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Re: The Obesity Paradox in COPD is Absent in US Never-Smokers.

Published on May 5, 2020in American Journal of Epidemiology4.473
· DOI :10.1093/AJE/KWZ244
Hailey R. Banack11
Estimated H-index: 11
(SUNY: State University of New York System),
Jay S. Kaufman64
Estimated H-index: 64
(McGill University),
Steven D. Stovitz19
Estimated H-index: 19
(UMN: University of Minnesota)
Abstract
  • References (11)
  • Citations (1)
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References11
Newest
: An obesity paradox in chronic obstructive pulmonary disease (COPD), whereby overweight/obese individuals have improved survival, has been well-described. These studies have generally included smokers. It is unknown whether the paradox exists in individuals with COPD arising from factors other than smoking. Nonsmoking COPD is understudied yet represents some 25%-45% of the disease worldwide. To determine whether the obesity paradox differs between ever- and never-smokers with COPD, 1,723 adult ...
1 CitationsSource
#2Michael Schomaker (UCT: University of Cape Town)H-Index: 20
Last. Mireille E. Schnitzer (McGill University)H-Index: 5
view all 6 authors...
Classical epidemiology has focused on the control of confounding but it is only recently that epidemiologists have started to focus on the bias produced by colliders. A collider for a certain pair of variables (e.g., an outcome Y and an exposure A) is a third variable (C) that is caused by both. In DAGs terminology, a collider is the variable in the middle of an inverted fork (i.e., the variable C in A -> C <- Y). Controlling for, or conditioning an analysis on a collider (i.e., through stratifi...
2 CitationsSource
5 CitationsSource
#1Elizabeth Rose Mayeda (UCSF: University of California, San Francisco)H-Index: 18
#2M. Maria Glymour (UCSF: University of California, San Francisco)H-Index: 43
The effects of overweight or obesity on survival after cancer diagnosis are difficult to discern based on observational data because these associations reflect the net impact of both causal and spurious phenomena. We describe two sources of bias that might lead to underestimation of the effect of increased body weight on survival after cancer diagnosis: collider stratification bias and heterogeneity in disease bias. Given the mixed evidence on weight status, weight change, and postdiagnosis surv...
8 CitationsSource
#1Hailey R. Banack (McGill University)H-Index: 11
#2Jay S. Kaufman (McGill University)H-Index: 7
Smoking is often identified as a confounder of the obesity–mortality relationship. Selection bias can amplify the magnitude of an existing confounding bias. The objective of the present report is to demonstrate how confounding bias due to cigarette smoking is increased in the presence of collider stratification bias using an empirical example and directed acyclic graphs. The empirical example uses data from the Atherosclerosis Risk in Communities (ARIC) study, a prospective cohort study of 15,79...
27 CitationsSource
In a wide variety of disease states, obese persons have been shown to experience lower mortality and better survival than that shown by the nonobese. These states include diabetes,1–3 coronary artery disease,4,5 heart failure,6 peripheral arterial disease,5 hypertension,7 chronic obstructive pulmonary disease,8 lung cancer,9 and esophageal adenocarcinoma.10 Superior survival among the obese patients has also been demonstrated after myocardial infarction,11 coronary revascularization,12 and angio...
80 CitationsSource
24 CitationsSource
#1Martin LajousH-Index: 25
#2Anne BijonH-Index: 10
Last. Miguel A. HernánH-Index: 81
view all 7 authors...
Background: Obesity is associated with increased mortality in the general population but, paradoxically, with decreased mortality in persons with diabetes. Methods: Among 88,373 French women participating in the E3N-EPIC study who were free of diabetes in 1990, we estimated the hazard ratios (HRs) and 95% confidence intervals (CIs) of mortality for body mass index (BMI) levels by diabetes status. Results: During an average 16.7 years of follow-up, 2421 cases of diabetes were identified and 3750 ...
55 CitationsSource
60 CitationsSource
#1Miguel A. HernánH-Index: 81
Last. James M. RobinsH-Index: 100
view all 3 authors...
The term "selection bias" encompasses various biases in epidemiology. We describe examples of selection bias in case control studies (eg, inappropriate selection of controls) and cohort studies (eg, informative censoring). We argue that the causal struc ture underlying the bias in each example is essentially the same: conditioning on a common effect of 2 variables, one of which is either exposure or a cause of exposure and the other is either the outcome or a cause of the outcome. This structure...
1,201 CitationsSource
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#1Tianshi David Wu (JHUSOM: Johns Hopkins University School of Medicine)
#1Tianshi David Wu (JHUSOM: Johns Hopkins University School of Medicine)H-Index: 3
Last. Emily P. Brigham (JHUSOM: Johns Hopkins University School of Medicine)H-Index: 10
view all 5 authors...
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