Tobacco Use and Uptake of COVID-19 Vaccinations in Finland: a General Population Study
Conditions: COVID-19, Smoking, Tobacco Use, Vaccination Refusal
Sponsor: Finnish Institute for Health and Welfare

Study protocol 
 
Tobacco use and uptake of COVID-19 vaccinations in Finland: a general population 
study 
 
Sebastián Peña,1 Laura Kestilä,1 Maria Rosaria Galanti,2,3 Ahmed Nabil Shaaban,2 Ida 
Henriette Caspersen,4 Per Magnus, 4 Hanna Nohynek,1 Suvi Parikka,1 Sakari Karvonen1  
 
Affiliations: 
1. Department of Public Health and Welfare, Finnish Institute for Health and Welfare, 
Mannerheimintie 166, 00271, Helsinki, Finland 
2. Department of Global Public Health, Karolinska Institutet, Sweden  
3. Centre for Epidemiology and Community Medicine, Stockholm Region 
 
 
 
Collaborators: 
Karolinska Institutet 
Norwegian Institute of Public Health 
 
Funding: 
The Tobrisk-CoV study is funded by NordForsk (project number 105544) 
 
Protocol history 
Start date: March 29, 2022 
Version 1.0: Posted on July 29, 2022 
Version 2.0. Latest revision: September 27, 2022 
 
Revision history:  
- 
Summary of changes:  
Version 1.0 mentioned a Figure 1 but did not include it. 
We added a discussion on how we will handle incident COVID-19 cases in the 
analyses. 
 
Version 2.0 was finalised before data analyses, only some data wrangling was carried out.  
 
 

Background 
 
The COVID-19 pandemic continues to spread worldwide, resulting in more than 541 million 
cases and more than 6.3 million deaths worldwide by June 15, 2022.1 Rapid viral 
dissemination in many parts of the world has led to the emergence of several variants of 
concern, with greater transmissibility and severity.2,3  
 
During 2020, many countries resorted to strict public health and social measures as means to 
curb the pandemic, including large-scale lockdowns, closure of schools and public spaces, 
travelling restrictions and face masks mandates. In 2021, most high-income countries began 
rolling out COVID-19 vaccinations, with the promise of reducing incident COVID-19 
infections and preventing hospitalizations and deaths. Achieving high vaccination coverage 
is crucial for several reasons: (i) to reduce the number of new COVID-19 cases,4 (ii) to 
diminish selective pressures leading to the emergence of new variants of concern,5 (iii) to 
prevent severe COVID-19 outcomes, such as hospitalizations and deaths,6 and (iv) to 
potentially reduce the risk of post-acute COVID-19 symptoms.7  
 
Smokers are at higher risk of COVID-19 hospitalizations and deaths8 and might thus benefit 
greatly from high vaccination coverage. However, evidence suggests that they may be less 
likely to be vaccinated, as smokers are less likely to adhere to preventive measures in general 
and have lower adherence to other vaccines.9,10 Reports of a protective role of smoking on 
the risk of COVID-19 infection might have also reduced the perceived risks from being 
infected 11. Evidence regarding tobacco use and COVID-19 vaccine hesitancy is mixed. 
Some studies have shown greater mistrust in COVID-19 vaccine benefits11-14 and greater 
vaccine hesitancy and lower vaccine acceptance compared to non-smokers,11,15,16 while other 
studies have reported no differences by smoking status or lower levels of vaccine hesitancy 
in smokers compared to non-smokers.17-20  
 
Vaccine uptake, however, is a dynamic process not only influenced by trust and risk 
perception, but by convenience factors related to physical and geographical availability, 
affordability, ability to understand and appeal of immunisation services.21 To our knowledge, 
two studies from convenience samples in Singapore and Palestine have examined vaccination 
uptake by smoking status.22,23 Both studies showed that smokers had higher odds of being 
vaccinated.22,23 However, these studies are not representative of the general population and 
relied on self-reported vaccination status, resulting in a higher risk of selection and 
information bias. Spacing between vaccine doses is also relevant, as a growing body of 
literature suggests that smokers develop a weaker immune response to COVID-19 vaccines.24 

