The Association Between Tobacco Use and COVID-19 Infection and Adverse Outcomes in Three Nordic Countries: a Pooled Analysis
Conditions: COVID-19, Respiratory Tract Infections, Infections, Pneumonia, Viral, Pneumonia, Virus Diseases, SARS-CoV-2 Infection
Sponsor: Karolinska Institutet

 
 
 
 
  
Study Protocol 
  
The association between tobacco use and COVID-19 infection and adverse outcomes in 
three Nordic countries: a pooled analysis 
  
March 25, 2022  
Revision history: this protocol has not been revised 
  
Ahmed Nabil Shaaban1, Sebastián Peña3, Ida Henriette Caspersen4, Filip Andersson1,2 Sakari 
Karvonen3, Per Magnus4, Maria Rosaria Galanti1,2 
  
1.   Department of Global Public Health, Karolinska Institute, K9 Global folkhälsa, K9 GPH, 171 
77 Stockholm, Sweden. 
2. Centre for Epidemiology and Community Medicine, Stockholm Region, (CES), Solnavägen 
1E (Torsplan), 113 65 Stockholm, Sweden 
3.  Department of Public Health and Welfare, Finnish Institute for Health and Welfare, 
Mannerheimintie 166, PO BOX 30, 00271, Helsinki, Finland. 
4.  Centre for Fertility and Health, Norwegian Institute of Public Health, Postbox 222 Skøyen, N-
0213 Oslo, Norway. 
  
  
Funding: 
The Tobrisk-CoV study is funded by NordForsk (project number 105544) 

 
 
 
 
Background 
The coronavirus disease (COVID-19) pandemic, caused by severe acute respiratory syndrome 
coronavirus 2 (SARS-CoV-2), has caused more than 470 million confirmed cases of COVID-19, 
and more than 6 million deaths around the world by March 25, 2022 (WHO, 2022). Since the 
beginning of the COVID-19 pandemic, several factors have been attributed to increasing the risk 
of infection and adverse outcomes of the COVID-19 disease. Among these factors, the 
relationship between tobacco use and COVID-19 infection and adverse disease outcomes 
remained controversial as studies kept reporting mixed findings. Early studies reported what 
seemed to be a protective effect of tobacco use on COVID-19 infection (Haddad et al.; Jiménez-
Ruiz et al., 2020), or hospitalizations due to COVID-19 (Farsalinos et al., 2020; Neira et al., 
2021). A more recent ongoing living rapid review, this time including a larger selection of studies 
with different study designs, found that smokers are at reduced risk of SARS-COVID-19 
infection compared to non-smokers (Relative risk 0.67, 95% Credible interval 0.60-0.75) 
(Simons et al., 2021). These findings opened the way for speculations and hypotheses on the 
potential mechanisms behind this protective role. However, results from most of these studies 
may be affected by selection bias as they reported findings from clinical samples or bias due to 
confounding as the structure of these published data permitted only univariate analysis.  
Results from studies that suffer from selection bias or bias due to confounding should be 
handled with caution as they may undermine years of public health education against tobacco 
use, a major cause of morbidity and mortality worldwide. Moreover, the role of tobacco use in 
disease progression such as disease requiring hospitalization, ICU, and death remains unclear 
as most of the previous studies focused more on the association between tobacco use and the 
risk of infection, but not the adverse outcomes. These facts call for studies that ensure 
addressing any knowledge gap on the relation between tobacco and COVID-19 by taking into 
consideration 1) decreasing the risk for confounding and selection bias; 2) increasing precision 

 
 
 
 
through a higher sample size, 3) further investigating the association between tobacco use and 
adverse disease outcomes. In most Nordic countries, the profile of tobacco use in the 
underlying populations allows the analysis of several types of tobacco use e.g. cigarette 
smoking and smokeless tobacco (snus) use, enabling further insights into the potential role of 
nicotine in the association between tobacco use and COVID-19. The use of smokeless tobacco 
is highly prevalent (even exceeding the prevalence of smoking among men in Sweden and 
Norway), which will allow us to disentangle a potential role of nicotine in the association 
between tobacco use and COVID-19.  
The aim of this study is to examine the associations between tobacco use, COVID-19 infection, 
and adverse disease outcomes by using pooled population-based data from three Nordic 
countries, adjusting for potential confounders. The population-based nature of the samples 
minimizes selection bias Using a pooled analysis will accrue a large sample size and increase 
the potential for well–powered sub-groups analyses.  
 
