BMC Public Health (Aug 2024)

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

  • Siyuan Liu,
  • Haozheng Zhou,
  • Wenjun He,
  • Jiao Yang,
  • Xuanhao Yin,
  • Sufelia Shalayiding,
  • Na Ren,
  • Yan Zhou,
  • Xinyi Rao,
  • Nuofan Zhang,
  • Man Xiong,
  • Yueying Wang,
  • Wenfu Yang,
  • Yibo Wu,
  • Jiangyun Chen

DOI
https://doi.org/10.1186/s12889-024-18559-x
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 12

Abstract

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Abstract Background 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. Methods 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. Findings 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. Conclusion 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. Highlights 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.

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