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Does active smoking worsen Covid-19?

      Abstract

      Probably it does.

      Keywords

      Letter

      It was with great interest that we read the recent meta-analysis of 5 studies conducted in China in which Lippi et al conclude that active smoking is not associated with severity of coronavirus disease 2019 (Covid-19), with an odds ratio (OR) of 1.69 (95% CI, 0.41-6.42) [
      • Lippi G
      • Henry BM.
      Active smoking is not associated with severity of coronavirus disease 2019 (COVID-19).
      ]. This conclusion has been publicized in society, conveying the idea that smoking is not a risk factor for developing severe Covid-19. From a historical perspective, Lippi et al's conclusion has intriguing parallelisms with a long-standing, scientific battle already settled.
      In 1951, Doll & Hill launched the British Doctors’ Study with the aim of prospectively resolving the controversy surrounding the causal relationship between active smoking and cancer [
      • Doll R
      • Peto R
      • Boreham J
      • Sutherland I
      Mortality in relation to alcohol consumption: a prospective study among male British doctors.
      ]. This herculean effort was justified, since strong evidence had emerged in earlier years, although previous case-control studies did not bear out causal inferences [
      • Doll R
      • Hill AB.
      Smoking and carcinoma of the lung.
      ]. The lack of prospective data impeded a consensus among the medical community and, among the most illustrious skeptics was Ronald Fisher, father of frequentist statistics [
      • Stolley PD
      When genius errs: RA Fisher and the lung cancer controversy.
      ]. The British Doctors’ Study was one of the most protracted studies in the history of medicine. The latest paper was signed by Richard Doll in 2004, shortly before his demise at the age of 92 [
      • Doll R
      • Peto R
      • Boreham J
      • Sutherland I
      Mortality from cancer in relation to smoking: 50 years observations on British doctors.
      ]. However, in the 50s, when the study began, most of the Western adult population smoked and the yearly mortality rate was tremendous. Jerome Cornfield did not want to wait that long and, in 1951, applied Bayesian statistics for the first time to reveal the causal association between smoking and cancer [
      • Cornfield J.
      A method of estimating comparative rates from clinical data. Applications to cancer of the lung, breast, and cervix.
      ]. It was a novel way to analyze health data, hastening by several decades the proof that smoking impacts the incidence of lung cancer and its mortality using frequentist analyses, as Sharon McGrayne so judiciously states in the book “The Theory that Would Not Die” [
      • McGrayne SB.
      The theory that would not die: how Bayes’ rule cracked the enigma code, hunted down Russian submarines, & emerged triumphant from two centuries of controversy.
      ].
      Therefore, we have done nothing more than to perceive a troubling parallelism while reading the results of Lippi et al, categorically denying that active smoking worsens the course of Covid-19 [
      • Lippi G
      • Henry BM.
      Active smoking is not associated with severity of coronavirus disease 2019 (COVID-19).
      ]. Beyond public health considerations, the authors must know that the conclusions they reach do no derive from their data, which is a classic example of the well-known “absence of evidence is not evidence of absence” error [
      • Altman DG
      • Bland JM
      Statistics notes: Absence of evidence is not evidence of absence.
      ]. Under the frequentist paradigm, Lippi's meta-analysis must be interpreted as an inconclusive outcome. According to the null hypothesis significance testing framework, if H0 is not rejected, judgment should basically be suspended.
      In contrast and as Jerome Cornfield proved [
      • Cornfield J.
      A method of estimating comparative rates from clinical data. Applications to cancer of the lung, breast, and cervix.
      ], Bayesian analyses address inference about research questions more directly and intuitively [
      • Kruschke J
      Doing Bayesian data analysis: a tutorial with R, JAGS, and Stan.
      ]. Consequently, they can be a more fitting option for meta-analysis based on just a few studies, as in this case, as they are better able to resolve the problem of inter-study heterogeneity [

      Röver C.Bayesian random-effects meta-analysis using the bayesmeta R package. arXiv Prepr. arXiv1711.08683. 2017.

      ]. In particular, Bayesian models estimate the probability of the parameters directly, bearing in mind the available data, which is not what the frequentist confidence interval pursues [
      • Van de Schoot R
      • Kaplan D
      • Denissen J
      • Asendorpf JB
      • Neyer FJ
      • Van Aken MAG
      A gentle introduction to Bayesian analysis: applications to developmental research.
      ].
      Smoking damages the airway and fosters the development of COPD and worsens outcomes during the course of bronchial infections [
      • Carmona-Bayonas A
      • Jiménez-Fonseca P
      • Echaburu JV
      • Antonio M
      • Font C
      • Biosca M
      • et al.
      Prediction of serious complications in patients with seemingly stable febrile neutropenia: validation of the clinical index of stable febrile neutropenia in a prospective cohort of patients from the finite study.
      ]. Therefore, as Cornfield did decades ago [
      • Cornfield J.
      A method of estimating comparative rates from clinical data. Applications to cancer of the lung, breast, and cervix.
      ] to establish a direct estimate of the probability that active smoking worsens Covid-19, we have reanalyzed Lippi et al's data using a Bayesian random-effects model performed by the R bayesmeta package [

      Röver C.Bayesian random-effects meta-analysis using the bayesmeta R package. arXiv Prepr. arXiv1711.08683. 2017.

