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. Author manuscript; available in PMC: 2018 Jun 1.
Published in final edited form as: Prev Med. 2017 Feb 9;99:94–98. doi: 10.1016/j.ypmed.2017.01.021

Believability of New Diseases Reported in the 2014 Surgeon General’s Report on Smoking: Experimental Results from a National Survey of US Adults

Diane B Francis 1, Seth M Noar 2,3, Sarah D Kowitt 4, Kristen L Jarman 3, Adam O Goldstein 3,5
PMCID: PMC5687516  NIHMSID: NIHMS855000  PMID: 28189803

Abstract

Background

Tobacco use is the leading cause of preventable disease and death globally. The 2014 Surgeon General’s Report included new diseases linked to smoking, including liver and colon cancer, diabetes and tuberculosis. As more diseases are linked to smoking, which diseases should we communicate to the public and what message source has the most impact?

Methods

Data were collected through a nationally representative phone survey of US adults (N=5,014), conducted from September 2014 through May 2015. We experimentally randomized participants to a 2 (new smoking disease messages- liver and colon cancers compared to diabetes and tuberculosis) by 4 (message sources-CDC, FDA, Surgeon General, and none) experiment. The outcome was message believability.

Results

About half the sample was female (51.5%) and 17.8% were a current smoker. Overall, 56% of participants said the messages were very believable. Cancer messages (liver and colon cancer) were significantly more believable than messages about chronic disease (tuberculosis and diabetes), 61% vs. 52%. Smokers were less likely to report both sets of new disease messages as very believable compared to non-smokers. Significantly more smokers intending to quit (44.5%) found the messages to be very believable compared to smokers not intending to quit (22.6%). Believability did not differ by message source.

Conclusion

Important differences exist in believability of disease messages about new tobacco-related information. Messages emphasizing the causal link between smoking and new diseases should be considered for use in mass media campaigns.

Keywords: smoking, campaigns, health communication, message, source

Introduction

Mass media campaigns are integral to tobacco control efforts, and they have the potential to prevent initiation and reduce the prevalence of tobacco use.1–3 Research suggests messages about the negative health consequences of smoking can be effective at influencing message processing and quit behaviors.4 Negative health consequences of smoking include diseases such as lung, bladder and stomach cancers, cardiovascular disease, respiratory disease, and reproductive complications.1,5 Smoking can also exacerbate chronic diseases such as pneumonia and respiratory tract infections.1,6 Smoking can further result in increased risk of premature mortality.5

The 2014 Surgeon General’s Report included ten new diseases causally linked to smoking, including liver and colon cancers, diabetes and tuberculosis.5i While previous Surgeon General’s Reports have reviewed some of those diseases (diabetes, for example),1 the 2014 report was the first to establish a direct, causal relationship between those diseases and smoking.5 As more diseases are linked to smoking, which diseases should we communicate to the public? Studies from other tobacco prevention research suggests when information about new diseases linked to smoking is communicated to the public, increases in awareness,7 smoking-related knowledge,8 risk perceptions,9 and quit behaviors follow.9 Messages about new diseases can potentially draw upon prior knowledge and beliefs to persuade smokers that smoking is even more dangerous than previously thought. Thus, it is important to investigate which messages about new diseases causally linked to smoking the public finds most believable.

Message believability, a component of the elaboration likelihood model,10,11 has been shown to influence perceived and actual message effectiveness.12,13 Message believability is also associated with knowledge, attitudes and beliefs,14 and is an important mediator between message exposure and subsequent smoking-related behaviors.12,13 One study assessing the effects of message believability showed that message believability was associated with intention to engage in smoking cessation behaviors.12 This suggests that assessing message believability during formative research could aid in the development of better promotion or marketing messages for smoking education campaigns, especially if those campaigns communicate the source or sponsor of the messages.14 Large-scale smoking campaigns, in turn, can impact downstream smokers’ behaviors such as cessation and quit behaviors.3,4 Thus, one way to increase message processing is through message believability.12

Source factors also affect message effectiveness.15,16 Messages from more believable sources may be more persuasive, and thus have more impact, than those from sources deemed not believable.11,16 The processes by which source factors influence message processing are also explicated in the elaboration likelihood model of persuasion.11 In prior research, message source influenced the perceived impact of tobacco education messages.17 However, the effect of source factors have mainly been investigated between contrasting sources such as non-profits and the tobacco industry.

