Risk profiling of tobacco epidemic and estimated number of smokers living in China: a cross-sectional study based on PBICR

• Published in: BMC public health 2024-08, Vol.24 (1), p.2219-12
• Main Authors: Liu, Siyuan, Zhou, Haozheng, He, Wenjun, Yang, Jiao, Yin, Xuanhao, Shalayiding, Sufelia, Ren, Na, Zhou, Yan, Rao, Xinyi, Zhang, Nuofan, Xiong, Man, Wang, Yueying, Yang, Wenfu, Wu, Yibo, Chen, Jiangyun
• Format: Article
• Language: English
• Subjects: Adult; Bayes Theorem; Bayesian analysis; Bayesian geostatistical models; China; China - epidemiology; Cigarette smoking; Cross-Sectional Studies; Developing countries; Distribution; Drug addiction; Economic development; Epidemics; Exposure; Feature selection; Female; Forecasts and trends; Gender; Geographical distribution; Geostatistics; Humans; Inequalities; LDCs; Male; Mathematical models; Mental disorders; Middle Aged; Municipalities; Passive smoking; Population; Predictions; Prevalence; Provinces; Public health; Questionnaires; Regions; Remote sensing; Risk; Risk Assessment; Risk factors; Smoke; Smokers; Smokers - statistics & numerical data; Smoking; Smoking - epidemiology; Smoking cessation; Smoking prevalence; Spatial Analysis; Spatial data; Spatial inequality; Spatial variations; Statistics; Tobacco; Tobacco smoke; Tobacco Smoke Pollution - statistics & numerical data; Variables; Young Adult
• ISSN: 1471-2458; 1471-2458
• DOI: 10.1186/s12889-024-18559-x

Evidence on the prevalence of smoking in China remains insufficient, with most previous studies focusing on a single region. However, smoking prevalence exhibits significant inequalities across the entire country. This study aimed to evaluate the risk of tobacco prevalence across the country, taking into account spatial inequalities. The data used in this study were collected in 23 provinces, 5 autonomous regions, and 4 municipalities directly under the central government in 2022. Large population survey data were used, and a Bayesian geostatistical model was employed to investigate smoking prevalence rates across multiple spatial domains. Significant spatial variations were observed in smokers and exposure to secondhand smoke across China. Higher levels of smokers and secondhand smoke exposure were observed in western and northeastern regions. Additionally, the autonomous region of Tibet, Shanghai municipality, and Yunnan province had the highest prevalence of smokers, while Tibet, Qinghai province, and Yunnan province had the highest prevalence of exposure to secondhand smoke. We have developed a model-based, high-resolution nationwide assessment of smoking risks and employed rigorous Bayesian geostatistical models to help visualize smoking prevalence predictions. These prediction maps provide estimates of the geographical distribution of smoking, which will serve as strong evidence for the formulation and implementation of smoking cessation policies. Our study investigated the prevalence of smokers and exposure to secondhand smoke in different spatial areas of China and explored various factors influencing the smoking prevalence. For the first time, our study applied Bayesian geostatistical modeling to generate a risk prediction map of smoking prevalence, which provides a more intuitive and clear understanding of the spatial disparities in smoking prevalence across different geographical regions, economic levels, and development status. We found significant spatial variations in smokers and secondhand smoke exposure in China, with higher rates in the western and northeastern regions.