https://orcid.org/0000-0002-5165-8393
Figures
Abstract
Objective
Smoking rates among patients with rheumatoid arthritis (RA) exceed those in the general population. This study identified smoking cessation strategies used in patients with RA and synthesized data on their effects.
Methods
We conducted a systematic review of studies that reported effects of interventions for smoking cessation in patients with RA. We searched 5 electronic databases until March 2022. Screening, quality appraisal, and data collection were done independently by 2 reviewers.
Results
We included 18 studies reporting interventions for patients or providers: 14 evaluated strategies for patients (5 education on cardiovascular risk factors including smoking, 3 educational interventions on smoking cessation alone, 3 education with nicotine replacement and counseling, and 1 study each: education with nicotine replacement, counseling sessions alone, and a social marketing campaign). Smoking cessation rates ranged from 4% (95% CI: 2%-6%, 24 to 48 weeks) for cardiovascular risk education to 43% (95% CI: 21%-67%, 104 weeks) for counseling sessions alone. The pooled cessation rate for all interventions was 22% (95% CI: 8%-41%, 4 weeks to 104 weeks; 9 studies). Four interventions trained providers to ascertain smoking status and provide referrals for smoking cessation. The pooled rates of referrals to quit services increased from 5% in pre-implementation populations to 70% in post-implementation populations.
Citation: Lopez-Olivo MA, Sharma G, Singh G, James J, Krause KJ, Cinciripini P, et al. (2022) A systematic review with meta-analysis of the effects of smoking cessation strategies in patients with rheumatoid arthritis. PLoS ONE 17(12): e0279065. https://doi.org/10.1371/journal.pone.0279065
Editor: Jesse T. Kaye, University of Wisconsin-Madison, UNITED STATES
Received: May 6, 2022; Accepted: November 14, 2022; Published: December 15, 2022
Copyright: © 2022 Lopez-Olivo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This study was supported by the Tobacco Settlement Funds Research Resource Program (Maria E. Suarez-Almazor, PhD, Principal Investigator), in part by a Cancer Prevention Fellowship for Justin James (National Cancer Institute R25 grant CA056452, Shine Chang, PhD, Principal Investigator), and in part by The University of Texas MD Anderson Cancer Center’s Cancer Center Support Grant from the National Cancer Institute (CA016672, Peter WT Pisters, MD, Principal Investigator).
Competing interests: The authors have declared that no competing interests exist.
Introduction
Smoking is the leading cause of preventable disease. More than 8 million people a year die from diseases related to tobacco use [1]. In 2020, 22% of the world population (approximately 1.3 billion) were current tobacco users [2]. Smoking thus poses a great global economic burden, leading to an annual net loss equivalent to 1.8% of the gross domestic product in the world, owing in large part to lost productivity and increased use of health services [3].
Rheumatoid arthritis (RA) is a chronic condition that typically starts in middle-age and is slightly more frequent in women. The disease has a major impact on quality of life. It is associated with significant comorbidities and early mortality. No curative therapy is available, and in most cases, treatment is required for life. It affects approximately 1% of the global population and poses a great economic burden to society [4,5].
Smoking rates among patients with rheumatoid arthritis (RA) have been reported to be as high as 30%, exceeding the overall smoking rates reported in general populations [6,7]. Previous epidemiological studies have identified smoking as an important risk factor for RA [8–10]. The risk has been estimated 40% higher among ever smokers compared to never smokers [8]. In 2001, a study reported more than 65% of people living with RA have smoked cigarettes [11]. By 2016, this number had increased to more than 80% [12]. Furthermore, evidence suggest that half of the patients with RA are current smokers at the time of disease onset [11] and up to 26% continue to smoke despite the risk for increased RA complications, hospitalizations due to cardiovascular disease and respiratory tract infections, osteoporosis, and poor response to treatment [13,14]. Factors associated with smoking persistence in patients with RA are thought to be a combination of biological, psychological and social factors including managing RA pain, using smoking as a coping strategy, behavior carried from adolescence, and/or limited knowledge about the effects of smoking on the disease [15].
