Tobacco Use and Uptake of COVID-19 Vaccinations in Finland: A Population-Based Study.

INTRODUCTION: People who smoke are at higher risk of Coronavirus Disease-2019 (COVID-19) hospitalizations and deaths and might benefit greatly from high COVID-19 vaccination coverage. Studies on tobacco use and COVID-19 vaccine uptake in the general population are lacking. AIMS AND METHODS: We conducted a cohort study utilizing linked data from 42 935 participants from two national surveys in Finland (FinSote 2018 and 2020). Exposures were smoking and smokeless tobacco (snus) use. The primary outcome was the uptake of two COVID-19 vaccine doses. Secondary outcomes were the uptake of one COVID-19 vaccine dose; three COVID-19 vaccine doses; time between the first and second dose; and time between the second and third dose. We examined the association between tobacco use and COVID-19 vaccine uptake and between-dose spacing in Finland. RESULTS: People who smoke had a 7% lower risk of receiving two COVID-19 vaccine doses (95% confidence interval [CI] = 0.91; 0.96) and a 14% lower risk of receiving three doses (95% CI = 0.78; 0.94) compared to never smokers. People who smoked occasionally had a lower risk of receiving three vaccine doses. People who currently used snus had a 28% lower uptake of three doses (95% CI = 0.56; 0.93) compared to never users but we did not find evidence of an association for one or two doses. We did not find evidence of an association between tobacco use and spacing between COVID-19 vaccine doses. CONCLUSIONS: People who smoke tobacco products daily, occasionally, and use snus had a lower uptake of COVID-19 vaccines. Our findings support a growing body of literature on lower vaccination uptake among people who use tobacco products. IMPLICATIONS: People who smoke or use snus might be a crucial target group of public health efforts to increase COVID-19 vaccinations and plan future vaccination campaigns. CLINICAL TRIALS REGISTRATION NUMBER: NCT05479383.

Uncovering Sociological Effect Heterogeneity Using Tree-Based Machine Learning.

Individuals do not respond uniformly to treatments, such as events or interventions. Sociologists routinely partition samples into subgroups to explore how the effects of treatments vary by selected covariates, such as race and gender, on the basis of theoretical priors. Data-driven discoveries are also routine, yet the analyses by which sociologists typically go about them are often problematic and seldom move us beyond our biases to explore new meaningful subgroups. Emerging machine learning methods based on decision trees allow researchers to explore sources of variation that they may not have previously considered or envisaged. In this article, the authors use tree-based machine learning, that is, causal trees, to recursively partition the sample to uncover sources of effect heterogeneity. Assessing a central topic in social inequality, college effects on wages, the authors compare what is learned from covariate and propensity score-based partitioning approaches with recursive partitioning based on causal trees. Decision trees, although superseded by forests for estimation, can be used to uncover subpopulations responsive to treatments. Using observational data, the authors expand on the existing causal tree literature by applying leaf-specific effect estimation strategies to adjust for observed confounding, including inverse propensity weighting, nearest neighbor matching, and doubly robust causal forests. We also assess localized balance metrics and sensitivity analyses to address the possibility of differential imbalance and unobserved confounding. The authors encourage researchers to follow similar data exploration practices in their work on variation in sociological effects and offer a straightforward framework by which to do so.