      ]. The model assumes a normal prior (with mean 0, no effect in the logarithmic odds ratio scale, and standard deviation 1) for the µ effect parameter. As for the heterogeneity parameter τ, we chose a half-Student-t prior with scale 0.5, as recommended in the literature [

      Röver C.Bayesian random-effects meta-analysis using the bayesmeta R package. arXiv Prepr. arXiv1711.08683. 2017.

      ]. The code and data are available upon request to the authors. Fig. 1 displays the forest plot. The Bayesian meta-analysis suggests that active smoking increases the severity of Covid-19 with an odds ratio of 1.79 (95% credible interval, 0.86-4.13). There is a 95% posterior probability of the disease following a worse course in a smoker versus a non-smoker; thus, Lippi et al's categorical conclusion based on the frequentist analysis does not hold up. Fig. 2 shows the distribution of log odds ratio that tilts broadly to the right of 0, revealing a deleterious effect of smoking on the evolution of Covid-19. This conclusion is more compatible with both the data available, as well as the impact of active smoking in patients with pneumonia and other infections [
      • Bello S
      • Menéndez R
      • Antoni T
      • Reyes S
      • Zalacain R
      • Capelastegui A
      • et al.
      Tobacco smoking increases the risk for death from pneumococcal pneumonia.
      ,
      • Liapikou A
      • Makrodimitri S
      • Deskata K
      • Katsaras M
      • Triantafillidou C
      • Dimakou K
      • et al.
      The impact of smoking on community acquired pneumonia course and outcomes.
      ]. Moreover, given the aggressiveness Covid-19 displays in the airway, it would be bizarre that it should be the only respiratory disease not affected by smoking. The discrepancy between the conclusion reached by Lippi et al and the true message contained in the data is a good example of the danger of misreading non-significant or inconclusive frequentist results [
      • Altman DG
      • Bland JM
      Statistics notes: Absence of evidence is not evidence of absence.
      ]. Therefore, as in the 1950s, if you are a smoker, the Bayesian analysis provides you with yet another good reason to quit in times of Covid-19.
      Fig. 1
      Fig. 1Forest plot. The x axis is displayed in logarithmic scale.
      Fig. 2
      Fig. 2Marginal density plot for the harmful effect of smoking (log odds ratio).

      References

        • Lippi G
        • Henry BM.
        Active smoking is not associated with severity of coronavirus disease 2019 (COVID-19).
        Eur J Int Med. 2020; (Elsevier)
        • Doll R
        • Peto R
        • Boreham J
        • Sutherland I
        Mortality in relation to alcohol consumption: a prospective study among male British doctors.
        Int J Epidemiol. 2005; 34 (Oxford University Press): 199-204
        • Doll R
        • Hill AB.
        Smoking and carcinoma of the lung.
        Br Med J BMJ. 1950; 2 (Publishing Group): 739
        • Stolley PD
        When genius errs: RA Fisher and the lung cancer controversy.
        Am J Epidemiol. 1991; 133 (Oxford University Press): 416-425
        • Doll R
        • Peto R
        • Boreham J
        • Sutherland I
        Mortality from cancer in relation to smoking: 50 years observations on British doctors.
        Br J Cancer. 2005; 92 (Nature Publishing Group): 426-429
        • Cornfield J.
        A method of estimating comparative rates from clinical data. Applications to cancer of the lung, breast, and cervix.
        J Natl Cancer Inst. 1951; 11 (Oxford University Press): 1269-1275
        • McGrayne SB.
        The theory that would not die: how Bayes’ rule cracked the enigma code, hunted down Russian submarines, & emerged triumphant from two centuries of controversy.
        Yale University Press, 2011
        • Altman DG
        • Bland JM
        Statistics notes: Absence of evidence is not evidence of absence.
        Bmj Br Med J. 1995; 311 (Publishing Group): 485
        • Kruschke J
        Doing Bayesian data analysis: a tutorial with R, JAGS, and Stan.
        Academic Press, 2014
      1. Röver C.Bayesian random-effects meta-analysis using the bayesmeta R package. arXiv Prepr. arXiv1711.08683. 2017.

        • Van de Schoot R
        • Kaplan D
        • Denissen J
        • Asendorpf JB
        • Neyer FJ
        • Van Aken MAG
        A gentle introduction to Bayesian analysis: applications to developmental research.
        Child Dev. 2014; 85 (Wiley Online Library): 842-860
        • Carmona-Bayonas A
        • Jiménez-Fonseca P
        • Echaburu JV
        • Antonio M
        • Font C
        • Biosca M
        • et al.
        Prediction of serious complications in patients with seemingly stable febrile neutropenia: validation of the clinical index of stable febrile neutropenia in a prospective cohort of patients from the finite study.
        J Clin Oncol Am Soc Clin Oncol. 2015; (JCO–2014)
        • Bello S
        • Menéndez R
        • Antoni T
        • Reyes S
        • Zalacain R
        • Capelastegui A
        • et al.
        Tobacco smoking increases the risk for death from pneumococcal pneumonia.
        Chest. 2014; 146 (Elsevier): 1029-1037
        • Liapikou A
        • Makrodimitri S
        • Deskata K
        • Katsaras M
        • Triantafillidou C
        • Dimakou K
        • et al.
        The impact of smoking on community acquired pneumonia course and outcomes.
        Eur Respir Soc. 2016;