Örebro University
School of Medical Sciences Degree project, 15 ECTS June 2020
Are smokers more vulnerable considering disease severity in
COVID-19?
Version two
Author: Maja Lund, MS Supervisor: Matz Larsson Associate professor, Örebro University Hospital, Sweden
Abstract
Background: COVID-19 is an ongoing pandemic. As of 11 May 2020, there are 4 013 728 confirmed cases and 278 993 deaths. Smoking has been named as one possible factor regarding illness progression and severity.
Aim: The aim of this systematic literature review is to examine if smokers are more at risk considering disease severity.
Methods: This is a systematic literature study using the PubMed database. Inclusion and exclusion criteria were specified by using the PICOS format. Free text words and MeSH words were combined to create a search plan. The search was conducted twice, 26April 2020 and 12 May 2020. Full text articles were examined for eligibility by using inclusion and exclusion criteria. An estimation of bias was conducted by using the MINORS criteria.
Result: A total of seven articles were included. Of those, 5 reported a statistically significant relationship between smoking and disease progression or death. Of these, 4 articles found statistical significance when correcting for confounders (hypertension, COPD, ischemic heart disease, cardiac insufficiency).
Conclusions: The result of this systematic literature review suggests that smoking enhances the severity of COVID-19. Due to the limited number of patients combined with a narrow geographic area being studied, more research is needed to further evaluate and establish the relationship between smoking and COVID-19.
Abbreviations
ACE-2 – Angiotensinogen converting enzyme 2 ARDS – Acute Respiratory Distress Syndrome ASE – Acute Smoke Exposure
CFR – Case Fatality Rate
COPD – Chronic Obstructive Pulmonary Disease DPP4 – Dipeptidyl Peptidase 4
MERS – Middle Eastern Respiratory Syndrome MeSH – Medical Subject Headings
MINORS - Methodological Items for Non Randomized Studies N/A – Not applicable
PICOS – Population, Intervention, Comparison, Outcome, Study design RT-PCR- Real Time Polymerase Chain Reaction
SARS – Severe Acute Respiratory Syndrome TMPRSS2 - Transmembrane Serine Protease 2 WHO – World Health Organization
Table of contents
1. Introduction………...………5
1.1 COVID-19……….…....5
1.2 SARS………...5
1.3 MERS………..…..5
1.4 Possible relationship between smoking and COVID-19……….……..6
2. Aim……….6 3. Method………...………7 3.1 Design………...………7 3.2 Inclusion criteria……….………...7 3.3 Exclusion criteria……….………..7 3.4 Search plan………7 3.5 Filter stages………...…8 3.6 Risk of bias………...…9 3.7 Ethical considerations………..….9 4. Results………..…..9 4.1 Search……….………..9
4.2 Smoking and COVID-19………...………..12
4.3 Risk of bias……….…….15
5. Discussion……….……16
6. Conclusion………...…19
7. Acknowledgement………..……….19
1. Introduction 1.1 COVID-19
The Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2). COVID-19 is a respiratory illness with a reported mortality rate of 2-4% [1]. It first started to spread in December 2019 in the Wuhan province, China and has as of 11March 2020 been declared as a pandemic by the World Health Organization (WHO) [2]. The virus primarily affects the lower respiratory tracts and gives the infected person symptoms ranging from flu-like symptoms, mild fever and a light cough to pneumoniae as well as Acute Respiratory Distress Syndrome (ARDS) [3]. As main entrance, the virus uses the receptor Angiotensin-converting enzyme 2 (ACE-2) [4]. ACE-2 is expressed in the lung parenchyma, airway epithelia[5] as well as renal, gastrointestinal and cardiovascular tissue [6]. The virus is reported to spread from human-to-human in close proximity via droplets or direct contact [7].
As of 11 May 2020, 4 013 728 confirmed cases and 278 993 confirmed deaths have been registered globally due to COVID-19 [8].
