JOURNAL ARTICLE

Social Participation and Persistent Smoking Among Older Chinese With Smoking-Related Morbidity.

  • Published In: Journals of Gerontology Series B: Psychological Sciences & Social Sciences, 2023, v. 78, n. 9. P. 1572 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Gao, Manjing; Park, Soojin; Lee, Chioun 3 of 3

Abstract

This article examines the national prevalence and social determinants of persistent smoking—defined as continued smoking despite having smoking-related chronic diseases—among Chinese adults aged 45 to 80, using data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018. Findings indicate that about 24% of older men and 3% of older women persistently smoke, with higher risks among younger, nonpartnered, nonretired, and less educated individuals. Social participation is significantly associated with persistent smoking, but the effect varies by activity type: sedentary social activities like playing Mahjong, chess, or cards increase the likelihood of persistent smoking, whereas physical social activities such as community-organized dancing, fitness, and qigong are linked to reduced risk. The study highlights the need for culturally tailored public health interventions addressing sociocultural factors and targeting specific social contexts to reduce persistent smoking among older Chinese adults with chronic illnesses.

Additional Information

  • Source:Journals of Gerontology Series B: Psychological Sciences & Social Sciences. 2023/09, Vol. 78, Issue 9, p1572
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2023
  • ISSN:1079-5014
  • DOI:10.1093/geronb/gbad080
  • Accession Number:170744897
  • Copyright Statement:Copyright of Journals of Gerontology Series B: Psychological Sciences & Social Sciences is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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