This data package, titled Data from: Cross-sectional personal network analysis of adult smoking in rural areas, includes several files. First, there are annonymized raw data files in .rds file format (ego_data.rds & alter_data.rds). Second, there is the R code that allow the replication of various statistical analyses. Interested parts may consult the R code as .pdf file format (Supplementary_Material_R_Code.pdf), .Rmd file format (that can be run to create the .pdf file format) and the .R file format (that can be accesed with R and RStudio). Moreover, the labels files are useful for recreating the Supplementary Material pdf file.

Readers should know that this dataset corresponds to the study (paper) Cross-sectional personal network analysis of adult smoking in rural areas.

The ego_data.rds file includes 20 variables by 76 observations (respondents) while the alter_data.rds file includes 46 variables by 1681 observations (social contacts). We collected this information by deploying a personal network analysis research design. Initially, we interviewed 83 respondents (dubbed egos). Due to missing data, we kept in the analysis 76 egos and dropped seven respondents. We recruited the respondents using a link-tracining sampling framework. We started from a number of six seeds. We interviewed the seeds then we asked them to recommend other people in the study. We continued in a referee-referral fashion until 83 interviews were completed. The study was performed in a small rural Romanian community (4124 residents): Lerești (Argeș county).

Our study was carried out in accordance with the recommendations, relevant guidelines, and regulations (specifically, those provided by the Romanian Sociologists Society, i.e., the professional association of Romanian sociologists). The research was performed in accordance with the Declaration of Helsinki. The research protocol was approved by a named institutional/licensing committee. Specifically, the Ethics Committee of the Center for Innovation in Medicine (InoMed) reviewed and approved all these study procedures (EC-INOMED Decision No. D001/09-06-2023 and No. D001/19-01-2024). All participants gave written informed consent. The privacy rights of the study participants were observed. The authors did not have access to information that could identify participants. Face-to-face interviews were collected between September 13 – 23, 2023, in Lerești, Romania. After each interview, information that could identity the participants were anonymized. Before conducting the interview, we provided each participant with a dossier containing informative materials about the project's objectives, how the data would be analyzed and reported, and their participation rights (e.g., the right to withdraw from the project at any time, even after the interview was completed). All study participants gave their written informed consent prior to enrolment in the study.

The variables in the ego_data.rds file are as follows:

(1) "networkCanvasEgoUUID" (unique alpha numeric code for each observation);

(2) "ego_age" (the age of each study participant);

(3) "ego_age.cen" (the age of each study participant, centered);

(4) "ego_educ_b" (the education of each ego, binary);

(5) "ego_educ_f" (the education of each ego, educational achievement);

(6) "ego_marital.s_f" (the marital status of each ego);

(7) "ego_occupation.cat2_f" (the occupation of each ego);

(8) "ego_occupation_b" (the occupation of each ego, unemployed vs employed);

(9) "ego_relstatus_b" (whether the ego is in a relationship or not);

(10) "ego_sex_f" (the sex of the ego assigned at birth; male & female);

(11) "ego_sex_n" (the sex of the ego assigned at birth; 0 = male & 1 = female);

(12) "ego_smk_status_b1" (smoking status: 1 smoking, 0 others);

(13) "ego_smk_status_b2" (smoking status: 1 former smoker, 0 others);

(14) "ego_smk_status_b3" (smoking status: 1 not a smoker, 0 others);

(15) "ego_smkstatus_f" (smoking status: former smoker, never-smoker, non-smoker (smoked too little), occasional smoker, smoker);

(16) "ego_smoking_3cat" (smoking status: non-smoker, former smoker, smoker);

(17) "net.size" (number of social contacts, alters, that were elicited by an ego);

(18) "net.components" (number of strong components in the personal network);

(19) "net.deg.centralization" (personal network degree centralization);

(20) "net.density" (personal network density).