So, does source matter in the believability of new information about tobacco-caused chronic diseases? And if so, from which source should messages be attributed in a communication campaign?.18 In this study, we investigated believability of messages communicated from three government sources. The Surgeon General and Centers for Disease Control and Prevention (CDC) have wide-ranging experience communicating smoking health risks to the public.3,5,19,20 And, while both the CDC and the Food and Drug Administration (FDA) have conducted national mass media campaigns aimed at preventing smoking in the past few years,3 the FDA has only recently started communicating about the health consequences of smoking. Lastly, outside of a few nonprofit organizations, government sources are the ones most likely to communicate about the health consequences of smoking to the wider public.15 The public, therefore, may have differing perceptions about messages communicated from these government sources, and this is important to understand to aid government agencies in making their communications as impactful as possible.

We posit that considering information about new diseases was included in the 2014 Surgeon General’s report, the public may be most likely to believe the messages if they were attributed to the Surgeon General.19–21 It is also possible other sources could be equally or even more persuasive, such as the CDC or FDA.15 To that end, we conducted an experiment to 1) determine the believability of messages about new diseases linked to smoking in the 2014 Surgeon General’s report and 2) examine the influence of message source on believability of those messages among US adults.

Methods

Sample and measures

Data were collected through a nationally representative phone survey of US adults, which used two independent and non-overlapping random digit dialing frames (both landline and cell-phone), representing ~98% of total households. The survey was conducted from September 2014 through May 2015, and assessed regulatory constructs such as tobacco product use, tobacco constituent perceptions, and tobacco regulatory agency credibility. Low-income respondents, individuals living in higher cigarette use regions were oversampled. Specifically, both random digit dialing frames were stratified by household income and smoking rates at the county-level, where the poorest counties with the highest smoking rates were oversampled. In addition, to maximize the number of young adults (<25 years), cell phone numbers were oversampled. Within the landline frame, if more than one eligible adult resided in the household, young adults and smokers were sampled at a higher rate than older adult nonsmokers. A total of 5,014 participants over the age of 18 completed the survey. The weighted response rate—calculated using American Association for Public Opinion Research (AAPOR) Response Rate 4—was 42%, which is comparable to other national tobacco surveys.22,23 Using AAPOR standards, the response rate is the number of respondents who completed the survey as a proportion of all eligible and likely-eligible persons. Sample weights were computed to adjust for non-response and calibrate the sample to population counts on the following variables: census region, age, education, gender, ethnicity, phone type, and regional smoking rates. For more details on the sampling and data collection procedures, please refer to Boynton et al., 2016.24

The survey included a 2 (disease type) by 4 (source) experiment. For disease type, we tested two new cancers (liver and colon) and two new well-known chronic diseases (diabetes and tuberculosis) reported as causally linked to smoking in the 2014 Surgeon General’s report. Both of these chronic diseases and cancers the public has had heard about and likely has concerns.25–27 Participants were randomly assigned to one of two messages: Message 1(The [source] recently linked smoking cigarettes to more diseases, such as liver cancer and colon cancer) or Message 2 (The [source] recently linked smoking cigarettes to more diseases, such as tuberculosis and diabetes).

For source type, messages were from one of four randomly assigned sources: Surgeon General, FDA, CDC, or no source as a control. The no source message began, “Smoking cigarettes was recently linked to more diseases, such as…”. Believability of these messages was assessed with the question “how believable is this message?” with response options of very (coded as 3), somewhat (2), or not at all (1).

