Background

At the end of 2019, COVID-19 spread globally. In March 2020, WHO declared it a pandemic [1], which has led to significant years of life loss [2].and excess mortality [3]. Smoking is a closely related factor to COVID-19. On the one hand, smoking has been shown to upregulate ACE2 expression, increasing susceptibility to COVID-19 [4]. On the other hand, COVID-19 severity is significantly higher in smokers compared to non-smokers [5]. Therefore, it is necessary to reduce smoking behavior to promote health during the COVID-19 outbreak.

Some previous studies have reported changes in smoking behavior during the COVID-19 pandemic and identified influencing factors. Some studies suggest that smoking behavior has decreased during the pandemic due to concerns about the perceived harm of smoking during COVID-19 [6], difficulties purchasing cigarettes due to pandemic-related lockdowns, and the inability to smoke in public places due to mask-wearing requirements [7]. However, other studies have indicated a significant increase in smoking behavior during COVID-19 due to anxiety, depression, stress, and other factors [8, 9]. The multitude of factors influencing smoking during COVID-19 necessitates identifying high-risk populations and targeting the most significant influencing factors to reduce smoking behavior. However, none of these studies have investigated the primary influencing factors and high-risk populations for smoking behavior during the COVID-19 pandemic.

Classification and Regression Tree Analysis (CART) is a decision tree method developed by Breiman and colleagues. Using CART, it is possible to identify the most significant influencing factors for relative risk and explore the interaction between influencing factors and the most critical influencing factors to form the branches of the classification and regression tree, dividing the population into high-risk subgroups [10]. It is a nonparametric program that begins tree development by examining all predictor variables and selecting the variable (parent node) that can best predict the desired classification. The data in this parent node is divided into two classifications (child nodes): one predicts the response variable classification, and the other does not. This binary recursive splitting process is repeated for each child node until further splitting is no longer possible [11]. Over the years, as CART has developed, it has been increasingly used in the medical field [12,13,14,15], and in the smoking field, it is mainly used to identify high-risk populations for the use of tobacco substitutes [16], the combination of risk factors for smoking and the strongest predictive indicators [17], as well as the prediction of smoking cessation outcomes [18].

The present study

Overall, smoking is a risk factor for COVID-19 infection and severity. Prior studies have analyzed the influencing factors of smoking behavior during the pandemic, but these studies only explored the relationship between influencing factors and changes in smoking behavior. This study aims to address the limitations of these studies. Specifically, CART analysis was used to explore the factors that most deeply influence smoking behavior in the population and to analyze the interactions between this factor and other influencing factors to identify high-risk populations for increased smoking behavior.

Method

Data and procedure

The data used in this study is conducted in 23 provinces, 5 autonomous regions, and 4 municipalities directly under the central government from June 20, 2022, to August 31, 2022. In this time, China was still experiencing the peak of the COVID-19 pandemic, with an increase of 442 − 77,402 cases per day [19]. During the investigation, China implemented a dynamic “Zero-COVID” policy, taking prompt actions to contain the outbreak of COVID-19 in the local area [20]. The specific measures include medically lockdown those who have had close contact with confirmed cases; large-scale nucleic acid testing; citywide home quarantine; the use of electronic health codes when entering public places; travel restrictions; and advocating for mask-wearing in public spaces. In certain situations, staff will remind individuals to wear masks or they will be prohibited from entering. During the policy implementation, China rigorously enforced the policy, and the policy was well implemented [21, 27]. However, the results of this study suggest that lockdown is associated with a decrease in smoking behavior. This may be due to the inability to purchase cigarettes during the home quarantine period [7] and an increase in motivation to quit smoking due to an increased perception of the harm of COVID-19 [28].

According to the CART model, currently, the subgroup with a high acceptation degree of passive smoking, have no smoker smoked around them, and a length of smoked of 30 years or more has the highest smoking rate during the COVID-19 pandemic. The acceptation degree of passive smoking is the main determinant of smoking behavior during the pandemic. This may be because people are more attentive to personal health protection during the COVID-19 pandemic and are more sensitive to the perceived harmfulness of tobacco [29], which may lead to a lower acceptance of secondhand smoke [30], resulting in a reduction in smoking behavior.

