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White hat bias: examples of its presence in obesity research and a call for renewed commitment to faithfulness in research reporting


‘White hat bias’ (WHB) (bias leading to distortion of information in the service of what may be perceived to be righteous ends) is documented through quantitative data and anecdotal evidence from the research record regarding the postulated predisposing and protective effects of nutritively sweetened beverages and breastfeeding, respectively, on obesity. Evidence of an apparent WHB is found in a degree sufficient to mislead readers. WHB bias may be conjectured to be fuelled by feelings of righteous zeal, indignation toward certain aspects of industry or other factors. Readers should beware of WHB, and our field should seek methods to minimize it.

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  1. Allison DB, Mattes RD . Nutritively sweetened beverage consumption and obesity: the need for solid evidence on a fluid issue. JAMA 2009; 301: 318–320.

    Article  CAS  Google Scholar 

  2. Cope MB, Allison DB . Critical review of the World Health Organization's (WHO) 2007 report on ′evidence of the long-term effects of breastfeeding: systematic reviews and meta-analysis′ with respect to obesity. Obes Rev 2008; 9: 594–605.

    Article  CAS  Google Scholar 

  3. Mattes RD, Shikany JM, Allison BD . What is the demonstrated value of moderating nutritively sweetened beverage consumption in reducing weight gain or promoting weight loss? An evidence-based review and meta-analysis of randomized studies. (Submitted for publication).

  4. James J, Thomas JT, Cavan D, Kerr D . Preventing childhood obesity by reducing consumption of carbonated drinks: cluster randomised controlled trial. BMJ 2004; 328: 123743.

    Article  Google Scholar 

  5. Ebbeling CB, Feldman HA, Osganian SK, Chomitz VR, Ellenbogen SJ, Ludwig DS . Effects of decreasing sugar-sweetened beverage consumption on body weight in adolescents: a randomized, controlled pilot study. Pediatrics 2006; 117: 673–680.

    Article  Google Scholar 

  6. Vartanian LR, Schwartz MB, Brownell KD . Effects of soft drink consumption on nutrition and health: a systematic review and meta-analysis. Am J Public Health 2007; 97: 667–675.

    Article  Google Scholar 

  7. Sterne JAC, Egger M . Regression methods to detect publication and other bias in meta-analysis. In: Rothstein HR, Sutton AJ, Borenstein M (eds). Publication Bias in Meta-Analysis. John Wiley & Sons Ltd: West Sussex, UK, 2005.

    Google Scholar 

  8. Horta B, Bahl R, Martines J, Victora C . Evidence of the Long-Term Effects of Breastfeeding: Systematic Reviews and Meta- Analysis. World Health Organization Publication: Geneva, Switzerland, 2007.

    Google Scholar 

  9. Woloshin S, Schwartz LM, Casella SL, Kennedy AT, Larson RJ . Press releases by academic medical centers: not so academic? Ann Intern Med 2009; 150: 613–618.

    Article  Google Scholar 

  10. Raben A, Vasilaras TH, Møller AC, Astrup A . Sucrose compared with artificial sweeteners: different effects on ad libitum food intake and body weight after 10 wk of supplementation in overweight subjects. Am J Clin Nutr 2002; 76: 721–729.

    Article  CAS  Google Scholar 

  11. Brownell KD, Warner KE . The perils of ignoring history: big tobacco played dirty and millions died. How similar is Big Food? Milbank Q 2009; 87: 259–294.

    Article  Google Scholar 

  12. Levene M, Roberts P (eds). The Massacre in History (Studies on War and Genocide). Berghahn Books: Oxford, UK, 1999.

    Google Scholar 

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We gratefully acknowledge Dr Alfred A Bartolucci for his comments on our data analysis and Dr Lenny Vartanian for sharing his data file. Supported in part by the NIH grant P30DK056336. The opinions expressed are those of the authors and not necessarily those of the NIH or any other organization with which the authors are affiliated.

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Correspondence to D B Allison.

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Cope, M., Allison, D. White hat bias: examples of its presence in obesity research and a call for renewed commitment to faithfulness in research reporting. Int J Obes 34, 84–88 (2010).

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