Welcome to CDC stacks | Bias in calculation of attributable fractions using relative risks from non-smokers only - 37820 | CDC Public Access
Stacks Logo
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.
 
 
Help
Clear All Simple Search
Advanced Search
Bias in calculation of attributable fractions using relative risks from non-smokers only
Filetype[PDF-95.95 KB]


Details:
  • Personal Authors:
    Flegal, Katherine M.
  • Keywords:
  • Pubmed ID:
    25210928
  • Pubmed Central ID:
    PMC4731856
  • Description:
    Studies of weight and mortality sometimes state that the mortality relative risks for obesity from nonsmokers are valid estimates of the relative risks for obesity in both smokers and nonsmokers. Extending this idea, several influential articles have used relative risks for obesity from nonsmokers and attributable fraction methods for unadjusted risks to estimate attributable fractions of deaths in the entire population (smokers and nonsmokers combined). However, stratification by smoking is a form of adjustment for confounding. Simplified examples show that the use of relative risks from only 1 stratum to estimate attributable fractions, without incorporating data on the stratification variable, gives incorrect results for the entire population. Even if the mortality relative risks for obesity from nonsmokers are indeed valid in both smokers and nonsmokers, these relative risks nonetheless need to be treated as adjusted relative risks for the purpose of calculating attributable fractions for the whole sample.

  • Document Type:
    Journal Article
  • Collection(s):
    CDC Public Access
  • Funding:
    CC999999/Intramural CDC HHS/United States
  • Main Document Checksum:
No Related Documents.
You May Also Like:
  1. Smoking-attributable medical expenditures by age, sex, and smoking status estimated using a relative risk approach☆
    Personal Author:
    Maciosek, Michael V.; Xu, Xin; Butani, Amy L.; Pechacek, Terry F.;
    Published:
    Jun 04 2015
    Source:
    Prev Med. 77:162-167.
    Description:
    Objective : To accurately assess the benefits of tobacco control interventions and to better inform decision makers, knowledge of medical expenditures by age, gender, and smoking status is essential. : Method : We propose an approach to distribute sm...
    File Type:
    Filetype[PDF - 519.32 KB]
  2. Smoking and Mortality in Tianjin, China: A Death Registry–Based Case-Control Study, 2010–2014
    Personal Author:
    Li, Wei; Jiang, Guo-hong; Wang, De-zheng; Zhang, Hui; Xu, Zhong-liang; Zhang, Ying; Zheng, Wen-long; Xue, Xiao-dan; Peto, Richard; Lam, Tai-Hing;
    Published:
    Aug 16 2018
    Source:
    Prev Chronic Dis. 15.
    Description:
    Introduction : We conducted a mortality case-control study to assess the risks of all-cause and major causes of death attributable to smoking in Tianjin from 2010 through 2014. The death registry–based study used data from The Tianjin All Causes of...
    File Type:
    Filetype[PDF - 352.46 KB]
  3. Quantifying the disease impact of cigarette smoking with SAMMEC II software.
    Personal Author:
    Shultz, J M; Novotny, T E; Rice, D P;
    Published:
    1991 May-Jun
    Source:
    Public Health Rep. 106(3):326-333
    Description:
    Smoking-Attributable Mortality, Morbidity, and Economic Costs Software, Release II (SAMMEC II) has been developed for the Office on Smoking and Health, Public Health Service, to permit rapid calculation of deaths, years of potential life lost, direct...
    File Type:
    Filetype[PDF - 1.29 MB]
  4. Population Attributable and Preventable Fractions: Cancer Risk Factor Surveillance, and Cancer Policy Projection
    Personal Author:
    Shield, Kevin D.; Parkin, D. Maxwell; Whiteman, David C.; Rehm, Jürgen; Viallon, Vivian; Micallef, Claire Marant; Vineis, Paolo; Rushton, Lesley; Bray, Freddie; Soerjomataram, Isabelle;
    Published:
    Aug 01 2016
    Source:
    Curr Epidemiol Rep. 3(3):201-211.
    Description:
    The proportions of new cancer cases and deaths that are caused by exposure to risk factors and that could be prevented are key statistics for public health policy and planning. This paper summarizes the methodologies for estimating, challenges in the...
    File Type:
    Filetype[PDF - 680.32 KB]
  5. Excess deaths associated with underweight, overweight, and obesity: An evaluation of potential bias
    Personal Author:
    Flegal, Katherine M.; Graubard, Barry I.; Williamson, David F.; Gail, Mitchell H.;
    Corporate Author:
    National Center for Health Statistics (U.S.). National Center for Chronic Disease Prevention and Health Promotion (U.S.). National Cancer Institute (U.S.).
    Published:
    June 2018
    Series:
    Vital and health statistics. Series 3, Data evaluation and methods research ; no 42
    DHHS publication ; no. 2018-1426
    Description:
    Objectives: Previous analyses of mortality data from the National Health and Nutrition Examination Survey published in 2005 found that relative to the normal weight category of body mass index (BMI), defined as a BMI of 18.5–24.9, underweight (BMI ...
    File Type:
    Filetype[PDF - 687.21 KB]