Abstract
BackgroundIn 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.DiscussionIn 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.SummaryWe 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.
Highlights
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
Summary: We show that alternative approaches to percentile categorisation, such as transformations of the exposure variable or factorial polynomials, can be implemented using standard statistical software packages
Categorisation of built environment characteristics While the title of our article draws attention to the use of tertiles, somewhat akin to the “disappointing dichotomies” raised in the clinical context [9], we could have entitled this piece “quarrels with quartiles” or “quandaries with quintiles”; all of these approaches of exposure categorisation have been adopted in analyses of built environment effects on health
Summary
Categorisation of built environment characteristics While the title of our article draws attention to the use of tertiles, somewhat akin to the “disappointing dichotomies” raised in the clinical context [9], we could have entitled this piece “quarrels with quartiles” or “quandaries with quintiles”; all of these approaches of exposure categorisation have been adopted in analyses of built environment effects on health. We show that the linear model approach is consistent and the meta-analysis of the sub-samples approaches the equivalent analysis when fitting the model using the full data, while the meta-analysis of the tertile exposure studies becomes increasingly bumpy since each subsample has data-dependent tertile cut-points.
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