Smoking cessation delivery by general practitioners in Crete, Greece

Smoking cessation delivery by general practitioners in Crete, Greece Abstract Background Tobacco dependence treatment in clinical settings is of prime public health importance, especially in Greece, a country experiencing one of the highest rates of tobacco use in Europe. Methods Our study aimed to examine the characteristics of tobacco users and document rates of tobacco treatment delivery in general practice settings in Crete, Greece. A cross-sectional sample of patients (n = 2, 261) was screened for current tobacco use in 25 general practices in Crete, Greece in 2015/16. Current tobacco users completed a survey following their clinic appointment that collected information on patient characteristics and rates at which the primary care physician delivered tobacco treatment using the evidence-based 4 A’s (Ask, Advise, Assist, Arrange) model during their medical appointment and over the previous 12-month period. Multi-level modeling was used to analyze data and examine predictors of 4 A’s delivery. Results Tobacco use prevalence was 38% among all patients screened. A total of 840 tobacco users completed the study survey [mean age 48.0 (SD 14.5) years, 57.6% male]. Approximately, half of the tobacco users reported their general practitioner ‘asked’ about their tobacco use and ‘advised’ them to quit smoking. Receiving ‘assistance’ with quitting (15.7%) and ‘arranging’ follow-up support (<3%) was infrequent. Patient education, presence of smoking-related illness, a positive screen for anxiety or depression and the type of medical appointment were associated with 4 A’s delivery. Conclusion Given the fundamental importance of addressing tobacco treatment, increasing the rates of 4 A’s treatment in primary care settings in Greece is an important target for improving patient care. Introduction Tobacco use is the leading cause of premature death and disability and the largest threat to public health in Europe.1,2 Each year, more than 700 000 Europeans die from tobacco-related illness.2 The World Health Organization’s (WHO) European region has one of the highest proportions of death attributable to tobacco, with an estimated 16% of all deaths among adults over 30 years of age due to tobacco use.1 Despite the decline in the prevalence of tobacco use, more than 125 million Europeans (26% of the population) continue to smoke, representing the highest rate of tobacco use among all the WHO regions.3 Moreover, tobacco use imposes a huge economic burden on the European health care systems, with the direct health care costs alone estimated to be 100 billion Euros.4,5 There is overwhelming evidence attesting to the health and economic benefits of smoking cessation.6 Quitting smoking reduces the excess risk of smoking-related coronary heart disease, for example, by approximately 50% within 1 year, and to normal levels within 5 years.7 Smoking cessation is highly cost-effective with the cost per life-year saved estimated to be between €1500 and €3000.8,9 Tobacco use is also a priority among young European due to the fact that 94% of smokers start smoking before the age of 25 years and quitting smoking as early in life substantially reduces future disease risk.10 General practices have been identified as important settings for the delivery of smoking cessation treatment.1,4,11,12 The WHO1 and the European Network For Smoking and Tobacco Prevention13 have called for tobacco dependence to be a clinical priority of all health professionals.1,13 The 5A’s of smoking cessation are an internationally recognized evidence-based schema to guide interventions with tobacco users in all clinical settings including primary care.13,14 The 5 As include: ‘ask’ patients about smoking status; provide brief quit smoking ‘advice’; ‘assess’ readiness to quit smoking; ‘assist’ patients with making a quit attempt using behavioral techniques and pharmacotherapies; and, ‘arrange’ follow-up support throughout the quitting process. Internationally, and in Europe, a practice gap in rates of 5 As delivery in clinical settings has been documented.15–17 Greece has one of the highest rates of tobacco use among members of the European Union, estimated at 38% of the adult population.3 Little is known about the characteristics of tobacco users and current rates of tobacco treatment delivery in primary care settings in Greece. As a consequence, this study sought to examine the characteristics of tobacco users visiting general practitioners (GPs) in Crete, Greece and to document the rates of tobacco treatment delivery. We also examined patient-, GP- and clinic-level predictors of tobacco treatment delivery. Methods Design and setting Here, we report the cross sectional baseline data collected as part of the Global Bridges TiTAN Crete project. The purpose of the TiTAN Crete project (http://titan.uoc.gr/index_en.html) is to create a network of GPs trained in evidence-based smoking cessation treatments. Data collection took place in Crete, Greece between May 2015 and June 2016. GPs were surveyed and a cross-sectional sample of their patients was screened for current tobacco use. All current tobacco users were asked to complete a survey following their clinic appointment. The survey documented the characteristics of tobacco users and assessed rates at which their GPs delivered tobacco treatment during the patient’s same day medical appointment (index visit) and during the previous 12-month period. The study received ethics approval from the University Hospital of Heraklion Ethics Board (ref# 18078). Procedures During recruitment, all GPs located in the regions of Heraklion and Rethymnon in Crete, Greece were invited to participate in the study by email. To be eligible for the study GPs need to be: currently working in Primary Health Care practice in the geographic recruitment area. Twenty-five of twenty-six eligible GPs agreed to participate (response rate 96.2%). A follow-up telephone call was made by a member of the project team to confirm interest in participation. Informed consent was obtained from all participating GPs. GPs completed a questionnaire to document demographic characteristics including: age, gender, number of years practicing medicine, previous cessation training and personal smoking status. A standardized description of clinic characteristics was assembled; it included details of practice size, geography, payment methods and use of an electronic medical record. Within all participating GP’s offices, consecutive patients (n = 2261) were screened for eligibility using a brief written survey administered in the waiting room of the practice upon arrival for their appointment. Eligibility criteria included: being 18-years of age or older; a current tobacco user (≥1 cigarette per day on most days of the week); attending clinic for a non-urgent medical visit; and, ability to understand Greek. Eligible patients who agreed to participate in the study provided informed consent (n = 840, response rate 97.7%) and were asked to complete a brief survey following their medical appointment. The patient survey collected demographic variables (age, sex, ethnicity, years of formal education, occupation, income and postal code), a brief medical history and smoking related variables. The presence of any smoking-related illness was documented including heart disease, stroke, chronic obstructive pulmonary disease and cancer. A validated Greek version of the four-item Patient Health Questionnaire (PHQ), a tool used by health care professionals for diagnosing mental health disorders was administered.18,19 The two-item Heaviness of Smoking Index (HSI) was used to assess the degree of nicotine dependence.20,21 The HSI score ranges from 0 to 6 with higher HSI scores reflecting greater nicotine dependence. Smoking history was assessed by documenting the number of years a participant had been smoking. The number of previous quit attempts (lasting 24-h or longer) in the past year was also documented. Consistent with previous research, performance in the delivery of each of the 4 A’s (ask, advise, assist, arrange) was assessed using an exit survey.22 The survey instructed participants to respond either ‘yes’, ‘no’ or ‘don’t know’ when asked whether their GP asked them about their smoking status (ask); advised them to quit smoking (advise); provided assistance with quitting (assist); or arranged follow-up support (arrange). For ‘assist,’ we further examined the type of assistance provided including whether or not the GP: prescribed pharmacotherapy, provided self-help materials; or set a quit date. Participants were asked to respond regarding their receipt of those interventions during that day’s clinic appointment (i.e. the index appointment) as well as at any time in the previous 12-months. We chose not to measure rates at which providers assessed readiness to quit smoking in the present study. A research assistant coordinated all screening and data collection activities in clinic waiting rooms. For patients unable to read/write the research assistant completed the survey by interview. The Consolidated Standards of Reporting Trials flow diagram for the study is presented as Supplementary figure S1. Statistical analysis Descriptive statistics assessed GP characteristics, patient characteristics and rates of delivery of the 4 A’s. Given the clustered nature of data collection, each participating patient was linked to their GP. Multi-level modeling was used to account for provider-level clustering.23 An intra-class correlation coefficient (ICC) was calculated to describe the variation among GPs in rates of 4 A’s delivery and significance was assessed. The ICC ranges from 0 to 1 with higher scores indicating larger variation among providers in rates of 4 A’s delivery.23 In order to understand the patient-level, GP-level, and clinic-level factors associated with each outcome, separate multi-level logistic regression analyzes were completed. A block approach was used to examine the factors from each of the three levels that were associated with each outcome in a separate logistic regression model. Variables from each level that were significantly associated with each outcome at P < 0.1 were included in a final model; only variables that were significantly associated with each outcome at P < 0.05 were retained in the final model. Results were reported as adjusted odds ratios (AOR) and 95% confidence intervals (CI). SAS 9.4 was used to conduct multi-level modeling. Results The majority of GPs were under the age of 50-years (95.8%) and practicing in rural settings. Twenty-five percent of GPs reported current personal tobacco use while less than thirty-five percent of them reported they had the necessary skills to support their patients with quitting. Supplementary table S1 presents data on characteristics of providers. The prevalence of tobacco use among the patients was estimated at 38%, their mean age was 48.0 (SD 14.5) years, and 57.6% were male (table 1). The majority of tobacco users had smoked for more than 20-years (67.4%) and reported high rates of daily tobacco consumption (mean 21.1 SD ± 11.9). Overall, 65.4% of participants reported smoking within the first 30-min of waking while 58% of them were thinking of quitting in the next 6-months. Less than 40% of those surveyed reported making a quit attempt in the previous year. The majority of respondents reported low self-efficacy with quitting while 71.0% of tobacco users rated their GP’s advice to quit as important or very important. Table 1 Socio-demographic and tobacco-related characteristics of primary care patients sampled (n = 840) in Crete, Greece Parameter Response Value Age Mean years (SD) 48.0 (14.5) Sex % Male 57.6% Number of cigarettes (daily) Mean years (SD) 21.1 (11.9) Education (years) 0–6 21.7% 7–9 20.1% 10–12 30.1% 12+ 27.9% Nationality Greek 97.9% Smoking-related illnessa Yes 18.8% Depressionb Score of ≥ 3 6.9% Anxietyc Score of ≥ 3 15.5% Cigarettes/day <5 4.8% 6–15 32.3% 16–25 39.4% 26–40 19.9% >40 3.7% Time to