Research paper about smoking ads

timer Asked: Jul 3rd, 2018
account_balance_wallet $80

Question description

This is kind of research paper. 5 pages and MLA format.

It is about advertisement in general, but please focus on smoking ad in the video below.

You can use all of the sources that I have attached. Also, you can use Tim Wu book "The attention Merchant".

Do not bring other sources beside these.

Please write how it effect the audience and how companies release fake ads etc..

bs_bs_banner REVIEW doi:10.1111/add.12680 Do doctors’ smoking habits influence their smoking cessation practices? A systematic review and meta-analysis Maria J. Duaso1, Máirtín S. McDermott2, Agurtzane Mujika3, Edward Purssell4 & Alison While5 Department of Postgraduate Research, Florence Nightingale School of Nursing and Midwifery, King’s College London, London, UK,1 School of Information Systems and Technology, University of Wollongong, Wollongong, Australia,2 Faculty of Nursing, University of Navarra, Pamplona, Spain,3 Florence Nightingale School of Nursing and Midwifery, King’s College London, London, UK4 and Department of Postgraduate Research, Florence Nightingale School of Nursing and Midwifery, King’s College London, London, UK5 ABSTRACT Aims To assess the association between doctors’ smoking status and the use of the ‘5As’ of smoking cessation. Methods A systematic search of 11 databases covering English and Spanish language publications since 1996 was undertaken. Studies were included if they reported doctors’ smoking status (current, former or never smoker) and rates of practising any of the 5As of smoking cessation (Ask; Advise; Assess; Assist; and Arrange). Frequencies and proportions were extracted from individual papers and risk ratios (RR) were calculated. A random-effects meta-analysis model was used to assess the effect of the doctor’s personal smoking history. Covariate effects were explored using metaregression for three pre-specified study characteristics: doctors’ role, smoking prevalence of the sample and study quality. Results Twenty studies were included in this systematic review. The RR of always asking patients about their smoking was not associated significantly with doctors’ smoking status [RR = 0.98; 95% confidence interval (CI) = 0.94–1.02; P = 0.378; I2 = 0.00%; 10 studies]. Meta-analysis suggested that doctors who were current smokers had a 17% increased risk of not advising their patients to quit compared with never-smokers (RR = 0.83; 95% CI = 0.77–0.90; P < 0.000; I2 = 82.14%; 14 studies). However, high levels of heterogeneity were found that were not explained by the meta-regression. Regarding assisting patients to quit, never smokers were more likely to counsel than current smokers (RR = 0.92; 95% CI = 0.85–0.99; P = 0.036; I2 = 0.00%; three studies) but less likely to make a referral (RR = 1.40; 95% CI = 1.09–1.79; P = 0.009; I2 = 0.00%; five studies). No statistically significant differences were found in arranging future contact by smoking status (RR = 0.80; 95% CI = 0.52–1.23; P = 0.315; I2 = 47.03%; four studies). Conclusions Smoking status of doctors may affect their delivery of smoking cessation treatments to patients, with smokers being less likely than non-smokers or ex-smokers to advise and counsel their patients to quit but more likely to refer them to smoking cessation programmes. Keywords 5As, doctor, meta-analysis, smoker, smoking cessation, systematic review, tobacco. Correspondence to: Maria J. Duaso, Department of Postgraduate Research, Florence Nightingale School of Nursing and Midwifery, King’s College London, London SE1 8WA, UK. E-mail: Submitted 8 November 2013; initial review completed 18 February 2014; final version accepted 30 June 2014 INTRODUCTION Doctors play a leading role in reducing tobacco use. Given the frequency with which they encounter members of the public, they are ideally placed to make a significant impact on rates of smoking, which remains the single most preventable cause of death and illness in the world today [1]. Physician’s interventions can be effective in helping patients to stop smoking. The long-term success rate of those quitting unaided is 2–3%, with advice given © 2014 Society for the Study of Addiction by doctors increasing this by a further 1–3% [2]. While the effect of such advice may seem small, if implemented routinely it could have a major impact at the population level. Since 1996 the US guidelines have recommended the ‘Five As approach’ (5As) for treating tobacco use and dependence [3]: ask about tobacco use; advise to quit; assess willingness to make a quit attempt; assist in quit attempt; and arrange follow-up. This approach is endorsed by the World Health Organization (WHO) [4], Addiction, 109, 1811–1823 1812 Maria J. Duaso et al. and similar guidelines without the mnemonic exist in the United States [5] and elsewhere [6,7]. Despite these recommendations, the current implementation of the 5As is suboptimal and varies widely among doctors [8]. One major source of the varied implementation of stop smoking advice appears to be doctors’ own beliefs and attitudes towards smoking cessation. A systematic review of 13 studies [9] found that a sizable minority of primary care doctors held negative attitudes towards broaching smoking cessation with their patients. The doctors’ primary concern was that it was too timeconsuming, followed by beliefs that such intervention would be ineffective, and they had no confidence in their smoking cessation promotion ability. An earlier UK study [10] found that the majority (57%) of general practitioners (GPs) surveyed did not feel that it was appropriate to check the smoking status of their patients at each visit, a sizeable minority (39%) felt that it was inappropriate to advise all smokers to stop smoking at each visit and 23% felt that it was inappropriate for GPs to provide assistance to all smokers who wanted to stop. As well as beliefs about smoking cessation, health professionals also share the prevailing societal beliefs about smoking; for example, that smoking relieves stress and that smoking cessation will have a negative impact on mood [11,12], despite evidence to the contrary [13], which can also lead to a reduced likelihood of providing advice to quit [14]. One potential source both of these negative beliefs and of variations in practice are the doctors’ own health behaviours. Several studies have suggested that doctors who smoke have less positive attitudes towards smoking cessation [15,16] and are less likely to both take patient’s smoking history and provide advice than non-smoking colleagues [17–20]. However, the extent to which personal smoking behaviour affects the physician’s clinical practice is an important public health issue that has not been comprehensively investigated. The aim of the current review, therefore, is to determine if doctors’ smoking status (current, ex- or never smoker) is associated with engagement in smoking cessation, defined as practising any of the ‘5 As’: asking, advising, assessing, assisting or arranging follow-up. METHOD Eligibility criteria Studies were selected for this review if they: 1 Reported and categorized doctors’ current smoking status, including whether they had previously smoked. 2 Reported doctors’ current smoking cessation practices comparable to the 5As of smoking cessation. 3 Reported statistical relationships between (1) and (2). 4 Were published in English or Spanish. © 2014 Society for the Study of Addiction 5 Were published from 1996 onwards. Studies were excluded if: 1 Past smoking history was not reported. Previous studies have suggested that health professionals with a past smoking history may have different preventive practices to those who have never smoked [21]. 2 Studies only reported doctors’ attitudes towards smoking cessation, confidence in their ability to help patients to quit or intention to counsel their patients. 3 Reported smoking cessation practices that were not comparable to the 5As of smoking cessation. 4 Doctors’ data were not reported independently, but mixed with data from other health professionals (e.g. nurses, dentists) or unqualified staff (e.g. medical students). Study identification Five electronic English databases (MEDLINE, EMBASE, PsycINFO, CINAHL and ERIC) and six Spanish databases (IBECS, Scielo, CUIDEN, ENFISPO, LILACS and MEDES) were searched using a combination of free text search terms [including doctor$, GP$ (quit$ or stop$ or ceas$ or giv$) adj smoking]. An example of MEDLINE full search strategy can be found in the Supporting information, Table S1. Database searches were run up to 15 February 2013. No attempt was made to access unpublished studies or other ‘grey’ literature. Two reviewers working independently screened all abstracts and titles (M.J.D. for English databases and A.M. for Spanish ones) selecting potentially eligible studies for full text evaluation. Reviewers working independently and in duplicate (M.J.D., A.M. and M.McD.) determined the eligibility of full text reports. A subsample of screened decisions was reviewed by a second author (M.McD.). A kappa coefficient agreement of 0.91 was reached. Data extraction from all finally included papers was doublechecked by an independent reviewer (A.W. or M.J.D.). Disagreements were resolved by consensus. Data synthesis Smoking status data were extracted. Doctors were grouped into current smokers, former smokers or never smokers. All smoking cessation practices reported in the included studies were grouped into five categories (5As). A coding checklist was developed and variables were included in the analysis if the authors reported comparable measurements (see examples in Table 1). For the first variable (Ask), studies were included if they reported ‘always’ identifying tobacco users. The other four smoking cessation practices (Advise, Assess, Assist and Arrange) were converted into a dichotomous variable if several frequencies were reported. Always or more than Addiction, 109, 1811–1823 Doctors’ smoking and their smoking cessation practice 1813 Table 1 Five As for treating tobacco use and dependence. The five As Examples of interventions Ask always about or otherwise identify a patient’s smoking status