Identifying credible attribution sources for cigarette health warning labels in China: results from a cross-sectional survey of Chinese adults

Abstract

ObjectiveThe Framework Convention on Tobacco Control recommends health warning labels (HWLs) include an attribution source. Little is known regarding the perceived credibility and effectiveness of different message sources. This study examined perceptions of four HWL attribution sources among adults in China – the world’s largest consumer of cigarettes.DesignCross-sectional experimental survey design.ParticipantsData were collected in 2017 from a convenience sample of 1999 adults across four cities in China; 80% of the sample were current smokers.Main outcome measuresParticipants viewed four versions of the same HWL, each with a different attribution source: the China Center for Disease Control (ref. group); the regulatory arm of China’s domestic tobacco company (STMA); Liyuan Peng, China’s first lady; and the WHO. Respondents indicated which HWL was the most: (1) credible, (2) effective at making people quit and (3) effective at preventing youth initiation.ResultsMultinomial logistic regression models estimated adjusted relative risk ratios (aRRRs) of the three outcomes. Controlling for demographics and smoking status, HWLs attributed to STMA and Liyuan Peng, respectively, were perceived as significantly less credible (aRRR=0.81, p<0.001; aRRR=0.31, p<0.001), less effective at making people quit (aRRR=0.46, p<0.001; aRRR=0.24, p<0.001) and less effective at preventing young smoking (aRRR=0.52, p<0.001; aRRR=0.39, p<0.001) than the China CDC HWL. There were no significant differences in perceived effectiveness of between the WHO and China CDC HWLs. Participants viewed the WHO HWL as significantly more credible (aRRR=1.21, p<0.001) than the China CDC HWL.ConclusionResults suggest the unique role of health organisations in conveying smoking-related messages that appear credible and effective at motivating others to quit smoking or never start smoking in China. Findings can inform global recommendations regarding HWL attribution sources.