first cigarette After 60 mins 20.4% 31–60 mins 14.3% 6–30 mins 35.6% Within 5 mins 29.8% HSI High 20.6% Moderate 56.1% Low 23.3% Years of smoking 0–2 1.2% 3–9 7.9% 10–19 23.6% 20+ 67.4% Readiness to quitd Next 30 days 24.2% Next 6-months 34.2% Not ready to quit 41.6% Self-efficacy with quittinge Low (≤7/10) 85.5% High (>7/10) 14.5% Number of quit attempts in past year 0 61.4% 1–2 32.5% 3+ 6.1% Presence of Other smokers in the home Yes 58.1% Family/friends who smoke None 4.2% Some 38.9% Most 52.6% All 4.3% Perceived importance of quittingf Low (≤7/10) 37.5% High (>7/10) 62.5% Importance of doctor’s advice to quit Very important 21.4% Important 49.6% Somewhat important 18.6% Not at all important 10.4% Parameter Response Value Age Mean years (SD) 48.0 (14.5) Sex % Male 57.6% Number of cigarettes (daily) Mean years (SD) 21.1 (11.9) Education (years) 0–6 21.7% 7–9 20.1% 10–12 30.1% 12+ 27.9% Nationality Greek 97.9% Smoking-related illnessa Yes 18.8% Depressionb Score of ≥ 3 6.9% Anxietyc Score of ≥ 3 15.5% Cigarettes/day <5 4.8% 6–15 32.3% 16–25 39.4% 26–40 19.9% >40 3.7% Time to first cigarette After 60 mins 20.4% 31–60 mins 14.3% 6–30 mins 35.6% Within 5 mins 29.8% HSI High 20.6% Moderate 56.1% Low 23.3% Years of smoking 0–2 1.2% 3–9 7.9% 10–19 23.6% 20+ 67.4% Readiness to quitd Next 30 days 24.2% Next 6-months 34.2% Not ready to quit 41.6% Self-efficacy with quittinge Low (≤7/10) 85.5% High (>7/10) 14.5% Number of quit attempts in past year 0 61.4% 1–2 32.5% 3+ 6.1% Presence of Other smokers in the home Yes 58.1% Family/friends who smoke None 4.2% Some 38.9% Most 52.6% All 4.3% Perceived importance of quittingf Low (≤7/10) 37.5% High (>7/10) 62.5% Importance of doctor’s advice to quit Very important 21.4% Important 49.6% Somewhat important 18.6% Not at all important 10.4% a Self- Reported heart disease, stroke, heart failure/cancer/chronic obstructive pulmonary disease (COPD)? (1 = yes, 0 = no). b PHQ-4 for depression. c PHQ-4 for anxiety. d Which of the following best describes your feelings about smoking right now? (Responses: 1 = ready to quit in next 30 days, 0= ready to quit in next 6-months or not ready to quit). e On a scale of 1–10 how confident are you that you would be able to quit smoking at this time? (1 = not at all confident, 10 = extremely confident). f On a scale of 1–10 how important is it to you to quit smoking at this time? (Response: 1 = not at all important, 10 = extremely important. Table 1 Socio-demographic and tobacco-related characteristics of primary care patients sampled (n = 840) in Crete, Greece Parameter Response Value Age Mean years (SD) 48.0 (14.5) Sex % Male 57.6% Number of cigarettes (daily) Mean years (SD) 21.1 (11.9) Education (years) 0–6 21.7% 7–9 20.1% 10–12 30.1% 12+ 27.9% Nationality Greek 97.9% Smoking-related illnessa Yes 18.8% Depressionb Score of ≥ 3 6.9% Anxietyc Score of ≥ 3 15.5% Cigarettes/day <5 4.8% 6–15 32.3% 16–25 39.4% 26–40 19.9% >40 3.7% Time to first cigarette After 60 mins 20.4% 31–60 mins 14.3% 6–30 mins 35.6% Within 5 mins 29.8% HSI High 20.6% Moderate 56.1% Low 23.3% Years of smoking 0–2 1.2% 3–9 7.9% 10–19 23.6% 20+ 67.4% Readiness to quitd Next 30 days 24.2% Next 6-months 34.2% Not ready to quit 41.6% Self-efficacy with quittinge Low (≤7/10) 85.5% High (>7/10) 14.5% Number of quit attempts in past year 0 61.4% 1–2 32.5% 3+ 6.1% Presence of Other smokers in the home Yes 58.1% Family/friends who smoke None 4.2% Some 38.9% Most 52.6% All 4.3% Perceived importance of quittingf Low (≤7/10) 37.5% High (>7/10) 62.5% Importance of doctor’s advice to quit Very important 21.4% Important 49.6% Somewhat important 18.6% Not at all important 10.4% Parameter Response Value Age Mean years (SD) 48.0 (14.5) Sex % Male 57.6% Number of cigarettes (daily) Mean years (SD) 21.1 (11.9) Education (years) 0–6 21.7% 7–9 20.1% 10–12 30.1% 12+ 27.9% Nationality Greek 97.9% Smoking-related illnessa Yes 18.8% Depressionb Score of ≥ 3 6.9% Anxietyc Score of ≥ 3 15.5% Cigarettes/day <5 4.8% 6–15 32.3% 16–25 39.4% 26–40 19.9% >40 3.7% Time to first cigarette After 60 mins 20.4% 31–60 mins 14.3% 6–30 mins 35.6% Within 5 mins 29.8% HSI High 20.6% Moderate 56.1% Low 23.3% Years of smoking 0–2 1.2% 3–9 7.9% 10–19 23.6% 20+ 67.4% Readiness to quitd Next 30 days 24.2% Next 6-months 34.2% Not ready to quit 41.6% Self-efficacy with quittinge Low (≤7/10) 85.5% High (>7/10) 14.5% Number of quit attempts in past year 0 61.4% 1–2 32.5% 3+ 6.1% Presence of Other smokers in the home Yes 58.1% Family/friends who smoke None 4.2% Some 38.9% Most 52.6% All 4.3% Perceived importance of quittingf Low (≤7/10) 37.5% High (>7/10) 62.5% Importance of doctor’s advice to quit Very important 21.4% Important 49.6% Somewhat important 18.6% Not at all important 10.4% a Self- Reported heart disease, stroke, heart failure/cancer/chronic obstructive pulmonary disease (COPD)? (1 = yes, 0 = no). b PHQ-4 for depression. c PHQ-4 for anxiety. d Which of the following best describes your feelings about smoking right now? (Responses: 1 = ready to quit in next 30 days, 0= ready to quit in next 6-months or not ready to quit). e On a scale of 1–10 how confident are you that you would be able to quit smoking at this time? (1 = not at all confident, 10 = extremely confident). f On a scale of 1–10 how important is it to you to quit smoking at this time? (Response: 1 = not at all important, 10 = extremely important. Rates of 4 A’s delivery At the index visit 50.3% of patient reported receiving advice to quit smoking from their GP however, only 11.1% of patients reported receiving assistance with smoking cessation. A similar pattern was documented for the previous 12-months. Discussing and prescribing quit smoking medications and the provision of self-help material occurred infrequently at both the index visit and during the previous 12-months (table 2). ICCs indicate substantial inter-provider variability for ‘ask’, ‘advice’ and ‘assist’ (table 3). ICCs for the specific forms of assistance (i.e. self-materials, prescribe pharmacotherapy) and arrange were non-significant. Figure 1 provides a visual depiction of rates of 4 A’s delivery by GP. Table 2 Rates of 4 A’s tobacco treatment among GPs at index visit and previous 12-months, in Crete, Greece Parameter % Index visitn = 752 % Previous 12-monthsn = 805 ICC P-value ICC Ask 55.7 63.2 0.494 0.006 Advise     Quit smoking 50.3 58.3 0.422 0.006     Health hazards 32.1 46.4 0.292 0.007 Assist     General assistance 11.1 15.7 0.459 0.024     Set quit date 4.4 3.5 0.687 0.195     Provide self-help material 2.7 5.7 0.431 0.172     Discuss medications 5.3 7.8 0.884 0.354     Prescribe medication 0.9 1.5 0.883 0.499 Arrange 2.8 2.5 0.688 0.296 Parameter % Index visitn = 752 % Previous 12-monthsn = 805 ICC P-value ICC Ask 55.7 63.2 0.494 0.006 Advise     Quit smoking 50.3 58.3 0.422 0.006     Health hazards 32.1 46.4 0.292 0.007 Assist     General assistance 11.1 15.7 0.459 0.024     Set quit date 4.4 3.5 0.687 0.195     Provide self-help material 2.7 5.7 0.431 0.172     Discuss medications 5.3 7.8 0.884 0.354     Prescribe medication 0.9 1.5 0.883 0.499 Arrange 2.8 2.5 0.688 0.296 ICC: intra-class correlation coefficient, describes variation in tobacco treatment among providers sampled and is measured on a scale from 0 to 1, with a value close to 0 indicating the clusters were all similar. Intra-Provider ICC = provider variance/total variance. P values: reports on significance level of the GP-level variation observed. Table 2 Rates of 4 A’s tobacco treatment among GPs at index visit and previous 12-months, in Crete, Greece Parameter % Index visitn = 752 % Previous 12-monthsn = 805 ICC P-value ICC Ask 55.7 63.2 0.494 0.006 Advise     Quit smoking 50.3 58.3 0.422 0.006     Health hazards 32.1 46.4 0.292 0.007 Assist     General assistance 11.1 15.7 0.459 0.024     Set quit date 4.4 3.5 0.687 0.195     Provide self-help material 2.7 5.7 0.431 0.172     Discuss medications 5.3 7.8 0.884 0.354     Prescribe medication 0.9 1.5 0.883 0.499 Arrange 2.8 2.5 0.688 0.296 Parameter % Index visitn = 752 % Previous 12-monthsn = 805 ICC P-value ICC Ask 55.7 63.2 0.494 0.006 Advise     Quit smoking 50.3 58.3 0.422 0.006     Health hazards 32.1 46.4 0.292 0.007 Assist     General assistance 11.1 15.7 0.459 0.024     Set quit date 4.4 3.5 0.687 0.195     Provide self-help material 2.7 5.7 0.431 0.172     Discuss medications 5.3 7.8 0.884 0.354     Prescribe medication 0.9 1.5 0.883 0.499 Arrange 2.8 2.5 0.688 0.296 ICC: intra-class correlation coefficient, describes variation in tobacco treatment among providers sampled and is measured on a scale from 0 to 1, with a value close to 0 indicating the clusters were all similar. Intra-Provider ICC = provider variance/total variance. P values: reports on significance level of the GP-level variation observed. Table 3 Final model for multi-level analysis of GPs and patient-level characteristics associated with rates of 4 A’s delivery in Crete, Greece Parameter Ask Advise(quit smoking) Advise(health hazards) Assist Arrange GP-level variables Gender Female 1.00 Male 2.88 (1.06, 7.86)* Patient-level variables Age 18–24 years 1.00 1.00 25–39 years 0.24 (0.05, 1.26) 0.08 (0.01, 0.79)* 40–54 years 1.40 (0.33, 5.93) 0.05 (0.01, 0.56)* 55–64 years 1.59 (0.35, 7.24) 0.20 (0.02, 2.15) ≥65 years 1.63 (0.36, 7.50) 0.06 (0.01, 0.70)* Education 0–6 1.00 1.00 7–9 0.53 (0.31, 0.91)* 0.64 (0.36, 1.12) 10–12 0.43 (0.26, 0.72)** 0.47 (0.27, 0.80)** 12+ 0.51 (0.30, 0.88)* 0.62 (0.35, 1.08) Smoking-related illness No 1.00 1.00 Yes 2.07 (1.27, 3.37)** 2.05 (1.29, 3.27)** Anxiety, depression, or other mental illness No 1.00 1.00 Yes 1.83 (1.04, 3.23)* 4.67 (2.23, 9.75)*** Purpose of visit Medical examination 1.00 1.00 1.00 Prescription 0.74 (0.49, 1.12) 1.02 (0.69, 1.51) 0.61 (0.41, 0.91)* Other/missing 0.26 (0.15, 0.45)*** 0.31 (0.18, 0.54)*** 0.19 (0.10, 0.35)*** Cigarettes/day <15 1.00 15–25 1.63 (0.35, 7.56) >25 6.51 (1.09, 38.85)* Time to first cigarette in the morning After 30 min 1.00 Within 30 min 0.24 (0.07, 0.92)* Random variancea GP 3.022 (1.113) 2.208 (0.816) 1.231 (0.463) 2.996 (1.329) Parameter Ask Advise(quit smoking) Advise(health hazards) Assist Arrange GP-level variables Gender Female 1.00 Male 2.88 (1.06, 7.86)* Patient-level variables Age 18–24 years 1.00 1.00 25–39 years 0.24 (0.05, 1.26) 0.08 (0.01, 0.79)* 40–54 years 1.40 (0.33, 5.93) 0.05 (0.01, 0.56)* 55–64 years 1.59 (0.35, 7.24) 0.20 (0.02, 2.15) ≥65 years 1.63 (0.36, 7.50) 0.06 (0.01, 0.70)* Education 0–6 1.00 1.00 7–9 0.53 (0.31, 0.91)* 0.64 (0.36, 1.12) 10–12 0.43 (0.26, 0.72)** 0.47 (0.27, 0.80)** 12+ 0.51 (0.30, 0.88)* 0.62 (0.35, 1.08) Smoking-related illness No 1.00 1.00 Yes 2.07 (1.27, 3.37)** 2.05 (1.29, 3.27)** Anxiety, depression, or other mental illness No 1.00 1.00 Yes 1.83 (1.04, 3.23)* 4.67 (2.23, 9.75)*** Purpose of visit Medical examination 1.00 1.00 1.00 Prescription 0.74 (0.49, 1.12) 1.02 (0.69, 1.51) 0.61 (0.41, 0.91)* Other/missing 0.26 (0.15, 0.45)*** 0.31 (0.18, 0.54)*** 0.19 (0.10, 0.35)*** Cigarettes/day <15 1.00 15–25 1.63 (0.35, 7.56) >25 6.51 (1.09, 38.85)* Time to first cigarette in the morning After 30 min 1.00 Within 30 min 0.24 (0.07, 0.92)* Random variancea GP 3.022 (1.113) 2.208 (0.816) 1.231 (0.463) 2.996 (1.329) Models adjusted for GP-level clustering effects; CI, confidence interval. Ask: 25 GPs; 1= Asked (n = 419), 0= Not Asked (n = 333). Advise (quit smoking): 25 GPs; 1= Advised (n = 378), 0= Not Advised (n = 372). Advise (health hazards): 25 GPs; 1= Advised health hazards (n = 241), 0= Not advised health hazards (n = 509). Assist: 25 GPs; 1= Assisted (n = 83), 0= Not Assisted (n = 668). Arrange: 25 GPs; 1= Arranged follow-up visit (n = 21), 0= Not arranged follow-up visit (n = 730). P-values calculated based on Wald Tests; *P < 0.05;**P < 0.01; *** P < 0.001. Empty cells: not significant variable in the final model. a Random effects reflecting deviation of clinic k from the overall mean for the particular clinic effects. Table 3 Final model for multi-level analysis of GPs and patient-level characteristics associated with rates of 4 