Abstract
Body mass indices (BMIs) are applied to monitor weight status and associated health risks in populations. Binary or multinomial logistic regression models are commonly applied in this context, but are only applicable to BMI values categorized within a small set of defined ad hoc BMI categories. This approach precludes comparisons with studies and models based on different categories. In addition, ad hoc categorization of BMI values prevents the estimation and analysis of the underlying continuous BMI distribution and leads to information loss. As an alternative to multinomial regression following ad hoc categorization, we propose a continuous outcome logistic regression model for the estimation of a continuous BMI distribution. Parameters of interest, such as odds ratios for specific categories, can be extracted from this model post hoc in a general way. A continuous BMI logistic regression that describes BMI distributions avoids the necessity of ad hoc and post hoc category choice and simplifies between-study comparisons and pooling of studies for joint analyses. The method was evaluated empirically using data from the Swiss Health Survey.
Highlights
Body mass index (BMI) is an anthropometric measure that is relatively easy to capture in epidemiological studies
The model results were insensitive to BMI measurement scales or categorization schemes and matched previously reported findings on the impact of smoking and sex of the individuals on BMI
It was obvious from the conditional BMI densities (Figure 2) that more restrictive models, e.g., a conditional normal distribution with or without sex- and smoking-specific variance[22], would describe the BMI distributions less accurately
Summary
Body mass index (BMI) is an anthropometric measure that is relatively easy to capture in epidemiological studies. The most prominent standard BMI categories, underweight, normal weight, overweight, and obesity as defined by the World Health Organization [WHO, 3], are commonly applied to ensure comparability and reproducibility of statistical analyses across epidemiological studies[4,5]. Such international standards are important for the communication of scientific results, for risk factor assessment and monitoring in populations, and for providing information to the general public. We propose that statistical analyses should be based on precise BMI measurements without ad hoc categorization, and parameters and interesting contrasts thereof should be categorized post hoc Such results would be interpretable and universally comparable between studies using any type of category
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