Prevalence and predictors of active and passive smoking in Saudi Arabia: A survey among attendees of primary healthcare centers in Riyadh
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».