Current cigarette use was measured with two items, asking participants “have you smoked at least 100 cigarettes in your entire life?” and “do you now smoke cigarettes every day, some days, or not at all?”. Participants who reported smoking at least 100 lifetime cigarettes and reported current smoking every day or some days were classified as smokers. Otherwise, participants were classified as non-smokers. Quit intentions were measured with the item “are you planning to quit smoking…” with response options for “within the next month”, “within the next 6 months”, “sometime in the future beyond 6 months”, or “are you not planning to quit”. This item was only asked of smokers. Participants who responded they were planning to quit within the next month or within the next 6 months were compared with smokers intending to quit sometime in the future and smokers not intending to quit.

Covariates included gender, age, race, ethnicity, education, household poverty status (above or below the 2014 poverty line based on household size and income reported by participants), and smoking status by quit intention.

Data Analysis

We used SAS version 9.4 survey procedures to account for the complex survey design and sampling weights. Since there were three ordered response options to the outcome variable (i.e., very, somewhat, not at all believable), we initially conducted an ordinal logistic regression analysis to assess predictors associated with warning believability. However, since the proportional odds assumption was violated (Χ2 = 148.94, DF = 20, p<0.0001) and few respondents chose the option “not at all believable” (n=435, 7.9%),28 we conducted analyses utilizing a multivariate logistic regression model, comparing adults who reported the warnings to be very believable with adults who reported the warnings to be somewhat or not at all believable. We conducted further analyses comparing smokers intending to quit with smokers not intending to quit.

We entered control variables (i.e., race, ethnicity, age, sex, education, household poverty status, smoking status, quit intentions), message warning, and message source simultaneously in the multivariate logistic regression model. Only individuals with complete data across all relevant variables were included in the analyses. In our final model, 141 observations (approximately 2.8% of the sample) were deleted because they were missing on one or more of the explanatory variables, which resulted in a final sample size of 4,873. Results include weighted percentages, adjusted odds ratios (AOR), and confidence intervals (CI). For all analyses, significance was set at p < 0.05.

Results

Table 1 provides weighted percentages for our sample (N=5,014). Most participants were female (51.5%), over the age of 25 (85.1%), White (67.9%) and non-Hispanic (85.8%). Participants tended to have some college education or higher (57.4%) and most were above the poverty line (75.3%). About one sixth reported being a current smoker (17.8%), and among current smokers, 19.5% reported not intending to quit.

Table 1.

Unweighted and weighted percentages for demographic and smoking-related variables, n=5014

Variable All adults
Unweighted n		 All adults
Unweighted %		 All adults
Weighted %
Gender
  Male 2372 47.3 48.5
  Female 2640 52.7 51.5
Age
  Young Adult, < 25 years 809 16.1 14.9
  Adult, 25+ years 4205 83.9 85.1
Race
  White 3473 69.6 67.9
  Black or African American 978 19.6 18.3
  Other or unknown 541 10.8 13.7
Ethnicity
  Hispanic 432 8.6 14.2
  Non-Hispanic 4568 91.4 85.8
Education
  12th grade, no diploma or less 524 10.5 11.2
  High school graduate or GED 1232 24.7 31.4
  Some college 1034 20.7 20.7
  Associate’s degree 496 9.9 10.5
  College degree 1060 21.2 15.7
  Master’s degree 507 10.2 8.1
  Professional or doctoral degree 144 2.9 2.4
Household poverty status
  Below the poverty line 868 17.3 15.9
  Above the poverty line 3772 75.2 75.3
  Refused to answer 374 7.5 8.8
Smoking Status
  Current Smoker 1151 23.0 17.8
  Not a current smoker 3856 77.0 82.2
Quit Intentions
  Current smoker who intends to quit in the next month or 6 months 528 46.4 48.2
  Current smoker who intends to quit in the future beyond 6 months 361 31.8 32.3
  Current smoker who does not intent to quit 248 21.8 19.5
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Table 2 shows the weighted logistic regression results (N=4,873). Overall, 56.4% said the messages were very believable; the remainder said the messages were somewhat (n=1690, 35.71%) or not at all (n=435, 7.86%) believable. A higher proportion of participants reported new tobacco-related cancer messages (liver and colon cancer) to be very believable than new tobacco-related disease messages (tuberculosis and diabetes), 61.1% vs. 52.3%. These results were confirmed in our final model where new tobacco-related cancer messages (liver and colon cancer) had significantly higher odds of being reported as very believable compared to other tobacco-related disease messages (tuberculosis and diabetes), (AOR: 1.45, 95% CI: 1.17, 1.80). No significant differences existed in message believability by message source (i.e., Surgeon General, FDA, CDC, no source).

