Continuous outcome logistic regression for analyzing body mass index distributions

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.