Categorisation of built environment characteristics: the trouble with tertiles

International Journal of Behavioral Nutrition and Physical Activity

Simon R White¹  Karen E Lamb² 

[1] Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK;Centre for Physical Activity and Nutrition Research, Deakin University, Burwood 3125, VIC, Australia

关键词: Statistical analysis;    Neighbourhood;    Built environment;    Exposure assessment;    Percentile categorisation;   

Others  :  1135835
DOI  :  10.1186/s12966-015-0181-9

 received in 2014-09-23, accepted in 2015-02-03,  发布年份 2015

【 摘 要 】

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.

Summary

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.

【 授权许可】

   
2015 Lamb and White; licensee BioMed Central.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】

• [1]Durand CP, Andalib M, Dunton GF, Wolch J, Pentz MA: A systematic review of built environment factors related to physical activity and obesity risk: implications for smart growth urban planning. Obes Rev 2011, 12:e173-82.
• [2]Giskes K, van Lenthe F, Avendano-Pabon M, Brug J: A systematic review of environmental factors and obesogenic dietary intakes among adults: are we getting closer to understanding obesogenic environments? Obes Rev 2011, 12:e95-106.
• [3]Williams J, Scarborough P, Matthews A, Cowburn G, Foster C, Roberts N, et al.: A systematic review of the influence of the retail food environment around schools on obesity-related outcomes. Obes Rev 2014, 15:359-74.
• [4]Sallis JF, Floyd MF, Rodríguez DA, Saelens BE: The role of built environments in physical activity, obesity, and CVD. Circulation 2012, 125:729-37.
• [5]Sugiyama T, Neuhaus M, Cole R, Giles-Corti B, Owen N: Destination and route attributes associated with adults’ walking: a review. Med Sci Sports Exerc 2012, 44:1275-86.
• [6]Duncan MJ, Spence JC, Mummery WK: Perceived environment and physical activity: a meta-analysis of selected environmental characteristics. Int J Behav Nutr Phys Activ 2005, 2:11. BioMed Central Full Text
• [7]Papas MA, Alberg AJ, Ewing R, Helzlsouer KJ, Gary TL, Klassen AC: The built environment and obesity. Epidemiol Rev 2007, 29:129-43.
• [8]Ball K, Crawford D, Salmon J, Timperio A, Giles-Corti B, Mishra G: Socioeconomic and neighbourhood inequalities in women’s physical activity, diet and obesity. The SESAW study. Deakin University, Melbourne; 2005.
• [9]Senn S: Disappointing dichotomies. Pharm Stat 2003, 2:239-40.
• [10]Epstein LH, Raja S, Daniel TO, Paluch RA, Wilfley DE, Saelens BE, et al.: The built environment moderates effects of family-based childhood obesity treatment over 2 years. Ann Behav Med 2012, 44:248-58.
• [11]McDonald K, Hearst M, Farbakhsh K, Patnode C, Forsyth A, Sirard J, et al.: Adolescent physical activity and the built environment: a latent class analysis approach. Health Place 2012, 18:191-8.
• [12]Muller-Riemenschneider F, Pereira G, Villanueva K, Christian H, Knuiman M, Giles-Corti B, et al.: Neighborhood walkability and cardiometabolic risk factors in Australian adults: an observational study. BMC Public Health 2013, 13:755. BioMed Central Full Text
• [13]Gomez LF, Sarmiento OL, Parra DC, Schmid TL, Pratt M, Jacoby E, et al.: Characteristics of the built environment associated with leisure-time physical activity among adults in Bogota, Colombia: a multilevel study. J Phys Act Health 2010, 7(Suppl 2):S196-203.
• [14]Hoehner CM, Allen P, Barlow CE, Marx CM, Brownson RC, Schootman M: Understanding the independent and joint associations of the home and workplace built environments on cardiorespiratory fitness and body mass index. Am J Epidemiol 2013, 178:1094-105.