Table 2.

Weighted logistic regression results comparing adults who reported the messages to be very believable vs. not at all or somewhat believable, n=4873

Variable N (%) Reported
very believable		 Very believable vs. not at all or
somewhat believable
Adjusted Odds Ratio (95% CI)
Message
  Message 1: Liver cancer and colon cancer 1552 (61.1) 1.45 (1.17, 1.80)
  Message 2: Tuberculosis and diabetes 1285 (52.3) REF
Source
  Source 1: FDA 672 (53.7) 0.78 (0.57, 1.06)
  Source 2: CDC 747 (58.8) 0.98 (0.72, 1.32)
  Source 3: Surgeon General 699 (55.5) 0.85 (0.64, 1.14)
  Source 4: No source 719 (58.4) REF
Gender
  Male 1283 (54.3) 0.85 (0.68, 1.06)
  Female 1557 (58.4) REF
Age
  Young Adult, < 25 years 449 (55.1) 0.98 (0.73, 1.30)
  Adult, 25+ years 2393 (56.7) REF
Race
  White 1937 (55.0) REF
  Black or African American 586 (57.4) 1.17 (0.88, 1.56)
  Other or unknown 304 (59.5) 1.05 (0.74, 1.48)
Ethnicity
  Hispanic 260 (61.8) 1.38 (0.94, 2.02)
  Non-Hispanic 2576 (55.5) REF
Education
  12th grade, no diploma or less 290 (56.8) 0.60 (0.30, 1.20)
  High school graduate or GED 656 (52.9) 0.54 (0.33, 0.90)
  Some college 579 (58.4) 0.68 (0.41, 1.12)
  Associate’s degree 263 (51.1) 0.50 (0.29, 0.87)
  College degree 636 (58.6) 0.63 (0.39, 1.03)
  Master’s degree 321 (63.3) 0.77 (0.46, 1.28)
  Professional or doctoral degree 87 (67.3) REF
Household poverty
  Below the poverty line 465 (53.9) 0.93 (0.66, 1.32)
  Above the poverty line 2176 (57.5) REF
  Refused to answer 202 (51.5) 0.80 (0.55, 1.17)
Smoking status, by quit intentions
  Current smoker who intends to quit in the next month or 6 months 270 (48.0) 0.66 (0.46, 0.96)
  Current smoker who intends to quit in the future beyond 6 months 149 (39.7) 0.48 (0.32, 0.71)
  Current smoker who does not intend to quit 55 (22.6) 0.22 (0.12, 0.39)
  Non-smoker 2357 (59.6) REF
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Participants who reported being a high school graduate or having a GED (AOR, 0.54, 95% CI: 0.33, 0.90) or having an associate’s degree (AOR, 0.50, 95% CI: 0.29, 0.87) had significantly lower odds of reporting the messages as very believable than individuals with professional or doctoral degrees. There were no statistical differences in message believability by race, ethnicity, age, sex, or household poverty status. There were also no significant interactions between message believability and age, smoking status, or sex.