 
Finland developed a national COVID-19 vaccination strategy and the vaccine rollout started 
on December 27, 2020. Vaccinations started with priority groups (health and social care 
personnel and people at high risk of severe COVID-19) but eventually were expanded to the 
whole population aged 5 and over.25 COVID-19 vaccines are provided free of charge and 
delivered nationwide by each municipality. Finland has administered primarily mRNA 
vaccines (Cominarty and Spikevax).25 Vaccination coverage in the Finnish population aged 
18 and older has reached 89.7% for the first dose, 87.6% for the second dose and 65.1% for 
the booster dose by June 14, 2022.26  
 
In this study, we will examine the association between tobacco use and COVID-19 vaccine 
uptake and between-dose spacing. Given conflicting results of previous studies, we expect to 
observe an association between tobacco use and vaccination uptake and dose-spacing, but 
the direction of the effect is unclear. We will expand current knowledge by examining two 
forms of tobacco use in Finland -smoking and smokeless tobacco use (snus)- and by 
analysing the spacing between vaccine doses. We will use data from nationally representative 
surveys in Finland linked to vaccination registries, which reduces the risk of selection and 
information bias.  
 
Methods 
 
The study protocol has been written in close accordance with the Strengthening the Reporting 
of Observational Studies in Epidemiology (STROBE) statement for cohort studies 27 
 
Study design  
The study design is a cohort study of nationally representative health surveys linked to 
COVID-19 vaccination data derived from the Finnish National Vaccination Register, using 
a unique personal identification number assigned to all residents in Finland.  
 
 
Setting and participants 
We will use data from two FinSote Surveys, which are cross-sectional population health 
surveys in Finland.  
 
Participants in the FinSote 2018 Survey were adults aged 20 years and over derived from the 
Population Register of Statistics Finland, which comprises all residents in Finland. This 
sampling frame includes people living in institutions and military conscripts.28 The survey 

was based on stratified random sampling, one stratum for each 19 counties. In 2017, 3300 
people were invited to participate from 18 research areas (2300 adults aged 20-74 and 1000 
adults aged 75+, total sample size 59400). Data was collected between October 2, 2017, and 
March 3, 2018. Participants received a self-administered questionnaire in Finnish, Swedish, 
English or Russian, which could be returned on paper or filled in electronically. The 
participation rate was 45%, resulting in a total of 26422 participants.28 Participants were 
asked for consent for registry linkage, resulting in an analytical sample of 14736 subjects.  
 
Participants in the FinSote 2020 Survey were adults aged 20 and over derived from the 
Digital and Population Data Services Registry, created in Jan 2020 after the merge between 
the Population Register of Statistics Finland and local register offices.29 This sampling frame 
includes all residents in Finland. The survey was based on a stratified random sample of 22 
regions (2000 adults aged 20-74, 800 adults aged 75+, total sample size 61600). Data 
collection started on September 14, 2020, and finished on February 8, 2021. Participants 
received a self-administered questionnaire in Finnish, Swedish, English or Russian, which 
could be returned on paper or filled in electronically. The analytical sample is 28199 
participants, with a participation rate of 46%. In 2020, consent to linkage was included in the 
overall consent, and as a result, we were able to link all participants in the analytical sample.  
 
We will link data from FinSote 2018 and 2020 surveys to the Finnish National Vaccination 
Register maintained by the Finnish Institute for Health and Welfare using a unique identifier 
assigned to all residents in Finland. The follow-up will be between December  27, 2020 (date 
of the first COVID-19 vaccination in the country) until May 31, 2022 or the latest available 
date.   
 
 
 
Outcomes 
Our primary outcome will be the vaccination uptake of at least two doses of a COVID-19 
vaccine. We will examine four secondary outcomes: 
(i) vaccination uptake of at least one dose of a COVID-19 vaccine,  
(ii) vaccination uptake of the complete COVID-19 vaccination scheme (two doses and a 
booster dose),  
(iii) proportion of participants with more than 7 months between the first and second COVID-
19 dose, and  
(iv) proportion of participants with more than 7 months between second dose and booster 
dose.  

 
We will include any approved COVID-19 vaccination in Finland by the latest available date 
(i.e. April 29, 2022):30 COVID-19 mRNA vaccines (Comirnaty and Spikevax), recombinant 
vaccines 
(Vaxzevria, 
COVID-19 
vaccine 
Janssen, 
COVISHIELD, 
Nuvaxovid,  
COVOVAX), and inactivated vaccines (BIBP/Sinopharm, COVAXIN, Coronavac). 
Participants who have received the COVID-19 vaccine Janssen would be excluded from 
analyses on the second dose, as the vaccine only requires a single dose. 
 
