distribution curve at the upper end than the lower (B and C).
International Journal of Epidemiology, 2019
Background The prediction of future obesity patterns is crucial for effective strategic planning. However, disproportionally changing body mass index (BMI) distributions pose particular challenges. Flexible modelling of the shape of BMI distributions may improve prediction performance. Methods We used data from repeated national health surveys conducted in Mexico, Colombia and Peru at four or five time points between 1988 and 2014. Data from all surveys except the last survey were used to construct prediction models for three obesity indicators (median BMI, overweight/obesity prevalence and obesity prevalence) for the time of the last survey. We assessed their performance using predicted curves, absolute prediction errors and comparison of actual and predicted distributions. With one method, we modelled the shape of BMI distributions assuming BMI follows a Box-Cox Power Exponential (BCPE) distribution, whose parameters were modelled as a function of interval or nominal 5-year age gr...
Lancet (London, England), 2016
Underweight and severe and morbid obesity are associated with highly elevated risks of adverse health outcomes. We estimated trends in mean body-mass index (BMI), which characterises its population distribution, and in the prevalences of a complete set of BMI categories for adults in all countries. We analysed, with use of a consistent protocol, population-based studies that had measured height and weight in adults aged 18 years and older. We applied a Bayesian hierarchical model to these data to estimate trends from 1975 to 2014 in mean BMI and in the prevalences of BMI categories (<18·5 kg/m(2) [underweight], 18·5 kg/m(2) to <20 kg/m(2), 20 kg/m(2) to <25 kg/m(2), 25 kg/m(2) to…