Does smoking affect the outcome of patient education and self-management in asthmatics?

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1 Patient Education and Counseling 49 (2003) 91±97 Does smoking affect the outcome of patient education and self-management in asthmatics? Frode Gallefoss a,*, Per Sigvald Bakke b a Section of Pulmonary Medicine, Medical Department, Vest-Agder Central Hospital, 4604 Kristiansand, Norway b Department of Thoracic Medicine, University Hospital of Bergen, Bergen, Norway Received 20 September 2001; received in revised form 27 February 2002; accepted 17 March 2002 Abstract Information on the potential effect of smoking on the outcome of patient education in asthma is lacking. We randomly allocated 78 asthmatics to either a control or intervention group. Intervention consisted of two 2-h group sessions followed by 1±2 individual sessions each by a nurse and a physiotherapist. Self-management was emphasised following a stepwise treatment plan at exacerbations. Smokers experienced more general practitioner (GP) visits (P ˆ 0:001) and absenteeism from work (P ˆ 0:02), a greater need for rescue medication (P ˆ 0:03), a larger drop in FEV1 (P ˆ 0:02) and worse St. George's respiratory questionnaire (SGRQ) scores (P < 0:001) compared to nonsmokers during the 1-year follow-up. In multiple linear and logistic regression models smoking was still associated with worse SGRQ scores, a drop in FEV1, higher need for GP visits and rescue medication and higher total costs. We, thus, conclude that smoking was associated with reduced health related quality of life, a drop in FEV1, increased need for rescue medication and GP visits and higher costs after patient education during the 1-year follow-up, compared to no smoking. # 2002 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Asthma; Patient education; Smoking; Self care 1. Introduction The damaging effects of smoking on the lungs are well established. Several epidemiological studies have concluded that smoking is a main predictor for a decline in lung function [1±3] and other studies have shown that smoking cessation slows down the decline in lung function [3±5]. Several guidelines today af rm that chronic obstructive pulmonary disease (COPD) is predominantly caused by smoking [6±9] and that smoking cessation is of major prognostic importance. No medication has to date shown to slow down the rate of decline in lung function in COPD. With regard to asthma in adults, there is no rm evidence that smoking is a predisposing or causal factor. Nevertheless smoking is a contributing factor for the course and morbidity of the disease [10]. Asthmatics who stop smoking have been shown to double the probability of remission of the disease [11]. Pedersen et al. revealed in his study that smoking asthmatics were resistant to inhaled corticosteroids [12]. One previous survey reported excess decline in FEV1 among smokers with newly diagnosed asthma compared to nonsmokers [13]. But besides this there is little evidence for the * Corresponding author. Tel.: ; fax: address: frode.gallefoss@vas.no (F. Gallefoss). effects of smoking on the outcome of randomised controlled asthma studies in adult asthmatics. An 11-year Medline search (1990±2001) on the MeSH-keywords ``asthma'', ``patient education'' and ``smoking'' or ``smoking cessation'' did not reveal reports trying to evaluate the impact of smoking on the effect of patient education in asthma. The present asthma education program has been shown in a randomised, controlled intervention study with 12-month follow-up, to improve quality of life, level of lung function [14] and compliance with inhaled corticosteroids [15] in patients with mild to moderate asthma. Further, the education program has been shown to reduce general practitioner (GP) consultation rates and absenteeism from work [16] and in a cost-effectiveness analysis to reveal better outcomes at lower costs [17]. The objectives of the present report were to investigate the possible in uence of smoking on the main outcomes of the study. 2. Subjects and methods 2.1. Study design The 78 consecutive asthmatics were between May 1994 and December 1995 included in the study after having /02/$ ± see front matter # 2002 Elsevier Science Ireland Ltd. All rights reserved. PII: S (02)