Ask—systematically identify all tobacco users at every visit Advise—strongly urge all tobacco users to quit Assess—determine willingness to make a quit attempt Assist—aid the patient in quitting attempt Arrange—ensure follow-up contact In a clear, strong and personalized manner, urge every tobacco user to quit Ask smokers about their interest in quitting smoking Provide brief counselling about how to quit smoking Give out written stop-smoking materials Discuss use of medications Discuss a quit date Refer to a smoking cessation class or programme Suggest a follow-up visit or telephone call about quitting smoking Schedule follow-up contact, in person or via telephone Source: Fiore 2000 [22]. occasionally was considered as practising and occasionally or less considered as not practising. Moderator variables Studies have suggested that professionals who perceive smoking cessation as ‘highly’ relevant to their practice are more likely to advise their patients to stop [23,24]. Doctors’ specialities were coded independently by two researchers according to whether smoking cessation was considered an essential part of their role (e.g. primary care practitioners, pulmonologists, cardiologists, oncologists, infertility specialists) or not (hospital doctors, nonvascular surgeons, etc.). Therefore, samples were classified into two groups, ‘specialist’ or ‘non-specialist’. Studies with mixed samples (specialist + non-specialist) were classified as ‘non-specialist’ (Table 2). Another potential factor that could affect between study heterogeneity is the general tobacco control activity at national level at the time that the study took place. Doctors’ smoking status might have less impact on smoking cessation efforts in countries with high legislative and preventive regulations. The smoking prevalence of the sample was extracted as proxy indicator of national levels of tobacco control activity, as these have been linked to smoking rates among health professionals, particularly doctors [40–42]. The quality of the studies was assessed using an adaptation of the Centre for Evidence-Based Management Survey Scoring System [43]. The adapted tool used a 0–6 scoring system to appraise the methodological quality of cross-sectional surveys, including representativeness of the sample, response rate, validity of the tool and assessment of statistical significance. Rather than defining a minimum value below which a study would be excluded, a study’s quality score was used as a covariate to assess heterogeneity. © 2014 Society for the Study of Addiction Data analysis Statistical analysis was carried out using Comprehensive Meta-analysis Software version 2 and Metafor for R. Frequencies and proportions were extracted from individual studies and three risk ratios (RR) (i.e. current versus never smoker; current versus ex-smoker; ex- versus never smoker) were calculated per each smoking cessation practice (5As). Random-effects meta-analysis models were used to assess the effect of the doctor’s personal smoking history on smoking cessation practices. Lack of homogeneity was tested using the χ2 Q statistic (significance level P < 0.1) and the descriptive percentage of variance due to heterogeneity among studies was assessed using the I2 statistic [44]. To assess the effect of the three potential moderator variables, univariable meta-regressions were carried out for each primary comparison (e.g. Ask/current versus never smokers). Interaction terms were calculated to test if the effect varied by covariate subgroup (speciality, smoking prevalence of the sample and quality score). Funnel plots were used to inspect visually for potential publication bias. Egger tests were conducted to test the symmetry of the plot [45]. Where there was evidence of asymmetry suggesting a possible publication bias, a sensitivity analysis and the ‘trim-and-fill’ method was carried out to assess the impact of each study on the combined effect [46]. RESULTS Description of studies After removing duplicates, a total of 3213 abstracts were screened. A total of 295 full texts were assessed for eligibility, from which 250 papers were excluded [see Preferred Reporting Items for Systematic Reviews and Addiction, 109, 1811–1823 © 2014 Society for the Study of Addiction 2007 2010 1994 1994 2009 2002 2004 2001 2000/4 2003 2000 Not stated 2000 Not stated 2000 Not stated 2003/5 2002 2003 2004 Aboyans 2009 [18] Araya 2012 [25] Easton 2001 [26] Easton 2001 [19] Freour 2011 [27] Jacot Sadowski 2009 [28] Jiang 2007 [20] Kossler 2002 [29] Meshefedjian 2010 [21] Ng 2007 [30] Ohida 2001 [15] Ozturk 2012 [31] Rico Lezama 2001 [32] Samuels 1997 [33] Sanchez 2003 [34] Schnoll 2006 [35] Sotiropoulos 2007 [36] Steinberg 2007 [37] Thankappan 2009 [38] Zylbersztejn 2007 [39] *Non-specialist. Survey year First author (publication year) Table 2 Details of included studies. France Chile US US France Switzerland China Austria Canada Indonesia Japan Turkey Uruguay Israel Ecuador Russia Greece US India Argentina Location 371 235 1452 1644 341 1856 3652 1395 618 447 3771 80 152 260 679 63 1284 336 339 6497 Sample size Cardiologists Hospital physicians* Non-primary care women physicians* Primary care women physicians Doctors specialized in infertility Primary care physicians Physicians* General practitioners General practitioners Physicians with primary clinical responsibilities* Physicians* Non-vascular surgeons* Hospital physicians* Hospital doctors* Physicians not specified* Oncologists Physicians from all specialities* General practitioners and general internists* Physicians (faculty and health service)* Hospital physicians* Physician role National National National National National National Regional National Regional Regional National Institutional Institutional Institutional Regional Institutional National sample Regional Regional National Sample origin Paper Paper Paper Paper Authors Authors Paper Paper Author Author Paper Paper Paper Author Paper Author Paper Paper Paper Paper Source of data 8.1 16.2 4.7 3.5 12.5 17.7 22.9 7.1 7.4 11.9 20.3 17.5 30.9 15.7 32.3 27.0 38.6 3.3 10.8 30.0 Smoking prevalence 4 2 4 4 4 4 6 4 5 5 5 2 0 2 4 2 5 5 5 2 Qualityscore 1814 Maria J. Duaso et al. Addiction, 109, 1811–1823 Records idenƟfied through database searching (n =5245) Full-text arƟcles assessed for eligibility (n =295) Figure 1 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram Included Studies included in qualitaƟve synthesis (n = 45 ) Records excluded based on Ɵtle, abstract and duplicity (n =2516) Full-text arƟcles excluded, with reasons (n = 250) (a) physicians’ smoking status not reported (b) No assessment of past smoking (c) no reporƟng of 5As smoking cessaƟon pracƟces (d) No associaƟons tested (e) Mixed sample Authors did not provide requested informaƟon for meta-analysis (n = 25) Studies included in Meta-analysis (n=20) Meta-Analyses (PRISMA) Fig. 1]. Of 45 studies that were deemed potentially relevant, the authors of 31 studies were contacted to obtain further information to determine their eligibility. Some of the authors no longer held the original data, while others could not be contacted. Authors were contacted on at least two occasions, and we were able to obtain additional data relating to six studies. Finally, 20 studies were included in this systematic review. The study characteristics are presented in Table 2. The year of publication ranged from 1997 [33] to 2012 [25,31]. The studies included were conducted in 17 countries, eight of them in Europe, four in North America, four in South America and four in Asia. Eight of the studies included samples of doctors for whom smoking cessation was an important part of their role as GPs [19,21,28,29,37], cardiologists [18], oncologists [35] and infertility specialists [27]. The study sample sizes ranged from 63 to 6497 participants. Half the studies included national samples, six were regional and four were conducted in single study sites. The prevalence of smoking among the doctors in the included studies ranged from a minimum of 3% in two studies from the United States [19,37] to 38.6% in the Greek study [36]. The quality scores of the included studies ranged from 0 to 6, with a median of 4 (interquartile range = 2–5) (Table 2). Survey response rates ranged from 18.2% [29] to 97.0% [36]. Regarding the use of the five smoking cessation practices investigated, 10 studies [18,21,25,27,28,30,32,34, © 2014 Society for the Study of Addiction 1815 Spanish databases (n =484) Records screened (n = 3213) Eligibility Screening IdenƟficaƟon Doctors’ smoking and their smoking cessation practice 35,38] included data on whether doctors always enquired about the smoking status of their patients (Table 3) and 14 studies [19–21,25–27,29,31,33– 36,38,39] on advising smokers to quit (Table 4). Assessing smokers’ motivation was reported in only one of the studies [35]. Assisting with a quit attempt can take many forms, and this was reflected by the different measurements reported (Table 5): counselling patients [21,27,28], providing written materials [15,21,35], setting a quit date [15,20,21,35], providing medications [15,20,21,28,31,35,37] and referring to a smoking cessation programme [15,21,28,31,35]. The fifth A, arranging a follow-up contact, was included in four studies [15,18,21,35] (Table 6). Asking patients about smoking status A wide range of frequencies of reported practices were found, with 96.2% of French cardiologists routinely assessing smoking [18] compared only to 7.1% of Indonesian physicians reporting doing so [30]. Only two of the 10 studies [35,38] found statistically significant differences between doctors’ smoking status (Table 3). The meta-analysis confirmed the results of these individual studies (Table 7), as the pooled RR of always asking patients about their smoking was not associated significantly with the doctors’ smoking status. Heterogeneity in results was not significant across any of the three comparisons. Addiction, 109, 1811–1823 1816 Maria J. Duaso et al. Table 3 Reported smoking cessation practices (ASK) by doctor’s smoking status in individual studies. Smoking status Study Aboyans 2009 [18] Current Former Never Araya 2012 [25] Current Former Never Freour 2011 [27] Current Former Never Jacot