A’s delivery in Crete, Greece Parameter Ask Advise(quit smoking) Advise(health hazards) Assist Arrange GP-level variables Gender Female 1.00 Male 2.88 (1.06, 7.86)* Patient-level variables Age 18–24 years 1.00 1.00 25–39 years 0.24 (0.05, 1.26) 0.08 (0.01, 0.79)* 40–54 years 1.40 (0.33, 5.93) 0.05 (0.01, 0.56)* 55–64 years 1.59 (0.35, 7.24) 0.20 (0.02, 2.15) ≥65 years 1.63 (0.36, 7.50) 0.06 (0.01, 0.70)* Education 0–6 1.00 1.00 7–9 0.53 (0.31, 0.91)* 0.64 (0.36, 1.12) 10–12 0.43 (0.26, 0.72)** 0.47 (0.27, 0.80)** 12+ 0.51 (0.30, 0.88)* 0.62 (0.35, 1.08) Smoking-related illness No 1.00 1.00 Yes 2.07 (1.27, 3.37)** 2.05 (1.29, 3.27)** Anxiety, depression, or other mental illness No 1.00 1.00 Yes 1.83 (1.04, 3.23)* 4.67 (2.23, 9.75)*** Purpose of visit Medical examination 1.00 1.00 1.00 Prescription 0.74 (0.49, 1.12) 1.02 (0.69, 1.51) 0.61 (0.41, 0.91)* Other/missing 0.26 (0.15, 0.45)*** 0.31 (0.18, 0.54)*** 0.19 (0.10, 0.35)*** Cigarettes/day <15 1.00 15–25 1.63 (0.35, 7.56) >25 6.51 (1.09, 38.85)* Time to first cigarette in the morning After 30 min 1.00 Within 30 min 0.24 (0.07, 0.92)* Random variancea GP 3.022 (1.113) 2.208 (0.816) 1.231 (0.463) 2.996 (1.329) Parameter Ask Advise(quit smoking) Advise(health hazards) Assist Arrange GP-level variables Gender Female 1.00 Male 2.88 (1.06, 7.86)* Patient-level variables Age 18–24 years 1.00 1.00 25–39 years 0.24 (0.05, 1.26) 0.08 (0.01, 0.79)* 40–54 years 1.40 (0.33, 5.93) 0.05 (0.01, 0.56)* 55–64 years 1.59 (0.35, 7.24) 0.20 (0.02, 2.15) ≥65 years 1.63 (0.36, 7.50) 0.06 (0.01, 0.70)* Education 0–6 1.00 1.00 7–9 0.53 (0.31, 0.91)* 0.64 (0.36, 1.12) 10–12 0.43 (0.26, 0.72)** 0.47 (0.27, 0.80)** 12+ 0.51 (0.30, 0.88)* 0.62 (0.35, 1.08) Smoking-related illness No 1.00 1.00 Yes 2.07 (1.27, 3.37)** 2.05 (1.29, 3.27)** Anxiety, depression, or other mental illness No 1.00 1.00 Yes 1.83 (1.04, 3.23)* 4.67 (2.23, 9.75)*** Purpose of visit Medical examination 1.00 1.00 1.00 Prescription 0.74 (0.49, 1.12) 1.02 (0.69, 1.51) 0.61 (0.41, 0.91)* Other/missing 0.26 (0.15, 0.45)*** 0.31 (0.18, 0.54)*** 0.19 (0.10, 0.35)*** Cigarettes/day <15 1.00 15–25 1.63 (0.35, 7.56) >25 6.51 (1.09, 38.85)* Time to first cigarette in the morning After 30 min 1.00 Within 30 min 0.24 (0.07, 0.92)* Random variancea GP 3.022 (1.113) 2.208 (0.816) 1.231 (0.463) 2.996 (1.329) Models adjusted for GP-level clustering effects; CI, confidence interval. Ask: 25 GPs; 1= Asked (n = 419), 0= Not Asked (n = 333). Advise (quit smoking): 25 GPs; 1= Advised (n = 378), 0= Not Advised (n = 372). Advise (health hazards): 25 GPs; 1= Advised health hazards (n = 241), 0= Not advised health hazards (n = 509). Assist: 25 GPs; 1= Assisted (n = 83), 0= Not Assisted (n = 668). Arrange: 25 GPs; 1= Arranged follow-up visit (n = 21), 0= Not arranged follow-up visit (n = 730). P-values calculated based on Wald Tests; *P < 0.05;**P < 0.01; *** P < 0.001. Empty cells: not significant variable in the final model. a Random effects reflecting deviation of clinic k from the overall mean for the particular clinic effects. Figure 1 View largeDownload slide Rates of ask, advise, assist, arrange in previous 12-months by GPs sampled in Crete, Greece Figure 1 View largeDownload slide Rates of ask, advise, assist, arrange in previous 12-months by GPs sampled in Crete, Greece Predictors of 4 A’s delivery The final model of the multi-level analysis examining predictors of 4 A’s delivery is presented as table 3. Male GPs were significantly more likely to ‘advise’ patients about the health hazards of smoking and the value of cessation (AOR 2.88; 95% CI 1.06, 7.86; P < 0.05). No other GP level variables were found to be significant in predicting 4 A’s delivery. ‘Asking’ about tobacco use occurred more frequently among patient with smoking related illness (AOR 2.07; 95% CI 1.27, 3.37; P < 0.01). ‘Advice’ regarding the health hazards of smoking was more likely to be delivered to patients with grade school education, a smoking-related illness (AOR 2.05; 95% CI 1.29, 3.27; P < 0.01), a positive screen for anxiety/depression (AOR 1.83; 95% CI 1.04, 3.23; P < 0.05) and who were seen in clinic for a medical examination or prescription. ‘Assistance’ with quitting was more frequently delivered to patients with a positive screen for anxiety or depression (AOR 4.67; 95% CI 2.23, 9.75; P < 00.1). Additionally a significant (P < 0.01) trend across age groups was seen in rates at which ‘advise’ was delivered; tobacco users of increasing age were advised to quit more frequently than younger patients. ‘Arranging’ follow-up was significantly more likely to occur among patients who smoked more than 25 cigarettes per day (AOR 6.51; 95% CI 1.09, 38.85; P < 0.05) and who smoked their first cigarettes 30 mins or more after waking in the morning. Discussion Study main results and highlights from the literature To our knowledge, this is the first study to report on the characteristics of tobacco users and rates of tobacco treatment delivery in primary care in Greece. Our study reveals a very high prevalence (38%) of smoking among patients seen in primary care. Tobacco users who participated in the present study reported high levels of readiness to quit, rated quitting as being of personal importance and identified their GP’s advice to quit as an important source of influence. All of our findings highlight the opportunity to intervene more effectively with tobacco users identified in primary care settings. Although, there is strong evidence,11–14 to support the primary care setting as a key environment for providing smoking cessation and a framework exists to integrate smoking cessation treatment into daily clinical practice, our study documented that a large proportion of tobacco users did not receive ‘advice’ to quit from their primary health care GPs in the previous year. Moreover, while ‘advice’ to quit is delivered to approximately 58.3% of all tobacco users, less than 15.7% received any type of ‘assistance’ with quitting in the last year.13,14,24 Our study adds to a large body of existing international surveys which have documented a similar practice gap in the rates of tobacco treatment delivery in primary care settings.14–16 Among the GPs sampled there was significant variability in the rates at which ‘ask’ and ‘advise’ was delivered. Providers in this study can be classified in three categories according to the rates of ‘ask’ and ‘advise’: high performers (>80% of patients received ‘ask’ and ‘advice’), moderate performers (40–70%) and low performers (<30%). The source of this variation and approaches to supporting low and moderate performing GPs with increasing rates of tobacco treatment delivery are important topics for future research. The profile of tobacco users identified in our study suggests a large proportion of patients are highly addicted, have high daily tobacco consumption rates, have other smokers in their home and report low levels of self-efficacy–all of which are factors known to be associated with difficulty with cessation.25,26 The reported rate of cigarette consumption in our study was 21.1 cigarettes per day, significantly higher than the European average (14.4 cigarettes/day) and slightly higher than population rates reported for Greece in the most recent Eurobarometer survey.3,27 These patients are more likely to benefit from formalized cessation assistance provided by trained clinicians employing evidence-based therapies such as pharmacotherapy and counseling. Importantly, more than half of participants in our study reported their readiness to quit smoking in the near future. This finding is similar to data from other studies.28,29 European tobacco treatment guidelines have called for tobacco addiction to be given the same attention by clinicians as other chronic diseases and chronic disease risk factors such as hypertension, diabetes and cholesterol management.13 These diseases are screened for regularly and treated aggressively using a combination of counseling and pharmacotherapy. Tobacco use has been described as unique in its prevalence, lethality and neglect.14 It has not been given the same attention as other chronic diseases or risk factors by primary care clinicians. Lack of training in evidence-based tobacco treatment during undergraduate and post-graduate medical training, low levels of self-efficacy, work load, time pressure, as well as patient resistance are some of the most important factors which are known to limit the adoption of tobacco treatment by GPs.14,30–32 Countries experiencing fiscal constraints have been found to report lower rates of smoking cessation advice.22 A prime opportunity for intervention is to transform clinicians’ knowledge and attitudes about the importance of addressing tobacco use and the important role they play in increasing their patient’s motivation to quit smoking. Training in evidence-based tobacco treatment has been shown to increase rates of tobacco treatment.11,33 In our study, only approximately one third of GPs had received training in smoking cessation in the past, highlighting the opportunity to enhance training in evidence-based tobacco treatment. Strong evidence demonstrates that multi-component interventions combining training and other physician and patient-level intervention strategies are the most effective method for increasing GP performance in the delivery of smoking cessation treatment and improving cessation rates among patients.11 These cost-effective interventions are particularly important for a country affected by economic challenges. Our study documented that several patient-level factors were associated with the frequency of 4 A’s delivery. Overall tobacco treatment advice is more frequently delivered to patients perceived to be at increased risk (i.e. have a smoking related illness), who suffer from anxiety or depression and who are older. Similar patterns have been previously reported.34,35 Interestingly, individuals with grade school education or less were more likely to be advised to quit; a pattern also previously documented.16,30 The rates of 4 A’s treatment were higher at appointments for medical examinations; these appointments may be longer in duration and thus provide more opportunity to discuss prevention, however, there is evidence to show that tobacco users are open to receiving advice to quit at other types of medical appointments in particular those during which acute symptoms are being experienced.16 Importantly, clinical practice guidelines emphasize that tobacco treatment be delivered to all patients who smoke and not a sub-population of smokers or during specific visits.13,14 There is strong evidence to show that quitting at a younger age increases life expectancy dramatically.10,36 Strengths and limitations One quarter of GPs sampled were tobacco users themselves. It is known that a physician’s personal tobacco use decreases the likelihood of tobacco treatment for patients in their practice.37 Previous reports have identified tobacco use among clinicians in Greece and other European countries to be similar to that of the general population.38,39 While our study did not find personal smoking cessation status was significantly associated with rates of tobacco treatment delivery this may be due to our sample size. Consideration should be given to supporting clinicians with quitting as a strategy for increasing rates of tobacco treatment delivery in their practice. We had very high rates of participation among GPs and their patients, a factor that we attribute to the high regard given to University-based medical research in Greece. Limitations of our study should also be considered. It is unclear how GPs s sampled in our study are representative of those practicing in other parts of Greece and Europe. Our primary care providers were relatively young (<50-years), and primarily working in publically funded clinics (vs. private practice) in rural settings. Most providers were affiliated with the university practice based research network located on the island of Crete. The generalizability of our findings to the rest