2 92 F. Gallefoss, P.S. Bakke / Patient Education and Counseling 49 (2003) 91±97 received ordinary consultation care at the outpatient chest clinic at a medium sized somatic hospital in Southern Norway. At inclusion, they signed a written consent and were then randomised to an intervention or a control group. The patients in the intervention group underwent an education program and were then followed by their GPs. The patients in the control group were transferred directly to their GPs. After 1 year, all the patients were invited to a follow-up examination. Eligible asthmatics were between 18 and 70 years of age and not suffering from any serious disease, such as unstable coronary heart disease, heart failure, serious hypertension, diabetes mellitus, kidney or liver failure. They were to have a pre-bronchodilator FEV1 equal to or higher than 80% of predicted value [18]. Furthermore, we required either a positive reversibility test [18], a documented 20% spontaneous variability (PEF or FEV1) or a positive metacholine test (PD20) [19]. A positive reversibility test required at least a 20% increase (PEF or FEV1) after inhalation of 400 ugh salbutamol. These measures were obtained from the participants charts. Of the eligible patients, the inclusion rate was 92% (78/85) Educational program The educational program consisted of two 2-h group sessions and 1±2h of individual counselling provided both by a nurse and a physiotherapist. The group sessions provided information on the elementary pathophysiology of asthma, the mechanisms of asthma drugs, how to cope with asthma and principles for self-management. During individual counselling, the same topics were covered with a personal approach and the patients were given an individual self-management plan aiming at early change of medication at the start of exacerbations/episodes of asthma attacks. The intervention group also received a specially made 19-page booklet aligning the information given during the educational sessions. Smoking cessation was recommended but individual counselling was not given unsolicited. The educational intervention is previously described in detail [14] Outcome variables The registration of GP visits and days off work due to asthma was done by self-registration at monthly intervals. Each patient was supplied with 12mailed envelopes with appropriate return address and 12standardised registration forms to be mailed the rst day of each month. Reminder letters were sent within 2weeks when needed. At the 12-month follow-up all the registration forms were checked with the patient to reassure the correctness of the registrations. At the 1-year follow-up, we used the St. George's respiratory questionnaire (SGRQ) [20]. The SGRQ is a diseasespeci c quality of life instrument and has 76 items which are weighted to produce three component scores: ``symptoms'' measuring distress due to respiratory symptoms; ``activity'' measuring the effect of disturbances to mobility and physical activity; ``impacts'' quantifying the psychosocial impact of the disease. A number of items in the ``symptoms'' component relate to the frequency of symptoms over the previous year. Both the ``activity'' and ``impacts'' components relate to the patient's current state. A ``total'' score is also calculated from all component items, thus providing a global estimation of the patients respiratory health. Each of these scores ranges from 0 to 100, a score of 100 indicating maximum disability [21]. A difference of four units indicates a slight clinical effect, a difference of 8 or 12U indicates moderate or very good clinical effects, respectively [22]. A total score of 10 U is considered within the normal range. Dispensed medication was reported from the actual dispensaries. Steroid inhaler compliance was calculated as a percentage ˆ dispensed medication = prescribed medication 100 during a 1-year follow-up and was recoded to de ned daily dosages (DDD) according to the anatomical therapeutic chemical (ATC) classi cation index [23] making us able to compare groups using for example beclomethasone and budesonide. Pre-bronchodilator spirometry was performed prior to randomisation and at 12months follow-up by standard methods [18] using a Jaeger MasterLab body box (WuÈrzburg, Germany). The costs were based on