Sadowski 2009 [28] Current Former Never Meshefedjian 2010 [21] Current Former Never Ng 2007 [30] Current Former Never Rico Lezama 2001 [32] Current Former Never Sanchez 2003 [34] Current Former Never Schnoll 2006 [35] Current Former Never Thankappan 2009 [38] Current Former Never ASK n % n % P 30 120 220 8.1 32.4 59.3 27 113 216 90 94.2 98.2 0.110 38 86 11 8.1 63.7 28.1 30 71 91 78.9 82.4 82 0.886 42 103 192 12.5 30.6 57.0 33 88 159 79 85 83 0.599 325 537 969 17.7 29.3 52.9 264 455 791 81.2 84.7 81.6 0.257 44 192 358 7.4 32.3 60.3 9 41 85 20.5 21.4 23.7 0.761 52 117 268 11.9 26.8 61.3 3 13 19 5.8 11.1 7.1 0.335 47 36 69 30.9 23.7 45.4 45 34 64 95.7 94.4 92.8 0.931 218 176 280 32.3 26.1 41.5 97 84 136 44.5 47.7 48.6 0.649 17 32 14 27.0 50.8 22.2 0 3 5 0 9.4 35.7 0.042 36 87 210 10.8 26.1 62.1 8 36 75 22.2 41.2 35.6 0.001 Overall P-value corresponds to χ2 or Yates’ χ2 correction if any expected frequency was below 1. Advising patients to quit smoking Six of the 14 studies included reported statistically significant lower rates of advising their patients to quit among doctors who smoked [19,20,25,29,34,36]. Primary random-effect meta-analysis suggested that, compared to never-smokers, current smokers had a 17% increased risk of not advising their patients to quit [RR = 0.83; 95% confidence interval (CI) = 0.77–0.90; P < 0.001]. They © 2014 Society for the Study of Addiction were also less likely to advise when compared to former smokers (RR = 0.82; 95% CI = 0.74–0.90; P < 0.000). There was evidence of heterogeneity in both comparisons (Q = 72.79, d.f. = 13, P < 0.000, I2 = 82.14) and (Q = 81.02 d.f. = 13, P = 0.000, I2 = 83.95), respectively. However, a mixed-effect meta-regression model indicated that heterogeneity in the RR was not significantly affected by doctors’ speciality, overall prevalence of the sample or study quality (Supporting information, Table S2). Addiction, 109, 1811–1823 Doctors’ smoking and their smoking cessation practice 1817 Table 4 Reported smoking cessation practices (ADVISE) by doctor’s smoking status in individual studies. Smoking status Study Araya 2012 [25] Current Former Never Easton 2001a [19] Current Former Never Easton 2001b [26] Current Former Never Freour 2011 [27] Current Former Never Jiang 2007 [20] Current Former Never Kossler 2002 [29] Current Former Never Meshefedjian 2010 [21] Current Former Never Ozturk 2012 [31] Current Former Never Samules 1997 [33] Current Former Never Sanchez 2003 [34] Current Former Never Schnoll 2006 [35] Current Former Never Sotiropoulos 2007 [36] Current Former Never Thankappan 2009 [38] Current Former Never Zylbersztejn 2007 [39] Current Former Never n ADVISE % n % 38 86 11 8.1 63.7 28.1 24 72 88 66 301 1030 4.7 21.5 73.7 55 316 1219 P 63.2 83.7 79.1 0.035 24 123 465 32 43 46 0.193 3.5 19.9 76.7 29 223 858 32 43 46 0.019 42 103 192 12.5 30.6 57.0 40 95 96 95 92 96 0.599 813 97 2642 22.9 2.7 74.4 432 65 1789 53.1 67 67.7 0.043 150 710 1245 7.1 33.7 59.1 104 504 1096 69.0 71.0 88.0 0.001 44 192 358 7.4 32.3 60.3 25 129 231 56.8 67.9 63.1 0.308 14 8 58 17.5 10.0 72.5 12 8 52 85.7 100.0 89.7 0.928 40 53 162 15.7 20.8 63.5 34 52 150 85.0 98.1 92.6 0.138 218 176 280 32.3 26.1 41.5 117 135 202 53.2 76.3 71.6 0.001 17 32 14 27.0 50.8 22.2 4 16 8 23.5 50.0 57.1 0.115 496 177 611 38.6 13.8 47.6 369 150 521 74.4 84.7 85.3 0.001 36 87 210 10.8 26.1 62.1 25 63 168 69.4 72.4 80.0 0.071 1916 1429 3039 30.0 22.4 47.6 1110 1140 2368 58.9 81.0 79.0 0.000 Overall P-value corresponds to χ2 or Yates’ χ2 correction if any expected frequency was below 1. © 2014 Society for the Study of Addiction Addiction, 109, 1811–1823 © 2014 Society for the Study of Addiction 12.5 30.6 57.0 17.7 29.3 52.9 22.9 2.7 74.4 7.4 32.3 60.3 20.3 28.0 51.8 17.5 10.0 72.5 27.0 50.8 22.2 3.3 22.9 73.8 325 537 969 813 97 2642 44 192 358 765 1054 1952 14 8 58 17 32 14 11 77 248 % 42 103 192 n – – – – – – – – – – – – 25 110 215 – – – 209 401 682 29 87 143 n – – – – – – – – – – – – 55.6 57.9 58.9 – – – 63.9 74.4 70.1 69.0 84.4 74.5 % 0.258 0.005 0.071 P ASSIST counselling – – – – – – 1 2 1 33 62 83 6 18 38 – – – – – – – – – n – – – – – – 5.9 6.3 7.1 4.3 5.9 4.3 14.3 9.6 10.7 – – – – – – – – – % 0.989 0.111 0.673 P ASSIST written matter Overall P-value corresponds to χ2 or Yates; χ2 correction if any expected frequency was below 1. Freour 2011 [27] Current Former Never Jacot Sadowski 2009 [28] Current Former Never Jiang 2007 [20] Current Former Never Meshefedjian 2010 [21] Current Former Never Ohida 2001 [15] Current Former Never Ozturk 2012 [31] Current Former Never Schnoll 2006 [35] Current Former Never Steinberg 2007 [37] Current Former Never Study Smoking status – – – – – – 1 3 3 29 53 90 20 90 151 37 6 172 – – – – – – n 4.6 6.2 6.5 – – – 5.9 9.4 21.4 – – – 3.8 5.0 4.6 47.6 48.1 42.4 – – – – – – % 0.354 0.453 0.410 0.123 P ASSIST quit date Table 5 Reported smoking cessation practices (ASSIST) by doctor’s smoking status in individual studies. 8 55 185 1 2 0 0 0 0 108 142 268 22 115 207 48 13 175 273 447 829 – – – n 72.7 71.4 74.6 5.9 6.3 0.0 0.0 0.0 0.0 14.1 13.5 13.7 50.0 61.5 58.0 5.9 13.4 6.6 83.5 82.9 85.2 – – – % ASSIST NRT 0.929 0.637 0.933 0.360 0.020 0.470 P – – – – – – – – – – – – – – – 40 5 116 224 350 631 – – – n – – – – – – – – – – – – – – – 4.9 5.2 4.4 68.5 64.9 64.9 – – – % 0.782 0.458 P ASSIST bupropion 38 42 95 – – – 1 3 1 7 4 24 12 8 15 16 63 105 – – – – – – n – – – 5.9 9.4 7.1 50.0 50.0 41.4 1.6 0.8 0.8 37.2 33.5 29.5 – – – 11.6 7.8 9.8 – – – % ASSIST referral 0.904 0.786 0.118 0.435 0.164 P 1818 Maria J. Duaso et al. Addiction, 109, 1811–1823 Doctors’ smoking and their smoking cessation practice Table 6 Reported smoking cessation practices (ARRANGE) by doctor’s smoking status in individual studies. Study Smoking status ARRANGE n % n 8.1 32.4 59.3 23 76.7 107 89.2 0.018 204 92.7 7.4 32.3 60.3 13 31.0 43 23.0 0.309 102 28.7 Aboyans 2009 [18] Current 30 Former 120 Never 220 Meshefedjian 2010 [21] Current 44 Former 192 Never 358 Ohida 2001 [15] Current 765 Former 1054 Never 1952 Schnoll 2006 [35] Current 17 Former 32 Never 14 20.3 28.0 51.8 27.0 50.8 22.2 % 2 9 29 P 0.3 0.9 0.015 1.5 2 11.8 5 15.6 0.935 2 14.3 1819 Two studies reported the use of bupropion when helping smokers to quit [20,28]. Data provided by the authors from a survey of 3385 Swiss primary care doctors suggests that a third prescribed bupropion [28], while fewer than 5% of hospital-based Chinese physicians used it when helping smokers to quit [20]. Overall, no statistically significant differences were found across the doctors’ smoking status (Table 7). Finally, the rates of referring to cessation services also varied. For example, 41% of non-vascular surgeons in a Turkish hospital referred their patients to specialist services [31], while fewer than 1% of a national sample of 3771 Japanese physicians reported doing so [15]. None of the five studies including data on referral found statistically significant differences by smoking status of the doctors. However, the pooled RR suggested that smokers are 1.4 times more likely to refer than never smokers (RR = 1.40; 95% CI = 1.09–1.79, P = 0.009) (Supporting information, Fig. S9). Arranging follow-up Overall P-value corresponds to χ2 or Yates’ χ2 correction if any expected frequency was below 1. Assisting in quit attempt Meta-analysis of three studies reporting on counselling of patients (Table 7) suggests that smokers were 8% less likely to counsel than never smokers and 14% less likely than former smokers (RR = 0.92; 95% CI = 0.85– 0.99, P = 0.036 and RR = 0.86; 95% CI = 0.79–0.94; P < 0.001, respectively) with no significant heterogeneity. None of the three studies reporting on assistance in the form of written materials found significant differences by doctors’ smoking status [15,21,35]. The pooled RRs were also non-significant (Table 7). Despite the well-known effectiveness of setting a quit date, overall rates of reported assistance to enable smokers to do so were low. Doctors’ smoking status did not appear to have any impact in individual studies [15,20,21,35] or pooled RRs (Table 7). Six studies reported doctors’ provision of nicotine replacement therapy (NRT) when assisting smokers to quit (Table 5). There were marked differences across the studies in terms of rates of provision, with a study of Russian oncologists providing none at all [35] compared to more than two-thirds of a national sample of US primary care doctors [19]. Of the seven studies, only one [20] found doctors’ smoking status to be a statistically significant factor in assisting smokers to quit using NRT, with 5.9% of current smokers prescribing NRT compared to 13.4% of former and 6.6% of never smokers (P = 0.02). The pooled RRs in the meta-analysis did not suggest an overall effect (Table 7). © 2014 Society for the Study of Addiction The last step of the 5As is to schedule a follow-up contact either in person or over the telephone. Once more, reported rates were variable (Table 6). Higher levels of follow-up were found among French cardiologists (92%) [18] compared to 24% of GPs in Montreal [21], and fewer than 1% of Japanese hospital-based doctors [15]. Two of the four studies found statistically significant differences, with smokers reporting lower levels of follow-up [15,18]. The pooled RRs in the meta-analysis did not suggest an overall effect (Table 7). However meta-regression suggests that the effect varies by speciality subgroup, with specialist doctors being more likely to arrange follow-up (RR = 4.89; 95% CI = 1.16–20.67; P = 0.031) (Supporting information, Table S2). Publication bias There was evidence of asymmetry in two of the metaanalyses: Asking current versus never smokers and Asking current versus ex-smokers (Supporting information, Table S3, Fig. S1), with fewer smaller studies showing no relationship than might be expected. Sensitivity analysis using the ‘leave-one-out’ method [47] revealed that the association of asking and smoking status remained statistically non-significant when any one of the studies was excluded. DISCUSSION We hypothesized that doctors’ own smoking status or smoking history could be a source of variation in the current variable and suboptimal implementation of recommended smoking cessation advice in the form of the Addiction, 