of Greece and/or southern Europe requires further investigation. It is also possible that an observation bias may have resulted in clinicians more consistently delivering tobacco treatment during the data collection period, resulting in higher rates of 4 A’s delivery being documented than normal. While patient-reported rates of 4 A’s delivery have been shown to be more accurate than physician self-report it is also possible that there may be some recall bias or over reporting by patients.40 In the present study, we reported on 4 of the 5 As strategies. We did not enquire about the ‘assess’ strategy. Conclusions This study has identified an important practice gap in the delivery of evidence-based smoking cessation treatments in primary care in Greece. Increasing the rates of 5 As tobacco treatment in primary care is an important target for quality improvement. Future research could examine the efficacy of training and practice-level interventions tailored to the unique profile of tobacco users and primary care providers in the Mediterranean and Eastern Europe and strategies for motivating patient not ready to quit, as well as cessation among health care providers. Funding This work was supported by Global Bridges: Healthcare Alliance for Tobacco Dependence Treatment and Pfizer Independent Grants for Learning and Change (GB-13522581). Conflicts of interest: A. Pipe has received educational and research support in the past from Pfizer and Johnson & Johnson, and has served as a consultant to Pfizer and Amgen. Key points This is the first study to report on the characteristics of tobacco users and rates of tobacco treatment delivery in primary care practice settings in Greece. Our findings revealed a very high prevalence of smoking among patients identified in the primary care settings sampled as well as high rates of daily tobacco use and nicotine addiction. While approximately half of all tobacco users received advice to quit, assistance with quitting was infrequent. This study has identified an important practice gap in the delivery of evidence-based smoking cessation treatments in primary care practices in Greece. Increasing the rates of 5As tobacco treatment in primary care settings is an important target for quality improvement. Acknowledgements This study was conducted in collaboration with the Practice Based Research Network in Primary Care in Crete. The authors would like to acknowledge the contributions of the network members: Anastasiou Fotini, Kounalakis Dimitrios, Makri Kornilia, Meramveliotakis Emmanouil, Papamastorakis Emmanouil, Pateli Rodanthi, Petraki Chrisa, Prokopiadou Dimitra, Stefanaki Ioanna, Symvoulakis Emmanouil, Tsakountakis Nikolaos, Tsiligianni Ioanna, Vasilaki Aggeliki, Vasilopoulos Theodoros. Supplementary data Supplementary data are available at EURPUB online. References 1 World Health Organization . WHO Global Report: Mortality Attributable to Tobacco, 2012 . Switzerland, Available at: http://www.who.int/tobacco/publications/surveillance/rep_mortality_attributable/en/ (15 January 2017, date last accessed). 2 European Commission. Tobacco Policy. European Commission , 2014 . Belgium, Available at: http://ec.europa.eu/health/tobacco/policy/index_en.htm (15 January 2017, date last accessed). 3 European Commission . Attitudes of Europeans Towards Tobacco: Special Eurobarometer 429, 2014 . Belgium, http://ec.europa.eu/public_opinion/archives/ebs/ebs_429_en.pdf (15 January 2017, date last accessed). 4 Tsalapati K , Vardavas CI , Athanasakis K , et al. Going up in ashes? Smoking-attributable morbidity, hospital admissions and expenditure in Greece . Eur J Public Health 2014 ; 24 : 477 – 9 . Google Scholar CrossRef Search ADS PubMed 5 European Commission . Attitudes of Europeans Towards Tobacco: Special Eurobarometer 385, 2012 . Belgium, http://ec.europa.eu/health//sites/health/files/tobacco/docs/eurobaro_attitudes_towards_tobacco_2012_en.pdf (16 January 2017, date last accessed). 6 U.S. National Cancer Institute and World Health Organization . The Economics of Tobacco and Tobacco Control. National Cancer Institute Tobacco Control Monograph 21. NIH Publication No. 16-CA-8029A. Bethesda, MD: U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute; and Geneva, CH: World Health Organization, 2016 . USA, Available at: https://cancercontrol.cancer.gov/brp/tcrb/monographs/21/docs/m21_complete.pdf (15 January 2017, date last accessed). 7 U.S. Department of Health and Human Services . The Health Consequences of Smoking—50 Years of Progress: A report of the Surgeon General, 2015 . USA, Available at: http://www.surgeongeneral.gov/library/reports/50-years-of-progress (16 January 2017, date last accessed). 8 Franco OH , der Kinderen AJ , De Laet C , et al. Primary prevention of cardiovascular disease: cost-effectiveness comparison . Int J Technol Assess Health Care 2007 ; 23 : 71 – 9 . Google Scholar CrossRef Search ADS PubMed 9 Eddy DM , Peskin B , Shcheprov A , et al. Effect of smoking cessation advice on cardiovascular disease . Am J Med Qual 2009 ; 24 : 241 – 9 . Google Scholar CrossRef Search ADS PubMed 10 Doll R , Peto R , Boreham J , Sutherland I . Mortality in relation to smoking: 50 years' observations on male British doctors . BMJ 2004 ; 328 : 1519 . Google Scholar CrossRef Search ADS PubMed 11 Papadakis S , McDonald P , Mullen K-A , et al. Strategies to increase the delivery of smoking cessation treatments in primary care settings: a systematic review and meta-analysis . Prev Med 2010 ; 51 : 199 – 213 . Google Scholar CrossRef Search ADS PubMed 12 Vardavas C , Symvoulakis M , Lionis C . Dealing with tobacco use and dependence within primary health care: time for action . Tob Induc Dis 2013 ; 11 : 6 . Google Scholar CrossRef Search ADS PubMed 13 European Network for Smoking and Tobacco Prevention (ENSP) , Behrakis P , Bilir N , Clancy L , et al. editors. Guidelines for Treating Tobacco Dependence . Brussels, Belgium : European Publishing , 2017 . 14 Fiore MC , Jaen CR , Baker TB , et al. Clinical Practice Guideline: Treating Tobacco Use and Dependence. Rockville, MD: US Department of Health and Human Services, Public Health Service, 2008 . USA, Available at: https://bphc.hrsa.gov/buckets/treatingtobacco.pdf (15 January 2017, date last accessed). 15 Katz DA , Vander Weg MW , Holman J , et al. The emergency department action in smoking cessation (EDASC) trial: impact on delivery of smoking cessation counseling . Acad Emerg Med 2012 ; 19 : 409 – 20 . Google Scholar CrossRef Search ADS PubMed 16 Papadakis S , Gharib M , Hambleton J . Delivering evidence-based smoking cessation treatment in primary care practice. Experience of Ontario family health teams . Can Fam Phys 2014 ; 60 : e362 – 71 . 17 Kruger J , O’Halloran A , Rosenthal A . Assessment of compliance with US Public Health Service Clinical Practice Guideline for tobacco by primary care physicians . Harm Reduct J 2015 ; 12 : 7 . Google Scholar CrossRef Search ADS PubMed 18 Löwe B , Wahl I , Rose M , et al. A 4-item measure of depression and anxiety: validation and standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general population . J Affect Disord 2010 ; 122 : 86 – 95 . Google Scholar CrossRef Search ADS PubMed 19 Karekla M , Pilipenko N , Feldman J . Patient health questionnaire: Greek language validation and subscale factor structure . Compr Psychiatr 2012 ; 53 : 1217 – 26 . Google Scholar CrossRef Search ADS 20 Chaiton MO , Cohen JE , McDonald PW , Bondy SJ . The Heaviness of Smoking Index as a predictor of smoking cessation in Canada . Addict Behav 2007 ; 32 : 1031 – 42 . Google Scholar CrossRef Search ADS PubMed 21 Pérez-Ríos M , Santiago-Pérez MI , Alonso B , et al. Fagerstrom test for nicotine dependence vs. heavy smoking index in a general population survey . BMC Public Health 2009 ; 9 : 493 . Google Scholar CrossRef Search ADS PubMed 22 Omole OB , Ngobale KN , Ayo-Yusuf OA . Missed opportunities for tobacco use screening and brief cessation advice in South African primary health care: a cross-sectional study . BMC Fam Pract 2010 ; 29 : 94 . Google Scholar CrossRef Search ADS 23 Donner A , Klar N . Design and Analysis of Cluster Randomization Trials in Health Research . London : Arnold , 2000 . 24 Stead LF , Buitrago D , Preciado N , et al. Physician advice for smoking cessation . Cochrane Database Syst Rev 2013 ; 5 : CD000165 . 25 Piper ME , McCarthy DE , Baker TB . Assessing tobacco dependence: a guide to measure evaluation and selection . Nicotine Tob Res 2006 ; 8 : 339 – 51 . Google Scholar CrossRef Search ADS PubMed 26 Burgess DJ , Fu SS , Noorbaloochi S , et al. Employment, gender, and smoking cessation outcomes in low-income smokers using nicotine replacement therapy . Nicotine Tob Res 2009 ; 11 : 1439 – 47 . Google Scholar CrossRef Search ADS PubMed 27 Symvoulakis M , Klinis S , Kakoliris N , et al. The association between tobacco use and perceptions of tobacco price strategies within primary care patients in rural Greece . Tob Prev Cessat 2016 ; 2 : 60 . Google Scholar CrossRef Search ADS 28 Schoretsaniti S , Filippidis FT , Vardavas CI , et al. 5-Year trends in the intention to quit smoking amidst the economic crisis and after recently implemented tobacco control measures in Greece . Addict Behav 2014 ; 39 : 140 – 5 . Google Scholar CrossRef Search ADS PubMed 29 Kaai S , Chung-Hall J , Sun M , et al. Predictors of quit intentions among adult smokers in Mauritius: Findings from the ITC Mauritius Survey . Tob Prev Cessat 2016 ; 2 : 69 . 30 Steinberg MB , Akincigil A , Delnevo CD , et al. Gender and age disparities for smoking-cessation treatment . Am J Prev Med 2006 ; 30 : 405 – 12 . Google Scholar CrossRef Search ADS PubMed 31 Vogt F , Hall S , Marteau TM . General practitioners' and family physicians' negative beliefs and attitudes towards discussing smoking cessation with patients: a systematic review . Addict 2005 ; 100 : 1423 – 31 . Google Scholar CrossRef Search ADS 32 Lionis C , Petelos E . The impact of the financial crisis on the quality of care in primary care: an issue that requires prompt attention . Qual Prim Care 2013 ; 21 : 269 – 73 . Google Scholar PubMed 33 Carson KV , Verbiest MEA , Crone MR , et al. Training health professionals in smoking cessation . Cochrane Database Syst Rev 2012 ; 5 : CD000214 . 34 Martinson BC , O'Connor PJ , Pronk NP , Rolnick SJ . Smoking cessation attempts in relation to prior health care charges: the effect of antecedent smoking-related symptoms? Am J Health Promot 2003 ; 18 : 125 – 32 . Google Scholar CrossRef Search ADS PubMed 35 Azuri J , Peled S , Kitai E , Vinker S . Smoking prevention and primary physician's and patient's characteristics . Am J Health Behav 2009 ; 33 : 710 – 7 . Google Scholar CrossRef Search ADS PubMed 36 Jha P , Ramasundarahettige C , Landsman V , et al. 21st-century hazards of smoking and benefits of cessation in the United States . N Engl J Med 2013 ; 368 : 341 – 50 . Google Scholar CrossRef Search ADS PubMed 37 Duaso MJ , McDermott MS , Mujika A , et al. Do doctors' smoking habits influence their smoking cessation practices? A systematic review and meta-analysis . Addict 2014 ; 109 : 1811 – 23 . Google Scholar CrossRef Search ADS 38 World Health Organization . WHO Tobacco Free Initiative: The Role of Health Professionals in Tobacco Control, 2005 . Switzerland, Available at: http://www.who.int/tobacco/resources/publications/wntd/2005/bookletfinal_20april.pdf (15 January 2017, date last accessed). 39 Pipe A , Sorensen M , Reid R . Physician smoking status, attitudes toward smoking, and cessation advice to patients: an international survey . Patient Educ Couns 2009 ; 74 : 118 – 23 . Google Scholar CrossRef Search ADS PubMed 40 Pbert L , Adams A , Quirk M , et al. The patient exit interview as an assessment of physician-delivered smoking intervention: a validation study . Health Psychol 1999 ; 18 : 183 – 8 . Google Scholar CrossRef Search ADS PubMed © The Author 2017. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The European Journal of Public Health Oxford University Press