Current smokers not intending to quit (AOR: 0.22, 95% CI, 0.12, 0.39), smokers intending to quit in the future beyond 6 months (AOR: 0.48, 95% CI: 0.32, 0.71), and smokers intending to quit in the next month or the next 6 months (AOR: 0.57, 95% CI, 0.42, 0.76) had lower odds of reporting messages as very believable, compared to non-smokers. When the referent group was changed, smokers intending to quit in the next month or the next 6 months (48%) had significantly higher odds of reporting the messages as very believable compared to smokers not intending to quit (22.6%) (AOR: 3.09, 95% CI: 1.60, 5.95) (data not shown in tables).

Discussion

Tobacco smoking has been causally linked to a range of diseases, and the 2014 Surgeon General’s Report included ten new diseases caused by smoking.5 To motivate reduction in smoking behaviors, what new messages should we communicate to the public and from what source? Our results indicate participants found new cancer messages more believable, regardless of message source, with 61% saying a liver/colon cancer message was very believable. The cancer-related messages were one and a half times more believable than the chronic disease messages (tuberculosis and diabetes). This higher believability is likely because smoking’s association with cancer is more familiar than with other chronic diseases.3 These results are consistent with communication research which suggests that augmenting prior information with new information influences message processing and beliefs.2

No effect of source on message believability was detected. The lack of effect could be the result of the somewhat similar sources used (Surgeon General, FDA, CDC),15 the existing credibility of those sources,16 or data collection mode (sources were heard and not seen). Message source has been found to influence the perceived impact of tobacco education messages in previous studies.17 However, in some cases this has been between contrasting sources such as a non-profit compared to a tobacco industry source.18 More importantly, in a communication campaign, the source depiction is likely to involve visual imagery that could augment source impact on message outcomes.16 Future research could compare different types of sources (e.g. government, tobacco industry, individuals impacted by smoking) to further explicate the effect of source attribution on message effectiveness in the context of new and existing tobacco communication. Studies should assess participants’ familiarity and prior knowledge of each source in addition to assessing message believability.

Non-smokers found the messages to be more believable than smokers. While expected, emerging research suggests non-smokers play an important role in communicating the effects of campaigns and other tobacco education messages.3 That is, there may be an indirect effect of tobacco education messages on current smokers through important others, some of whom are non-smokers.3,4,29,30 In one study, cessation support behaviors from non-smokers and the prevalence of people talking to family and friends about smoking increased after exposure to the Tips campaign.3 Therefore, influencing non-smokers could not only prompt social interactions and increase cessation support but could also influence quit-related behaviors among current smokers, especially among those intending to quit.

Smokers intending to quit in the next 6 months found the messages much more believable than smokers not intending to quit. Those intending to quit may be in an advanced readiness stage and may be more open to new information,31 attending to the messages more closely.32 This in turn, increases message believability. Smokers intending to quit may also find the message more personally relevant than those not intending to quit.12 This finding bodes well for future smoking prevention campaigns by providing preliminary experimental evidence for possible effective smoking prevention messages targeted at smokers intending to quit.

Individuals with lower levels of education found the messages less believable than those with higher levels of education. Although other studies have found differences in perceived effectiveness of smoking prevention messages by socioeconomic status (measured by education and income),33 caution should be taken when interpreting these results. As has been suggested, the visual content of the messages may be an important determinant of message believability and effectiveness among those less educated.33 Thus, future studies should consider testing visuals to accompany statements about new diseases caused by smoking.

No differences in message believability were found by race, ethnicity, age, sex, or household poverty status. One potential implication is that messages about new diseases linked to smoking targeted to these groups could work equally well across these sociodemographic characteristics. Further work is needed to fully ascertain which messages about new diseases certain subgroups find most believable. Such research could yield valuable evidence for which messages should be included in larger campaigns targeted at the general public.

