Search

Previous research yielded conflicting results on the association between cigarette smoking and risk of SARS-CoV-2 infection. Since the prevalence of smoking is high globally, the study of its impact on COVID-19 pandemic may have considerable implications for public health. This study is the first to investigate the association between the SARS-CoV-2 antibody sero-positivity and biochemically verified smoking status, to refine current estimates on this association. SARS-CoV-2-specific IgG and serum cotinine levels (a well-known marker of tobacco exposure) were assessed in a large sero-epidemiological survey conducted in the town of Troina (Sicily, Italy). A propensity score matching was carried out to reduce the effect of possible factors on SARS-CoV-2 infection risk among study participants. Of the 1785 subjects included in our study, one-third was classified as current smokers, based on serum cotinine levels. The overall proportion of subjects with positive serology for SARS-CoV-2 IgG was 5.4%. The prevalence of SARS-CoV-2 antibody positivity and previous COVID-19 diagnosis were reduced in smokers. This reduced prevalence persisted after adjusting for possible confounders (such as sex, age, previous infection, chronic conditions, and risk group) at regression analyses, and the point estimates based on the PS-matched models resulted consistent with those for the unmatched population. This study found a lower proportion of positive SARS-CoV-2 serology among current smokers, using direct laboratory measures of tobacco exposure and thus avoiding possible bias associated with self-reported smoking status. Results may also serve as a reference for future clinical research on potential pharmaceutical role of nicotine or nicotinic-cholinergic agonists against COVID-19. [ABSTRACT FROM AUTHOR]

This article reviews contemporary issues in survey research, connecting established methods to innovative tools and technologies like real-time sensors and computer vision. This link takes into account the idea about the "organical" nature of Big Data, which represents a challenge toward a modernization of population studies in the light of technological innovations. The adopted dominant paradigm of data gathering is web survey (computer-assisted web interviewing), which is explored through the formalization of chain-referral methods as respondent-driven sampling. The general orientation is toward a computational social science approach. Weaknesses of such methodology is studied and solutions are provided with insights from empirical research on panel management. Contribution from gamification techniques is critically discussed. [ABSTRACT FROM AUTHOR]