Elsevier

Social Science & Medicine

Volume 87, June 2013, Pages 9-15
Social Science & Medicine

Standards and classification: A perspective on the ‘obesity epidemic’

https://doi.org/10.1016/j.socscimed.2013.03.009Get rights and content

Highlights

  • Current lack of debate about the effect of the obesity classification process.

  • Standardized terminology, and BMI, obscures uncertainty.

  • An internationally standard BMI belies the body fat variation between populations.

  • Such trends have led to the overly simplistic implementation of the BMI.

Abstract

In this paper I critique the increasing standardization of obesity. Specifically, I consider two ‘definitional turns’: the way language has been standardized to such an extent that it obscures uncertainty and variation, and the appearance of objectivity through quantification and standardized measurement. These, I suggest, have fostered a simplified picture of obesity, promoting the classification of weight and thereby facilitating the emergence of the ‘obesity epidemic’. These definitional turns fail to acknowledge the distinctions between fat and mass and intraclass variation within weight categories. A consequence of this process of simplification has been the erroneous application of population level information to individuals in a clinical context, with potentially harmful results.

Introduction

There are now a multitude of studies reporting dramatically increasing levels of obesity over the last twenty to thirty years (Ahrens, Moreno, & Pigeot, 2011; Manios & Costarelli, 2011). These studies not only indicate that the number of obese individuals is increasing, and is as high as 33% in some countries (Flegal, Carroll, Ogden, & Curtin, 2011), but that average weight is also increasing (Finucane et al., 2011). A global analysis of data estimated that in 2008 over 205 million men and 297 million women over the age of 20 were obese (Finucane et al., 2011). Moreover, this includes increasing numbers of ‘morbidly obese’ individuals, skewing the distribution of weights towards the upper extreme (Yanovski & Yanovski, 2011).

The increase in obesity would not be so concerning if it were not for the increasing number of adverse health effects associated with it. To date studies have indicated relationships between obesity and a range of conditions including type 2 diabetes mellitus, fatty liver disease, endocrine and orthopaedic disorders and most of the major cardiovascular risk factors (Lobstein & Baur, 2005; Manios & Costarelli, 2011; Reilly et al., 2003). The increasing prevalence of obesity together with the indicated negative health effects have led some authors to define the current situation as an ‘obesity epidemic’ (Flegal, 2006).

Epidemiological data is often presented to underwrite these claims. Much of the data on which the estimates are based comes from national surveys using the Body Mass Index (BMI). The BMI derives from “Quetelet's index” (Smalley, Knerr, Kendrick, Colliver, & Owen, 1990) which was developed in the 1800's to chart the range of heights and weights of army conscripts (Oliver, 2006). In this original conception Quetelet noted a Gaussian (normal) distribution of weight to height ratios within the population, allowing for the description of the statistically average man (Oliver, 2006). Today the BMI calculated as weight (in kg)/height (in metres squared), is used to provide an estimate of body composition. Leaving aside the self-reported nature of much of the available survey data (Manson et al., 1995; Strauss, 1999; Yanovski & Yanovski, 2011), a question remains regarding the interpretation of changes in BMI. What does an increase of one BMI represent? Is there a linear trend with increasing weight, or is it a more complicated relationship such as a normal distribution or U-shaped relationship? Does each BMI increase of one have the same effect size on the specified outcome?

Continuous traits, such as weight or BMI, are not amenable to straightforward assessments in the same way as grouped data. Far easier is the assignment of risk to discrete classes or categories. Sex-based risk, for example, has an altogether simpler interpretation; a man may have one risk, a woman another. The creation and use of categories for underweight, normal weight, overweight, and obese have been central to the analysis and presentation of risk estimates, and indeed goes to the very core of data purporting that an obesity epidemic has emerged.

To date, while there has been much said of the shortcomings of the BMI, there has been little discussion of the way in which the BMI has been applied nor the processes through which the BMI has become the dominant tool on which obesity prevalence and risk have been determined. In this paper, I consider both the standardization and classification of obesity and the roles these have played within the ‘obesity epidemic’. In doing so I engage with the hitherto under explored processes that have reified the BMI as the measure of obesity. Specifically I argue that the language of obesity has been standardized to such an extent that it obscures uncertainty and variation in the assessment of obesity, and the quantification and standardized measurement of obesity has furthered this simplification and has facilitated the perception of an obesity epidemic, obscuring the nuances of the data collected. This, I contend, has important, and potentially harmful, effects when it is misapplied within the clinical context.