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Abstract

Abstract Background Tobacco dependence treatment in clinical settings is of prime public health importance, especially in Greece, a country experiencing one of the highest rates of tobacco use in Europe. Methods Our study aimed to examine the characteristics of tobacco users and document rates of tobacco treatment delivery in general practice settings in Crete, Greece. A cross-sectional sample of patients (n = 2, 261) was screened for current tobacco use in 25 general practices in Crete, Greece in 2015/16. Current tobacco users completed a survey following their clinic appointment that collected information on patient characteristics and rates at which the primary care physician delivered tobacco treatment using the evidence-based 4 A’s (Ask, Advise, Assist, Arrange) model during their medical appointment and over the previous 12-month period. Multi-level modeling was used to analyze data and examine predictors of 4 A’s delivery. Results Tobacco use prevalence was 38% among all patients screened. A total of 840 tobacco users completed the study survey [mean age 48.0 (SD 14.5) years, 57.6% male]. Approximately, half of the tobacco users reported their general practitioner ‘asked’ about their tobacco use and ‘advised’ them to quit smoking. Receiving ‘assistance’ with quitting (15.7%) and ‘arranging’ follow-up support (<3%) was infrequent. Patient education, presence of smoking-related illness, a positive screen for anxiety or depression and the type of medical appointment were associated with 4 A’s delivery. Conclusion Given the fundamental importance of addressing tobacco treatment, increasing the rates of 4 A’s treatment in primary care settings in Greece is an important target for improving patient care. Introduction Tobacco use is the leading cause of premature death and disability and the largest threat to public health in Europe.1,2 Each year, more than 700 000 Europeans die from tobacco-related illness.2 The World Health Organization’s (WHO) European region has one of the highest proportions of death attributable to tobacco, with an estimated 16% of all deaths among adults over 30 years of age due to tobacco use.1 Despite the decline in the prevalence of tobacco use, more than 125 million Europeans (26% of the population) continue to smoke, representing the highest rate of tobacco use among all the WHO regions.3 Moreover, tobacco use imposes a huge economic burden on the European health care systems, with the direct health care costs alone estimated to be 100 billion Euros.4,5 There is overwhelming evidence attesting to the health and economic benefits of smoking cessation.6 Quitting smoking reduces the excess risk of smoking-related coronary heart disease, for example, by approximately 50% within 1 year, and to normal levels within 5 years.7 Smoking cessation is highly cost-effective with the cost per life-year saved estimated to be between €1500 and €3000.8,9 Tobacco use is also a priority among young European due to the fact that 94% of smokers start smoking before the age of 25 years and quitting smoking as early in life substantially reduces future disease risk.10 General practices have been identified as important settings for the delivery of smoking cessation treatment.1,4,11,12 The WHO1 and the European Network For Smoking and Tobacco Prevention13 have called for tobacco dependence to be a clinical priority of all health professionals.1,13 The 5A’s of smoking cessation are an internationally recognized evidence-based schema to guide interventions with tobacco users in all clinical settings including primary care.13,14 The 5 As include: ‘ask’ patients about smoking status; provide brief quit smoking ‘advice’; ‘assess’ readiness to quit smoking; ‘assist’ patients with making a quit attempt using behavioral techniques and pharmacotherapies; and, ‘arrange’ follow-up support throughout the quitting process. Internationally, and in Europe, a practice gap in rates of 5 As delivery in clinical settings has been documented.15–17 Greece has one of the highest rates of tobacco use among members of the European Union, estimated at 38% of the adult population.3 Little is known about the characteristics of tobacco users and current rates of tobacco treatment delivery in primary care settings in Greece. As a consequence, this study sought to examine the characteristics of tobacco users visiting general practitioners (GPs) in Crete, Greece and to document the rates of tobacco treatment delivery. We also examined patient-, GP- and clinic-level predictors of tobacco treatment delivery. Methods Design and setting Here, we report the cross sectional baseline data collected as part of the Global Bridges TiTAN Crete project. The purpose of the TiTAN Crete project (http://titan.uoc.gr/index_en.html) is to create a network of GPs trained in evidence-based smoking cessation treatments. Data collection took place in Crete, Greece between May 2015 and June 2016. GPs were surveyed and a cross-sectional sample of their patients was screened for current tobacco use. All current tobacco users were asked to complete a survey following their clinic appointment. The survey documented the characteristics of tobacco users and assessed rates at which their GPs delivered tobacco treatment during the patient’s same day medical appointment (index visit) and during the previous 12-month period. The study received ethics approval from the University Hospital of Heraklion Ethics Board (ref# 18078). Procedures During recruitment, all GPs located in the regions of Heraklion and Rethymnon in Crete, Greece were invited to participate in the study by email. To be eligible for the study GPs need to be: currently working in Primary Health Care practice in the geographic recruitment area. Twenty-five of twenty-six eligible GPs agreed to participate (response rate 96.2%). A follow-up telephone call was made by a member of the project team to confirm interest in participation. Informed consent was obtained from all participating GPs. GPs completed a questionnaire to document demographic characteristics including: age, gender, number of years practicing medicine, previous cessation training and personal smoking status. A standardized description of clinic characteristics was assembled; it included details of practice size, geography, payment methods and use of an electronic medical record. Within all participating GP’s offices, consecutive patients (n = 2261) were screened for eligibility using a brief written survey administered in the waiting room of the practice upon arrival for their appointment. Eligibility criteria included: being 18-years of age or older; a current tobacco user (≥1 cigarette per day on most days of the week); attending clinic for a non-urgent medical visit; and, ability to understand Greek. Eligible patients who agreed to participate in the study provided informed consent (n = 840, response rate 97.7%) and were asked to complete a brief survey following their medical appointment. The patient survey collected demographic variables (age, sex, ethnicity, years of formal education, occupation, income and postal code), a brief medical history and smoking related variables. The presence of any smoking-related illness was documented including heart disease, stroke, chronic obstructive pulmonary disease and cancer. A validated Greek version of the four-item Patient Health Questionnaire (PHQ), a tool used by health care professionals for diagnosing mental health disorders was administered.18,19 The two-item Heaviness of Smoking Index (HSI) was used to assess the degree of nicotine dependence.20,21 The HSI score ranges from 0 to 6 with higher HSI scores reflecting greater nicotine dependence. Smoking history was assessed by documenting the number of years a participant had been smoking. The number of previous quit attempts (lasting 24-h or longer) in the past year was also documented. Consistent with previous research, performance in the delivery of each of the 4 A’s (ask, advise, assist, arrange) was assessed using an exit survey.22 The survey instructed participants to respond either ‘yes’, ‘no’ or ‘don’t know’ when asked whether their GP asked them about their smoking status (ask); advised them to quit smoking (advise); provided assistance with quitting (assist); or arranged follow-up support (arrange). For ‘assist,’ we further examined the type of assistance provided including whether or not the GP: prescribed pharmacotherapy, provided self-help materials; or set a quit date. Participants were asked to respond regarding their receipt of those interventions during that day’s clinic appointment (i.e. the index appointment) as well as at any time in the previous 12-months. We chose not to measure rates at which providers assessed readiness to quit smoking in the present study. A research assistant coordinated all screening and data collection activities in clinic waiting rooms. For patients unable to read/write the research assistant completed the survey by interview. The Consolidated Standards of Reporting Trials flow diagram for the study is presented as Supplementary figure S1. Statistical analysis Descriptive statistics assessed GP characteristics, patient characteristics and rates of delivery of the 4 A’s. Given the clustered nature of data collection, each participating patient was linked to their GP. Multi-level modeling was used to account for provider-level clustering.23 An intra-class correlation coefficient (ICC) was calculated to describe the variation among GPs in rates of 4 A’s delivery and significance was assessed. The ICC ranges from 0 to 1 with higher scores indicating larger variation among providers in rates of 4 A’s delivery.23 In order to understand the patient-level, GP-level, and clinic-level factors associated with each outcome, separate multi-level logistic regression analyzes were completed. A block approach was used to examine the factors from each of the three levels that were associated with each outcome in a separate logistic regression model. Variables from each level that were significantly associated with each outcome at P < 0.1 were included in a final model; only variables that were significantly associated with each outcome at P < 0.05 were retained in the final model. Results were reported as adjusted odds ratios (AOR) and 95% confidence intervals (CI). SAS 9.4 was used to conduct multi-level modeling. Results The majority of GPs were under the age of 50-years (95.8%) and practicing in rural settings. Twenty-five percent of GPs reported current personal tobacco use while less than thirty-five percent of them reported they had the necessary skills to support their patients with quitting. Supplementary table S1 presents data on characteristics of providers. The prevalence of tobacco use among the patients was estimated at 38%, their mean age was 48.0 (SD 14.5) years, and 57.6% were male (table 1). The majority of tobacco users had smoked for more than 20-years (67.4%) and reported high rates of daily tobacco consumption (mean 21.1 SD ± 11.9). Overall, 65.4% of participants reported smoking within the first 30-min of waking while 58% of them were thinking of quitting in the next 6-months. Less than 40% of those surveyed reported making a quit attempt in the previous year. The majority of respondents reported low self-efficacy with quitting while 71.0% of tobacco users rated their GP’s advice to quit as important or very important. Table 1 Socio-demographic and tobacco-related characteristics of primary care patients sampled (n = 840) in Crete, Greece Parameter Response Value Age Mean years (SD) 48.0 (14.5) Sex % Male 57.6% Number of cigarettes (daily) Mean years (SD) 21.1 (11.9) Education (years) 0–6 21.7% 7–9 20.1% 10–12 30.1% 12+ 27.9% Nationality Greek 97.9% Smoking-related illnessa Yes 18.8% Depressionb Score of ≥ 3 6.9% Anxietyc Score of ≥ 3 15.5% Cigarettes/day <5 4.8% 6–15 32.3% 16–25 39.4% 26–40 19.9% >40 3.7% Time to first cigarette After 60 mins 20.4% 31–60 mins 14.3% 6–30 mins 35.6% Within 5 mins 29.8% HSI High 