Current guidelines on smoking cessation recommend clinicians to ask about tobacco use, advise them to stop using tobacco, and provide behavioral intervention (i.e., counseling) in addition to approved pharmacotherapy for cessation [16]. For patients with RA, the benefits of smoking cessation are numerous [17] including slowing down the progression of chronic obstructive pulmonary disease [18], reduced risk of fractures after 10 years of quitting [19], risk of dying from lung cancer halved within ten years [20], the risk of hospitalization for cardiovascular events or respiratory tract infections decreasing for each additional year of smoking cessation [14], and reduced mortality risk within four years [21]. Immediate health benefits have also been reported such as improved blood pressure and lung function [22,23], improved disease activity, better response to RA treatment, reduced infective risks of immunosuppressive therapy, and improved success of medication dose reduction [17]. However, despite current guidelines on smoking cessation, only about 10% of rheumatology visits with patients who smoke include documentation of cessation counseling or follow-up advice [24]. The purpose of this systematic review of the literature was to identify smoking cessation strategies used in patients with RA and to synthesize data on smoking cessation outcomes, referrals to quit services, knowledge about cessation benefits, and health outcomes.
Methods
Registration
This systematic review was conducted following Cochrane methodological standards [25], and results were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement [26]. The protocol of this systematic review was submitted to the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD42020215287 (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=215287).
Eligibility criteria
We included experimental (i.e., controlled trials [randomized or not], uncontrolled studies [i.e., same population before and after], and implementation studies [i.e., different population before and after]) that reported the effects of smoking cessation interventions in adult patients with RA in any setting and were published in abstract or full-text format. We included any smoking cessation strategy reported by the authors. We excluded basic science studies (i.e., in vivo, in vitro, or animal studies), studies with different population (i.e., juvenile idiopathic arthritis or not RA), reporting development of methods, opinion pieces (i.e., not original research), or reviews. We also excluded studies not reporting data on the effects of the smoking cessation strategy being evaluated (i.e., studies not directly reporting quantitative data on the intervention). Therefore, qualitative studies, articles reporting tool development without evaluation, case reports, guidelines, protocols, and surveys reporting the prevalence of tobacco use in RA were excluded.
Information sources
We searched the literature in 5 electronic databases (MEDLINE, EMBASE, Cochrane, CINAHL, and Web of Science) for articles published from inception until March 2022. Sources of gray literature (unpublished records) were searched through ClinicalTrials.gov. In addition, the reference lists of included articles were hand-searched to identify other potentially relevant studies.
Search
We used a broad search strategy to capture all available studies, including terms related to smoking cessation and RA. An expert librarian (KJK) built the search strategy with input from the study team. Our search was limited to humans, but no other restrictions were imposed. S1 Table shows our search strategy for MEDLINE.
Study selection
For study selection, we followed a 2-step process using DistillerSR (Evidence Partners, Ottawa, Canada; https://www.evidencepartners.com). During the first step, two authors (GS and GS) independently screened titles and abstracts for possible inclusion. For the second step, relevant citations were retrieved in full text to determine the final eligibility for inclusion. Reasons for exclusion were independently recorded, and disagreements were resolved through discussion or, when needed, by a third author (MLO).
Data collection process
Two authors independently collected the data (GS, GS). Disagreements were resolved by consensus or, when needed, consultation of a third author (MLO). If more than 1 publication reported on the same study, the most recently published results were used. We contacted study authors when clarification was needed regarding any missing outcome data or inconsistencies. Data collected for analyses has been shared in the Open Science Framework repository (https://osf.io/hruyz/?view_only=887674593c43432aac52ddd74b93440b).
Data items
Data collected included: (i) study characteristics (author, year of publication, country, number of centers, funding, methods to assess risk of bias, design), (ii) participants’ characteristics (age, gender, ethnicity, socioeconomic status, pack-years, eligibility criteria), (iii) intervention characteristics (description, provider, timing, duration, etc.), and (iv) outcome data (number of events and number of participants per treatment group for dichotomous outcomes; mean and standard deviation and number of participants per treatment group for continuous outcomes). Our primary outcome was the smoking cessation rates as reported by the authors. Other outcomes included smoking cessation-relate outcomes (i.e., percent abstinent, reduction in the number of cigarettes, referrals to cessation services, assessment of smoking status by providers, number of cigarettes smoked), knowledge about cessation benefits, and RA-related outcomes (i.e., disease activity measured by the Disease Activity Score [27], pain [28], global health assessment [29,30], and function measured by the Health Assessment Questionnaire [31]).