Methods 
Setting and study design 
This is an observational study of pooled population-based samples in three Nordic countries. 
Country-specific data has already been analysed in previous studies in Sweden (Galanti, 2021), 
Finland (Peña et al., 2021), and Norway (Magnus et al., 2016). We will use the national 
personal number assigned to every resident in these three countries at birth or at immigration to 
obtain information on diagnoses of COVID-19 and adverse outcomes among individuals in 
these cohorts through record-linkage with regional and national databases.  
 
 

 
 
 
 
Data Sources  
We will pool data from three Nordic countries, Sweden, Finland, and Norway. Briefly, Swedish 
data comes from a historical cohort of 424,386 clients of public dental clinics aged 23 and older 
in the Stockholm region with inception between October 2015 and January 2020, with follow-up 
from February 2020 to December 2020. In Sweden, the public dental clinics (Folktandvården, 
FTV) provide routine preventive visits (oral check-ups) to all residents who choose to receive 
care in these clinics. At each health check-up smoking and snus use are ascertained as past 
use, current use, and amount of current use. The national personal numbers assigned to every 
resident in Sweden at birth or at immigration will be used to obtain information on diagnoses of 
COVID-19 and of other diseases through record-linkage with regional health care registers. 
Demographic information will be extracted through record-linkage with the register of the total 
population of the Stockholm region held by Statistics Sweden. 
The Finnish data will come from three pooled cross-sectional national health surveys in Finland 
(FinSote 2018-2020) of 44,199 participants aged 20 and older. The study samples included 
permanent residents in Finland from the FinSote surveys 2018, 2019, and 2020. The unique 
personal identifier assigned to all Finnish residents will be linked to the Communicable Diseases 
Registry to obtain information on diagnoses of COVID-19, to the Care Register for Health Care 
(HILMO) to obtain information on hospital admissions due to COVID-19, and to Statistics 
Finland Mortality Data to obtain information on deaths. Data on some sociodemographic 
characteristics will be also obtained from the Digital and Population Data Services Agency. 
The Norwegian data will be based on the Norwegian Mother, Father and Child Cohort Study 
(MoBa) (Magnus et al., 2016), and the Norwegian Influenza Pregnancy Cohort (NorFlu) (Laake, 
2018), with linkages to the Norwegian Surveillance System for Communicable Diseases (MSIS), 
the Norwegian Immunisation Registry (SYSVAK),  and the Norwegian Population Registry. 
MoBa is a nation-wide population-based cohort consisting of 280 000 participants, where 

 
 
 
 
parents were recruited during pregnancy from 1999 to 2008, while NorFlu is a pregnancy cohort 
consisting of 9 000 participants recruited in Oslo and Bergen during the swine flu pandemic in 
2009-2010. Since March 2020, cohort participants have been regularly invited to answer 
electronic questionnaires. In June 2020 and January 2021, participants were asked to report 
current smoking. Questions about snus and other tobacco use were asked in January 2021 
only. COVID-19 diagnoses are obtained from registry data (MSIS) based on PCR confirmed 
SARS-CoV-2 infection. Demographic information is extracted from the registries via linkage to 
the existing cohort databases. For the purpose of this study, all subjects who died before the 
onset of the pandemic (February 2020) in the three countries will be excluded from the analysis. 
We developed a harmonization protocol to create a comparable dataset. The harmonization 
process started with identifying all possible variables to be used in the pooled analyses. We 
then identified common variables across countries, and only variables available in at least 2 
countries will be included in the pooled analysis. Each country provided the format on which 
these variables were stored in their respective datasets. Finally, we elaborated a document 
describing the format for the harmonized variables. The principle of harmonization was to create 
variables with a minimum common format. The harmonized variables list is as follows:  
Exposure Variables 
Tobacco use (cigarette smoking and/or snus use) will be considered as exposure as follows: 
1. Current Cigarette Smoking 
Participants will be grouped as current cigarette smokers if they reported daily or occasional 
smoking. A categorical variable for current smoking status will be created (non-current smoker = 
1, current daily or occasional smokers=2) 
2. Current amount cigarette smoked/day 

 
 
 
 
Current amount of cigarette smoked/day will be included as a continuous variable. Also a 
categorical variable of the originally reported average daily consumption will be created as 
follows:  no smoking, 10 cigarettes per day (CPD) or less; 11-20 CPD; more than 20 CPD.   
3. Current Snus use  
Participants will be grouped as current snus users if they reported daily or occasional snus use. 
A categorical variable for current snus use will be created (non-current snus user= 1, current 
daily or occasional snus user= 2) 
 