20.6% Moderate 56.1% Low 23.3% Years of smoking 0–2 1.2% 3–9 7.9% 10–19 23.6% 20+ 67.4% Readiness to quitd Next 30 days 24.2% Next 6-months 34.2% Not ready to quit 41.6% Self-efficacy with quittinge Low (≤7/10) 85.5% High (>7/10) 14.5% Number of quit attempts in past year 0 61.4% 1–2 32.5% 3+ 6.1% Presence of Other smokers in the home Yes 58.1% Family/friends who smoke None 4.2% Some 38.9% Most 52.6% All 4.3% Perceived importance of quittingf Low (≤7/10) 37.5% High (>7/10) 62.5% Importance of doctor’s advice to quit Very important 21.4% Important 49.6% Somewhat important 18.6% Not at all important 10.4% Parameter Response Value Age Mean years (SD) 48.0 (14.5) Sex % Male 57.6% Number of cigarettes (daily) Mean years (SD) 21.1 (11.9) Education (years) 0–6 21.7% 7–9 20.1% 10–12 30.1% 12+ 27.9% Nationality Greek 97.9% Smoking-related illnessa Yes 18.8% Depressionb Score of ≥ 3 6.9% Anxietyc Score of ≥ 3 15.5% Cigarettes/day <5 4.8% 6–15 32.3% 16–25 39.4% 26–40 19.9% >40 3.7% Time to first cigarette After 60 mins 20.4% 31–60 mins 14.3% 6–30 mins 35.6% Within 5 mins 29.8% HSI High 20.6% Moderate 56.1% Low 23.3% Years of smoking 0–2 1.2% 3–9 7.9% 10–19 23.6% 20+ 67.4% Readiness to quitd Next 30 days 24.2% Next 6-months 34.2% Not ready to quit 41.6% Self-efficacy with quittinge Low (≤7/10) 85.5% High (>7/10) 14.5% Number of quit attempts in past year 0 61.4% 1–2 32.5% 3+ 6.1% Presence of Other smokers in the home Yes 58.1% Family/friends who smoke None 4.2% Some 38.9% Most 52.6% All 4.3% Perceived importance of quittingf Low (≤7/10) 37.5% High (>7/10) 62.5% Importance of doctor’s advice to quit Very important 21.4% Important 49.6% Somewhat important 18.6% Not at all important 10.4% a Self- Reported heart disease, stroke, heart failure/cancer/chronic obstructive pulmonary disease (COPD)? (1 = yes, 0 = no). b PHQ-4 for depression. c PHQ-4 for anxiety. d Which of the following best describes your feelings about smoking right now? (Responses: 1 = ready to quit in next 30 days, 0= ready to quit in next 6-months or not ready to quit). e On a scale of 1–10 how confident are you that you would be able to quit smoking at this time? (1 = not at all confident, 10 = extremely confident). f On a scale of 1–10 how important is it to you to quit smoking at this time? (Response: 1 = not at all important, 10 = extremely important. Table 1 Socio-demographic and tobacco-related characteristics of primary care patients sampled (n = 840) in Crete, Greece Parameter Response Value Age Mean years (SD) 48.0 (14.5) Sex % Male 57.6% Number of cigarettes (daily) Mean years (SD) 21.1 (11.9) Education (years) 0–6 21.7% 7–9 20.1% 10–12 30.1% 12+ 27.9% Nationality Greek 97.9% Smoking-related illnessa Yes 18.8% Depressionb Score of ≥ 3 6.9% Anxietyc Score of ≥ 3 15.5% Cigarettes/day <5 4.8% 6–15 32.3% 16–25 39.4% 26–40 19.9% >40 3.7% Time to first cigarette After 60 mins 20.4% 31–60 mins 14.3% 6–30 mins 35.6% Within 5 mins 29.8% HSI High 20.6% Moderate 56.1% Low 23.3% Years of smoking 0–2 1.2% 3–9 7.9% 10–19 23.6% 20+ 67.4% Readiness to quitd Next 30 days 24.2% Next 6-months 34.2% Not ready to quit 41.6% Self-efficacy with quittinge Low (≤7/10) 85.5% High (>7/10) 14.5% Number of quit attempts in past year 0 61.4% 1–2 32.5% 3+ 6.1% Presence of Other smokers in the home Yes 58.1% Family/friends who smoke None 4.2% Some 38.9% Most 52.6% All 4.3% Perceived importance of quittingf Low (≤7/10) 37.5% High (>7/10) 62.5% Importance of doctor’s advice to quit Very important 21.4% Important 49.6% Somewhat important 18.6% Not at all important 10.4% Parameter Response Value Age Mean years (SD) 48.0 (14.5) Sex % Male 57.6% Number of cigarettes (daily) Mean years (SD) 21.1 (11.9) Education (years) 0–6 21.7% 7–9 20.1% 10–12 30.1% 12+ 27.9% Nationality Greek 97.9% Smoking-related illnessa Yes 18.8% Depressionb Score of ≥ 3 6.9% Anxietyc Score of ≥ 3 15.5% Cigarettes/day <5 4.8% 6–15 32.3% 16–25 39.4% 26–40 19.9% >40 3.7% Time to first cigarette After 60 mins 20.4% 31–60 mins 14.3% 6–30 mins 35.6% Within 5 mins 29.8% HSI High 20.6% Moderate 56.1% Low 23.3% Years of smoking 0–2 1.2% 3–9 7.9% 10–19 23.6% 20+ 67.4% Readiness to quitd Next 30 days 24.2% Next 6-months 34.2% Not ready to quit 41.6% Self-efficacy with quittinge Low (≤7/10) 85.5% High (>7/10) 14.5% Number of quit attempts in past year 0 61.4% 1–2 32.5% 3+ 6.1% Presence of Other smokers in the home Yes 58.1% Family/friends who smoke None 4.2% Some 38.9% Most 52.6% All 4.3% Perceived importance of quittingf Low (≤7/10) 37.5% High (>7/10) 62.5% Importance of doctor’s advice to quit Very important 21.4% Important 49.6% Somewhat important 18.6% Not at all important 10.4% a Self- Reported heart disease, stroke, heart failure/cancer/chronic obstructive pulmonary disease (COPD)? (1 = yes, 0 = no). b PHQ-4 for depression. c PHQ-4 for anxiety. d Which of the following best describes your feelings about smoking right now? (Responses: 1 = ready to quit in next 30 days, 0= ready to quit in next 6-months or not ready to quit). e On a scale of 1–10 how confident are you that you would be able to quit smoking at this time? (1 = not at all confident, 10 = extremely confident). f On a scale of 1–10 how important is it to you to quit smoking at this time? (Response: 1 = not at all important, 10 = extremely important. Rates of 4 A’s delivery At the index visit 50.3% of patient reported receiving advice to quit smoking from their GP however, only 11.1% of patients reported receiving assistance with smoking cessation. A similar pattern was documented for the previous 12-months. Discussing and prescribing quit smoking medications and the provision of self-help material occurred infrequently at both the index visit and during the previous 12-months (table 2). ICCs indicate substantial inter-provider variability for ‘ask’, ‘advice’ and ‘assist’ (table 3). ICCs for the specific forms of assistance (i.e. self-materials, prescribe pharmacotherapy) and arrange were non-significant. Figure 1 provides a visual depiction of rates of 4 A’s delivery by GP. Table 2 Rates of 4 A’s tobacco treatment among GPs at index visit and previous 12-months, in Crete, Greece Parameter % Index visitn = 752 % Previous 12-monthsn = 805 ICC P-value ICC Ask 55.7 63.2 0.494 0.006 Advise     Quit smoking 50.3 58.3 0.422 0.006     Health hazards 32.1 46.4 0.292 0.007 Assist     General assistance 11.1 15.7 0.459 0.024     Set quit date 4.4 3.5 0.687 0.195     Provide self-help material 2.7 5.7 0.431 0.172     Discuss medications 5.3 7.8 0.884 0.354     Prescribe medication 0.9 1.5 0.883 0.499 Arrange 2.8 2.5 0.688 0.296 Parameter % Index visitn = 752 % Previous 12-monthsn = 805 ICC P-value ICC Ask 55.7 63.2 0.494 0.006 Advise     Quit smoking 50.3 58.3 0.422 0.006     Health hazards 32.1 46.4 0.292 0.007 Assist     General assistance 11.1 15.7 0.459 0.024     Set quit date 4.4 3.5 0.687 0.195     Provide self-help material 2.7 5.7 0.431 0.172     Discuss medications 5.3 7.8 0.884 0.354     Prescribe medication 0.9 1.5 0.883 0.499 Arrange 2.8 2.5 0.688 0.296 ICC: intra-class correlation coefficient, describes variation in tobacco treatment among providers sampled and is measured on a scale from 0 to 1, with a value close to 0 indicating the clusters were all similar. Intra-Provider ICC = provider variance/total variance. P values: reports on significance level of the GP-level variation observed. Table 2 Rates of 4 A’s tobacco treatment among GPs at index visit and previous 12-months, in Crete, Greece Parameter % Index visitn = 752 % Previous 12-monthsn = 805 ICC P-value ICC Ask 55.7 63.2 0.494 0.006 Advise     Quit smoking 50.3 58.3 0.422 0.006     Health hazards 32.1 46.4 0.292 0.007 Assist     General assistance 11.1 15.7 0.459 0.024     Set quit date 4.4 3.5 0.687 0.195     Provide self-help material 2.7 5.7 0.431 0.172     Discuss medications 5.3 7.8 0.884 0.354     Prescribe medication 0.9 1.5 0.883 0.499 Arrange 2.8 2.5 0.688 0.296 Parameter % Index visitn = 752 % Previous 12-monthsn = 805 ICC P-value ICC Ask 55.7 63.2 0.494 0.006 Advise     Quit smoking 50.3 58.3 0.422 0.006     Health hazards 32.1 46.4 0.292 0.007 Assist     General assistance 11.1 15.7 0.459 0.024     Set quit date 4.4 3.5 0.687 0.195     Provide self-help material 2.7 5.7 0.431 0.172     Discuss medications 5.3 7.8 0.884 0.354     Prescribe medication 0.9 1.5 0.883 0.499 Arrange 2.8 2.5 0.688 0.296 ICC: intra-class correlation coefficient, describes variation in tobacco treatment among providers sampled and is measured on a scale from 0 to 1, with a value close to 0 indicating the clusters were all similar. Intra-Provider ICC = provider variance/total variance. P values: reports on significance level of the GP-level variation observed. Table 3 Final model for multi-level analysis of GPs and patient-level characteristics associated with rates of 4 A’s delivery in Crete, Greece Parameter Ask Advise(quit smoking) Advise(health hazards) Assist Arrange GP-level variables Gender Female 1.00 Male 2.88 (1.06, 7.86)* Patient-level variables Age 18–24 years 1.00 1.00 25–39 years 0.24 (0.05, 1.26) 0.08 (0.01, 0.79)* 40–54 years 1.40 (0.33, 5.93) 0.05 (0.01, 0.56)* 55–64 years 1.59 (0.35, 7.24) 0.20 (0.02, 2.15) ≥65 years 1.63 (0.36, 7.50) 0.06 (0.01, 0.70)* Education 0–6 1.00 1.00 7–9 0.53 (0.31, 0.91)* 0.64 (0.36, 1.12) 10–12 0.43 (0.26, 0.72)** 0.47 (0.27, 0.80)** 12+ 0.51 (0.30, 0.88)* 0.62 (0.35, 1.08) Smoking-related illness No 1.00 1.00 Yes 2.07 (1.27, 3.37)** 2.05 (1.29, 3.27)** Anxiety, depression, or other mental illness No 1.00 1.00 Yes 1.83 (1.04, 3.23)* 4.67 (2.23, 9.75)*** Purpose of visit Medical examination 1.00 1.00 1.00 Prescription 0.74 (0.49, 1.12) 1.02 (0.69, 1.51) 0.61 (0.41, 0.91)* Other/missing 0.26 (0.15, 0.45)*** 0.31 (0.18, 0.54)*** 0.19 (0.10, 0.35)*** Cigarettes/day <15 1.00 15–25 1.63 (0.35, 7.56) >25 6.51 (1.09, 38.85)* Time to first cigarette in the morning After 30 min 1.00 Within 30 min 0.24 (0.07, 0.92)* Random variancea GP 3.022 (1.113) 2.208 (0.816) 1.231 (0.463) 2.996 (1.329) Parameter Ask Advise(quit smoking) Advise(health hazards) Assist Arrange GP-level variables Gender Female 1.00 Male 2.88 (1.06, 7.86)* Patient-level variables Age 18–24 years 1.00 1.00 25–39 years 0.24 (0.05, 1.26) 0.08 (0.01, 0.79)* 40–54 years 1.40 (0.33, 5.93) 0.05 (0.01, 0.56)* 55–64 years 1.59 (0.35, 7.24) 0.20 (0.02, 2.15) ≥65 years 1.63 (0.36, 7.50) 0.06 (0.01, 0.70)* Education 0–6 1.00 1.00 7–9 0.53 (0.31, 0.91)* 0.64 (0.36, 1.12) 10–12 0.43 (0.26, 0.72)** 0.47 (0.27, 0.80)** 12+ 0.51 (0.30, 0.88)* 0.62 (0.35, 1.08) Smoking-related illness No 1.00 1.00 Yes 2.07 (1.27, 3.37)** 2.05 (1.29, 3.27)** Anxiety, depression, or other mental illness No 1.00 1.00 Yes 1.83 (1.04, 3.23)* 4.67 (2.23, 9.75)*** Purpose of visit Medical examination 1.00 1.00 1.00 Prescription 0.74 (0.49, 1.12) 1.02 (0.69, 1.51) 0.61 (0.41, 0.91)* Other/missing 0.26 (0.15, 0.45)*** 0.31 (0.18, 0.54)*** 0.19 (0.10, 0.35)*** Cigarettes/day <15 1.00 15–25 1.63 (0.35, 7.56) >25 6.51 (1.09, 38.85)* Time to first cigarette in the morning After 30 min 1.00 Within 30 min 0.24 (0.07, 0.92)* Random variancea GP 3.022 (1.113) 2.208 (0.816) 1.231 (0.463) 2.996 (1.329) Models adjusted for GP-level clustering effects; CI, confidence interval. Ask: 25 GPs; 1= Asked (n = 419), 0= Not Asked (n = 333). Advise (quit smoking): 25 GPs; 1= Advised (n = 378), 0= Not Advised (n = 372). Advise (health hazards): 25 GPs; 1= Advised health hazards (n = 241), 0= Not advised health hazards (n = 509). Assist: 25 GPs; 1= Assisted (n = 83), 0= Not Assisted (n = 668). Arrange: 25 GPs; 1= Arranged follow-up visit (n = 21), 0= Not arranged follow-up visit (n = 730). P-values calculated based on Wald Tests; *P < 0.05;**P < 0.01; *** P < 0.001. Empty cells: not significant variable in the final model. a Random effects reflecting deviation of clinic k from the overall mean for the particular clinic effects. Table 3 Final model for multi-level analysis of GPs and patient-level characteristics associated with rates of 4 A’s delivery in Crete, Greece Parameter Ask Advise(quit smoking) Advise(health hazards) Assist Arrange