utilisation of care and unit costs (US$ 1 ˆ 7 Norwegian crowns (NOK), 1994). Patient copayments were included in the costs. Direct costs (costs borne by the health care system, community and family) and indirect costs (productivity loss and time costs borne by the individual, family, society or by the employer) were included. The cost analysis included the costs related to asthma education, hospital care for asthma, GP visits, pulmonary consultant visits, travel costs, the cost of pharmaceuticals (anti-asthmatics), time costs for those employed and absenteeism from work due to asthma. No discounting was undertaken on costs and consequences due to the short time perspective. The assumptions for the cost analysis are previously explained in detail [17] Statistics Data with obviously skewed distribution were displayed with median values as a measure of central tendency and 25 and 75% as a measure of dispersion (Tables 1 and 2) and differences between groups tested with Mann±Whitney U-test. Correspondingly, for normally distributed data, the mean and standard deviations (S.D.) were used and differences between groups tested with Student's t-test. Multiple logistic regression analyses were computed with a cut-off value of 30 U for SGRQ scores as dependant variable (Tables 3 and 4). Explanatory variables were age, sex, percentage change in FEV1 during the 12-month

3 F. Gallefoss, P.S. Bakke / Patient Education and Counseling 49 (2003) 91±97 93 Table 1 Baseline characteristics of asthmatics included in the control and intervention groups stratified by smoking habits Smokers (n ˆ 22) Non-smokers (n ˆ 56) Control (n ˆ 13) Intervention (n ˆ 9) Control (n ˆ 26) Intervention (n ˆ 30) Sex, women, n (%) 11 (85) 7 (78) 20 (77) 17 (57) Age, mean (S.D.) (years) 42(9) 41 (17) 46 (14) 41 (11) Packyears, median a 11 (4/21) 6 (4/17) ± ± Duration of symptoms, median (years) a,b 11 (3/25) 4 (2/8) 6 (3/26) 10 (4/37) Employed, n (%) 8 (62) 4 (44) 16 (62) 25 (83) FEV1, percentage predicted, mean (S.D.) 92(12) 92(13) 97 (18) 93 (13) FVC, percentage predicted, mean (S.D.) 103 (14) 102(13) 106 (15) 105 (12) a Median values (25 or 75%) are employed for non-normally distributed data. b Based on the question: how long have you had asthma symptoms? Table 2 Main study outcomes of patients in the control and intervention groups by smoking habits Asthma (n ˆ 71) Smokers (n ˆ 17) Non-smokers (n ˆ 54) P Median Mean (S.D.) Median Mean (S.D.) GP visits 2(1/7) 4 (5) 0 (0/1) 1 (0/1) b Absenteeism from work a 6 (0/52) 49 (88) 0 (0/0) 3 (12) 0.02 b Steroid inhaler compliance 62 (10/120) 67 (62) 55 (27/110) 74 (61) 0.82 b Dispensed b2-inhalers (DDD) 100 (50/300) 227 (280) 50 (0/150) 105 (193) 0.03 b Percent change in FEV1, mean (S.D.) 6.6 (13) 2.1 (12) 0.02 c SGRQ Total scores 45 (13) 24 (18) <0.001 c Activity 55 (18) 32(21) <0.001 c Impact 38 (16) 20 (20) c Symptoms 56 (17) 32(18) <0.001 c Total costs (NOK) (1900/36200) (10900) 4700 (3300/8800) 6900 (7400) 0.05 b Both mean (S.D.) and median (25 or 75%) values are displayed for non-normally distributed data. a Calculated for those having regular work, i.e. n ˆ 17 and 49 for the smokers and non-smokers, respectively. b Mann±Whitney U-test. c Student's t-test. follow-up, treatment group and smoke status. Likewise, multiple linear regression analysis was computed with the normally distributed percentage change in FEV1 during the 12-month follow-up as dependant variable (Fig. 2). Since, other main outcome variables (GP visits, absenteeism from work and medication data) showed skewed distribution, making us unable to perform multiple linear regressions, we categorised them to binary outcomes, running multiple Table 3 Multiple logistic regression analysis with SGRQ total score below or above 30 U at 12-month follow-up as dependent variable Explanatory variables Coefficient (b) S.E. P Odds ratio 95% CI Constant ± ± ± Age in years at baseline ±1.05 Sex a ±1.51 Change in FEV ±1.06 Treatment group b ±0.64 Smoke status c ±98.21 Coefficient (b) is the mathematical weighting of each variable in the model and S.E. it's estimated error; odds ratio: controlling for other variables in the model, for every unit increase in the explanatory variable, the odds of having the event of interest increase by this value. a Female. b Control group. c Non-smokers.