109, 1811–1823 1820 Maria J. Duaso et al. Table 7 Meta-analysis doctor’s reported practice of 5As smoking cessation by smoking status. Pooled effect size ASK Current versus never-smokers Current versus ex-smokers Ex- versus never-smokers ADVISE Current versus never-smokers Current versus ex-smokers Ex- versus never-smokers ASSIST counselling Current versus never-smokers Current versus ex-smokers Ex- versus never-smokers ASSIST written materials Current versus never-smokers Current versus ex-smokers Ex- versus never-smokers ASSIST quit date Current versus never-smokers Current versus ex-smokers Ex- versus never-smokers ASSIST NRT Current versus never-smokers Current versus ex-smokers Ex- versus never-smokers ASSIST bupropion Current versus never-smokers Current versus ex-smokers Ex- versus never-smokers ASSIST referral Current versus never-smokers Current versus ex-smokers Ex- versus never-smokers ARRANGE Current versus never-smokers Current versus ex-smokers Ex- versus never-smokers Heterogeneity Relative risk 95% CI P-value No of studies 0.98 0.96 1.01 0.94–1.02 0.92–1.00 0.96–1.05 0.378 0.087 0.774 10 10 10 0.83 0.82 1.02 0.77–0.90 0.74–0.90 0.99–1.05 0.000 0.000 0.112 0.92 0.86 1.07 0.85–0.99 0.79–0.94 1.00–1.14 1.07 0.85 1.23 d.f. P-value I2 8.77 6.18 12.95 9 9 9 0.459 0.722 0.165 0.00 0.00 30.51 14 14 14 72.79 81.02 22.99 13 13 13 0.000 0.000 0.042 82.14 83.95 43.46 0.036 0.000 0.046 3 3 3 0.08 0.76 2.48 2 2 2 0.961 0.683 0.290 0.00 0.00 19.30 0.75–1.51 0.57–1.28 0.94–1.61 0.720 0.443 0.139 3 3 3 0.41 2.10 1.91 2 2 2 0.816 0.349 0.385 0.00 4.97 0.00 0.85 0.87 1.10 0.64–1.14 0.67–1.13 0.94–1.30 0.272 0.295 0.234 4 4 4 4.87 1.16 1.74 3 3 3 0.181 0.763 0.628 38.42 0.00 0.00 0.98 0.93 0.98 0.93–1.03 0.79–1.10 0.93–1.03 0.412 0.418 0.412 6 6 6 1.84 9.78 1.84 5 5 5 0.871 0.082 0.871 0.00 48.87 0.00 1.06 1.05 1.00 0.97–1.15 0.96–1.16 0.93–1.08 0.176 0.283 0.949 2 2 2 0.10 0.05 0.13 1 1 1 0.749 0.830 0.722 0.00 0.00 0.00 1.40 1.19 1.17 1.09–1.79 0.92–1.53 0.96–1.42 0.009 0.177 0.114 5 5 5 1.66 0.37 0.37 4 4 4 0.798 0.985 0.985 0.00 0.00 0.00 0.80 0.92 0.95 0.52–1.23 0.64–1.33 0.87–1.02 0.315 0.667 0.162 4 4 4 5.66 4.43 3.03 3 3 3 0.129 0.219 0.387 47.03 32.25 1.10 Q CI = confidence interval. 5As. Overall, there was limited evidence in our metaanalysis to suggest this. Of the smoking cessation practices investigated, only advising smokers to quit, assisting with counselling and referring were found to vary by smoking status. With almost 60 years since the first research linking smoking and ill health [48], it may be that the negative impact of smoking on health is accepted to the point that doctors’ own smoking cessation practices are unaffected by their current or former smoking status. Also, the gradual increase of teaching on tobacco in medical schools [49] may have reduced any potential differences in practice. A recent systematic review of randomized trials concluded that participation in formal training in © 2014 Society for the Study of Addiction smoking cessation significantly increases the rate of quit advice provided to patients [50]. It would have been valuable to include training as a moderator variable, but unfortunately this was not reported in most of the studies. Former smokers advised and assisted with counselling more than current smokers, suggesting that stopping smoking might have a positive effect on smoking cessation practices. It is important to note that most of the studies included here were not designed to evaluate the association between smoking status and smoking cessation practices, and therefore may be underpowered to detect this. Interestingly, doctors who smoke overall were more likely to refer to smoking cessation services or equivalent Addiction, 109, 1811–1823 Doctors’ smoking and their smoking cessation practice than current and former smokers. A comparative study of 11 European Union (EU) countries concluded that GPs who smoke tended to feel less effective in helping patients to reduce tobacco consumption than non-smoking GPs (39.34 versus 48.18%, P < 0.01) [16]. It could be hypothesized that doctors do not personally feel able to assist with quitting if they themselves are smokers, and so refer out to other agencies. Our findings suggest that overall the delivery of the 5As is suboptimal. These results should be viewed with caution, as only a subset of studies addressing 5As delivery is included here. However, our results are consistent with other studies that have also found that, while patients are often asked about their smoking habits and advised to stop smoking, assistance and follow-up rates are lower [51–53]. Key findings from the Smoking Toolkit Study (STS) in England suggest that only a minority of smokers have discussed smoking with their GP in the past year, and just 25.9% have been offered a prescription or referred to a smoking cessation service [54]. The strengths of this review include a comprehensive search strategy of both English and Spanish databases including a wide range of countries, rigorous and reproducible extraction of data and the contacting of authors for further information. The use of a random-effect model and meta-regression analyses to explain heterogeneity also strengthens confidence in the estimation results presented here. A key limitation of this work is the between-study heterogeneity. This systematic review has brought together studies that are diverse in terms of setting, methodological quality and speciality of participants. Studies reported over a period of almost 20 years and both smoking prevalence and practices may have changed over this time. Reported smoking cessation practices varied widely across studies. We used doctors’ specialities as a proxy indicator of how integral smoking cessation was to their role. However, this has some limitations, as smoking role perceptions may vary according to national guidance or local policies. Table 7 shows high levels of heterogeneity across the studies that assess Advise practices that are not explained in the meta-regression; consequently, the overall effect must be viewed with caution. Conversely, a visual inspection of the forest plot (Supporting information, Fig. S3) suggests that, despite the different RR estimates of the individual studies, there is no inconsistency in the direction of the effect, with smoking doctors seeming to be less likely to advise their smoking patients to quit. Another limitation is the omission of grey literature in the search; however, due to the importance of the subject and the relative robustness of our findings in the sensitivity analysis there is little reason to think these are significant. © 2014 Society for the Study of Addiction 1821 Several recommendations can be made for future research based on our findings. This review confirms that provision of the 5As of smoking cessation among doctors remains low and variable. It appears that the impact of doctors’ own smoking behaviour is limited. Future research should investigate other reasons for the shortfall and variability in adherence to this recommended practice, and could also investigate whether interventions to reduce smoking among doctors increase the delivery of smoking cessation interventions. Finally, it would be interesting to ascertain whether the findings reported in this review are consistent in other professional groups providing valuable smoking cessation support, such as nurses, midwives, dentists or pharmacists. CONCLUSIONS Meta-analyses of the currently available studies suggest that, while the smoking status of doctors does not affect whether or not they monitor patient’s smoking status, it may have an impact on them advising their smoking patients to quit. Smoking doctors are at higher risk of not assisting their patients with counselling when compared to former smokers and non-smokers. Conversely, they seem more likely to refer their patients to a smoking cessation programme. These findings must be interpreted within the context of the limitations of the data, but suggest that smoking cessation among doctors might extend beyond their personal health and benefit their patients. Declaration of interests None. References 1. World Health Organization. WHO Report on the Global Tobacco Epidemic, 2008: The MPOWER Package. Geneva: World Health Organization; 2008. Available at http:// .pdf (accessed 28 July 2014) (Archived at http:// on 28 July 2014). 2. Stead L. F., Bergson G., Lancaster T. Physician advice for smoking cessation. Cochrane Database Syst Rev 2008; (2): CD000165. 3. Tobacco Use and Dependence Guideline Panel. Treating Tobacco Use and Dependence: 2008 Update. Washington, DC: US Department of Health and Human Services; 2008. 4. 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Fidler J. A., Shahab L., West O., Jarvis M. J., McEwen A., Stapleton J. A. et al. ‘The Smoking Toolkit Study’: a national study of smoking and smoking cessation in England. BMC Public Health 2011; 11: 1471–2458. Supporting Information Additional Supporting information may be found in the online version of this article at the publisher’s web-site: Figure S1 Funnel plots showing relation between the association of smoking cessation practices/smoking status of physicians (log risk ratio) and the standard error of the log risk ratio Figure S2 Forest plot ASK. Current smokers versus never smokers Figure S3 Forest plot ADVISE. Current smokers versus never smokers Figure S4 Forest plot ASSIST counseling current smokers versus never smokers Figure S5 Forest plot ASSIST providing written material. Current smokers versus never smokers Figure S6 Forest plot ASSIST setting up a quit date. Current smokers versus never smokers Figure S7 Forest plot ASSIST with nicotine replacement therapy (NRT). Current smokers versus never smokers Figure S8 Forest plot ASSIST with bupropion. Current smokers versus never smokers Figure S9 Forest plot ASSIST making a referral. Current smokers versus never smokers Figure S10 Forest plot ARRANGE. Current smokers versus never smokers Table S1 Medline search strategy. Table S2 Meta-regression. Table S3 Publication bias. Addiction, 109, 1811–1823 This document is a scanned copy of a printed document. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material.