GP-level variables Gender Female 1.00 Male 2.88 (1.06, 7.86)* Patient-level variables Age 18–24 years 1.00 1.00 25–39 years 0.24 (0.05, 1.26) 0.08 (0.01, 0.79)* 40–54 years 1.40 (0.33, 5.93) 0.05 (0.01, 0.56)* 55–64 years 1.59 (0.35, 7.24) 0.20 (0.02, 2.15) ≥65 years 1.63 (0.36, 7.50) 0.06 (0.01, 0.70)* Education 0–6 1.00 1.00 7–9 0.53 (0.31, 0.91)* 0.64 (0.36, 1.12) 10–12 0.43 (0.26, 0.72)** 0.47 (0.27, 0.80)** 12+ 0.51 (0.30, 0.88)* 0.62 (0.35, 1.08) Smoking-related illness No 1.00 1.00 Yes 2.07 (1.27, 3.37)** 2.05 (1.29, 3.27)** Anxiety, depression, or other mental illness No 1.00 1.00 Yes 1.83 (1.04, 3.23)* 4.67 (2.23, 9.75)*** Purpose of visit Medical examination 1.00 1.00 1.00 Prescription 0.74 (0.49, 1.12) 1.02 (0.69, 1.51) 0.61 (0.41, 0.91)* Other/missing 0.26 (0.15, 0.45)*** 0.31 (0.18, 0.54)*** 0.19 (0.10, 0.35)*** Cigarettes/day <15 1.00 15–25 1.63 (0.35, 7.56) >25 6.51 (1.09, 38.85)* Time to first cigarette in the morning After 30 min 1.00 Within 30 min 0.24 (0.07, 0.92)* Random variancea GP 3.022 (1.113) 2.208 (0.816) 1.231 (0.463) 2.996 (1.329) Parameter Ask Advise(quit smoking) Advise(health hazards) Assist Arrange GP-level variables Gender Female 1.00 Male 2.88 (1.06, 7.86)* Patient-level variables Age 18–24 years 1.00 1.00 25–39 years 0.24 (0.05, 1.26) 0.08 (0.01, 0.79)* 40–54 years 1.40 (0.33, 5.93) 0.05 (0.01, 0.56)* 55–64 years 1.59 (0.35, 7.24) 0.20 (0.02, 2.15) ≥65 years 1.63 (0.36, 7.50) 0.06 (0.01, 0.70)* Education 0–6 1.00 1.00 7–9 0.53 (0.31, 0.91)* 0.64 (0.36, 1.12) 10–12 0.43 (0.26, 0.72)** 0.47 (0.27, 0.80)** 12+ 0.51 (0.30, 0.88)* 0.62 (0.35, 1.08) Smoking-related illness No 1.00 1.00 Yes 2.07 (1.27, 3.37)** 2.05 (1.29, 3.27)** Anxiety, depression, or other mental illness No 1.00 1.00 Yes 1.83 (1.04, 3.23)* 4.67 (2.23, 9.75)*** Purpose of visit Medical examination 1.00 1.00 1.00 Prescription 0.74 (0.49, 1.12) 1.02 (0.69, 1.51) 0.61 (0.41, 0.91)* Other/missing 0.26 (0.15, 0.45)*** 0.31 (0.18, 0.54)*** 0.19 (0.10, 0.35)*** Cigarettes/day <15 1.00 15–25 1.63 (0.35, 7.56) >25 6.51 (1.09, 38.85)* Time to first cigarette in the morning After 30 min 1.00 Within 30 min 0.24 (0.07, 0.92)* Random variancea GP 3.022 (1.113) 2.208 (0.816) 1.231 (0.463) 2.996 (1.329) Models adjusted for GP-level clustering effects; CI, confidence interval. Ask: 25 GPs; 1= Asked (n = 419), 0= Not Asked (n = 333). Advise (quit smoking): 25 GPs; 1= Advised (n = 378), 0= Not Advised (n = 372). Advise (health hazards): 25 GPs; 1= Advised health hazards (n = 241), 0= Not advised health hazards (n = 509). Assist: 25 GPs; 1= Assisted (n = 83), 0= Not Assisted (n = 668). Arrange: 25 GPs; 1= Arranged follow-up visit (n = 21), 0= Not arranged follow-up visit (n = 730). P-values calculated based on Wald Tests; *P < 0.05;**P < 0.01; *** P < 0.001. Empty cells: not significant variable in the final model. a Random effects reflecting deviation of clinic k from the overall mean for the particular clinic effects. Figure 1 View largeDownload slide Rates of ask, advise, assist, arrange in previous 12-months by GPs sampled in Crete, Greece Figure 1 View largeDownload slide Rates of ask, advise, assist, arrange in previous 12-months by GPs sampled in Crete, Greece Predictors of 4 A’s delivery The final model of the multi-level analysis examining predictors of 4 A’s delivery is presented as table 3. Male GPs were significantly more likely to ‘advise’ patients about the health hazards of smoking and the value of cessation (AOR 2.88; 95% CI 1.06, 7.86; P < 0.05). No other GP level variables were found to be significant in predicting 4 A’s delivery. ‘Asking’ about tobacco use occurred more frequently among patient with smoking related illness (AOR 2.07; 95% CI 1.27, 3.37; P < 0.01). ‘Advice’ regarding the health hazards of smoking was more likely to be delivered to patients with grade school education, a smoking-related illness (AOR 2.05; 95% CI 1.29, 3.27; P < 0.01), a positive screen for anxiety/depression (AOR 1.83; 95% CI 1.04, 3.23; P < 0.05) and who were seen in clinic for a medical examination or prescription. ‘Assistance’ with quitting was more frequently delivered to patients with a positive screen for anxiety or depression (AOR 4.67; 95% CI 2.23, 9.75; P < 00.1). Additionally a significant (P < 0.01) trend across age groups was seen in rates at which ‘advise’ was delivered; tobacco users of increasing age were advised to quit more frequently than younger patients. ‘Arranging’ follow-up was significantly more likely to occur among patients who smoked more than 25 cigarettes per day (AOR 6.51; 95% CI 1.09, 38.85; P < 0.05) and who smoked their first cigarettes 30 mins or more after waking in the morning. Discussion Study main results and highlights from the literature To our knowledge, this is the first study to report on the characteristics of tobacco users and rates of tobacco treatment delivery in primary care in Greece. Our study reveals a very high prevalence (38%) of smoking among patients seen in primary care. Tobacco users who participated in the present study reported high levels of readiness to quit, rated quitting as being of personal importance and identified their GP’s advice to quit as an important source of influence. All of our findings highlight the opportunity to intervene more effectively with tobacco users identified in primary care settings. Although, there is strong evidence,11–14 to support the primary care setting as a key environment for providing smoking cessation and a framework exists to integrate smoking cessation treatment into daily clinical practice, our study documented that a large proportion of tobacco users did not receive ‘advice’ to quit from their primary health care GPs in the previous year. Moreover, while ‘advice’ to quit is delivered to approximately 58.3% of all tobacco users, less than 15.7% received any type of ‘assistance’ with quitting in the last year.13,14,24 Our study adds to a large body of existing international surveys which have documented a similar practice gap in the rates of tobacco treatment delivery in primary care settings.14–16 Among the GPs sampled there was significant variability in the rates at which ‘ask’ and ‘advise’ was delivered. Providers in this study can be classified in three categories according to the rates of ‘ask’ and ‘advise’: high performers (>80% of patients received ‘ask’ and ‘advice’), moderate performers (40–70%) and low performers (<30%). The source of this variation and approaches to supporting low and moderate performing GPs with increasing rates of tobacco treatment delivery are important topics for future research. The profile of tobacco users identified in our study suggests a large proportion of patients are highly addicted, have high daily tobacco consumption rates, have other smokers in their home and report low levels of self-efficacy–all of which are factors known to be associated with difficulty with cessation.25,26 The reported rate of cigarette consumption in our study was 21.1 cigarettes per day, significantly higher than the European average (14.4 cigarettes/day) and slightly higher than population rates reported for Greece in the most recent Eurobarometer survey.3,27 These patients are more likely to benefit from formalized cessation assistance provided by trained clinicians employing evidence-based therapies such as pharmacotherapy and counseling. Importantly, more than half of participants in our study reported their readiness to quit smoking in the near future. This finding is similar to data from other studies.28,29 European tobacco treatment guidelines have called for tobacco addiction to be given the same attention by clinicians as other chronic diseases and chronic disease risk factors such as hypertension, diabetes and cholesterol management.13 These diseases are screened for regularly and treated aggressively using a combination of counseling and pharmacotherapy. Tobacco use has been described as unique in its prevalence, lethality and neglect.14 It has not been given the same attention as other chronic diseases or risk factors by primary care clinicians. Lack of training in evidence-based tobacco treatment during undergraduate and post-graduate medical training, low levels of self-efficacy, work load, time pressure, as well as patient resistance are some of the most important factors which are known to limit the adoption of tobacco treatment by GPs.14,30–32 Countries experiencing fiscal constraints have been found to report lower rates of smoking cessation advice.22 A prime opportunity for intervention is to transform clinicians’ knowledge and attitudes about the importance of addressing tobacco use and the important role they play in increasing their patient’s motivation to quit smoking. Training in evidence-based tobacco treatment has been shown to increase rates of tobacco treatment.11,33 In our study, only approximately one third of GPs had received training in smoking cessation in the past, highlighting the opportunity to enhance training in evidence-based tobacco treatment. Strong evidence demonstrates that multi-component interventions combining training and other physician and patient-level intervention strategies are the most effective method for increasing GP performance in the delivery of smoking cessation treatment and improving cessation rates among patients.11 These cost-effective interventions are particularly important for a country affected by economic challenges. Our study documented that several patient-level factors were associated with the frequency of 4 A’s delivery. Overall tobacco treatment advice is more frequently delivered to patients perceived to be at increased risk (i.e. have a smoking related illness), who suffer from anxiety or depression and who are older. Similar patterns have been previously reported.34,35 Interestingly, individuals with grade school education or less were more likely to be advised to quit; a pattern also previously documented.16,30 The rates of 4 A’s treatment were higher at appointments for medical examinations; these appointments may be longer in duration and thus provide more opportunity to discuss prevention, however, there is evidence to show that tobacco users are open to receiving advice to quit at other types of medical appointments in particular those during which acute symptoms are being experienced.16 Importantly, clinical practice guidelines emphasize that tobacco treatment be delivered to all patients who smoke and not a sub-population of smokers or during specific visits.13,14 There is strong evidence to show that quitting at a younger age increases life expectancy dramatically.10,36 Strengths and limitations One quarter of GPs sampled were tobacco users themselves. It is known that a physician’s personal tobacco use decreases the likelihood of tobacco treatment for patients in their practice.37 Previous reports have identified tobacco use among clinicians in Greece and other European countries to be similar to that of the general population.38,39 While our study did not find personal smoking cessation status was significantly associated with rates of tobacco treatment delivery this may be due to our sample size. Consideration should be given to supporting clinicians with quitting as a strategy for increasing rates of tobacco treatment delivery in their practice. We had very high rates of participation among GPs and their patients, a factor that we attribute to the high regard given to University-based medical research in Greece. Limitations of our study should also be considered. It is unclear how GPs s sampled in our study are representative of those practicing in other parts of Greece and Europe. Our primary care providers were relatively young (<50-years), and primarily working in publically funded clinics (vs. private practice) in rural settings. Most providers were affiliated with the university practice based research network located on the island of Crete. The generalizability of our findings to the rest of Greece and/or southern Europe requires further investigation. It is also possible that an observation