1.2 The Severe Acute Respiratory Syndrome (SARS)
The Severe Acute Respiratory Syndrome (SARS) had an outbreak beginning in 2002. It was a disease caused by the severe acute respiratory syndrome coronavirus (SARS-CoV). SARS was first reported to spread in China and became the first pandemic of the 21th century, reaching Europe, North America and South East Asia[9]. It was established that ACE-2 was the main receptor for SARS-CoV [10], the same main receptor as SARS-CoV-2 [4]. Smoking was suggested to be a protecting factor against SARS but it has not been confirmed by any study [11]. Men were found to have a higher case fatality rate (CFR) compared to women [12]. 1.3 The Middle Eastern Respiratory Syndrome (MERS)
The Middle Eastern Respiratory syndrome (MERS) is a disease caused by coronavirus MERS-CoV that started to spread in 2013 in the Kingdom of Saudi Arabia. MERS primarily affects the lower part of the respiratory tract and targets the receptor dipeptidyl peptidase (DPP4) which has been proven to be upregulated in smokers and patients with COPD [13]. Patients with a history of smoking were reported to be more susceptible for MERS-CoV and additionally having a higher CFR [14].
1.4 Possible relationship between smoking and COVID-19
A concern has been raised regarding how smoking may have negative effects on the outcome of COVID-19 [15]. A study found that patients who are obese and smokers, are more likely to develop a severe form of COVID-19 [16]. It is widely known that smoking is a risk factor for developing COPD, cardiovascular disease and cancer. Smoking also surpresses the immune system. Smokers are more prone to respiratory infections in general and risk of influenza as well as severity of influenza in smokers, are higher compared with nonsmokers [17].
Studies conducted in China have also shown a higher percentage of men being more severly affected by the disease [18]. China had in 2015 a smoking prevalence of 27.7 %, with 52.1% being men and 2.7 % being women [19]. It has been discussed if the poorer prognosis in men with COVID-19 could be linked to the the higher prevalance of smoking amongst men compared to women in China [20]. Though, it has been argued that there is no link between current smoking and disease severity of COVID-19 [22][21].
A decreased expression of ACE-2, the receptor for SARS-CoV and SARS-CoV-2, has been studied in the lung tissue of rats after chronic cigarette smoke exposure [22]. In experiments conducted using human tissue, an elevated gene expression of ACE-2 has been found amongst smokers compared to non-smokers [23]. The gene expression of the receptor was statistically significantly pronounced amongst long-term smokers [6] and ever smoking has been suggested to increase the pulmonary expression of ACE-2 with 25 % [24]. SARS-CoV-2 requires activation by spike proteins which in turn need to be primed by two host-cell
enzymes, Transmembrane Serine protease 2 (TMPRSS2) and Furin. TMPRSS2 has not been proven to have a higher expression within the group of smokers, but the expression of Furin has been shown to be higher amongst smokers, but not to the same extent as ACE-2 [24]. Examining if smoking has an effect on the pulmonary expression of ACE-2 in single
bronchial cells, there has been found that smoking causes hyperplasia in goblet cells as well as a loss of club cells. Smokers had a higher expression of ACE-2 in goblet cells resulting in a different pulmonary expression of ACE-2 compared to non-smokers [24].
2. Aim
The aim of this systematic literature review is to investigate if previous or current smoking increases disease severity in COVID-19.
3. Method 3.1 Design
Systematic literature review. 3.2 Inclusion criteria
This literature review was constructed using the PRISMA Statement guidelines [25] together with the population, intervention, comparison, outcome and study design (PICOS) format. The PICOS format is below specified as:
Population: Current smokers/former smokers Intervention: COVID-19
Comparison:
Outcome: Disease progression of COVID-19 Study design: English
3.3 Exclusion criteria
The following 5 criteria was used in order to exclude articles in this study: 1. Articles not written in English
2. Articles not available in full text 3. Articles not stating conflict of interest 4. Articles based on animal studies
5. Articles based on secondhand sources e.g. review articles 3.4 Search plan
The database PubMed was used to conduct a detailed literature search. Search words were formed based using the PICOS format. Free text words were combined with Medical Subject Headings (MesH). Population based words were separated with “OR” and intervention-based words, together with outcome-based words, were separated with “AND”. The first search was carried out on 26 April 2020 and the second search on 12 May 2020. The second search was conducted in order to enhance the number of studies included in the qualitative synthesis and also to provide this review with updated material due to the nature of the ongoing pandemic.
Table 1. Search plan and criteria.
Inclusion criteria Search words Filter Population Patients who are smokers/former
smokers
“smoking” “marijuana smoking”
“tobacco smoke pollution”
“Smok*” “cigarette*” “vaping”
“vape” “electronic cigarettes”
“hookah” “water pipe”
“e-cigarette”
Intervention COVID-19 “COVID-19” “severe acute respiratory syndrome coronavirus 2” “covid*” “coronavirus” “sars-cov2” “corona” “ncov*”
Comparison
Outcome Mortality rate and/or infectious rate of COVID 19
“COVID-19” “severe acute
respiratory syndrome coronavirus 2” “covid*” “coronavirus” “sars-cov2” “corona” “ncov*”
Study design Written in English “English”
3.5 Filter stages
After conducting the first and second search respectively the titles were screened, and exclusion criteria applied. Abstracts were read and screened using inclusion and exclusion criteria at the second stage. The full text of remaining articles was read at the third and final stage. After inclusion and exclusion criteria were applied, the selected articles could be incorporated in the qualitative synthesis.