Outcome Variables 
The study will include five outcomes: any diagnosis of COVID-19, hospital admission for 
COVID-19, length of hospital stay for COVID-19; intensive care use due to COVID-19, and 
death attributable to COVID-19. The follow-up period for all outcomes was from February 2020 
until December 2020.  
1. Any diagnosis of COVID-19: 
Sweden:   at least a positive polymerase chain reaction test (PCR) reported by the laboratories 
to Sweden’s national electronic surveillance system for communicable diseases, SmiNet 
Finland: cases with a positive SARS-CoV-2 RT-PCR, either informed by a laboratory or by a 
physician as a record of an ICD-10 code U07.1 (which requires a positive SARS-CoV-2 RT-
PCR). 
Norway: a positive test for SARS-CoV-2 based on PCR obtained from The Norwegian 
Surveillance System for Communicable Diseases (MSIS) or the presence of antibodies for 
SARS-CoV-2. 

 
 
 
 
Out of these definitions a categorical variable will be created (No recorded COVID-19 
diagnosis= 1, registered COVID-19 diagnosis= 2) 
2. Hospital admission for COVID-19 
Any hospital admission with a diagnosis of COVID-19 (ICD-10 codes U071 and U072). The 
diagnosis could be registered either as a main or as a concomitant diagnosis.  
A. A categorical variable for hospital admission with any diagnosis of COVID-19 (either 
main or secondary diagnosis) will be created (No admissions= 1, any admission= 2). 
B. A categorical variable for hospital admission with COVID-19 as the main diagnosis only 
(No admissions= 1, any admission= 2). 
3. Intensive unit care because of a diagnosis of COVID-19 
Admission to an intensive care unit (ICU) because of a diagnosis of COVID-19 (ICD-10 codes 
as above). A categorical variable for intensive unit care because of a diagnosis of COVID-19 
(No/Yes) will be created (No ICU care= 1, any ICU care= 2). 
4. Death for COVID-19 
Death due to COVID-19 will be established using the Swedish, Finnish, and Norwegian Cause 
of Death Registries. All deaths occurring during the follow-up period with COVID-19 registered 
as the main cause will be included. The restriction to main cause will be done to maximize the 
specificity of the diagnosis (Ioannidis, 2021). A categorical variable for death due to COVID-19 
will be created (Alive at the end of follow-up= 1, Death during follow-up= 2).  
Covariates 
We will include sex, age, cohabitation, education, employment, co-morbidity due to a tobacco-
related disease and country of residence as covariates in the study. The covariates will be 
defined as follows:  

 
 
 
 
1. Sex 
Sex will be categorized as male (= 1), female (= 2). 
2. Age 
We will use age as a continuous variable (in years). 
3. Cohabitation 
We will define cohabitation as living alone (=1) or living with others (= 2). 
4. Education 
We will categorize education into three groups: Less than high school (= 1), high school (= 2), 
and university (= 3). 
5. Employment  
We will categorize current employment status as: no current employment, student or retired (= 
1), currently part-time or full-time employed (= 2). This information is available in Sweden and 
Norway.  
6. Comorbidity due to any tobacco-related disease 
We will define any comorbidity due to a tobacco-related disease as: No tobacco-related 
comorbidity (= 1), any tobacco-related comorbidity (= 2). This information is available in Sweden 
and Norway. 
7. Country 

 
 
 
 
We will create a categorical variable with the country of residence as follows: Sweden (= 1), 
Norway (= 2), Finland (=3). 
 
Date variables 
1. Year of latest assessment of cigarette smoking 
Format: YYYY 
2. Year of latest assessment of snus use 
Format: YYYY 
3. Date of COVID-19 Diagnosis 
Format: DD/MM/YYYY 
4. Date of Hospital Admissions due to COVID-19 
Format: DD/MM/YYYY 
5. Date of Intensive Care Admission due to COVID-19 
Format: DD/MM/YYYY 
6. Date of hospital discharge 
Format: DD/MM/YYYY 
 
 

 
 
 
 