The variables in the alter_data.rds file are as follows:

(1) "alter_age" (the age of the alter);

(2) "alter_age.cen" (the age of the alter - centered);

(3) "alter_btw" (alter's betweenness score);

(4) "alter_btw.cen" (alter's betweenness score - centered);

(5) "alter_deg" (alter's degree score);

(6) "alter_deg.cen" (alter's degree score - centered);

(7) "alter_educ_b" (alter's education);

(8) "alter_educ_f" (alter's education);

(9) "alter_marital.s_f" (alter's marital status);

(10) "alter_relstatus_b" (alter's marital status - binary variable);

(11) "alter_sex_f" (alter's sex assigned at birth);

(12) "alter_sex_n" (alter's sex assigned at birth; 1 - female; 0 - male);

(13) "alter_smk_status_b1" (alter's smoking status; 1 smoker, 0 others);

(14) "alter_smk_status_b2" (alter's smoking status; 1 former smoker, 0 others);

(15) "alter_smk_status_b3" (alter's smoking status; 1 non-smoker, 0 others);

(16) "alter_smoking_3cat" (alter's smoking status: three categories - smoker, non-smoker, former smoker);

(17) "assortativity_score_fsmoker" (assortativity score for alter, former smoker);

(18) "assortativity_score_nsmoker" (assortativity score for alter, non-smoker);

(19) "assortativity_score_smoker" (assortativity score for alter, smoker);

(20) "ego.alter_meet_f" (ego's meeting frequency with alter);

(21) "ego_alter_meet_b" (ego's meeting frequency with alter, binary variable);

(22) "ego.alter_meet_n" (ego's meeting frequency with alter, numerical codes);

(23) "alter_rel.w.ego_f" (type of alters in an ego's network);

(24) "networkCanvasUUID" (alpha numeric code for alter);

(25) "networkCanvasEgoUUID" (alpha numeric code for ego);

(26) "ego_smkstatus_f" (smoking status: former smoker, never-smoker, non-smoker (smoked too little), occasional smoker, smoker);

(27) "ego_smoking_3cat" (three categories, smoking status: former smoker, non-smoker, smoker);

(28) "ego_type_fsmk" (former smoking egos by type of ego-alter relationship);

(29) "ego_type_nsmk" (non smoking egos by type of ego-alter relationship);

(30) "ego_type_smk" (smoking egos by type of ego-alter relationship);

(31) "ego_sex_f" (ego's sex, binary);

(32) "ego_sex_n" (ego's sex, numerical code, 1 female, 0 male);

(33) "ego_educ_b" (ego's education, binary variable)

(34) "ego_age" (ego's age)

(35) "ego_age.cen" (ego's age, centered)

(36) "ego_relstatus_b" (ego's marital status, binary variable)

(37) "ego_occupation_b" (ego's employment status, binary variable)

(38) "net.components" (number of strong components in the personal network)

(39) "net.deg.centralization" (degree centralization score in the personal networ)

(40) "net.density" (density score in the personal network)

(41) "prop_fsmokers" (proportion of former smokers in the personal network - alters)

(42) "prop_fsmokers.cen" (proportion of former smokers in the personal network, centered- alters)

(43) "prop_nsmokers" (proportion of non-smokers in the personal network- alters)

(44) "prop_nsmokers.cen" (proportion of non-smokers in the personal network, centered- alters)

(45) "prop_smokers" (proportion of smokers in the personal network- alters)

(46) "prop_smokers.cen" (proportion of smokers in the personal network, centered- alters)

ABSTRACT Smoking is considered a chronic disease and one of the leading causes of preventable death in the world. The quality of life is an important measure of health impact and its correlation with nicotine dependence levels and smoking is unclear. We evaluated the quality of life of smokers and its correlation with smoke load and the nicotine dependence level. Smokers of both sexes and with no diagnosis of clinical diseases were included in this study. We evaluated their quality of life and level of nicotine dependence through questionnaires. The sample consisted of 48 individuals, 27 women and 21 men. There was a negative correlation between vitality and the amount of years these individuals have smoked (p=0.009;r=-0.27), as well as the general health condition and pack/years (p=0.02; r=-0.23), and the current amount of cigarettes consumed per day (p=0.006;r=-0.29). We can also observe a negative correlation between functional capacity and the Fagerström questionnaire score (p=0.004;r=-0.3). We concluded that the smoke load and the nicotine dependence levels were related to worse quality of life indices of the smoking population.

