Literature DB >> 17135625

Correcting biases in estimates of mortality attributable to obesity.

James A Greenberg1.   

Abstract

OBJECTIVE: To assess whether a recent study that found a relatively small number of excess deaths attributable to obesity may have underestimated by not correcting for statistical biases. RESEARCH METHODS AND PROCEDURES: This prospective cohort study used data from the First National Health and Nutrition Examination Survey Epidemiologic Follow-Up Study. Survival analyses were conducted using 9690 individuals 32 to 87 years of age and 1886 all-cause deaths during a 9.1-year follow-up. Corrections were made for the reputed regression-dilution bias by using the average BMI during the decade before follow-up as predictor. Corrections for the reputed reverse-causation bias were made by excluding participants with a history of serious illness. Attributable fractions were calculated and used to estimate excess deaths.
RESULTS: The uncorrected estimate of excess deaths attributable to obesity (BMI > or =30) was 41.9, using 18.5 to 25 kg/m(2) as ideal-weight category. Using average BMI as predictor increased the estimate to 93.3. Correcting for reverse-causation effects increased the estimate further to 131.1 (range, 93.3 to 169.0). The uncorrected hazard ratio, 1.25, was increased to 1.41 by using average BMI as predictor, and then to 2.40 by correcting for reverse causation. Using BMI 21 to 25 kg/m(2) and 23 to 25 kg/m(2) as ideal-weight categories increased the corrected estimates to 144.6 (range, 80.5 to 177.2) and 164.1 (range, 103.8 to 194.9), respectively. Larger increases were found for overweight and Grade 2 to 4 obesity (BMI > or =35 kg/m(2)). For overweight, the uncorrected estimate using 18.5 to 25 kg/m(2) as ideal-weight category was -88.3 and the corrected estimate using 23 to 25 kg/m(2) as ideal-weight category was 205.4 (range, 114.5 to 296.3). DISCUSSION: Correcting for statistical biases and using higher ideal-weight categories increased the estimate of excess deaths attributable to obesity by approximately 400% and changed the negative estimate for overweight to a large positive estimate.

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Year:  2006        PMID: 17135625     DOI: 10.1038/oby.2006.242

Source DB:  PubMed          Journal:  Obesity (Silver Spring)        ISSN: 1930-7381            Impact factor:   5.002


  16 in total

1.  Response to "Biased Corrections or Biased About Corrections"

Authors:  Katherine M Flegal; Barry I Graubard; David F Williamson; Mitchell H Gail
Journal:  Obesity (Silver Spring)       Date:  2008-04-24       Impact factor: 5.002

2.  Overweight adults may have the lowest mortality--do they have the best health?

Authors:  Anna Zajacova; Jennifer Beam Dowd; Sarah A Burgard
Journal:  Am J Epidemiol       Date:  2011-01-12       Impact factor: 4.897

3.  Role of a plausible nuisance contributor in the declining obesity-mortality risks over time.

Authors:  Tapan Mehta; Nicholas M Pajewski; Scott W Keith; Kevin Fontaine; David B Allison
Journal:  Exp Gerontol       Date:  2016-09-17       Impact factor: 4.032

4.  Shape of the BMI-mortality association by cause of death, using generalized additive models: NHIS 1986-2006.

Authors:  Anna Zajacova; Sarah A Burgard
Journal:  J Aging Health       Date:  2011-05-10

5.  Support needs of overweight African American women for weight loss.

Authors:  Janet L Thomas; Diana W Stewart; Ian M Lynam; Christine M Daley; Christie Befort; Robyn M Scherber; Andrea E Mercurio; Kolawole S Okuyemi; Jasjit S Ahluwalia
Journal:  Am J Health Behav       Date:  2009 Jul-Aug

6.  Body mass trajectories and mortality among older adults: a joint growth mixture-discrete-time survival analysis.

Authors:  Anna Zajacova; Jennifer Ailshire
Journal:  Gerontologist       Date:  2013-01-25

Review 7.  Modeling obesity histories in cohort analyses of health and mortality.

Authors:  Samuel H Preston; Neil K Mehta; Andrew Stokes
Journal:  Epidemiology       Date:  2013-01       Impact factor: 4.822

8.  Body mass index at various ages and mortality in Chinese women: impact of potential methodological biases.

Authors:  X Zhang; X-O Shu; W-H Chow; G Yang; H Li; J Gao; Y-T Gao; W Zheng
Journal:  Int J Obes (Lond)       Date:  2008-05-06       Impact factor: 5.095

Review 9.  Obesity and mortality: are the risks declining? Evidence from multiple prospective studies in the United States.

Authors:  T Mehta; K R Fontaine; S W Keith; S S Bangalore; G de los Campos; A Bartolucci; N M Pajewski; D B Allison
Journal:  Obes Rev       Date:  2014-06-09       Impact factor: 9.213

10.  Lifetime body size and reproductive factors: comparisons of data recorded prospectively with self reports in middle age.

Authors:  Benjamin J Cairns; Bette Liu; Suzanne Clennell; Rachel Cooper; Gillian K Reeves; Valerie Beral; Diana Kuh
Journal:  BMC Med Res Methodol       Date:  2011-01-17       Impact factor: 4.615

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