3.6 Risk of bias
Risk of bias was measured using the Methodological Items for Non Randomized Studies (MINORS) template. All studies included were assessed regarding bias by using this template. A score of 0 indicates that the requested information is not reported. A score of 1 indicates that the requested information is reported but inadequate. A score of 2 states that the information is
reported and adequate. The ideal global score for a non-comperative study using MINORS is 16 [26].
3.7 Ethical considerations
No original data has been applied in this systematic literature review. The data applied has been excerpted from already published scientific written material.
The authors of the articles included in this review have used different approaches to describe their ethical considerations. Of the 7 articles included in the qualitative synthesis one did not state that an ethical approval had been sought. Two stated that the requirement regarding informed written consent was “waived due to the context of emerging diseases”. One study did not provide a written informed consent and did not mention an oral consent having been performed. One study stated that an oral consent had been acquired.
4.
Result
4.1 SearchA flow chart of stages regarding inclusion, exclusion and result is presented in Figure 1a respectively Figure 1b. When combining the various search words above provided, the first search (Figure 1a) resulted in 105 articles. A number of 53 articles remained after screening records by title for eligibility. The process of screening records by abstract resulted in 18 articles assessed for eligibility. After assessing the full-text articles for eligibility 6 were chosen to be included in this review. The reasons for excluding 15 of the full text articles were defined as; review article (6), population not relevant (5), intervention not relevant (3) and not available in full text (1).
A second search was accomplished by combining the various search words resulting in 141 articles. After removing the 105 articles from the first search, 36 studies remained. Six were excluded after reading the title. Thirty articles were screened by abstract and after the screening was completed 13 studies was read in full. The full text studies were excluded due to being review articles (2), describing a non-relevant population (3) or describing a not relevant intervention (4). A total of seven studies were included in the qualitative synthesis after a first and second search had been performed.
Figure 1a. PRISMA flow chart describing the first search process.
Records identified through database searching
(n=105)
Records identified through database searching (n=105) Sc re ening Sc re ening Inc lude d Inc lude d El igi bi lity El igi bi lity Ide ntif ic ation Ide ntif ic ation
Records excluded by title (n=52)
Records excluded by title (n=52) Records screened by abstract (n=53) (n = ) Records excluded (n=35) Records excluded (n=35)
Full-text articles assessed for eligibility
(n=18)
Full-text articles excluded Review article (n=6) Population not relevant (n=5) Intervention not relevant (n=3)
Not available in full text (n=1)
Studies included in qualitative synthesis
Figure 1b. PRISMA flow chart describing the second search process.
Records identified through database searching
(n=141)
Records identified through database searching (n=141) Sc re ening Sc re ening Inc lude d Inc lude d El igi bi lity Ide ntif ic ation Ide ntif ic ation
Records excluded due to having been included in the first search
(n=105)
Records excluded due to having been included in the first search
(n=105) Records excluded by title (n=6) Records excluded by title (n=6)
Full-text articles assessed for eligibility (n=13) Studies added to qualitative synthesis (n=4) Studies added to qualitative synthesis (n=4) Records screened by abstract (n=30) (n = ) Records screened by abstract (n=30) (n = )
Full-text articles excluded Review article (n=2) Population not relevant (n=3) Intervention not relevant (n=4)
4.2 Smoking and COVID-19
Liu, W et al. [27] conducted a study which followed 78 patients hospitalized with pneumonia in Wuhan, China due to COVID-19. After two weeks, 11 patients (14,1%) of the total 78 presented symptoms of a severe type and were therefore included in a progression group. The stabilization/improvement group contained 67 patients and had 2 (3%) patients with a history of smoking in comparison with the progression group which had 3 (27,3%) patients with a history of smoking. P-value here was stated as 0.018
A univariate logistic analysis gave the factor a history of smoking a p-value of 0.011, presenting that a history of smoking is one factor statistically significantly associated with progression of COVID-19. No statistically significant relationship between any comorbidity and illness progression was found. The multivariable logistic analysis defined a history of smoking(p-value=0.018) as one of the risk factors associated with progression of illness [27].