Statistical analysis 
Risk ratios (RR) and their corresponding 95% CI for COVID-19 infection, hospitalization, ICU, or 
death due to COVID-19 will be estimated through generalized linear models (GLM). We will use 
the Poisson family with robust standard errors and the maximum likelihood optimization 
algorithm to estimate the relative risk of a confirmed COVID-19 case, hospital admission, ICU, 
or death due to COVID-19 (Zou, 2004). First, adjusted Poisson regression (Zou, 2004) will be 
applied to assess the influence of exposure (smoking or snus) on the study’s outcomes. If we 
assume that 𝑦𝑖𝑗 is the observed binary outcome (COVID-19 infection, hospital admission, ICU, 
or death due to COVID-19) for subject 𝑖 in country 𝑗, the specification and the equation of the 
Poisson model is as follows: 
 
log(𝑝𝑖𝑗) = 𝛽0 + 𝛽1𝑋1𝑖𝑗+ ∑𝛽𝑘
𝑅
𝑘
 𝑋𝑖𝑗𝑘                               (1) 
where 𝑝𝑖𝑗 is the probability of the outcome 𝑦𝑖𝑗,  𝑋1𝑖𝑗 is the exposure (smoking/snus), 𝛽0  is the 
intercept, 𝛽1 is the regression coefficient corresponding to the exposure,  𝑋𝑖𝑗𝑘 represents the 
subject’s values of 𝑅 covariates, and 𝛽𝑘 is a regression coefficient corresponding to each 
covariate. Exponentiating 𝛽1 gives the corresponding relative risk, RR.  
We will then apply a multilevel regression (Goldstein, 1995) which assumes that each country 
has its own COVID-19 infection probability, and this varies from one country to another. This will 
be the primary analytical model. In this multilevel model, the Poisson regression for COVID-19 
infection will include an additional term 𝑢𝑗 , which is the country-level random effects as a 
predictor variable: 
log(𝑝𝑖𝑗) = 𝛽0 + 𝛽1𝑋1𝑖𝑗+ ∑𝛽𝑘
𝑅
𝑘
 𝑋𝑖𝑗𝑘+ 𝑢𝑗                 (2) 
In this model, the probability depends on the value of the random effects 𝑢𝑗 which is the totality 
of unmeasured country-level variables that predict COVID-19 infection and are uncorrelated 
with the individual variables in the model.  

 
 
 
 
As a secondary analysis, we will use a negative binomial count model (Hilbe, 2011, 2014) to 
assess inpatient length of stay (i.e. the cumulative number of days of hospitalization) as a 
measure of disease severity among tobacco users compared to non-tobacco users. 
We will carry out stratified analyses by sex, age, education, and time period.  
We will carry out three sensitivity analyses. First, we will analyse the data adding tobacco-
related comorbidity and employment as additional confounders with data from Sweden and 
Norway. Second, we will analyse only data prospectively collected to explore the influence of 
information bias. For this purpose, we will include data from Sweden and Finnish data from 
2018 and 2019. This data will be analyzed with Cox proportional hazards models with the same 
specifications as the primary analysis. We will use time in study as the timescale. Participants 
will be censored due to end of follow-up or the occurrence of the event.  
Third, for death of COVID-19, we will also include a competing risk analysis to take right-
censoring due to a death non-attributable to COVID-19 into account. Competing risk means that 
a subject can experience one of a set of different events during the study period. Accordingly, 
the use of traditional methods of survival analysis, such as the Kaplan-Meier survival function, 
will result in incidence estimates that are biased upward, irrespective of the independence 
between the competing events (Austin et al., 2016; Berry et al., 2010; Kim, 2007; Southern et 
al., 2006). Instead, we will use two models to fit a regression in the presence of competing risk, 
the cause-specific hazard modeling and the sub-distribution hazard modeling (Austin et al., 
2016). The cause-specific hazard function implies the instant rate of occurrence of a specific 
event in subjects who have not experienced yet any of the different types of events (Andersen 
et al., 2002; Austin et al., 2016). If we consider two types of events, death due to COVID-19 
causes and death due to non-COVID-19 causes, then the cause-specific hazard of COVID-19 
death implies the instant rate of COVID-19 death in subjects who have not yet experienced 

 
 
 
 
either event (Austin et al., 2016). In the cause-specific hazards regression model, we will model 
the effect of covariates by using the traditional Cox proportional hazards model after censoring 
individuals with competing events at the time of their occurrence. 
Unlike the cause-specific hazard function, the revised risk set in the sub-distribution hazard 
function (Fine & Gray, 1999), also known as the cumulative incidence function regression 
model, allows to include subjects who are event-free and subjects who already experienced a 
competing event . In the sub-distribution hazard function, we will model the effect of covariates 
by using a proportional hazards model through the cumulative incidence function that allows for 
the estimation of the incidence of the occurrence of an event while taking competing risks into 
consideration (Austin et al., 2016; Fine & Gray, 1999). 
All analyses will be conducted with STATA®, version 17 (StataCorp LP, College Station, Texas, 
USA) and R version 4.1.2. We will incorporate the complex sampling design in Finland in all 
analyses.  
 
 
 
 
 
 
 
 

 
 
 
 
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