Categorisation of built environment characteristics: the trouble with tertiles

BACKGROUND: In the analysis of the effect of built environment features on health, it is common for researchers to categorise built environment exposure variables based on arbitrary percentile cut-points, such as median or tertile splits. This arbitrary categorisation leads to a loss of information...

Full description

Saved in:
Bibliographic Details
Published in:The international journal of behavioral nutrition and physical activity 2015-02, Vol.12 (1), p.19-19, Article 19
Main Authors: Lamb, Karen E, White, Simon R
Format: Article
Language:English
Subjects:
Analysis
Australia
Body Mass Index
Commerce
computer software
Debate
Environment Design
Female
Health aspects
Health Behavior
Humans
Models, Biological
nutrition
Obesity - etiology
Obesity - prevention & control
physical activity
Research Design
researchers
Residence Characteristics
Social aspects
socioeconomic status
Supermarkets
Transportation
women
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:BACKGROUND: In the analysis of the effect of built environment features on health, it is common for researchers to categorise built environment exposure variables based on arbitrary percentile cut-points, such as median or tertile splits. This arbitrary categorisation leads to a loss of information and a lack of comparability between studies since the choice of cut-point is based on the sample distribution. DISCUSSION: In this paper, we highlight the various drawbacks of adopting percentile categorisation of exposure variables. Using data from the SocioEconomic Status and Activity in Women (SESAW) study from Melbourne, Australia, we highlight alternative approaches which may be used instead of percentile categorisation in order to assess built environment effects on health. We discuss these approaches using an example which examines the association between the number of accessible supermarkets and body mass index. We show that alternative approaches to percentile categorisation, such as transformations of the exposure variable or factorial polynomials, can be implemented easily using standard statistical software packages. These procedures utilise all of the available information available in the data, avoiding a loss of power as experienced when categorisation is adopted.We argue that researchers should retain all available information by using the continuous exposure, adopting transformations where necessary.
ISSN:1479-5868
1479-5868
DOI:10.1186/s12966-015-0181-9