Parameter values from the simulation for random interaction across cohorts.

The metadata set does not comprise any description or summary. The information has not been provided.

2 Introduction Smoking habits may change during COVID-19 pandemic and this information is relevant to improve COVID-19 prevention and control measures for smoking population. The aim of the study was to analyze the consumption of tobacco, levels of nicotine dependence and motivation for smoke cessation during COVID-19 pandemic in Brazil. Also to identify previous knowledge of smokers about use of tobacco and complications from COVID-19. Methods In this survey, observational prospective study, 122 smokers responded an online form (Google Forms). Between April and May 2020 (M1), patients provided general data and history of smoking habits. Fagerstrom and Wisconsin scale were also measured. Participants answered questions about prior knowledge of smoking and complications of COVID-19. Participants answered how pandemic period is influencing their habits and consumption of tobacco. After 30 to 45 days (M2), the same questionnaires were reapplied. Results Daily consumption of cigarettes did not change (p=0.85). In motivation for smoking cessation, no differences were identified between M1 and M2 (p=0.17), also for the level of nicotine dependence (Fagerstrom) (p=0.68) and nicotine withdrawal symptoms (Wisconsin Scale) (p=0.85). We identified a correlation between cigarettes/day before pandemic with motivation for smoking cessation (r= 0,19; p= 0,03) and nicotine dependence level (r= 0,61; p= P<0.001). No significant correlations were observed between load consumption in M1 (during pandemic) and motivation (r= 0,13; p= 0,12). Although a significant association were observed with nicotine dependence level (r= 0,69, p= P<0.001). Conclusions We conclude that consumption, motivation, and levels of nicotine dependence were not modified during the COVID-19 pandemic in Brazil.

BackgroundSmoking is a modifiable risk factor for Alzheimer’s disease (AD). However, smoking-related effects on intrinsic brain activity in high-risk AD population are still unclear.ObjectiveWe aimed to explore differences in smoking effects on brain function between healthy elderly and amnestic mild cognitive impairment (aMCI) patients using ReHo mapping.MethodsWe identified 64 healthy elderly controls and 116 aMCI patients, including 98 non-smoking and 18 smoking aMCI. Each subject underwent structural and resting-state functional MRI scanning and neuropsychological evaluations. Regional homogeneity (ReHo) mapping was used to assess regional brain synchronization. After correction for age, gender, education, and gray matter volume, we explored the difference of ReHo among groups in a voxel-wise way based on analysis of covariance (ANCOVA), followed by post hoc two-sample analyses (p < 0.05, corrected). Further, we correlated the mean ReHo with neuropsychological scales.ResultsThree groups were well-matched in age, gender, and education. Significant ReHo differences were found among three groups, located in the left supramarginal gyrus (SMG) and left angular gyrus (AG). Specifically, non-smoking aMCI had lower ReHo in SMG and AG than smoking aMCI and controls. By contrast, smoking aMCI had greater AG ReHo than healthy controls (p < 0.05). Across groups, correlation analyses showed that left AG ReHo correlated with MMSE (r = 0.18, p = 0.015), clock drawing test (r = 0.20, p = 0.007), immediate recall (r = 0.36, p < 0.001), delayed recall (r = 0.34, p < 0.001), and auditory verbal learning test (r = 0.20, p = 0.007).ConclusionSmoking might pose compensatory or protective effects on intrinsic brain activity in aMCI patients.

aSNPs located within 500 kb of given gene.bOdds ratios for current smokers adjusted for sex, age, family history of smoking-related cancers, and pack years smoked; odds ratios for former smokers adjusted for sex, age, family history, and age at smoking cessation; odds ratios for never smokers adjusted for sex, age, family history, and exposure to secondhand tobacco smoke.cBayes factor greater than or equal to 3; included in the final model.