Exposures 
We will examine two forms of tobacco use: smoking and smokeless tobacco (snus) use. We 
will assess smoking status with the following question: Do you smoke currently (cigarettes, 
cigars or pipe)? (a) yes, daily, (b) occasionally, (c) not at all, (d) I have never smoked. We 
will create a categorical variable with the following categories: never smokers, former 
smokers, current occasional smokers and current daily smokers.  
 
We will assess snus use with the following question: Do you currently use any of the 
following products? Snus (Swedish type moist stuff) (a) yes, daily, (b) occasionally, (c) not 
at all, (d) I have never used. We will create a categorical variable with the following 
categories:  never users, former users, current users (daily and occasional combined). Data 
on smoking status is available for all participants, while data on snus use was available for 
participants aged 20-74 years old.  
 
Potential confounders 
We will adjust for several potential sociodemographic confounders: age, sex, marital status, 
educational tertile, and participation in social activities. All variables will be analysed at 
baseline. We will define marital status as those married or in a registered relationship or 
cohabiting versus those separated or divorced, widowed or single. We will calculate 
educational tertiles for each survey year using the years of education reported in the question 
“How many years altogether have you attended school or studied full time?”. We will analyse 
participants’ mother tongue using data from national registries and will categorise it into 
three categories: Finnish, Swedish and other. We will measure participation in social 
activities with a question about participation in the activities of any club, association, hobby 
group or religious or spiritual community. We will categorise participation into the following: 
no participation, occasional and active. 
 
 
 

Potential mediators 
We will take the incidence of COVID-19 infections into account (see below). We will obtain 
data on confirmed COVID-19 cases from the Finnish National Infectious Disease Register 
maintained by the Finnish Institute for Health and Welfare. Confirmed COVID-19 cases will 
be defined as those 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). Reporting of COVID-19 positive cases was mandatory for 
laboratories and physicians throughout the study period.  
 
Effect modifiers 
We will examine potential effect modification by reporting stratified results by age (as a 
categorical variable), sex and educational level.  
 
Data sources and measurement 
Data sources and measurements for all variables were described above. More details on the 
exact variables can be found in the Supplementary Appendix.  
 
Bias 
Confounding bias 
We will reduce the risk of confounding by adjusting for potential confounders that causally 
precede the exposure and are associated with the outcome. We built a directed acyclic graph 
to guide the identification of potential confounders and mediators (Figure 1). Adjusting for 
mediators can result in an underestimation of the association and potentially introduce M-
bias (a type of collider bias).31,32  
 
 
 
 
 
 
 
 
 
 
 
 

Figure 1. Directed acyclic graph of the causal association between tobacco use and COVID-
19 vaccinations uptake 
 
 
 
 
A special concern were participants who had a COVID-19 infection. Tobacco use is 
associated with COVID-19 infection and, in turn, having a COVID-19 infection can 
influence the willingness to vaccinate or delay COVID-19 vaccination. Current Finnish 
guidelines recommend to the general adult population, after having a COVID-19 infection,  
to: (i) wait at least two months before getting the first COVID-19 dose, (ii) wait at least four 
to six months after the COVID-19 infection or the first dose, whichever happens first. People 
with two doses will not be given a third dose if they have had COVID-19. Having a COVID-
19 infection, according to our DAG, would act as a mediator. Excluding individuals who 
have had the infection (or adjusting for it) would result in estimating the direct effect between 
tobacco use and COVID-19 vaccination uptake and not the total effect. For this reason, our 
main analyses will report the outcomes without adjusting for having a COVID-19 infection. 
In sensitivity analyses, we will report estimates stratified by COVID-19 infection status and 
also we will use causal inference methods (e.g. marginal structural methods and G-formula) 
to account for this time-varying estimate. We expect, however, that this will not make a large 
difference, as the COVID-19 incidence prior to the start of the COVID-19 vaccinations was 
very low and continued to be low until the rise of the Omicron variant in mid-December 
2021.33 In practice, this means that a very small proportion of our sample will experience a 
COVID-19 infection during the study period. 
 