RESEARCH AND PRACTICE Effects of Different Types of Antismoking Ads on Reducing Disparities in Smoking Cessation Among Socioeconomic Subgroups Sarah J. Durkin, PhD, Lois Biener, PhD, and Melanie A. Wakefield, PhD Tobacco use inflicts the greatest burden of illness on those least able to afford it.1,2 An enormous challenge for tobacco control is how to tackle the consistently higher levels of smoking prevalence found among disadvantaged groups,3–5 especially because these gaps may be widening.6,7 Televised antismoking campaigns provide an effective population-wide method of preventing smoking uptake,8,9 promoting adult smoking cessation,10 and reducing adult smoking prevalence,11 and research indicates that some types of ads may be more effective than others. Antismoking messages that produce strong emotional arousal, particularly personal stories or graphic portrayals of the health effects of smoking, tend to perform well12; they are perceived to be more effective than others, are more memorable, and generate more thought and discussion.13–16 However, it is unclear whether different types of messages might maintain, increase, or mitigate the disparities in smoking prevalence across population subgroups. Research on subgroup differences in responses to a range of anti-tobacco ads has not found systematic differences by gender, race/ ethnicity, or nationality.13,17–19 A review of the literature on the use of mass media concluded that in comparison with their effects on other populations, campaigns have often been less effective, sometimes equally effective, but rarely more effective in promoting cessation among socioeconomically disadvantaged populations.20 However, many of the less effective generalaudience campaigns were hampered by minimal reach to smokers of low socioeconomic status (SES) because they were low-cost campaigns unable to afford extensive media exposure.20 Most research examining longer-term quit rates in the context of large-scale, well-funded antismoking campaigns found comparable quit rates or reductions in smoking prevalence in low- and high-SES groups.21–28 However, to our Objectives. We assessed which types of mass media messages might reduce disparities in smoking prevalence among disadvantaged population subgroups. Methods. We followed 1491 adult smokers over 24 months and related quitting status at follow-up to exposure to antismoking ads in the 2 years prior to the baseline assessment. Results. On average, smokers were exposed to more than 200 antismoking ads during the 2-year period, as estimated by televised gross ratings points (GRPs). The odds of having quit at follow-up increased by 11% with each 10 additional potential ad exposures (per 1000 points, odds ratio [OR] = 1.11; 95% confidence interval [CI] = 1.00, 1.23; P < .05). Greater exposure to ads that contained highly emotional elements or personal stories drove this effect (OR = 1.14; 95% CI 1.02, 1.29; P < .05), which was greater among respondents with low and midsocioeconomic status than among high–socioeconomic status groups. Conclusions. Emotionally evocative ads and ads that contain personalized stories about the effects of smoking and quitting hold promise for efforts to promote smoking cessation and reduce socioeconomic disparities in smoking. (Am J Public Health. 2009;99:2217–2223. doi:10.2105/AJPH.2009.161638) knowledge, no population-based research has examined the relationship between the degree of exposure to different types of antismoking messages and quit rates between low- and high-SES groups. A variety of theories29–38 provide guidance about which styles of ads may best encourage quitting, especially among members of lower socioeconomic groups. Consistent with these theories, reviews of the effects of antismoking advertising have concluded that advertisements that evoke strong emotional responses through negative visceral imagery or personal stories about the health effects of smoking can increase attention, generate greater recall and appeal, and influence smoking beliefs and intentions.12,39,40 Recent research indicates that self-relevant emotional reactions (i.e., emotional reflections about one’s life, body, or behavior that are triggered by the ad41) may be especially persuasive, because they affect perceptions of future risk of becoming ill,42 which in turn have been linked with reduced cigarette consumption, increased intentions to quit, and quit attempts.43 December 2009, Vol 99, No. 12 | American Journal of Public Health Antismoking ads that use strong graphic imagery of the health effects of smoking are likely to be predominately associated with high negative emotional arousal, but personal stories of the consequences of smoking may evoke high or low levels of emotion depending on the particular story and the degree to which smokers relate to the characters.38 However, less emotional personal testimonials may still be more effective than other types of less emotional ads because there is no explicit persuasive intent against which smokers may react38,44 and because health information is presented in a story-based format, which people learn to process naturally from an early age.45 Because lower-SES groups tend to have a greater degree of resistance to messages from the health care sector,46 lower health literacy levels,47,48 greater likelihood of belief in myths about cancer risks and prevention,49 and less perception that smoking increases a person’s chance of getting cancer,48 we proposed that emotional messages and personal stories might be especially influential. Presenting antismoking messages in an emotional or personal testimonial Durkin et al. | Peer Reviewed | Research and Practice | 2217 RESEARCH AND PRACTICE format may convey health information to these smokers in a way that is difficult to discount, natural and easy to process, and likely to arouse emotions that lead to increased perceptions of susceptibility to smoking-related diseases and motivation to quit.38,42,44 Drawing on the only previous study to examine the effect on adult quitting of the degree of exposure to antismoking ads,10 we first hypothesized that when all types of advertisements were considered together, greater exposure to these antismoking ads would be associated with greater likelihood of quitting by follow-up. Our second hypothesis was that particular types of antismoking ads (those containing highly emotional elements or personal testimonials about the effects of smoking) would be associated with a greater chance of successful quitting by follow-up than would exposure to ads without these elements. Finally, we hypothesized that highly emotional or personal testimonial ads would be especially effective among lower-SES groups. METHODS Our data came from the first 2 waves of the UMass Tobacco Study, a longitudinal survey of Massachusetts adults designed to investigate responses to the Massachusetts Tobacco Control Program. During the period surrounding the data collection for the baseline survey (1999–2002), 134 different anti-tobacco television ads aired in Massachusetts. Viewers were exposed to a range of ads from the Massachusetts Tobacco Control Program and the American Legacy Foundation, along with a small proportion from the New York State Tobacco Program where media markets overlapped state lines (i.e. Albany, Schenectady, Troy). Between January 2001 and June 2002, the baseline survey obtained a probability sample from 6739 adults, oversampling adult smokers, young adults (aged 18–30 years), and recent quitters. Of residential households sampled, 66% were successfully screened, and 70% of eligible adults were interviewed (overall response rate = 46%). Recontact was attempted with all adults in the baseline sample (n = 4991) between January 2003 and July 2004, and a follow-up rate of 56% (n = 2805) was achieved. We analyzed data only from respondents who were baseline smokers, were successfully recontacted at follow-up, and lived within the 3 largest media markets in Massachusetts for which ratings data were available (n =1491). Measures Outcome measure—cessation. At each wave, a current smoker was defined as a respondent reporting lifetime consumption of at least 100 cigarettes who currently smoked some days or every day. Cessation was defined as abstinence from smoking for at least 1 month at the time of the follow-up interview. Predictors. We assessed the emotional intensity of individual tobacco control ads aired by state sponsors or the American Legacy Foundation by asking 18 adult independent raters to view and rate them. We determined the presence of emotionally arousing content by the mean score (on a scale of 1–7) on 3 items describing the ads as emotional, intense, and powerful. Ads with scores equal to or above the midpoint were classified as highly emotional. This process provided ratings for 74% of the individual ads aired by the state sponsors and the American Legacy Foundation. The remaining ads were viewed by researchers and categorized by characteristics known to relate to strong emotions. Of the 134 ads aired, 35.1% were rated as highly emotional ads. Ads were categorized as personal testimonials if they portrayed people describing their personal experiences with smoking or how smoking affected their lives or the lives of their families. Often the ads depicted an individual talking to the audience about his or her pain and suffering in a familiar setting such as a home or a hospital. Of all 134 ads, 31.3% were rated as personal testimonial ads (64% of these were rated as highly emotional). Personal testimonial ads categorized by researchers achieved a concordance rate over 95%, and discrepancies were discussed and resolved. We categorized 20.2% of the ads as both highly emotional and personal testimonial, 13.4% as highly emotional but not personal testimonial, 11.2% as personal testimonial but not highly emotional, and 53.7% as neither. The box on the next page contains a description and examples of each of these types of ads. 2218 | Research and Practice | Peer Reviewed | Durkin et al. Ads categorized as highly emotional, personal testimonial, or both were considered together (44.8%) and were compared with ads without these elements, the comparison ads. We ascertained the volume of broadcast, measured in gross ratings points (GRPs), of antismoking ads aired in Massachusetts from Nielsen Media Research monitoring records. GRPs represent the sum of all household rating points achieved by a schedule of advertisements for a particular period within a particular media market. For