4 94 F. Gallefoss, P.S. Bakke / Patient Education and Counseling 49 (2003) 91±97 Table 4 Multiple logistic regression analysis with the same explanatory variables as in Table 3, revealing only the effect of smoke status on various dependant variables Dependant variables Coefficient (b) S.E. P Odds ratio 95% CI SGRQ sub-score Activity a ±51.37 Impact a ±45.43 Symptoms a ±50.48 Collection of rescue medication b ±24.4 Steroid inhaler compliance c ±5.35 GP visits d ±23.14 Absenteeism from work e ±11.85 Coefficient (b) is the mathematical weighting of each variable in the model and S.E. it's estimated error; odds ratio: controlling for other variables in the model, for every unit increase in the explanatory variable, the odds of having the event of interest increase by this value. a Score < 30 U. b No collection of short-acting b-agonist inhalations. c Steroid inhaler compliance < 75%. d No GP visits during the 12-month follow-up. e No absenteeism from work during the 12-month follow-up (only those employed included in the model). logistic regression analyses (Tables 3 and 4) to assess how smoke status was correlated with these outcomes. Outliers were included in the analyses. All data were primarily registered in Data-Ease Version All analyses were performed on Compaq computers applying the statistical package for the social sciences (SPSS) Version 7.5. Permission to establish a person register was given from the National Data Supervision Centre. The methodological procedures were in accordance with the ethical standards of the Helsinki declaration as approved by the Regional Ethical Committee. 3. Results In the intervention group, ve asthmatics failed to complete the educational program. The reasons for not nishing the program were priority because of hectic work (n ˆ 2), alcoholism (n ˆ 1), failure to meet at educational classes for unknown reasons (n ˆ 2). Another two patients were withdrawn from the study during the follow-up due to lack of co-operation (n ˆ 2). Five of those asthmatics withdrawn were smokers, leaving us with only four smokers in the intervention group at the 1-year follow-up. The withdrawn intervention group patients did not, to our knowledge, have any serious deterioration in their obstructive lung disease. In the control group, none of the asthmatics were withdrawn. Baseline characteristics of asthmatics included in the control and intervention groups strati ed by smoking habits were comparable (Table 1). One asthmatic in each treatment group reported stopping smoking during the 1-year followup. If not stratifying by treatment group, smokers were more frequent GP visitors than non-smokers (Table 2). The 82% of smokers visited their GP on one or more occasions during the 12-month follow-up compared to 39% of non-smokers (P ˆ 0:002, w 2 -test). Fig. 1 shows a more detailed categorisation of these data. Smokers also dispensed more shortacting b2-agonists, had a larger drop in FEV1 during the 1-year follow-up, reported worse SGRQ scores and higher total costs than non-smokers (Table 2). Fig. 1. Proportions of patients making GP visits during the 12-month follow-up.

5 F. Gallefoss, P.S. Bakke / Patient Education and Counseling 49 (2003) 91±97 95 Fig. 2. Multiple linear regression analysis with percentage change in FEV1 as dependent variable. As the results from simple strati cation on smoking status indicated (Table 2), smokers may have had a limited effect of patient education. However, these results could be explained partly or mostly by other factors of which the proportional differences in smoking habits in the two treatment groups probably would be of greatest in uence. Therefore, in order to adjust for other covariates, regression models were constructed to reveal if smoking continued to be associated with limited effects of patient education. Table 3 reveals that smoking continued to be clearly correlated with the less favourable SGRQ total scores of 30 U or more when adjusting for age, sex, change in FEV1 during the 1-year follow-up for the treatment group and smoke status. Table 4 displays similar correlations for the SGRQ sub-scores. Smoking was also an important independent predictor for asthmatics needing rescue medication and GP consultations (Table 4, all the P < 0:03). Smoking was also in a multiple linear regression model moderately associated with an unfavourable drop in FEV1 (Fig. 2) of 7.6% during the 1-year follow-up (95% con- dence interval (CI) from 14.5 to 0.67, for the model r ˆ 0:396, b ˆ 2.56, P ˆ 0:03). Thus, a larger drop in FEV1 by smoking was revealed than what was suggested in Table 2. Likewise, in a multiple logistic regression model with total costs below and above NOK 20,000 as dependent variable, and sex, age, treatment group and smoking status as independent variables, smoking was a strong predictor of high costs (odds ratio ˆ 28:3, P ˆ 0:006). 