title: Categorisation of built environment characteristics: the trouble with tertiles.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Centre for Physical Activity and Nutrition Research, Deakin University, Burwood, VIC, 3125, Australia. karen.lamb@deakin.edu.au.
authors.forename: Karen E
authors.initials: KE
authors.lastname: Lamb
authors.affiliation: Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, CB2 0SR, UK. simon.white@mrc-bsu.cam.ac.uk.
authors.forename: Simon R
authors.initials: SR
authors.lastname: White
mesh_terms: D001315:Australia; D015992:Body Mass Index; D003132:Commerce; D004779:Environment Design; D005260:Female; D015438:Health Behavior; D006801:Humans; D008954:Models, Biological; D009765:Obesity / Q000209:etiology / Q000517:prevention & control; D012107:Research Design; D012111:Residence Characteristics; D014186:Transportation