example, 30 GRPs for an ad or program indicates that 30% of the households in a given media market were tuned to that program at that time. GRPs for an ad or program summed over a given time provide estimates of how often the total media market has potentially been exposed to an ad or type of ad over that period. For example, 1000 GRPs for a 2-year period can indicate that all (100%) of the target population has been exposed, 10 times on average, to an ad or program. GRPs provide estimates of potential exposure to ads for households within a particular population area, but they do not equate to actual individual exposure. Some viewers may have been reached more often and some less often, depending on their TV-watching frequency. We computed the sum of monthly GRPs for tobacco control ads for each of the media markets and merged this with the individual adult data according to the interview month and the media market in which the respondent lived. GRPs for 2 ads sponsored by American Legacy Foundation were not able to be identified by Nielsen Media Research, so the GRPs for these ads were removed from the analysis. For these unidentified ads there were only 2.62 GRPs in 2001 and 228.96 in 2002. We computed 3 ad exposure measures for each respondent, 1 for total tobacco control ads (state and American Legacy Foundation ads combined), 1 for the tobacco control ads that contained highly emotional or personal testimonial elements, and 1 for the comparison ads (Table 1). Each measure was a sum of GRPs for 24 months, reflecting an individual’s total potential exposure to ads over the 2 years prior to the date of the baseline interview in the media market where the respondent lived. We divided these sums by American Journal of Public Health | December 2009, Vol 99, No. 12 RESEARCH AND PRACTICE Emotionally Evocative and Comparison Antismoking Ads Highly Emotional and Personal Testimonial Ads Personal stories of the health effects of smoking experienced by narrators or by close family members. Examples: Rick Stoddard series; I can’t breathe campaign (Pam Laffin); Shower; Janet Sackman Personal Testimonial Ads That Were Not Highly Emotional Personal stories of the quitting process, including quitting motivation, quitting strategies, how family/friends were supportive; how much better narrators feel now they’ve quit. Examples: Chuck; Birthday; Teacher; Wonderful Grandfather; I Did It Highly Emotional Ads That Did Not Include Personal Testimonials Anti-tobacco industry ads that depict the victims of tobacco; depictions of family scenes with a family member missing because of smoking-related death; depictions of a person exposing family/friends to environmental smoke. Scenes of credible people realizing the harmful nature of environmental smoke. Examples: Baby Monitor; Body Bags NYC; Kids; Ghost; Careful series. Comparison Ads Ads that depict how smoking effects fitness, appearance, and social standing; ads that use information-based approaches detailing ill effects of smoking (including environmental smoke effects); humorous ads that highlight the ridiculous nature of smoking; anti-tobacco industry ads that use humor/irony or statistics/information to attack the industry. Examples: Stamina; House Party; Auto-shop; Numbers; Smelly puking habit ads; Daily Dose series Note. Detailed descriptions of the ads are available for viewing on the Media Campaign Resource Center (MCRC) Web site: http://www. 1000 to aid interpretation of effects: each 1-unit increase in the 3 GRP measures represented 10 additional potential exposures over the 24-month period to (1) all tobacco control ads, (2) highly emotional or personal testimonial ads, and (3) comparison ads. Because the baseline data collection period spanned 18 months, there was wide variation in potential exposure to the ads among individual smokers who were interviewed at different times and in different media markets. Moderator variable. Socioeconomic status was determined by education and income. Respondents with high school or less education and with an income of $50 000 per year or less were classified as low SES. Those who had at least some college education and earned more than $50 000 per year were classified as high SES. Participants who had lower levels of education but a higher income level or who had higher education but a lower income were classified as mid-SES. At baseline, 218 smokers did not provide their income or their education level and were therefore categorized as undetermined SES. Covariates. Covariates included minority status (minority versus non-Hispanic White), gender, age (at baseline), and addiction level (heavy or light). Respondents who reported smoking within 30 minutes of waking or smoking more than 20 cigarettes per day were classified as heavily addicted. We classified smokers at a lower addiction level if they reported not smoking within 30 minutes of waking and smoking fewer than 20 cigarettes per day. We also included as a covariate usual TV watching between 8 PM and 11 PM in a typical week (0–3 days/week, 4–6 days/ week, or 7 days/week). We included this as a proxy measure of individual TV-watching frequency. We also included the number of months between the baseline and follow-up interviews (range = 21–35 months; 89% of the sample was reinterviewed between 21 and 26 months after baseline) and the media market in which the respondent lived as covariates in all analyses. Statistical Analysis For the baseline sample, we computed survey weights to adjust for the probability of selection. As in any longitudinal study, attrition from wave to wave may have reduced representativeness. Analyses of the baseline differences between adult respondents at followup and those who failed to respond indicated December 2009, Vol 99, No. 12 | American Journal of Public Health that responders were significantly more likely to be older, female, non-Hispanic White, and more educated. We used these variables in an iterative raking procedure to create adjustments to the weights, which yielded distributions on these demographic characteristics at follow-up that either were identical to those at baseline or differed by at most 0.4 percentage points. Multivariate logistic regression analyses tested our first hypothesis, that total potential exposure to tobacco control ads would predict quitting at follow-up, as well as whether there was an interaction between total potential exposure to tobacco control ads and SES status. We also used multivariate logistic regression to test our second hypothesis, that exposure to ads with highly emotional or personal testimonial elements would raise the probability of quitting by follow-up, compared with exposure to the comparison ads. We added interaction terms to the multivariate logistic regression to test our third hypothesis, that the emotionally evocative or personal testimonial ads would be especially effective among respondents with low SES. We ran a set of multiple logistic regression analyses separately for each SES group to Durkin et al. | Peer Reviewed | Research and Practice | 2219 RESEARCH AND PRACTICE TABLE 1—Sample Characteristics for the Total Sample and by SES Group: UMass Tobacco Study, 2001–2004 Total Sample (n = 1491) Low SES (n = 348) Mid SES (n = 459) High SES (n = 466) Undetermined SES (n = 218) Age, y, mean (SE) 40.5 (0.5) 43.4 (0.9) 38.7 (0.7) 38.7 (0.9) 42.8 (1.4) Interview gap, mo, mean (SE) 23.8 (0.1) 23.6 (0.2) 24.0 (0.1) 23.7 (0.1) 23.7 (0.1) Total tobacco control ad GRPs,a mean (SE) 853.4 (2.2) 850.6 (5.0) 854.2 (3.4) 856.6 (4.4) 849.9 (4.9) Total tobacco control ad GRPs,b mean (SE) 20 480 (50) 20 410 (120) 20 500 (80) 20 560 (110) 20 400 (120) Average monthly HE/PT GRPs,a mean (SE) 438.2 (1.9) 440.5 (3.8) 439.9 (3.6) 434.8 (3.1) 438.0 (5.8) 10 520 (190) 415.2 (1.7) 10 570 (90) 410.0 (3.6) 10 560 (90) 414.4 (3.0) 10 430 (70) 421.8 (2.6) 10 510 (140) 411.9 (5.4) 9960 (40) 9840 (90) 9950 (70) 10 120 (60) 9890 (130) Continuing smoker 82.4 87.1 81.8 80.8 79.0 Quitter 17.6 12.9 18.2 19.2 21.0 Summed HE/PT GRPs,b mean (SE) Average monthly comparison GRPs,a mean (SE) Summed comparison GRPs,b mean (SE) Quitting status at follow-up, % Education, % Some college or above 51.5 0 51.7 100 38.7 High school or lower Not disclosed 46.1 2.5 100 0 48.3 0 0 0 44.5 16.9 £ $50 000 41.8 100 51.7 0 8.6 > $50 000 45.8 0 48.3 100 7.1 Not disclosed 12.4 0 0 0 84.3 Income, % Race/Ethnicity, % Minority 16.1 23.1 13.8 12.6 16.1 83.9 76.9 86.2 87.4 83.9 Women 55.2 51.8 56.7 53.1 61.6 Men 44.8 48.2 43.3 46.9 38.4 Heavyc 59.5 66.8 64.0 48.0 61.0 Lightd 40.5 33.2 36.0 52.0 39.0 0–3 d/wk 4–6 d/wk 32.2 26.1 37.1 18.6 30.2 26.2 27.3 33.7 37.9 22.6 7 d/wk 41.8 44.3 43.5 39.0 39.5 85.8 Non-Hispanic White Gender, % Addiction level, % TV-watching frequency, % Media market, % Boston, MA 87.0 82.7 86.1 92.1 Albany–Schenectady–Troy, NY 2.8 3.6 3.4 1.0 3.8 Providence–New Bedford, MA 10.2 13.7 10.5 6.8 10.5 Note. GRPs = gross ratings points; HE/PT = highly emotional/personal testimonial; SES = socioeconomic status. a Monthly average GRPs of state-sponsored and American Legacy Foundation–sponsored ads aired in the 24 months before the baseline interview. b Summed GRPs of state-sponsored and American Legacy Foundation–sponsored ads aired in the 24 months before the baseline interview. c Smoked within 30 minutes of waking or smoked more than 20 cigarettes per day. d Did not smoke within 30 minutes of waking and smoked fewer than 20 cigarettes per day. provide odds ratios for Figure 1. All analyses included as covariates age, gender, minority status, addiction level, TV-watching frequency, number of months between baseline and follow-up interviews, and the media market in which each participant resided. RESULTS Of the 1491 individuals who were smoking at baseline, 16.1% had quit for 1 month or more at the time of the follow-up interview. Just under half had, at most, a high school education (46.1%), and 41.8% earned $50 000 per year 2220 | Research and Practice | Peer Reviewed | Durkin et al. or less; 24.6% reported both of these low-SES indicators. Half had more than a high school education (51.5%), and 45.8% earned more than $50 000 per year; 29.8% reported both of these high-SES indicators. A further 30.9% had 1 low- and 1 high-SES indicator (mid-SES group), and 14.7% did not disclose their American Journal of Public Health | December 2009, Vol 99, No. 12 RESEARCH AND PRACTICE TABLE 2—Effects of Potential Exposure to 2 Types of Ads on Odds of Quitting Smoking: UMass Tobacco Study, 2001–2004 Main Predictors Highly emotional or OR (95% CI) 1.14** (1.02, 1.29) personal testimonial ad GRPsa Comparison ad GRPsa FIGURE 1—Likelihood of quitting smoking at follow-up (odds ratios) associated with potential exposure to each 10 additional highly emotional or personal testimonial ads, by socioeconomic status (SES) group: UMass Tobacco Study, 2001–2004. 