Our findings have implications for smoking-related mass media campaigns. New mass media tobacco campaigns should continue to consider messages about newer diseases caused by smoking. Such messages activates a person’s prior knowledge, influence message processing and prompt quit behaviors.3 Evaluations of campaigns suggest repeated cycles of messages are necessary to sustain high levels of quit behaviors.34 For example, the CDC-led Tips from Former Smokers campaign begin in 2012 with advertisements depicting patients with smoking-related heart disease, head and neck cancer, and stroke.3 That campaign reached 78% of US smokers and 74% of non-smokers, and quit attempts among smokers (of 1 day or more in the prior three months) increased by 12%.3 To maintain the effects of Tips, the CDC launched additional advertisements in 2013 featuring other negative health consequences of smoking such as chronic obstructive pulmonary disease, and lung and colorectal cancer.3,35 Advertisements about new cancers linked to smoking such as those tested in this study could be used to expand the current Tips campaign and might have greater impact, building on public knowledge of smoking as a cause of lung cancer.

The study has some limitations. The phone survey allowed for a nationally representative sample of adults. However, messages were read to participants, and may have been processed differently when heard (versus being viewed). We were not able to assess participants’ familiarity or prior knowledge of the diseases or sources. We also did not assess perceived prevalence of the diseases under study among the public. Familiarity, prior knowledge and perceived prevalence are factors that should be considered in future studies. The experiment included four new diseases caused by smoking—two cancers and two other types of diseases—and they were grouped in pairs. For practical reasons, we combined cancer vs. non-cancer conditions. Although not ideal, this allowed us to test certain cancers compared to other diseases. Further studies are needed on other new health consequences of smoking. In this study, we used the term “linked” to test the messages. It is possible that using more definitive language such as “cause” could have influenced the findings. Research is needed to determine if other types of cancers or diseases provide similar findings. Another limitation is the use of a single item to measure believability. Whenever possible, multi-item measures should be used to evaluate message content, including topic relevance and knowledge about the topic.10 Other message effectiveness measures such as cognitive elaboration should also be included in future studies. We did not evaluate full campaign advertisements; rather, this study evaluated believability of specific messages that could later be tested as part of future smoking prevention campaigns. To that end, we used single sentences to describe these new diseases caused by smoking. Our findings can serve as quantitative, experimental formative research to guide future message development. Studies should also be conducted to ascertain the believability and persuasiveness of messages among adolescents.

Conclusion

This study highlights important differences in the types of information about new diseases caused by smoking that the public finds believable. Smoking as a cause of novel cancers was more believable to smokers and non-smokers. Messages emphasizing the casual link between smoking cigarettes and a variety of new diseases should be considered for use in mass media campaigns, especially those that build on prior knowledge such as novel cancers.

Highlights.

  • New tobacco-caused cancer message was more believable than a message about other new tobacco-caused diseases.

  • Message believability did not differ by message source.

  • Mass media campaigns that focus on new diseases caused by smoking may be particularly effective.

Acknowledgments

Funding: This work was supported by P50CA180907 from the National Cancer Institute and the FDA Center for Tobacco Products (CTP). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the Food and Drug Administration.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

The authors report no conflict of interest.

i

The Surgeon General’s report included to new health consequences with casual links to smoking: Liver cancer, colorectal cancer, age-related macular degeneration, congenital defects, tuberculosis, diabetes, ectopic pregnancy, male sexual function, rheumatoid arthritis, and immune function.

