How weight change is modelled in population studies can affect research findings: empirical results from a large-scale cohort study. Issue 6 (6th June 2014)

• Record Type: Journal Article
• Authors: Paige, E; Korda, R J; Banks, E; Rodgers, B
• Is Part Of: BMJ open. Volume 4:Issue 6(2014)
• Publication Date: 2014-06-06
• Subjects: education -- cohort studies; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2014-004860
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: To investigate how results of the association between education and weight change vary when weight change is defined and modelled in different ways. Design: Longitudinal cohort study. Participants: 60 404 men and women participating in the Social, Environmental and Economic Factors (SEEF) subcomponent of the 45 and Up Study—a population-based cohort study of people aged 45 years or older, residing in New South Wales, Australia. Outcome measures: The main exposure was self-reported education, categorised into four groups. The outcome was annual weight change, based on change in self-reported weight between the 45 and Up Study baseline questionnaire and SEEF questionnaire (completed an average of 3.3 years later). Weight change was modelled in four different ways: absolute change (kg) modelled as (1) a continuous variable and (2) a categorical variable (loss, maintenance and gain), and relative (%) change modelled as (3) a continuous variable and (4) a categorical variable. Different cut-points for defining weight-change categories were also tested. Results: When weight change was measured categorically, people with higher levels of education (compared with no school certificate) were less likely to lose or to gain weight. When weight change was measured as the average of a continuous measure, a null relationship between education and annual weight change was observed. No material differences in the education and weight-change relationship were found when comparing weight change defined as an absolute (kg) versus a relative (%) measure. Results of the logistic regression were sensitive to different cut-points for defining weight-change categories. Conclusions: Using average weight change can obscure important directional relationship information and, where possible, categorical outcome measurements should be included in analyses.