1.00 (0.98, 1.01) Interview gap, mo 1.00 (0.92, 1.08) SES Low (Ref) 1.00 Mid 1.70** (1.02, 2.83) High education or income (undetermined SES). Table 1 displays characteristics of the sample by demographic subgroup. On average, smokers were exposed to 853.4 (SE = 2.2) tobacco control ad GRPs per month, or an average overall total of 20 480 (SE = 50) tobacco control ad GRPs over the 24 months that preceded the baseline interview. These comprised 438.2 (SE =1.9) highly emotional or personal testimonial ad GRPs per month (10 520 GRPs overall) and 415.2 (SE =1.7) comparison ad GRPs per month (9960 GRPs overall). Our analysis of the effect of potential exposure to total tobacco control ads indicated that this was a significant predictor of quitting status at follow-up: the odds of having quit increased by 11% with each 10 additional potential antismoking ad exposures (per 1000 GRPs, odds ratio [OR] =1.11; 95% confidence interval [CI] =1.00, 1.23; P < .05). We found no significant interaction between total potential exposures and SES status (interaction c2 = 5.21; P > .05). Our analysis of the effect of potential exposure to different types of ads indicated that level of potential exposure to emotionally evocative or personal testimonial ads was a significant predictor of quitting at follow-up (Table 2). For each 10 additional potential exposures over the 2-year period to these types of ads, the odds that smokers quit were 1.14 times as high. However, level of potential exposure to comparison ads was not a significant predictor of quitting at follow-up (OR = 0.93; 95% CI = 0.61, 1.40; P > .05; Table 2). We also examined the interaction between potential exposure to each type of ad and SES group. We observed a significant interaction between SES group and potential exposure to the emotionally evocative ads (interaction c2 = 9.57; P < .05), but no interaction between SES group and the comparison ads (interaction c2 =1.52; P > .05). Figure 1 shows the odds ratios for the relationship between potential exposure to highly emotional or personal testimonial ads and quitting, calculated for each separate SES group after adjustment for all covariates. The figure shows an increased likelihood of quitting for each 10 additional potential exposures to an emotionally evocative or personal testimonial ad for respondents in the low-SES group, the mid-SES group, and the undetermined-SES group. By contrast, smokers in the high-SES group showed a decreased likelihood of quitting with each 10 additional potential exposures to these types of ads. We also conducted an alternate set of analyses without the undetermined-SES group, and the overall interaction findings remained the same. DISCUSSION Potential exposure to all antismoking ads was associated with a greater likelihood of quitting at follow-up; the odds of baseline smokers having quit at follow-up increased by 11% with each 10 additional potential exposures to a tobacco control antismoking ad (or 1000 antismoking ad GRPs). This confirms our first hypothesis and is consistent with the only previous study to examine this question in December 2009, Vol 99, No. 12 | American Journal of Public Health 0.93 (0.61, 1.40) Age,b y Undetermined Race/ethnicity 1.70* (0.95, 3.03) 2.11** (1.07, 4.14) White (Ref) 1.00 Minority 0.55* (0.29, 1.04) Gender Women (Ref) 1.00 Men 1.09 (0.75, 1.59) Addiction level Heavyc Lightd (Ref) 0.42*** (0.29, 0.60) 1.00 TV-watching frequency, d/wk 0–3 (Ref) 1.00 4–6 1.09 (0.70, 1.71) 7 1.00 (0.63, 1.58) Media market Boston, MA (Ref) Albany–Schenectady– 1.00 0.43 (0.01, 15.22) Troy, NY Providence– 0.85 (0.34, 2.13) New Bedford, MA Note. CI = confidence interval; GRPs = gross ratings points; OR = odds ratio; SES = socioeconomic status. a Summed GRPs, divided by 1000, of state-sponsored and American Legacy Foundation–sponsored ads aired in the 24 months before the baseline interview. b The continuous age variable (each smoker’s baseline age in years (range = 18–83 y) was included as a covariate in each analysis. The odds ratio of 1.00 was rounded up from 0.997. c Smoked within 30 minutes of waking or smoked more than 20 cigarettes per day. d Did not smoke within 30 minutes of waking and smoked fewer than 20 cigarettes per day. *P < .10; *P < .05; ***P < .001. Durkin et al. | Peer Reviewed | Research and Practice | 2221 RESEARCH AND PRACTICE adults.10 When converted to relative risks associated with exposure to 5000 GRPs, as in the previous study,10 this effect equates to a 48.9% increase in the relative risk of quitting. This is somewhat greater than the 10% increase found by Hyland et al.,10 but similar to the 40% increase estimated by Levy et al.50 We also found that emotionally evocative ads drove this effect, confirming our second hypothesis. Smokers who were exposed to more highly emotional and personal testimonial ads were significantly more likely to have quit smoking by follow-up: the odds of baseline smokers having quit by follow-up increased by 14% with each 10 additional potential exposures to these ads (70.6% increase in relative risk of quitting per 5000 emotionally evocative ad GRPs). Potential exposure to the comparison ads was not associated with quitting. Our results are consistent with previous laboratory-based research that showed that highly emotional antismoking ads are more likely to be recalled, to be perceived as more effective, and to be thought about and discussed.13–16 Often public health agencies are reluctant to air hard-hitting emotional ads. However, our findings underscore the importance of developing emotionally evocative ads rather than messages that are considered more palatable and upbeat. Our findings also add to the theory and emerging literature on the utility of narrative communication in persuasion.38,42,44 Narratives, by contrast to ads featuring experts or scientific demonstrations, can reduce the tendency toward counterargument (e.g., self-exemptions), increase viewers’ insight into what it would be like to have a specific illness, and increase perceptions of group and personal vulnerability through identification with characters in the ads. We found no interaction between the extent of potential exposure to all tobacco control antismoking ads considered together and SES, consistent with the majority of previous research examining the overall effects of wellfunded campaigns on quitting and smoking prevalence across SES groups.23–26 Our study adds to this literature by examining the relationship between quitting and the extent of potential exposure, rather than only whether respondents were in the jurisdiction or community that was exposed. Consistent with our third hypothesis, we found an interaction between SES and the level of potential exposure to emotionally evocative or personal testimonial ads. The pattern of quitting across groups indicated that greater potential exposure to these types of ads was associated with a greater likelihood of quitting among low-SES, mid-SES, and undeterminedSES groups but not in the high-SES group. This indicates that extensive exposure to emotionally evocative antismoking messages may be particularly effective among populations with the highest smoking rates (low SES) and with the highest proportion of smokers (mid-SES). Thus, the pattern of greater effect among lowSES than high-SES groups indicates that wide distribution of these highly emotional and story-based ads may contribute to the reduction of socioeconomic disparities in smoking. A limitation of our study was that GRPs measure potential exposure at a population level rather than confirmed individual-level exposure; however, studies have shown a strong association between GRP levels and self-reported recall of ads.16,51 A strength of our study was matching media exposure data to the timing of interviews and to the media market of each individual over an extended period (24 months) and examining the effects of the extent of potential exposure to antismoking ads. We also adjusted for variation between individuals in the time between the baseline and follow-up interviews, which avoided potential problems of inflated quit rates among some respondents caused by a longer period until follow-up (followup range = 21–35 months). Our findings indicate that public health agencies may contribute to reducing smoking rates in their communities, especially among socioeconomically deprived populations, by developing and widely airing emotionally evocative antismoking ads and ads that feature personalized stories about the effects of smoking and the experience of quitting. j About the Authors Sarah J. Durkin and Melanie A. Wakefield are with the Centre for Behavioural Research in Cancer, The Cancer Council Victoria, Melbourne, Australia. Lois Biener is with the Center for Survey Research at the University of Massachusetts, Boston. Correspondence should be sent to Sarah Durkin, PhD, Centre for Behavioural Research in Cancer, The Cancer Council Victoria, 1 Rathdowne St, Carlton, Victoria, 2222 | Research and Practice | Peer Reviewed | Durkin et al. Australia, 3053 (e-mail: Reprints can be ordered at by clicking the ‘‘Reprints/Eprints’’ link. This article was accepted April 8, 2009. Contributors All authors helped to design the study. S. J. Durkin completed the analyses and led the writing. L. Biener designed and supervised the survey data collection, contributed to the writing, and supervised the study. M. A. Wakefield completed ad coding and contributed to the writing. Acknowledgments Sarah J. Durkin was supported by an International Union Against Cancer Yamagiwa-Yoshida Memorial International Cancer Study grant. Melanie A. Wakefield was supported by a National Health and Medical Research Council Principal Research Fellowship. This research was funded by grants from the National Cancer Institute’s Tobacco Research Initiative for State and Community Interventions (CA86257 to L. Biener and CA86273 to M. A. Wakefield). We thank Catherine Garrett and Cecilia Shiner for preparing the advertising exposure data and the longitudinal data set and Glen Szczypka for preparation of GRP data. Human Participant Protection The protocol was approved by the institutional review board of the University of Massachusetts, Boston. References 1. Siahpush M, Borland R, Yong HH. Sociodemographic and psychosocial correlates of smoking-induced deprivation and its effect on quitting: findings from the International Tobacco Control Policy Evaluation Survey. Tob Control. 