Section snippets

Classification: making up obese people

But what does it mean to talk of classification? Indeed what do we mean by classification? To answer these questions I draw on Bowker and Starr (1999) who define a classification system as:

“a set of boxes (metaphorical or literal) into which things can be put to then do some kind of work – bureaucratic or knowledge production.” (Bowker & Starr, 1999, p.10)

In order for classification to occur the categories must be consistent, mutually exclusive, and complete – that is, no object from the same

Facilitating classification

As suggested, an important aspect of classification is the ability to identify similarities upon which to group items (or in the case of obesity, individuals). However, while there has been a great deal of discussion regarding the BMI levels associated with obesity, there has been little discussion about the establishment of the categories themselves and the implications of these categories and how they have facilitated the perception of obesity epidemic.

In the remainder of this paper I

Implementing standards: a cautionary note

Identifying the process of standardization is important, for alongside the increasing standardization there has been a creep of overly simplified usage of obesity classification and the not un-problematic application of population level standards to individuals. Again, this move has been gradual and the current individual application of the BMI differs substantially to its origins as ‘Quetelet's index’ where the purpose was to determine population averages and ranges, rather than using it for a

Conclusion

The BMI is now the dominant tool on which data purporting the obesity epidemic is based. While there has been discussion regarding the limits of the BMI, and the setting of thresholds for categories, to date there has been a paucity of debate regarding the limitations imposed by neither the categorization itself nor the ongoing standardization of these categories. In this paper, I have addressed this deficit, and have suggested that the act of classifying individuals and populations as

Acknowledgements

This work has benefited from participation within the Identification and prevention of Dietary- and lifestyle-induced health EFfects In Children and infantS (IDEFICS) study. The IDEFICS study is funded through the European Commission Sixth Framework Programme. Area 2: Epidemiology of food-related diseases and allergies. Topic 5.4.2.1.: Influence of diet and lifestyle on children's health (Integrated Project). Contract n° 016181 (FOOD). I would also like to thank Dr Garrath Williams and Dr

References (73)

  • S.E. Barlow

    Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report

    Pediatrics

    (2007)
  • A. Bocquier et al.

    Overweight and obesity: knowledge, attitudes and practices of general practitioners in France

    Obesity Research

    (2005)
  • G.C. Bowker et al.

    Sorting things out. Classification and its consequences

    (1999)
  • E.E. Calle et al.

    Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. Adults

    New England Journal of Medicine

    (2003)
  • A.L. Caplan

    The concepts of health, illness, and disease

  • M.R. Carnethon et al.

    Association of weight status with mortality in adults with incident diabetes

    JAMA

    (2012)
  • V.R. Chomitz et al.

    Promoting healthy weight among elementary school children via a health report card approach

    Archives in Pediatric and Adolescent Medicine

    (2003)
  • R. Colls et al.

    Re-thinking ‘the obesity problem’

    Geography

    (2010)
  • P. Conrad

    The medicalization of society. on the transformation of human conditions into treatable disorders

    (2007)
  • J. Dupré

    Scientific classification

    Theory, Culture & Society

    (2006)
  • C.B. Ebbeling et al.

    Tracking pediatric obesity. An index of uncertainty?

    Journal of the American Medical Association

    (2008)
  • B. Evans

    Measuring fatness, governing bodies: the spatialities of the body mass index (BMI) in anti-obesity politics

    Antipode

    (2009)
  • N. Finer

    Better measures of fat mass – beyond BMI

    Clinical Obesity

    (2012)
  • K.M. Flegal

    Defining obesity in children and adolescents: epidemiologic approaches

    Critical Reviews In Food Science and Nutrition

    (1993)
  • K.M. Flegal

    Commentary: the epidemic of obesity — what's in a name?

    International Journal of Obesity and Related Metabolic Disorders

    (2006)
  • K.M. Flegal et al.

    Prevalence and trends in obesity among US adults, 1999–2008

    Journal of the American Medical Association

    (2011)
  • K.M. Flegal et al.

    Overweight in children: definitions and interpretation

    Health Education Research

    (2006)
  • D.S. Freedman et al.

    Racial/ethnic differences in body fatness among children and adolescents

    Obesity (Silver Spring)

    (2008)
  • W.P. Fu et al.

    Screening for childhood obesity: international vs population-specific definitions. Which is more appropriate?

    International Journal of Obesity and Related Metabolic Disorders

    (2003)
  • S.L. Gortmaker et al.

    Increasing pediatric obesity in the United States

    American Journal of Diseases of Children

    (1987)
  • C. Grimmett et al.

    Telling parents their child's weight status: psychological impact of a weight-screening program

    Pediatrics

    (2008)
  • I. Hacking

    Symposium papers, comments and an abstract: the sociology of knowledge about child abuse

    Noûs

    (1988)
  • I. Hacking

    The social construction of what?

    (1999)
  • W.R. Harlan et al.

    Secular trends in body mass in the United States, 1960–1980

    American Journal of Epidemiology

    (1988)
  • M.R. Hebl et al.

    Weighing the care: physicians' reactions to the size of a patient

    International Journal of Obesity

    (2001)
  • S.S. Herrick

    Obesity

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