Risk of bias in individual studies
Two authors independently appraised the quality of the studies (GS, GS). Disagreements were resolved by consensus or, when needed, consultation of a third author (MLO). The risk of bias tool (RoB 2.0) for randomized controlled trials and the risk of bias in nonrandomized studies (ROBINS-I) were used to appraise study quality, and the RoBvis tool was used to generate traffic-light plots [32]. The RoB 2.0 questions evaluate the randomization process, the effect of assignment and adherence to the intervention, missing outcome data, measurement of outcomes, and selection of the reported result. Each item was judged as having a high or low risk of bias, some concerns, or no information. ROBINS-I was used to assess bias caused by confounding, selection of participants, classification of the interventions, deviations from intended interventions, missing data, measurements of outcomes, and selection of reported results. These domains were judged as having a low, moderate, serious, or critical risk of bias, or no information. An overall judgment of the risk of bias was obtained using domain-level judgments. A study with a critical risk of bias in any single domain or a serious risk of bias in 2 or more domains was judged to have an overall critical risk of bias.
Summary measures
Dichotomous data were analyzed as risk ratios with their corresponding 95% confidence intervals (CIs). Continuous data were analyzed as mean differences (MD) with their corresponding 95% CI. For studies without a comparison group, rates were pooled and their 95% CI was calculated. Similarly, for implementation studies with different participants assessed before and after intervention, we pooled the rates (before and after the implementation) separately without a test of difference and calculated their 95% CI.
Synthesis of results
Eligibility for synthesis.
Data were pooled if at least 2 studies reported on the same outcome regardless of when was the follow-up assessed. Combining different follow-up times allowed us to determine an estimate independent of time. However, we also evaluated differences between short and long-term studies (≤6 vs > 6 months).
Preparing for synthesis.
A random-effects model was used to pool studies. To pool rates of uncontrolled studies, we used the Freeman-Tukey arcsine transformation to stabilize variances and conduct a meta-analysis using inverse variance weights. The resulting estimates and CI boundaries were back-transformed into proportions. Separate analyses were performed for before-and-after studies using the Cochrane methodology to pool paired MDs. An imputed conservative correlation coefficient of 0.8 was used when the within-groups correlation coefficient was not reported [33]. When studies did not report means, we used the median values [34]. Ranges were transformed into standard deviations (SDs) using previously validated methods [35]. Mean and SD were calculated from a frequency distribution with intervals using the formulas: mean = SUM(FREQ*interval midpoint)/SUM(FREQ) and SD = SQRT((n*SUM(FREQ*mean2)—SUM(FREQ*mean)2)/n(n-1).
Statistical and synthesis methods.
All statistical tests performed were 2-sided, and we considered a P value of less than 0.05 statistically significant. Analyses were conducted using STATA version 15 (StataCorp LP, College Station, TX).
Methods to explore heterogeneity.
Study heterogeneity was assessed by using the I2 statistic. An I2 value greater than 50% was considered to indicate substantial inconsistency. Subgroup analyses were performed to investigate whether study characteristics (design, follow-up, and type of intervention and control used) could explain the inconsistency observed.
Risk of bias across studies
The risk of publication bias was assessed through funnel plots and an Egger regression test when 10 or more studies reporting on the same outcome were available.
Certainty assessment
A summary-of-findings table was created following the GRADE approach to rate the quality of the evidence for each outcome [36]. We expressed certainty using 4 categories: (i) high quality of evidence, that is, further research is very unlikely to change our confidence in the effect estimate; (ii) moderate quality, that is, further research is likely to have an important impact on our confidence in the effect estimate and may change the estimate; (iii) low quality, that is, further research is very likely to have an important impact on our confidence in the effect estimate and is likely to change the estimate; and (iv) very low quality, that is, we are uncertain about the estimate.
