An early-stage epidemic: a systematic review of correlates of smoking among Chinese women

Int J Behav Med. 2014 Aug;21(4):653-61. doi: 10.1007/s12529-013-9367-1.

Abstract

Background: Despite the historically low smoking prevalence among Chinese women, there is a trend of future increase.

Purpose: We systematically reviewed the correlates of smoking among Chinese girls and women.

Method: We conducted a systematic review of literature on correlates of smoking among Chinese women using Medline and China Academic Journals databases. Following the PRISMA statement, two investigators independently searched for literature, identified and reviewed papers, assessed the quality of the papers, and extracted information. The characteristics of studies and correlates of smoking were synthesized separately for youth and adults.

Results: A total of 15 articles (11 on adults, 4 on youth) met the inclusion criteria. Based on these studies, peer smoking was the most consistent correlate of smoking among Chinese girls. Among Chinese women, partner smoking, job-related stress, and exposure to cigarettes made for women were consistent correlates of smoking. Knowledge of harms and negative attitudes towards smoking were found to be negatively associated with smoking.

Conclusion: Overall, the evidence base for smoking among Chinese women is limited. Although smoking among Chinese women is still at an early stage, it is becoming more prevalent among specific population subgroups, such as rural-to-urban migrant workers. Although further research is needed, findings from the current study provide a roadmap for research and policy on prevention of smoking among Chinese girls and women.

Publication types

  • Research Support, N.I.H., Extramural
  • Review
  • Systematic Review

MeSH terms

  • Adolescent
  • Adult
  • Child
  • China / epidemiology
  • Female
  • Humans
  • Middle Aged
  • Occupations / statistics & numerical data
  • Peer Group*
  • Prevalence
  • Rural Population
  • Smoking / epidemiology*
  • Spouses / statistics & numerical data
  • Young Adult