• [15]Ollberding NJ, Nigg CR, Geller KS, Horwath CC, Motl RW, Dishman RK: Food outlet accessibility and fruit and vegetable consumption. Am J of Health Promot 2012, 26:366-70.
• [16]Lamichhane AP, Puett R, Porter DE, Bottai M, Mayer-Davis EJ, Liese AD: Associations of built food environment with body mass index and waist circumference among youth with diabetes. Int J Behav Nutr Phys Act 2012, 9:81. BioMed Central Full Text
• [17]Laxer RE, Janssen I: The proportion of youths’ physical inactivity attributable to neighbourhood built environment features. Int J Health Geogr 2013, 12:31. BioMed Central Full Text
• [18]Bottino CJ, Rifas-Shiman SL, Kleinman KP, Oken E, Redline S, Gold D, et al.: The association of urbanicity with infant sleep duration. Health Place 2012, 18:1000-5.
• [19]Coogan PF, White LF, Evans SR, Palmer JR, Rosenberg L: The influence of neighborhood socioeconomic status and walkability on TV viewing time. J Phys Act Health 2012, 9:1074-9.
• [20]Rundle A, Neckerman KM, Freeman L, Lovasi GS, Purciel M, Quinn J, et al.: Neighborhood food environment and walkability predict obesity in New York City. Environ Health Perspect 2009, 117:442-7.
• [21]Leung CW, Laraia BA, Kelly M, Nickleach D, Adler NE, Kushi LH, et al.: The influence of neighborhood food stores on change in young girls’ body mass index. Am J Prev Med 2011, 41:43-51.
• [22]Van Hulst A, Gauvin L, Kestens Y, Barnett TA: Neighborhood built and social environment characteristics: a multilevel analysis of associations with obesity among children and their parents. Int J Obes 2013, 37:1328-35.
• [23]Burgoine T, Forouhi NG, Griffin SJ, Wareham NJ, Monsivais P: Associations between exposure to takeaway food outlets, takeaway food consumption, and body weight in Cambridgeshire, UK: population based, cross sectional study. British Med J 2014, 348:g1464.
• [24]Altman DG, Royston P: The cost of dichotomising continuous variables. Br Med J 2006, 332:1080.
• [25]Bennette C, Vickers A: Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents. BMC Med Res Methodol 2012, 12:21. BioMed Central Full Text
• [26]Brenner H, Blettner M: Controlling for continuous confounders in epidemiologic research. Epidemiology 1997, 8:429-34.
• [27]Naggara O, Raymond J, Guilbert F, Roy D, Weill A, Altman DG: Analysis by categorizing or dichotomizing continuous variables is inadvisable: an example from the natural history of unruptured aneurysms. Am J Neuroradiol 2011, 32:437-40.
• [28]Royston P, Altman DG, Sauerbrei W: Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med 2006, 25:127-41.
• [29]van Walraven C, Hart RG: Leave’em alone - why continuous variables should be analyzed as such. Neuroepidemiology 2008, 30:138-9.
• [30]MacCallum RC, Zhang S, Preacher KJ, Rucker DD: On the practice of dichotomization of quantitative variables. Psychol Methods 2002, 7:19-40.
• [31]Schulgen G, Lausen B, Olsen JH, Schumacher M: Outcome-oriented cutpoints in analysis of quantitative exposures. Am J Epidemiol 1994, 140:172-84.
• [32]Farrington DP, Loeber R: Some benefits of dichotomization in psychiatric and criminological research. Crim Behav Ment Health 2000, 10:100-22.
• [33]Royston P, Sauerbrei W: Multivariable model-building: a pragmatic approach to regression anaylsis based on fractional polynomials for modelling continuous variables. John Wiley & Sons, Ltd, Chichester, UK; 2008.
• [34]Helfand M: Evidence Synthesis for Decision Making [Special issue]. Med Decis Making 2013, 33:597-730.
• [35]Jackson D, Riley R, White IR: Multivariate meta-analysis: potential and promise. Stat Med 2011, 30:2481-98.