bias may have resulted in clinicians more consistently delivering tobacco treatment during the data collection period, resulting in higher rates of 4 A’s delivery being documented than normal. While patient-reported rates of 4 A’s delivery have been shown to be more accurate than physician self-report it is also possible that there may be some recall bias or over reporting by patients.40 In the present study, we reported on 4 of the 5 As strategies. We did not enquire about the ‘assess’ strategy. Conclusions This study has identified an important practice gap in the delivery of evidence-based smoking cessation treatments in primary care in Greece. Increasing the rates of 5 As tobacco treatment in primary care is an important target for quality improvement. Future research could examine the efficacy of training and practice-level interventions tailored to the unique profile of tobacco users and primary care providers in the Mediterranean and Eastern Europe and strategies for motivating patient not ready to quit, as well as cessation among health care providers. Funding This work was supported by Global Bridges: Healthcare Alliance for Tobacco Dependence Treatment and Pfizer Independent Grants for Learning and Change (GB-13522581). Conflicts of interest: A. Pipe has received educational and research support in the past from Pfizer and Johnson & Johnson, and has served as a consultant to Pfizer and Amgen. Key points This is the first study to report on the characteristics of tobacco users and rates of tobacco treatment delivery in primary care practice settings in Greece. Our findings revealed a very high prevalence of smoking among patients identified in the primary care settings sampled as well as high rates of daily tobacco use and nicotine addiction. While approximately half of all tobacco users received advice to quit, assistance with quitting was infrequent. This study has identified an important practice gap in the delivery of evidence-based smoking cessation treatments in primary care practices in Greece. Increasing the rates of 5As tobacco treatment in primary care settings is an important target for quality improvement. Acknowledgements This study was conducted in collaboration with the Practice Based Research Network in Primary Care in Crete. The authors would like to acknowledge the contributions of the network members: Anastasiou Fotini, Kounalakis Dimitrios, Makri Kornilia, Meramveliotakis Emmanouil, Papamastorakis Emmanouil, Pateli Rodanthi, Petraki Chrisa, Prokopiadou Dimitra, Stefanaki Ioanna, Symvoulakis Emmanouil, Tsakountakis Nikolaos, Tsiligianni Ioanna, Vasilaki Aggeliki, Vasilopoulos Theodoros. Supplementary data Supplementary data are available at EURPUB online. References 1 World Health Organization . WHO Global Report: Mortality Attributable to Tobacco, 2012 . Switzerland, Available at: http://www.who.int/tobacco/publications/surveillance/rep_mortality_attributable/en/ (15 January 2017, date last accessed). 2 European Commission. Tobacco Policy. European Commission , 2014 . Belgium, Available at: http://ec.europa.eu/health/tobacco/policy/index_en.htm (15 January 2017, date last accessed). 3 European Commission . Attitudes of Europeans Towards Tobacco: Special Eurobarometer 429, 2014 . Belgium, http://ec.europa.eu/public_opinion/archives/ebs/ebs_429_en.pdf (15 January 2017, date last accessed). 4 Tsalapati K , Vardavas CI , Athanasakis K , et al. Going up in ashes? Smoking-attributable morbidity, hospital admissions and expenditure in Greece . Eur J Public Health 2014 ; 24 : 477 – 9 . Google Scholar CrossRef Search ADS PubMed 5 European Commission . Attitudes of Europeans Towards Tobacco: Special Eurobarometer 385, 2012 . Belgium, http://ec.europa.eu/health//sites/health/files/tobacco/docs/eurobaro_attitudes_towards_tobacco_2012_en.pdf (16 January 2017, date last accessed). 6 U.S. National Cancer Institute and World Health Organization . The Economics of Tobacco and Tobacco Control. National Cancer Institute Tobacco Control Monograph 21. NIH Publication No. 16-CA-8029A. Bethesda, MD: U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute; and Geneva, CH: World Health Organization, 2016 . USA, Available at: https://cancercontrol.cancer.gov/brp/tcrb/monographs/21/docs/m21_complete.pdf (15 January 2017, date last accessed). 7 U.S. Department of Health and Human Services . The Health Consequences of Smoking—50 Years of Progress: A report of the Surgeon General, 2015 . USA, Available at: http://www.surgeongeneral.gov/library/reports/50-years-of-progress (16 January 2017, date last accessed). 8 Franco OH , der Kinderen AJ , De Laet C , et al. Primary prevention of cardiovascular disease: cost-effectiveness comparison . Int J Technol Assess Health Care 2007 ; 23 : 71 – 9 . Google Scholar CrossRef Search ADS PubMed 9 Eddy DM , Peskin B , Shcheprov A , et al. Effect of smoking cessation advice on cardiovascular disease . Am J Med Qual 2009 ; 24 : 241 – 9 . Google Scholar CrossRef Search ADS PubMed 10 Doll R , Peto R , Boreham J , Sutherland I . Mortality in relation to smoking: 50 years' observations on male British doctors . BMJ 2004 ; 328 : 1519 . Google Scholar CrossRef Search ADS PubMed 11 Papadakis S , McDonald P , Mullen K-A , et al. Strategies to increase the delivery of smoking cessation treatments in primary care settings: a systematic review and meta-analysis . Prev Med 2010 ; 51 : 199 – 213 . Google Scholar CrossRef Search ADS PubMed 12 Vardavas C , Symvoulakis M , Lionis C . Dealing with tobacco use and dependence within primary health care: time for action . Tob Induc Dis 2013 ; 11 : 6 . Google Scholar CrossRef Search ADS PubMed 13 European Network for Smoking and Tobacco Prevention (ENSP) , Behrakis P , Bilir N , Clancy L , et al. editors. Guidelines for Treating Tobacco Dependence . Brussels, Belgium : European Publishing , 2017 . 14 Fiore MC , Jaen CR , Baker TB , et al. Clinical Practice Guideline: Treating Tobacco Use and Dependence. Rockville, MD: US Department of Health and Human Services, Public Health Service, 2008 . USA, Available at: https://bphc.hrsa.gov/buckets/treatingtobacco.pdf (15 January 2017, date last accessed). 15 Katz DA , Vander Weg MW , Holman J , et al. The emergency department action in smoking cessation (EDASC) trial: impact on delivery of smoking cessation counseling . Acad Emerg Med 2012 ; 19 : 409 – 20 . Google Scholar CrossRef Search ADS PubMed 16 Papadakis S , Gharib M , Hambleton J . Delivering evidence-based smoking cessation treatment in primary care practice. Experience of Ontario family health teams . Can Fam Phys 2014 ; 60 : e362 – 71 . 17 Kruger J , O’Halloran A , Rosenthal A . Assessment of compliance with US Public Health Service Clinical Practice Guideline for tobacco by primary care physicians . Harm Reduct J 2015 ; 12 : 7 . Google Scholar CrossRef Search ADS PubMed 18 Löwe B , Wahl I , Rose M , et al. A 4-item measure of depression and anxiety: validation and standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general population . J Affect Disord 2010 ; 122 : 86 – 95 . Google Scholar CrossRef Search ADS PubMed 19 Karekla M , Pilipenko N , Feldman J . Patient health questionnaire: Greek language validation and subscale factor structure . Compr Psychiatr 2012 ; 53 : 1217 – 26 . Google Scholar CrossRef Search ADS 20 Chaiton MO , Cohen JE , McDonald PW , Bondy SJ . The Heaviness of Smoking Index as a predictor of smoking cessation in Canada . Addict Behav 2007 ; 32 : 1031 – 42 . Google Scholar CrossRef Search ADS PubMed 21 Pérez-Ríos M , Santiago-Pérez MI , Alonso B , et al. Fagerstrom test for nicotine dependence vs. heavy smoking index in a general population survey . BMC Public Health 2009 ; 9 : 493 . Google Scholar CrossRef Search ADS PubMed 22 Omole OB , Ngobale KN , Ayo-Yusuf OA . Missed opportunities for tobacco use screening and brief cessation advice in South African primary health care: a cross-sectional study . BMC Fam Pract 2010 ; 29 : 94 . Google Scholar CrossRef Search ADS 23 Donner A , Klar N . Design and Analysis of Cluster Randomization Trials in Health Research . London : Arnold , 2000 . 24 Stead LF , Buitrago D , Preciado N , et al. Physician advice for smoking cessation . Cochrane Database Syst Rev 2013 ; 5 : CD000165 . 25 Piper ME , McCarthy DE , Baker TB . Assessing tobacco dependence: a guide to measure evaluation and selection . Nicotine Tob Res 2006 ; 8 : 339 – 51 . Google Scholar CrossRef Search ADS PubMed 26 Burgess DJ , Fu SS , Noorbaloochi S , et al. Employment, gender, and smoking cessation outcomes in low-income smokers using nicotine replacement therapy . Nicotine Tob Res 2009 ; 11 : 1439 – 47 . Google Scholar CrossRef Search ADS PubMed 27 Symvoulakis M , Klinis S , Kakoliris N , et al. The association between tobacco use and perceptions of tobacco price strategies within primary care patients in rural Greece . Tob Prev Cessat 2016 ; 2 : 60 . Google Scholar CrossRef Search ADS 28 Schoretsaniti S , Filippidis FT , Vardavas CI , et al. 5-Year trends in the intention to quit smoking amidst the economic crisis and after recently implemented tobacco control measures in Greece . Addict Behav 2014 ; 39 : 140 – 5 . Google Scholar CrossRef Search ADS PubMed 29 Kaai S , Chung-Hall J , Sun M , et al. Predictors of quit intentions among adult smokers in Mauritius: Findings from the ITC Mauritius Survey . Tob Prev Cessat 2016 ; 2 : 69 . 30 Steinberg MB , Akincigil A , Delnevo CD , et al. Gender and age disparities for smoking-cessation treatment . Am J Prev Med 2006 ; 30 : 405 – 12 . Google Scholar CrossRef Search ADS PubMed 31 Vogt F , Hall S , Marteau TM . General practitioners' and family physicians' negative beliefs and attitudes towards discussing smoking cessation with patients: a systematic review . Addict 2005 ; 100 : 1423 – 31 . Google Scholar CrossRef Search ADS 32 Lionis C , Petelos E . The impact of the financial crisis on the quality of care in primary care: an issue that requires prompt attention . Qual Prim Care 2013 ; 21 : 269 – 73 . Google Scholar PubMed 33 Carson KV , Verbiest MEA , Crone MR , et al. Training health professionals in smoking cessation . Cochrane Database Syst Rev 2012 ; 5 : CD000214 . 34 Martinson BC , O'Connor PJ , Pronk NP , Rolnick SJ . Smoking cessation attempts in relation to prior health care charges: the effect of antecedent smoking-related symptoms? Am J Health Promot 2003 ; 18 : 125 – 32 . Google Scholar CrossRef Search ADS PubMed 35 Azuri J , Peled S , Kitai E , Vinker S . Smoking prevention and primary physician's and patient's characteristics . Am J Health Behav 2009 ; 33 : 710 – 7 . Google Scholar CrossRef Search ADS PubMed 36 Jha P , Ramasundarahettige C , Landsman V , et al. 21st-century hazards of smoking and benefits of cessation in the United States . N Engl J Med 2013 ; 368 : 341 – 50 . Google Scholar CrossRef Search ADS PubMed 37 Duaso MJ , McDermott MS , Mujika A , et al. Do doctors' smoking habits influence their smoking cessation practices? A systematic review and meta-analysis . Addict 2014 ; 109 : 1811 – 23 . Google Scholar CrossRef Search ADS 38 World Health Organization . WHO Tobacco Free Initiative: The Role of Health Professionals in Tobacco Control, 2005 . Switzerland, Available at: http://www.who.int/tobacco/resources/publications/wntd/2005/bookletfinal_20april.pdf (15 January 2017, date last accessed). 39 Pipe A , Sorensen M , Reid R . Physician smoking status, attitudes toward smoking, and cessation advice to patients: an international survey . Patient Educ Couns 2009 ; 74 : 118 – 23 . Google Scholar CrossRef Search ADS PubMed 40 Pbert L , Adams A , Quirk M , et al. The patient exit interview as an assessment of physician-delivered smoking intervention: a validation study . Health Psychol 1999 ; 18 : 183 – 8 . Google Scholar CrossRef Search ADS PubMed © The Author 2017. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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The European Journal of Public HealthOxford University Press

Published: Nov 13, 2017

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