Zhang et al. [28] performed a clinical investigation of 140 patients hospitalized with COVID-19 in Wuhan, China. The patients were categorized into two groups based on their clinical presentation, one group being nonsevere and one being severe. A total of 82 patients were placed in the nonsevere group and 58 patients placed in the severe group. In the severe group, 4(6.9%) patients were noted as being past smokers and three (3.7%) of the patients in the nonsevere group were past smokers. Two (3.4%) patients in the severe group were current smokers and 0 patients in the nonsevere group were current smokers. These findings were not statistically significant.
Smoking index was defined as cigarettes smoked per day x years of smoking. Of all 140 patients who were smokers and included in the study, 3 patients had a smoking index of <400 and six had a smoking index of ≥400. In the group of nonsevere patients there was one patient with a smoking index of <400 and two with a index of ≥400. Two patients were registered as having a smoking index of two <400 in the severe group and 4 patients had a smoking index of ≥400. No statistical significance was found [28].
Wang, R et al. [29] studied patients diagnosed with COVID-19 in the city of Fuyang, China. The study consisted of 125 hospitalized patients. Smoking history was collected as history of smoking and current smoker. Sixteen (12.8%) of the patients enrolled had a history of smoking. The number of critical patients in the study was 25 and of those 7 patients were current smokers
(28%). In the non-critical patients group consisting of 100 patients, 9 were current smokers (9%). The P-value was 0.027, making this finding statistically significant [29].
Yu t et al. [30] performed a study consisting of 95 hospitalized patients. Endpoints were ARDS, pneumonia and pneumonia exacerbation. Based on the findings from two consecutive CT scans, 70 patients were identified with pneumonia and divided into two groups. Two groups with one being a pneumonia exacerbation group consisting of 19 patients with four (21,1%) of them being tobacco smokers, and one pneumonia relieve group consisting of 51 patients with one (2%) being tobacco smoker. A multivariate analysis was conducted, finding a statistic significant correlation between smoking and pneumonia exacerbation. No correlation was found between smoking and pneumonia or smoking and ARDS [30].
Zheng, Y et al. [31] studied 73 patients hospitalized with COVID-19 in the Hubei province, China. Eight patients (10,9%) were current smokers. The patients were on time of admission clinically classified as having ordinary symptoms. After a follow up was conducted patients were divided into two groups, severe/critical and ordinary. Two patients (6,7%) were current smokers in the severe/critical group and 28 (93,3%) patients were non-smokers. The group clinically classified as ordinary had 6 (14,0%) patients who were current smokers and 37 (86,0%) patients as non-smokers. A comparison between the two groups were completed, indicating that the difference in smoking between them was not statistically significant while also indicating that smoking was not a negative factor regarding disease progression [31]. Mehra, MR et al. [32] conducted a study evaluating the relationship of drug therapy and cardiovascular diseases using material from an observational database with data from 169 hospitals situated in 11 countries in North America, Asia and Europe. The study contained 8910 patients diagnosed with COVID-19 who were admitted to hospital and registered as having died in the hospital or having been discharged. The current or remote history of smoking amongst patients was collected as part of their coexisting condition. Of the 8910 patients, 445(5,5 %) were current smokers and 1410(16,8 %) were former smokers. In a multivariable logistic regression analysis, current smoking was found to be associated with a higher risk of death (OR=1.79(95% CI 1.29-2.47)). Thus, supporting an association between smoking and disease severity in COVID-19 [32].
Hu, L et al. [33], studied a total of 323 hospitalized patients. Onehundredandeightysix of them were diagnosed with COVID-19 by Real Time Polymerase Chain Reaction (RT-PCR). A number of 137 patients tested RT-PCR negative and were diagnosed with COVID-19 due to their clinical picture. The patients were categorized into a non-severe, severe and critical disease group. The non-severe group (n=151) had 12 (7,9%) patients with a smoking history. The severe group (n=146) covered 22 (15,1%) patients with a smoking history. The critical disease group (n=26) consisted of 4 (15,4) patients with a smoking history. No statistical difference was found between the aforementioned three groups in terms of smoking. A univariate logistic regression analysis was carried out identifying 27 categorical variables associated with clinical outcome, naming smoking as one variable. When using a multivariate logistic regression model, eight variables were found to be associated with unfavorable clinical outcome with smoking being named as one [33].
