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Rezende LFM, Lee DH, Ferrari G, Giovannucci E. Confounding due to pre-existing diseases in epidemiologic studies on sedentary behavior and all-cause mortality: a meta-epidemiologic study. Ann Epidemiol. 2020;52:7-14. doi:10.1016/j.annepidem.2020.09.009
We examined the influence of confounding due to pre-existing diseases in prospective studies on sedentary behavior and all-cause mortality.
We analyzed 25 studies included in systematic reviews. The risk of confounding due to pre-existing diseases was assessed by five methodologic characteristics.
Sedentary behavior was associated with higher all-cause mortality. Studies with short average follow-up length had stronger magnitudes of association: 1 to less than 5 years (hazard ratio [HR], 1.58; 95% confidence interval [CI], 1.28–1.94), 5 to 9 years (HR, 1.24; 95% CI, 1.16–1.31), and 10 years or more of follow-up (HR, 1.20; 95% CI, 1.10–1.31). Studies that did not adjust for diseases at baseline, did not exclude deaths in the first years of follow-up, and did not exclude participants with diseases/conditions showed stronger associations. Studies with higher risk of confounding because of pre-existing diseases (HR, 1.40; 95% CI, 1.27–1.54) showed stronger association than lower risk studies (HR, 1.18; 95% CI, 1.10–1.27). Studies excluding participants with diseases at baseline had weaker associations compared with studies adjusting for diseases in models.
Sedentary behavior was associated with increased all-cause mortality, although confounding due to pre-existing diseases may bias the magnitude of the association.
Leandro F. M. Rezende ScD, Dong Hoon Lee ScD, Gerson Ferrari PhD, Edward Giovannucci MD, ScD
a Universidade Federal de São Paulo, Escola Paulista de Medicina, Departamento de Medicina Preventiva, Sao Paulo, SP, Brazil
b Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
c Laboratorio de Ciencias de la Actividad Física, el Deporte y la Salud, Facultad de Ciencias Médicas, Universidad de Santiago de Chile, USACH, Santiago, Chile
d Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
e Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA