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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: maria.duaso@kcl.ac.uk
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.
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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.
cdc.gov/tobacco/media_communications/countermarketing/mcrc/index.htm.
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: sarah.durkin@cancervic.org.au).
Reprints can be ordered at http://www.ajph.org 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.
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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.
Purchase answer to see full
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