Prevalence and predictors of active and passive smoking in Saudi Arabia: A survey among attendees of primary healthcare centers in Riyadh
Bibliographic record
Abstract
INTRODUCTION: Smoking remains a leading cause of preventable diseases worldwide, including cancer, heart disease, and respiratory disorders. In Saudi Arabia, the prevalence of smoking has been increasing, particularly among men and adolescents. However, limited research has focused on the prevalence and predictors of active and passive smoking in the region, particularly within the adult population. Understanding the sociodemographic and health-related factors that influence smoking behaviors can inform tobacco control strategies. The aim of the study is to investigate the prevalence and predictors of active and passive smoking among adults attending primary healthcare centers in Riyadh, Saudi Arabia. METHODS: This cross-sectional study was conducted in Riyadh, Saudi Arabia, between March and July 2023, targeting patients aged ≥18 years who visited primary healthcare centers. Multistage cluster sampling was used to select 48 healthcare centers from an initial list of 103 centers. Participants were recruited from the waiting areas, and a total of 14239 individuals completed an electronic questionnaire. The questionnaire assessed sociodemographic information, smoking behavior, and health conditions. Data were analyzed using SPSS version 26.0 for Windows, with Descriptive statistics and multivariable logistic regression analyses to identify factors associated with active and passive smoking. Statistical significance was set at p<0.05. RESULTS: The prevalence of active smoking was 17.3% and passive smoking was 16.5% among the participants. The multivariate logistic regression analysis identified several key predictors for both active and passive smoking. Male gender, larger household size, and lower income were significant factors for active smoking, with individuals in larger households (3-5 members) (AOR=1.48; 95% CI: 1.22-1.79) and those earning between 10000-19000 Saudi Arabian Riyals (AOR=0.56; 95% CI: 0.41-0.75) showing higher odds. Perceived health status also played a role, with those reporting good health (AOR=2.96; 95% CI: 1.68-5.25) having higher odds of smoking. Males were more likely to engage in active smoking compared to females (AOR=2.59; 95% CI: 2.23-3.02). For passive smoking, similar trends were observed, with larger households (AOR=2.27; 95% CI: 1.387-3.721) and male gender (AOR=2.59; 95% CI: 2.23-3.02) being significant predictors. CONCLUSIONS: The study highlights male gender, larger household size, lower income, and better perceived health status as significant predictors for both active and passive smoking behaviors in Riyadh, Saudi Arabia. These factors should be prioritized in public health strategies aimed at reducing tobacco exposure and promoting cessation. Further research is needed to explore the broader societal factors contributing to smoking behavior and exposure to secondhand smoke in the country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".