2007;16(2):e2. 2. Jarvis MJ, Wardle J. Social patterning of individual health behaviours: the case of cigarette smoking. In: Marmot W, ed. Social Determinants of Health. Oxford, UK: Oxford University Press; 1999. 3. Barbeau EM, Krieger N, Soobader MJ. 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Health Educ Monogr. 1974;2: 354–386. 30. Fishbein M, Ajzen I. Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley; 1975. 31. Ajzen I. The theory of planned behaviour. Organ Behav Hum Decis Process. 1991;50:179–211. 32. Baumeister RF, Vohs KD, DeWall CN, Zhang L. How emotion shapes behavior: feedback, anticipation, and reflection, rather than direct causation. Pers Soc Psychol Rev. 2007;11(2):167–203. 19. Wakefield M, Szczypka G, Terry-McElrath Y, et al. Mixed messages on tobacco: comparative exposure to public health, tobacco company and pharmaceutical company sponsored tobacco-related television campaigns in the United States, 1999–2003. Addiction. 2005; 100(12):1875–1883. 37. Green MC, Brock TC. The role of transportation in the persuasiveness of public narratives. J Pers Soc Psychol. 2000;79(5):701–721. 22. Owen L. Impact of a telephone helpline for smokers who called during a mass media campaign. Tob Control. 2000;9(2):148–154. 23. Warnecke RB, Langenberg P, Wong SC, Flay BR, Cook TD. The second Chicago televised smoking 45. Green MC, Brock TC, Strange JJ. Narrative Impact: Social and Cognitive Foundations. Hillsdale, NJ: Erlbaum; 2002. 46. Boulware LE, Cooper LA, Ratner LE, LaVeist TA, Powe NR. Race and trust in the health care system. Public Health Rep. 2003;118(4):358–365. 47. Peters E, Vastfjall D, Slovic P, Mertz CK, Mazzocco K, Dickert S. Numeracy and decision making. Psychol Sci. 2006;17:407–413. 48. Viswanath K, Breen N, Meissner H, et al. Cancer knowledge and disparities in the information age. J Health Commun. 2006;11:1–17. 49. Scroggins TG Jr, Bartley TK. Enhancing cancer control: assessing cancer knowledge, attitudes, and beliefs in disadvantaged communities. J La State Med Soc. 1999;151(4):202–208. 50. Levy DT, Chaloupka F, Gitchell J. The effects of tobacco control policies on smoking rates: a tobacco control scorecard. J Public Health Manag Pract. 2004; 10(4):338–353. 51. Southwell BG, Barmada CH, Hornik RC, Maklan DM. Can we measure encoded exposure? Validation evidence from a national campaign. J Health Commun. 2002;7(5):445–453. 34. Eagly AH, Chaiken S. The Psychology of Attitudes. Fort Worth, TX: Harcourt Brace Jovanovich; 1993. 35. Escalas JE, Moore MC, Britton JE. Fishing for feelings? Hooking viewers helps! J Consum Psychol. 2004;14(1-2):105–114. 21. An LC, Schillo BA, Kavanaugh AM, et al. Increased reach and effectiveness of a statewide tobacco quitline after the addition of access to free nicotine replacement therapy. Tob Control. 2006;15(4):286–293. 44. Kreuter MW, Green MC, Cappella J, et al. Narrative communication in cancer prevention and control: a framework to guide research and application. Ann Behav Med. 2007;33(3):221–235. 33. Cohen J. Attitude, affect and consumer behavior. In: Moore BS, Isen AM, eds. Affect and Social Behavior. Cambridge, UK: Cambridge University Press; 1990: 152–206. 18. Flynn BS, Worden JK, Bunn JY, Dorwaldt AL, Connolly SW, Ashikaga T. Youth audience segmentation strategies for smoking-prevention mass media campaigns based on message appeal. Health Educ Behav. 2007; 34(4):578–593. 20. Niederdeppe J, Kuang X, Crock B, Skelton A. Media campaigns to promote smoking cessation among socioeconomically disadvantaged populations: what do we know, what do we need to learn, and what should we do now? Soc Sci Med. 2008;67(9):1343–1355. 43. Romer D, Jamieson P. The role of perceived risk in starting and stopping smoking. In: Slovic P, ed. Smoking: Risk, Perception, and Policy. Thousand Oaks, CA: Sage; 2001:64–80. 36. Forgas JP. Mood and judgement: the Affect Infusion Model (AIM). Psychol Bull. 1995;117(1):39–66. 38. Hinyard LJ, Kreuter MW. Using narrative communication as a tool for health behavior change: a conceptual, theoretical, and empirical overview. Health Educ Behav. 2007;34(5):777–792. 39. Farrelly MC, Niederdeppe J, Yarsevich J. Youth tobacco prevention mass media campaigns: past, present, and future directions. Tob Control. 2003;12(Suppl 1): i35–i47. 40. Wakefield M, Flay B, Nichter M, Giovino G. Effects of anti-smoking advertising on youth smoking: a review. J Health Commun. 2003;8:229–247. 41. Burnkrant RE, Unnava HR. Self-referencing: a strategy for increasing processing of message content. Pers Soc Psychol Bull. 1989;15(4):628–638. 42. Dunlop SM, Wakefield M, Kashima Y. Can you feel it? Negative emotion, risk, and narrative in health communication. Media Psychol. 2008;11(1):52–75. December 2009, Vol 99, No. 12 | American Journal of Public Health Durkin et al. | Peer Reviewed | Research and Practice | 2223 Copyright of American Journal of Public Health is the property of American Public Health Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. 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Wr1 Essay 2 Prompt Interpreting Advertisements Using concepts from at least one of the following essays – Solomon’s “Masters of Desire,” Craig’s “Men’s Men and Women’s Women,” Schor’s “Selling to Children,” Frank’s “Commodify Your Dissent,” Pozner’s “Dove’s ‘Real Beauty’ Backlash,” or Tolentino’s Tolentino, “How Empowerment Became Something for Women to Buy” – and from Wu’s The Attention Merchants as an interpretative lens, write a critical analysis of how a contemporary advertisement “manipulates” consumers in order to sell a product, with negative consequences. Take one contemporary advertisement (not mentioned in the essays you are using) as an example for your analysis and explain how it uses images and text to influence its target audience. Then, describe and assess the effects of this ad on individuals or on society: what messages does it send, besides encouraging people to buy the products it depicts, that then become part of our wider culture? How do these messages affect individuals’ attitudes or lives? How might such messages affect society as a whole? This portion should also be critical; in other words, you must start from the assumption that the messages that the ad sends could create or contribute to a problem. Keep this in mind when choosing an ad. In addition to the essays, you should find one other article in Irvine Valley College library databases (such as Ebsco, Jstor, or ProQuest) that helps you extend and deepen your analysis of the advertisement by providing you with further interpretive tools or by helping you describe the potential negative effects the ad might have on society. Be sure to do the following in your essay:  Precisely identify and describe the specific desire or fear the ad is appealing to or exploiting.  Use sources as a lens for your interpretation of the ad and explanation its potential effects. This means you will need to effectively summarize and synthesize the pertinent ideas contained in both your chosen essay, from Wu, and from the source you find in the databases.  Analyze the specific elements and techniques the ad uses to appeal to or manipulate consumers. Include sufficient description for the reader to understand how the ad creates its effects, but don’t overdo it; connect your description clearly to analysis.  Analyze and evaluate the effects that this advertisement might have on individuals or on society. For example, what values does it communicate? What stereotypes does it reinforce? What political or social messages does it co-opt or exploit and thus weaken?  Properly cite both sources and the advertisement that you analyze. Include in-text citations and works cited entries at the end of the paper.  Avoid simplistic analyses that explain how your ad appeals to customers through lower prices or better products. The final version of this essay should be 5 pages in length, typed in 12-point font, and formatted according to MLA guidelines. In addition, you should include a Works Cited page with entries for the essays that you use, as well as an entry for the advertisement that you are analyzing. Important dates: Bring in and present the ad that you will be analyzing on Thursday, June 28. A 5 page draft is due on Tuesday, July 3. Please upload to Canvas and bring three copies for peer review. The final, 5 page version of the paper is due on Friday, July 6, at noon.

Tutor Answer

School: UT Austin


WR 1
July 3, 2018
Smoking ad
An advertisement is an announcement or a promotion to the public using mediums or
channels that are public to promote a given service, product or event service. Advertisements on
smoking were banned some time back at around 1971. The smoking advertisement on the
television disappeared after the passing of the Cigarette Smoking Act in 1970, which stipulated
that all the smoking advertisement should be banned (Mistry et al. 217). However, the cigarette
smoking was only prohibited in the TV where there was an advertisement that was done in the
newspaper as well as magazines. To these advertisements, the Congress declared some
mandatory packages that should be included in the cigarette packages that were being sold within
the United States. The mandatory words were "cigarette smoking may be dangerous to your
health." This is the slogan that now that since then is in most of the cigarette packages to ensure
that the individuals that are smoking are aware that smoking may be dangerous to their health.
From the banning of the smoking advert, the adverts that began were the antismoking advert
though this has not stopped individuals from smoking as well as the manufacturing of the
cigarettes have not seized from manufacturing them and selling them. However, the
manufacturing companies produce the cigarette and ensure that they indicate on the paper that
smoking is dangerous for an individual’s health. This is as per the act that was passed regarding
smoking (Duaso et al. 823). Regardless of Smoking advertisements being banned on the
television it however is still advertised in other media with the indication of the warning that was
stipulated by the congress.

The smoking ads in America have been seen to seduce the teens to enter into smoking
using their commercials, billboards as well as maga...

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