Table 2 Study characteristics of included studies.
4.3 Risk of bias
Risk of bias was calculated using the MINORS format. A score of zero states that information for the criteria was not reported. A score of one corresponds to reported but inadequate. A score of 2 is equivalent to reported and adequate. The ideal score for a non-comparative study is 16 [26]. The criteria for unbiased assessment of the study endpoint was not applicable (N/A) on the studies included since blinding was irrelevant. Collection of data was not reported as being Author Geographic location Study size Smoking Female/Male ratio
Endpoint Found association between smoking and severity/mortality of COVID-19 Mehra, MR et al North America, Asia, Europe 8910 Current smokers: 491 (5.5 %) Former smokers: 1493 (16.8 %) 3571 (40.1 %) / 5339 (59.9 %) Death Yes
Hu, L et al China 323 Smoking
history: 38 (11.8 %)
157 (48.6%)/
166 (51.4 %) progression Disease Yes
Yu, T et al China 95 Tobacco
smoking: 8 (8.4 %) 42 (44.2 %)/ 53 (55.8 %) exacerbation Pneumonia /ARDS Yes/No
Zheng, Y et al China 73 Current
smokers: 8 (10.9 %)
33 (45.2 %) /
40 (54.8 %) progression Disease No
Zhang, Jin-jin et
al China 140 smokers:2 Current
Past smokers: 7 Smoking index: <400 = 3 ≥400 = 6 69 (49.3 %) / 71 (50.7 %) progression Disease No
Liu W et al China 78 History of
smoking: 5 (6.4%)
39 (50 %) /
39 (50 %) progression Disease Yes
Wang, R et al China 125 History of
smoking: 16 (12.8 %)
54 (43.2%) /
prospectively collected in the included studies thus resulting in a score of zero. Overall risk of bias was deemed as an unclear risk of bias (Table 3).
Table 3. Estimation of bias according to the MINORS score. Author A clearly stated aim Inclusion of consecutive patients Prospective collection of data Endpoints appropriate to the aim of the study Unbiased assessment of the study endpoint Follow-up period appropriate to the aim of the study Loss to follow up less than 5% Prospective calculation of the study size Total points Mehra, Mr et al 2 1 0 1 N/A 2 2 0 8/16 Zheng, Y et al 2 1 0 1 N/A 1 2 0 7/16 Hu, L et al 2 2 0 2 N/A 1 2 1 10/16 Yu, T et al 2 1 0 2 N/A 1 2 1 9/16 Zhang, J-J et al 2 2 0 2 N/A 1 2 1 10/16 Liu, W et al 2 1 0 2 N/A 2 2 0 9/16 Wang, R et al 2 1 0 2 N/A 1 2 1 9/16
5. Discussion
Of the 7 articles included in this review, 5 found a statistically significant relationship between smoking and disease progression and/or risk of death of COVID-19. There is a clear correlation between smoking and hypertension, diabetes mellitus type 2 as well as ischemic heart disease [17]. Smoking is also the driving cause to developing COPD [34]. It is reasonable that these factors could affect the outcome of COVID-19. Of the 5 studies 4 also controlled for confounding factors and found that smoking, regardless of the confounders, worsens the outcome of COVID-19. Thus, indicating that smoking has a direct negative impact on the outcome of COVID-19.
Studies in rats have suggested that smoking downregulates ACE-2 and could by doing so also work as a protective in COVID-19. It has been suggested that nicotine could be used as a preventive instrument regarding COVID-19 infection [35]. No study presented results that could support this. Therefore, the results do not support any recommendations as to using nicotine as a treatment in COVID-19. Nicotine is a harmful substance as well as highly addictive and should consequently not be considered at all in treatment but can obviously be used as an aid for smoking COVID-19 patients to quit smoking.
Smoking status was collected using different methods as well as different notations. Wang, R et al, Zheng Y et al, Yu, T et al and Liu W et al [36][31][30][27] noted their patients smoking status as a history of smoking and did not describe the smoking status more at depth. By doing so, placing current smokers in the same group as previous smokers the result of their respective studies might apply to both groups. Zhang, J et al collected smoking history as years of smoking cessation, the amount of smoking and the years of smoking history which could in part also be presented as smoking index and by doing so giving a more detailed smoking status.
Mandeep, R. Mehra et al collected smoking history in form of current smokers, former smokers or nonsmokers [32]. Thus, providing a more nuanced result regarding smoking and its potential relationship with COVID-19. Collecting data regarding smoking history could be subjected to fault since patients might under-report their smoking history. Patients who quit smoking at the time of disease onset could be registered as former smokers even though their smoking cessation was quite recent.