 
 

Selection bias 
FinSote surveys 2018 and 2020 are based on a stratified random sample design that represents 
the Finnish population. However, non-participation in the survey could potentially introduce 
nonresponse bias if participants are systematically different from non-participants. For this 
purpose, we will take into account the complex survey design in all analyses and use the 
inverse probability weights (IPW) created by the Finnish Institute for Health and Welfare 
using a model with register-based data for the whole sample on age, sex, marital status, 
educational level, mother tongue and place of residence. The IPW method allows correcting 
for survey non-participation and the weighting factor of each participant is a combination of 
the inverse of the participation probability and the sampling probability. Hence, the set of 
weighted coefficients represents the Finnish population.34  
 
Information bias 
The risk of information bias from measurement error of the exposure and confounders is 
inherent to most population health surveys. However, our data is almost entirely collected 
before assessment of the outcome, reducing the possibility that the exposure is affected by 
the occurrence of the outcome (i.e. that COVID-19 vaccination affects tobacco use). COVID-
19 vaccination in Finland started on December 27, 2020, with a focus on health and 
residential care workers. The number of doses available in the first months was limited, so it 
is unlikely that participants from FinSote 2020 received the vaccination before the data 
collection. As sensitivity analyses, we will carry out the same analyses but excluding 
participants who have received a vaccination dose prior to February 8, 2021, when the data 
collection of FinSote 2020 ended. In addition, smoking status was collected relatively close 
to the COVID-19 vaccination, reducing (although not excluding it completely) the risk of 
exposure misclassification. 
 
The COVID-19 vaccination has been implemented nationally by public providers, meaning 
that all COVID-19 vaccines provided have been registered in the Finnish National 
Vaccination Register. We estimate the risk of loss to follow-up and information bias of the 
outcome to be close to zero.  
 
Study size 
The sample size was determined by the available data from FinSote surveys 2018 and 2020. 
We consider it not necessary to provide a sample size calculation.  
 
 
 

Quantitative variables 
We described the categorisation of exposures and confounders above. We will model age as 
a continuous variable and tested non-linearity by comparing the linear model with penalised 
smoothing splines using a likelihood ratio test.35 The variable years of education will be 
modelled as categorical to be able to account for secular changes in years of education during 
the study years. In other words, educational attainment increases over time and it is therefore 
more appropriate to calculate the educational attainment within each survey wave. 
 
 
 
Statistical methods 
We will use Poisson regression with robust standard errors to estimate the relative risk of 
confirmed COVID-19 cases. We will fit the following Poisson model: 
 
(1) 
𝑙𝑜𝑔 𝜇𝑖 = 𝛽0 + 𝛽1𝑆𝑖 + 𝛽𝑥𝑋𝑖
∗ + 𝜌1𝑅𝑎  
 
where 𝑖 denotes the individual, 𝛽0is the intercept; 𝛽1is the coefficient of interest for exposure 
to tobacco 𝑆; a vector of covariates 𝑋∗ (i.e., sex, age, marital status, years of education, 
mother tongue and participation in social activities); and fixed effects 𝜌1𝑅𝑎 for wellbeing 
area 𝑎. We will report the exponentiated coefficient as the relative risk estimate for 
unadjusted and adjusted models with their corresponding 95% confidence intervals. 
 
We will examine non-linearity between age and the outcome by comparing the linear model 
with penalised smoothing splines35 using a likelihood ratio test with the Wald method.36 We 
will use the model with splines if it performs better than the linear model, using a p-value of 
0.05 as the threshold for this decision. 
 
We will examine potential effect modification by reporting stratified analyses by age, sex 
and educational level. If there are noticeable differences, we will test for multiplicative 
interactions between tobacco use and sex, age and educational level by including an 
interaction term in equation 1. We will introduce the interaction terms one by one for each 
of the examined interactions. We will use a likelihood ratio test to compare the model with 
and without the interaction. A p-value lower than 0.05 in the likelihood ratio test will be used 
as an indication of the presence of an interaction. If there is evidence of an statistically 
significant interaction, we will include the interaction term in the final model.  
 

Our primary method to handle missing data will be multiple imputation, given that a missing 
at random assumption is the most likely in our data. We will examine patterns of missing 
data and use multiple imputation using chained equations (MICE). This procedure will be 
combined with the survey design tools to take the complex sampling design into account.  
 
In sensitivity analyses, we will report estimates using complete case analyses (i.e. taking only 
the complex sampling design into account). We will also report the same estimates as the 
main analyses, but restricting the follow-up time to when vaccination coverage reached 60% 
and 80%. This will allow us to understand whether these associations changed over time and 
also provide estimates that might be comparable to countries with lower vaccination 
coverage, thus increasing the external validity of our estimates.  
 
We will analyse the data using R version 4.1.1 and include the statistical code as a 
Supplementary Appendix.  
 
 
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