At Odds: Concerns Raised by Using Odds Ratios for Continuous or Common Dichotomous Outcomes in Research on Physical Activity and Obesity

Gina S Lovasi,Christopher Weiss,Darby Jack,Andrew Rundle,Lindsay J Underhill,Catherine Richards

doi:10.2174/1874297101205010013

Abstract

PURPOSE: Research on obesity and the built environment has often featured logistic regression and the corresponding parameter, the odds ratio. Use of odds ratios for common outcomes such obesity may unnecessarily hinder the validity, interpretation, and communication of research findings. METHODS: We identified three key issues raised by the use of odds ratios, illustrating them with data on walkability and body mass index from a study of 13,102 New York City residents. RESULTS: First, dichotomization of continuous measures such as body mass index discards theoretically relevant information, reduces statistical power, and amplifies measurement error. Second, odds ratios are systematically higher (further from the null) than prevalence ratios; this inflation is trivial for rare outcomes, but substantial for common outcomes like obesity. Third, odds ratios can lead to incorrect conclusions during tests of interactions. The odds ratio in a particular subgroup might higher simply because the outcome is more common (and the odds ratio inflated) compared with other subgroups. CONCLUSION: Our recommendations are to take full advantage of continuous outcome data when feasible and to use prevalence ratios in place of odds ratios for common dichotomous outcomes. When odds ratios must be used, authors should document outcome prevalence across exposure groups.

Highlights

Research on environmental determinants of physical activity and obesity [1,2,3,4] has generated interest among urban planners and public health practitioners, and contributes to ongoing policy discussions [5, 6]
The Active Living Research online literature database which brings together much of the published work on environmental determinants of physical activity and obesity [7] reveals that 44% of papers with quantitative results reported odds ratios (Fig. 1)
We identified key concerns with logistic regression in physical activity and obesity research (Table 1), issues which apply to other research fields [8, 9]

Summary

Introduction

Research on environmental determinants of physical activity and obesity [1,2,3,4] has generated interest among urban planners and public health practitioners, and contributes to ongoing policy discussions [5, 6]. The use of logistic regression frequently involves dichotomizing continuous measures such as physical activity or body mass index (BMI) (Fig. 1). Odds ratios diverge from prevalence ratios as the outcome prevalence in the reference group increases

Results

Conclusion