IntroductionEpidemiologically, cigarette smoking is a well-known risk factor for the pathogenesis of rheumatoid arthritis (RA). However, there has been few plausible explanations why cigarette smoking aggravated RA. We investigated the causal effect of smoking in experimental model of arthritis development.MethodsDuring induction of experimental arthritis with collagen challenge, mice were exposed to a smoking environment with 3R4F cigarettes. Generated smoke was delivered to mice through a nose-only exposure chamber (ISO standard 3308). Human cartilage pellet was challenged by cigarette smoke extract to identify citrullinating potential in vitro.ResultsCigarette smoke exacerbated arthritis in a collagen-induced arthritis (CIA) model. Exposure to smoke accelerated the onset of arthritis by 2 weeks compared to the conventional model without smoke. Citrullination of lung tissue as well as tarsal joints were revealed in smoke-aggravated CIA mice. Interestingly, tracheal cartilage was a core organ regarding intensity and area size of citrullination. The trachea might be an interesting organ in viewpoint of sharing cartilage with joint and direct smoke exposure. Anti-CCP antibodies were barely detected in the serum of CIA mice, they were significantly elevated in cigarette smoke group. Citrullinated antigens were increased in the serum of smoke-exposed mice. Lastly, a cigarette smoke extract enhanced human cartilage citrullination in vitro.ConclusionsMissing link of arthritic mechanism between smoke and RA could be partially explained by tracheal citrullination. To control tracheal cartilage citrullination may be beneficial for preventing arthritis development or aggravation if cigarette smoke is becoming a risk factor to pre-arthritic individual.

Tobacco smoking is one of the largest preventable causes of death and disease in Australia. In 2017-18, 13.8% of adults aged 18 years and over were daily smokers (2.6 million people), down from 14.5% in 2014-15. The decrease is a continuation of the trend over the past two decades, in 1995, 23.8% of adults were daily smokers. \r \r Additionally the proportion of adults who have never smoked is increasing over time, from 49.4% in 2007-08 to 52.6% in 2014-15 and 55.7% in 2017-18.

Definition of abbreviations: ARIC  =  Atherosclerosis Risk in Communities; B58C  =  British 1958 Birth Cohort; BHS  =  Busselton Health Study; CARDIA  =  Coronary Artery Risk Development in Young Adults; CHS  =  Cardiovascular Health Study; FHS  =  Framingham Heart Study; Health ABC  =  Health, Aging, and Body Composition; KORA  =  Cooperative Health Research in the Region of Augsburg; LBC1921  =  Lothian Birth Cohort 1921; LBC1936  =  Lothian Birth Cohort 1936; PIVUS  =  Prospective Investigation of the Vasculature in Uppsala Seniors; RS  =  Rotterdam Study; SAPALDIA  =  Swiss Study on Air Pollution and Lung Diseases in Adults; SE  =  standard error; SHIP  =  Study of Health in Pomerania.*Data shown are the effect estimates (β and SE) of the time and smoking-by-time interaction terms in the preliminary mixed effects model fully adjusted for all specified variables except the SNP terms. Time represents the rate of change in FEV1 in never smokers and the smoking-by-time interaction term represents the effects of the other three smoking patterns on the rate of change in FEV1, compared with never smokers. Smoking categories are defined as persistent (smoke throughout follow-up), intermittent (stop and/or start smoking during follow-up) and former (smoke only prior to start of follow-up).†Effect estimates in smoking categories are added to estimates in never smokers to compute the actual rate of change in each group (for example, in ARIC, the point estimate of the rate of change in FEV1 in persistent smokers was −14.0 − 12.4  =  −26.4 mL/year).