References

  • 1.US Department of Health and Human Services. The health consequences of smoking: a report of the Surgeon General. Atlanta, GA: Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion; 2004. [Google Scholar]
  • 2.Noar SM. A 10-year retrospective of research in health mass media campaigns: Where do we go from here? J Health Commun. 2006;11(1):21–42. doi: 10.1080/10810730500461059. [DOI] [PubMed] [Google Scholar]
  • 3.McAfee T, Davis KC, Alexander RL, Jr, Pechacek TF, Bunnell R. Effect of the first federally funded US antismoking national media campaign. Lancet. 2013 doi: 10.1016/S0140-6736(13)61686-4. [DOI] [PubMed] [Google Scholar]
  • 4.Durkin S, Brennan E, Wakefield M. Mass media campaigns to promote smoking cessation among adults: an integrative review. Tob Control. 2012;21(2):127–138. doi: 10.1136/tobaccocontrol-2011-050345. [DOI] [PubMed] [Google Scholar]
  • 5.US Department of Health and Human Services. The health consequences of smoking—50 Years of Progress: a report of the Surgeon General. Atlanta, GA: Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion; 2014. [Google Scholar]
  • 6.World Health Organization. WHO global report: mortality attributable to tobacco. Geneva, Switzerland: 2012. [Google Scholar]
  • 7.Miller CL, Quester PG, Hill DJ, Hiller JE. Smokers' recall of Australian graphic cigarette packet warnings & awareness of associated health effects, 2005–2008. BMC Public Health. 2011;11:238. doi: 10.1186/1471-2458-11-238. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Noar SM, Francis DB, Bridges C, et al. The impact of strengthening cigarette pack warnings: Systematic review of longitudinal observational studies. Soc Sci Med. 2016;164:118–129. doi: 10.1016/j.socscimed.2016.06.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Swayampakala K, Thrasher JF, Hammond D, et al. Pictorial health warning label content and smokers' understanding of smoking-related risks-a cross-country comparison. Health Educ Res. 2015;30(1):35–45. doi: 10.1093/her/cyu022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Chaiken S. Heuristic versus systematic information processing and the use of source versus message cues in persuasion. J Pers Soc Psychol. 1980;39(5):752. [Google Scholar]
  • 11.Petty RE, Cacioppo JT. The elaboration likelihood model of persuasion. Adv Exp Soc Psychol. 1986;19:123–205. [Google Scholar]
  • 12.Cornacchione J, Smith SW. The effects of message framing within the stages of change on smoking cessation intentions and behaviors. Health Commun. 2012;27(6):612–622. doi: 10.1080/10410236.2011.619252. [DOI] [PubMed] [Google Scholar]
  • 13.Kim Y-J. The role of regulatory focus in message framing in antismoking advertisements for adolescents. J Advert. 2006;35(1):143–151. [Google Scholar]
  • 14.Yale RN. Measuring Narrative Believability: Development and Validation of the Narrative Believability Scale (NBS-12) J Commun. 2013;63(3):578–599. [Google Scholar]
  • 15.Samu S, Bhatnagar N. The efficacy of anti - smoking advertisements: the role of source, message, and individual characteristics. Intl J Nonprofit & Voluntary Mark. 2008;13(3):237–250. [Google Scholar]
  • 16.Schmidt AM, Ranney LM, Pepper JK, Goldstein AO. Source Credibility in Tobacco Control Messaging. Tob Regul Sci. 2016;2(1):31–37. doi: 10.18001/TRS.2.1.3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Bansal-Travers M, Hammond D, Smith P, Cummings KM. The impact of cigarette pack design, descriptors, and warning labels on risk perception in the US. Am J Prev Med. 2011;40(6):674–682. doi: 10.1016/j.amepre.2011.01.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Byrne S, Guillory JE, Mathios AD, Avery RJ, Hart PS. The unintended consequences of disclosure: effect of manipulating sponsor identification on the perceived credibility and effectiveness of smoking cessation advertisements. J Health Commun. 2012;17(10):1119–1137. doi: 10.1080/10810730.2012.665425. [DOI] [PubMed] [Google Scholar]