It would be preferable to internationally agree on a fixed way of collecting smoking status in future studies regarding smoking and its impact on various diseases.
Of the 7 articles in this review, just one [32] presented data from other countries/regions than China. The number of participants in China who were noted as smokers were low compared to the number of smokers in China. It is possible that the different ways as to how the included studies approached collecting smoking status, contributed to underreporting of smoking history amongst included patients. China is the biggest consumer as well as producer, of tobacco in the World and around every third cigarette being sold globally is sold in China thus making tobacco a big contributing factor to their economy. More than half of China’s adult male population are smokers [37]. Since Chinese men have been found to smoke to a higher extent compared to Chinese women it would be fair to suggest that they would also be overrepresented. Men between ages of 15-24 have a smoking prevalence of 36.5 % and between ages of 45-64 it rises
to 60 %. The female demographic have a lower smoking prevalence which is as low as 0.5 % in the age group 15-24 and increases to 6.9 % amongst the group of 65 and older [19]. In the included studies all but one [27] had a higher prevalence of male patients compared to female patients. The one study who did not have a higher prevalence of male patients compared to female patients had an even gender ratio. This, in part, could support the theory that men are more vulnerable for COVID-19 compared to women but can also reflect that smoking is much more common amongst Chinese men than women.
SARS and MERS were caused by coronaviruses and were also said to have had a higher number of smokers and higher CFR [1]. Since many key features are shared between these viruses, e.g. SARS-CoV and SARS-CoV-2 have been found to share the same main receptor, it is not surprising to find results indicating that also COVID-19 is of similar character.
Researching a topic which is currently part of the cause of an ongoing pandemic that due to its nature changes over time, naturally results in various limitations. One of the limitations with this review is that it portrays a small window of this pandemic. With a total of 36 new articles were assessed after the second literature search it is reasonable to assume that the pandemic is still in its early stages. Since the situation is ever changing and being updated on a day-to-day basis, it was challenging excluding articles due to the deadline and possibly risking overlooking any key facts that were added to the database. However, it was necessary in order to fit the time frame given writing this paper.
Another limitation regarding the search is the use of only one database which was PubMed. After searching various databases, PubMed was chosen since it had the most material and was deemed being of high quality as well as widely recognized.
Since the quantity of available material regarding COVID-19 have been of limited, it would be possible to argue that review articles should have been included. The reason for not using review-articles in this literature review is that the author(s) of the material could purely use sources that are supporting their thesis while excluding others, and by doing so not giving the reader the complete picture. Thus, in order to give the most transparent picture of the current field of research as possible, review articles were excluded.
This review studies articles which covers a narrow geographical area with only one study presenting data from other locations than China [32]. A possible reason for this is the search plan used in this review, but also due to the fact that the pandemic started in China which in turn provided more time for data to be collected in that area. Another reason could be that the
studies have not yet been translated to English, which was one of the inclusions criteria for this review, and by lacking that factor excluding them from the search.
This review did not find any studies were other forms of smoking, e.g. vaping or marijuana smoking, than the smoking of tobacco cigarettes was studied. No articles regarding second- or third hand smoke exposure were found during the literature search process. Consumers of smoking products presents a behavior which involves an increased hand to mouth contact. This could possibly be making them more exposed to COVID-19. Water pipe smokers can also be part of a shared transmission. It has been described how SARS-CoV-2 possibly attaches to second hand aerosol and second hand smoke consequently becoming part of dust as dust particles which subsequently, can be inhaled by another person in the house causing them to become infected [38]. This ongoing pandemic have resulted in lock-down measures around the globe which results in citizens being forced to stay more indoors than they normally would, and by doing so, also possibly being more exposed to second hand as well as third hand smoke exposure [39]. Another potential source of disease transmission related to smoking is the fact that smokers cough more and spit saliva [40]. This review found no study nor discussion of that potential way of transmission.
6. Conclusion
The result of this systematic literature review suggests that smoking enhances the severity of COVID-19. Due to a limited number of patients combined with a narrow geographic area being studied, more research is needed to further evaluate and establish the role of smoking in COVID-19. High quality reports of smoking, oral tobacco, E-cigarettes and heat not burn products in hospital journals as well as studies, are needed.
7. Acknowledgement
The assistance provided by my supervisor Matz Larsson was of great help and much appreciated.
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