According to the CART model, during the COVID-19 pandemic, people are more likely to smoke when they are in the presence of have no smoker smoked around, which is contrary to previous research results. Previous studies have shown that individuals are more likely to start smoking when family and friends around them smoke [31, 32]. This may be due to an increase in personal protection awareness during the COVID-19 pandemic. As COVID-19 primarily affects the respiratory system, wearing a mask is an important preventive measure against COVID-19 [33]. During the period of this study, China was still experiencing the peak of the COVID-19 pandemic [19]. Despite the presence of individual variations, due to the Chinese government’s advocacy for mask usage and the concurrent increase in public health awareness among the population, there is a high level of acceptance and compliance with mask-wearing during the COVID-19 pandemic [34]. Even in 2023, when the COVID-19 pandemic has largely subsided, residents continue to exhibit good mask-wearing habits [35]. When people remove their masks to smoke, others may become more attentive to wearing masks due to fear of contracting COVID-19. Thus, when people are not smoking around them, individuals may be more likely to smoke. This conclusion needs to be verified in other countries. This result is opposite to our Logistic regression results, which may be due to CART examining the interaction between variables, which is why CART is widely used in exploring risk factors [36, 37]. Additionally, due to nicotine dependence, those with a longer smoking history have stronger nicotine dependence and more severe withdrawal symptoms, making it harder for them to reduce smoking behavior [38]. Therefore, our study shows that individuals with a length of smoking of 30 years or more are more likely to smoke during the COVID-19 pandemic. The group with a length of smoking of 40 years or more is not significant in the multiple regression results but is included in the CART model. There are two possible reasons for this. On the one hand, CART has greater resistance to multicollinearity compared to other parametric methods [36, 37]. On the other hand, CART is a decision tree model that only considers which variables can better predict the increase in smoking behavior during the COVID-19 pandemic and form the best classification, without considering variable significance issues.

Having a chronic illness is also a significant predictor, as non-chronically ill individuals are more likely to smoke. Smoking is strongly associated with chronic diseases [39], and China’s disease spectrum has shifted towards chronic, non-communicable diseases [40]. Additionally, chronic illness patients have a higher severe disease rate after contracting COVID-19 [41, 42]. To reduce the harm of chronic diseases, doctors are more likely to advise chronic illness patients to quit smoking, and patients are also more likely to accept smoking cessation advice from doctors [43, 44].

Regarding these issues, first, more attention should be paid to long-term smokers, and more specialized smoking cessation help should be provided to them. For example, the smoking cessation clinic actively promoted in China is an effective method [45]. Secondly, for individuals who are more exposed to secondhand smoke, tobacco education should be strengthened to enhance awareness of the hazards of secondhand smoke. Moreover, due to the requirement to wear masks in public areas during the COVID-19 period, smoking behavior has also been reduced. Therefore, during the COVID-19 pandemic, the “mask protection” effect can be fully utilized to guide smoking cessation behavior. Even if individuals around them are not smoking, environmental smoke may still carry and spread the virus, so it is necessary to wear masks and avoid smoking. Finally, doctors and non-chronically ill patients should also raise awareness of smoking cessation. Tobacco causes great harm to human health, and doctors’ smoking cessation advice is feasible in promoting patient smoking cessation [46], making doctors an important candidate in promoting smoking cessation, and doctors should also actively provide smoking cessation help to non-chronically ill patients.

Finally, this study explored the high-risk groups for smoking, and future studies should also delve deeper into the triggers for smoking cessation to provide a guiding direction for tobacco control policies and to form a continuity study to enrich policy guidelines.

Strength and limitation

Our study conducted a national survey using quota sampling, which can balance differences between regions and reflect the situation nationwide. Secondly, we focused on smoking behavior during the COVID-19 period and comprehensively analyzed the factors that influence smoking behavior in the context of epidemic prevention and control. Finally, our study results further revealed the mutual interactions between the most important risk factors and other influencing factors, thus identifying the high-risk group for smoking during the COVID-19 period.

However, this study also has some limitations. First, the study is a cross-sectional survey and does not establish causal relationships. Second, there may be other risk factors that affect smoking behavior during the COVID-19 period that were not included in this study. Finally, the sensitivity of CART in this study was relatively low, probably because of the small number of smokers in this study, which was large sample size gap between smokers and non-smokers. But even so, the accuracy of CART was high.

Conclusion

In general, this study was based on a national sample and used CART analysis to explore the high-risk population for increased smoking behavior during the COVID-19 period. The results showed that people with a high acceptation degree of passive smoking, have no smokers smoked around, and a length of smoking of ≥ 30 years were the subgroups with the highest smoking behavior during the COVID-19 period. Acceptation degree of passive smoking was the strongest predictor of smoking behavior during the COVID-19 period. It is important to pay more attention to long-term smokers and non-chronic disease patients, raise awareness of the hazards of smoking and secondhand smoke, and take advantage of the “mask effect” during the epidemic period to reduce smoking behavior during the COVID-19 pandemic.