  • 19.Alberg AJ, Shopland DR, Cummings KM. The 2014 Surgeon General's report: commemorating the 50th Anniversary of the 1964 Report of the Advisory Committee to the US Surgeon General and updating the evidence on the health consequences of cigarette smoking. Am J Epidemiol. 2014;179(4):403–412. doi: 10.1093/aje/kwt335. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Antman E, Arnett D, Jessup M, Sherwin C. The 50th anniversary of the US surgeon general's report on tobacco: what we've accomplished and where we go from here. J Am Heart Assoc. 2014;3(1):e000740. doi: 10.1161/JAHA.113.000740. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Blum A. Blowing smoke: the lost legacy of the 1964 Surgeon General's report on smoking and health. Oncology (Williston Park) 2014;28(5):418–422. [PubMed] [Google Scholar]
  • 22.Agaku IT, King BA, Husten CG, Bunnell R, Ambrose BK, Hu SS, Holder-Hayes E, Day HR. Tobacco Product Use Among Adults — United States, 2012–2013. MMWR Morb Mortal Wkly Rep. 2014;63(25):542–547. [PMC free article] [PubMed] [Google Scholar]
  • 23.Behavioral Risk Factor Surveillance System. [Accessed 03/18/2016];2013 Summary Data Quality Report with Response Rates. 2014 http://www.cdc.gov/brfss/annual_data/annual_2013.html.
  • 24.Boynton MH, Agans RP, Bowling JM, et al. Understanding how perceptions of tobacco constituents and the FDA relate to effective and credible tobacco risk messaging: A national phone survey of U.S. adults, 2014–2015. BMC Public Health. 2016;16(1):516. doi: 10.1186/s12889-016-3151-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Salinas JL, Mindra G, Haddad MB, Pratt R, Price SF, Langer AJ. Leveling of Tuberculosis Incidence - United States, 2013–2015. MMWR Morb Mortal Wkly Rep. 2016;65(11):273–278. doi: 10.15585/mmwr.mm6511a2. [DOI] [PubMed] [Google Scholar]
  • 26.Menke A, Casagrande S, Geiss L, Cowie CC. Prevalence of and trends in diabetes among adults in the united states, 1988–2012. JAMA. 2015;314(10):1021–1029. doi: 10.1001/jama.2015.10029. [DOI] [PubMed] [Google Scholar]
  • 27.U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta, GA: Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. [Google Scholar]
  • 28.Stokes ME, Davis CS, Koch GG. Categorical data analysis using SAS. SAS institute; 2012. [Google Scholar]
  • 29.Hall MG, Peebles K, Bach LE, Noar SM, Ribisl KM, Brewer NT. Social Interactions Sparked by Pictorial Warnings on Cigarette Packs. Int J Environ Res Public Health. 2015;12(10):13195–13208. doi: 10.3390/ijerph121013195. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Thrasher JF, Abad-Vivero EN, Huang L, et al. Interpersonal communication about pictorial health warnings on cigarette packages: Policy-related influences and relationships with smoking cessation attempts. Soc Sci Med. 2016;164:141–149. doi: 10.1016/j.socscimed.2015.05.042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Prochaska JO, DiClemente CC, Norcross JC. In search of how people change. Applications to addictive behaviors. Am Psychol. 1992;47(9):1102–1114. doi: 10.1037//0003-066x.47.9.1102. [DOI] [PubMed] [Google Scholar]
  • 32.Moorman M, van den Putte B. The influence of message framing, intention to quit smoking, and nicotine dependence on the persuasiveness of smoking cessation messages. Addict Behav. 2008;33(10):1267–1275. doi: 10.1016/j.addbeh.2008.05.010. [DOI] [PubMed] [Google Scholar]
  • 33.Niederdeppe J, Farrelly MC, Nonnemaker J, Davis KC, Wagner L. Socioeconomic variation in recall and perceived effectiveness of campaign advertisements to promote smoking cessation. Soc Sci Med. 2011;72(5):773–780. doi: 10.1016/j.socscimed.2010.12.025. [DOI] [PubMed] [Google Scholar]
  • 34.Wakefield MA, Spittal MJ, Yong HH, Durkin SJ, Borland R. Effects of mass media campaign exposure intensity and durability on quit attempts in a population-based cohort study. Health Educ Res. 2011;26(6):988–997. doi: 10.1093/her/cyr054. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Huang LL, Thrasher JF, Abad EN, et al. The U.S. National Tips From Former Smokers Antismoking Campaign: Promoting Awareness of Smoking-Related Risks, Cessation Resources, and Cessation Behaviors. Health Educ Behav. 2015;42(4):480–486. doi: 10.1177/1090198114564503. [DOI] [PMC free article] [PubMed] [Google Scholar]

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