IntroductionMigrant populations usually report higher smoking rates than locals. At the same time, people with a migration background have little or no access to regular smoking cessation treatment. In the last two decades, regular smoking cessation courses were adapted to reach out to Turkish- and Albanian-speaking migrants living in Switzerland. The main aims of the current study were (1) to analyze the effects of an adapted smoking cessation course for Turkish- and Albanian-speaking migrants in Switzerland on attitudes toward smoking and smoking behavior; and (2) to elucidate whether changes in attitudes toward smoking were associated to changes in smoking behavior in the short- and in the long-term.MethodsA total of 59 smoking cessation courses (Turkish: 37; Albanian: 22) with 436 participants (T: 268; A: 168) held between 2014 and 2019 were evaluated. Attitudes toward smoking and cigarettes smoked per day were assessed at baseline and 3-months follow-up. One-year follow-up calls included assessment of cigarettes smoked per day. Data were analyzed by means of structural equation modeling with latent change scores.ResultsParticipation in an adapted smoking cessation course led to a decrease of positive attitudes toward smoking (T: β = −0.65, p < 0.001; A: β = −0.68, p < 0.001) and a decrease of cigarettes smoked per day in the short-term (T: β = −0.58, p < 0.001; A: β = −0.43, p < 0.001) with only Turkish-speaking migrants further reducing their smoking in the long-term (T: β = −0.59, p < 0.001; A: β = −0.14, p = 0.57). Greater decreases in positive attitudes were associated with greater reductions of smoking in the short-term (T: r = 0.39, p < 0.001; A: r = 0.32, p = 0.03), but not in the long-term (T: r = −0.01, p = 0.88; A: r = −0.001, p = 0.99).ConclusionThe adapted smoking cessation courses fostered changes in positive attitudes toward smoking that were associated with intended behavior change in the short-term. The importance of socio-cognitive characteristics related to behavior change maintenance to further increase treatment effectiveness in the long-term is discussed.

Dataset of a mini-review assessing the use of behavior change theories in clinical trials assessing behavioral interventions for smoking cessation in healthy adults.

Summary dataset (.csv file) and R script (.R file) for the manuscript entitled:

Smoking does not accelerate leukocyte telomere attrition: a meta-analysis of 18 longitudinal cohorts.

The column names are explained at the beginning of the R script.

The objective of this study is to evaluate whether a combination of financial incentives and deposit contracts improves cessation among low- to moderate-income smokers. The estimated treatment effects on cessation are positive, but imprecise, with confidence intervals containing effect sizes estimated by prior studies of financial incentives alone and deposit contracts alone. A combined incentives and deposit contract program for Medicaid enrollees, with incentives offering up to $300 for smoking cessation and use of support services, produced a positive but imprecisely estimated effect on biochemically-verified cessation relative to usual care and with no detectable difference in cessation rates between different treatment arms.

Three datasets described in manuscript are preprocessed data (imputed). One meta-data file for all (variables are the same), and R codes used for analyses can be found here. If you want to replicate main analyses of the findings, you can use the R script in 'Analyses_EMA_Data.html' and two pre-processed datasets: 'dfimp_smokers.csv' and 'dfimp_nonsmokers.csv'. The follow-up analyses were done on dataset dfimp_smokerswith.csv'. Please see 'Follow_up_Analyses_EMA_Data.html'. This should enable replication. R scripts from 'Preparation_descriptives_EMA_Data.html' were preparatory steps with some descriptive statistics on non-anonymized dataset that is not shared, this does provide some insight into construction and presented descriptives. Link to same and other materials is: https://doi.org/10.17605/OSF.IO/J2WQK

Note: values in bold have 95% CI that do not cross 1.00;*IAT analyses control for IAT order effects.

Opinions on health / details on smoking behaviour / how to stop smoking / buying behaviour / opinions on effect on health of smoking / situation wherein respondent smokes / opinions on smoking / smoking behaviour of relatives / relations / friends / leisure behaviour, hobbies / reading behaviour ( in particular regarding advertisements ) / knowledge of special advertisements for cigarettes, cigars etc. / knowledge of smoking behaviour of vips / image of forms of smoking / situations in which r. could have stopped smoking / neuroticism. Background variables: basic characteristics/ occupation/employment/ income/capital assets/ readership, mass media, and 'cultural' exposure

Predictors of correctly identifying the nicotine source of cigarettes, e-cigarettes, smokless tobacco, and nicotine pouches and correctly identifying the content of tobacco-free nicotine e-cigarettes.

Health Related Correlates of Smoking in Old Order Amish from Lancaster County

Cigarette smoking and SNPs sequences of 142 controls (C), 55 cardiovascular artery disease (CAD), 15 urinary bladder cancer (UBCa) and 31 lung cancer (LCa) patients.