4. Discussion and conclusion This study reveals that smoking after patient education was associated with less favourable health related quality of life as measured by SGRQ. Likewise, smoking was a predictor of a clinically and statistically signi cant drop in FEV1. A greater proportion of smokers had a need for rescue medication and GP visits during the 1-year follow-up compared to non-smokers Discussion The results from this study should be interpreted with caution, since the number of smokers 22/78 (28%) was low at baseline and, especially since the number of smokers in the intervention group at the 1-year follow-up was very low (four). Hence, the low numbers of smokers refrained us from doing proper sub-group analysis. Nevertheless, the data indicate that smokers may have had less favourable outcomes after patient education. In particular, the results from the multiple linear and logistic regression analyses seemed on the whole robust enough for proper judgement. To our knowledge reports are lacking on the effect of smoke status on the outcome of patient education in asthma. We, therefore, believe that our data may be suggestive for future research. Smoking was clearly correlated to increased SGRQ total and sub-scores at the 1-year follow-up implying worse health related quality of life among smoking asthmatics compared to non-smoking. Smoking was also correlated to a clinically signi cant drop in FEV1 after a 1-year follow-up when adjusting for the positive effect of patient education on FEV1 as previously reported [14]. This is a clinically and statistically signi cant nding, since one of the intentions with adequate medication and patient education for asthma is to reduce the decline in FEV1 seen with sub-optimal treatment and care. Our results thereby may support the theory of inhaled steroid resistance previously reported by Pedersen et al. [12]. As Table 2 might indicate, smokers were attributed to more GP visits, more absenteeism from work and a greater need for rescue medication than non-smokers. The logistic regression models cannot ascertain this nding, since we are not able to do multiple linear regression analyses when the dependent variables are skewed. However, the multiple logistic regression analyses (Tables 4 and 5) revealed a signi cant correlation between smoking and asthmatics who needed GP visits and rescue medication during the 1-year follow-up, thereby supporting the results displayed in Table 2. We did not nd any statistically signi cant effect of

6 96 F. Gallefoss, P.S. Bakke / Patient Education and Counseling 49 (2003) 91±97 Table 5 Multiple linear regression analyses with percentage change in FEV1 during a 12-month follow-up as dependant variable Unstandardised regression coefficient (b) S.E. 95% CI P Constant ± Age in years ± Sex a ± Treatment group b ± Smoke status c to a Female. b Control group. c Non-smokers. smoking on steroid inhaler compliance and absenteeism from work in the multiple logistic regression analyses. However, the numbers were small and one should be cautious to interpret these results either way. Both a greater need for GP consultations and rescue medication, worse SGRQ scores, a drop in FEV1 and higher total costs among smokers point in the same direction and is most likely attributed to a more symptomatic disease [24] and a higher incidence of recurrent wheeze [25] among smoking asthmatics compared to non-smoking. Thus, current smoking might be a serious threat to the otherwise positive effects of patient education and self-management. One may also wonder to what extent the obvious excellent effects of patient education in our study could be biased by the drop-out of smokers during education. To our knowledge, such bias could very well be present in other patient education studies, but is rarely discussed. Researchers in the eld should aggregate their data in meta-analyses to reveal with statistical signi cance whether smokers in control and education groups have different dropout rates during education and dissimilar effectiveness and cost outcomes compared to non-smokers. Our results, thus, may accentuate the importance of smoking cessation among asthmatics. This is a challenging task among subjects having a tendency to perceive that they are not at personal risk from their smoking [26]. The main question of interest would be to evaluate the effect of patient education on those asthmatics who continue to smoke compared to those who stop. This is an important topic for future research and involves both increased number of smoking asthmatics being investigated and more emphasise on smoking cessation when asthmatics are educated. In the planning of such research, one should be careful to consider the smokers tendency not wanting to participate in patient education [27±29]. Abdulwadud et al. found in his study that the attendance at an asthma education program was 65% for never-smokers, 31% for former smokers and only 4% for current smokers [30]. One should also acknowledge the potentially high dropout rates from patient education among smoking asthmatics. In years to come, we will most likely experience clinical research with focus on how to tailor patient education to various personality traits. In this context, smoking habits and the psychology associated to smoking will most likely be an important predictor for the personal adaptation of patient education Practice implications These results indicate that smoking may seriously affect the positive effects of patient education in asthma, since smokers may well more readily drop out from asthma education than non-smokers. Thus, a randomised, controlled trial on asthma education may well nish with more smokers in the control group than in the education group. Since, smoking asthmatics have worse prognostic factors than nonsmoking asthmatics, this may interfere with reported results. We believe this possible source of selection bias is previously underestimated in the literature. We suggest a critical focus on the effects of smoking in future asthma education studies and to design large trials that are able to challenge our hypothesis with statistical signi cance. 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