Results
Study selection
Flow of studies.
We retrieved 837 citations. After removal of duplicates, 601 abstracts were screened. Of these, only 47 moved to the full-text screening. Fig 1 shows the diagram of study selection with the reasons for exclusion in each phase. Twenty-five publications (18 studies) met our study eligibility criteria [37–61].
CT.gov, ClinicalTrials.gov; JIA, juvenile idiopathic arthritis.
Study characteristics
Among the 18 studies included in the review, 10 studies were reported in abstract format (Table 1). Three studies were randomized controlled trials [37,46,58], and the rest were nonrandomized studies: 10 were uncontrolled trials (same participants evaluated at baseline and follow-ups) [42,44,47,48,50–54,59], and 5 were implementation studies (different participants evaluated before and after implementation of the intervention) [38–41,45]. Sample sizes ranged from 20 to 970. Seven studies included patients with RA and other conditions [38–40,50,51,54,59].
Participants’ characteristics
Mean age ranged from 50 to 65 years (S3 Table). The percentage of female participants ranged from 51% to 91%. In 7 studies, all participants were current tobacco users [37–40,47,48,50]. The remaining studies included a mixture of former and current tobacco users, with rates of current tobacco users ranging from 11% to 36%.
Intervention characteristics
The studies were divided by type of intervention (Table 2). There were 6 types of interventions targeting patients: 1 study reporting on a social marketing campaign [45]; 3 reporting on a health education intervention alone [44,51,52], 1 reporting on health education combined with nicotine replacement therapy (NRT) [48], 3 reporting on health education combined with NRT plus counseling sessions [37,50,54], 1 reporting on counseling sessions alone [47], and 4 reporting on lifestyle education focused on cardiovascular risk with goal setting [43,46,53,56,59]. There were 2 types of interventions targeting providers: 3 studies (18%) reported using electronic health record prompts to assess smoking status and offer training on how to refer to Quit line support services [38–40], and 1 study reported using a screening questionnaire to assess smoking status to offer referrals [41].
Risk of bias within studies.
S1 Fig shows the traffic-light plot of the risks of bias in the included studies. The randomized controlled trials were rated as having an overall low risk of bias, although concerns about randomization were raised for 2 studies. However, for the nonrandomized trials, all studies were judged to have methodological concerns and 9 were considered to have an overall serious risk of bias.
Outcomes of interventions targeting patients
Smoking cessation.
Nine studies reported on this outcome [37,42,44,47,48,50,51,54,56], but only 2 studies defined cessation: 1 as total abstinence in the last 7 days [50] and the other as total abstinence for at least 2 weeks, as verified by a carbon monoxide reading of 0 to 6 parts per million [54]. The remaining studies reported the rates at which participants self-reported quitting smoking without additional details regarding time frame. Rates of smoking cessation varied according to the type of intervention evaluated (Fig 2). The lowest pooled rate was observed in studies evaluating lifestyle education in the context of cardiovascular risk combined with goal setting sessions (4%, 95% CI: 2% to 6%), and the highest rate was reported by a study evaluating counseling sessions for 2 years (43%, 95% CI: 21% to 67%). The pooled rate of smoking cessation from all studies after any intervention (follow-up ranged from 4 weeks to 2 years) was 22% (95% CI: 8% to 41%; I2 = 96%; n = 9 studies). Table 3 shows the results of comparative studies evaluating this outcome [37,54,58]. No differences were observed in any of the 3 controlled trials comparing the intervention to a control group.
*Smoking cessation rates for Aimer 2017 and Zeun 2015 are reported for the intervention and control group in separate rows because both strategies provided smoking cessation services. In the control group in Zeun 2015, patients were referred to their general practitioner for smoking cessation services. ES, effect estimate (proportion of people quitting); CI, confidence interval; NRT, nicotine replacement therapy; CVD, cardiovascular disease. Inconsistency scores for the subgroups (I2): Education+NRT+Counseling = 80.2%; Education+NRT = 90.3%; CVD education+Goal setting = 0%; Education = 0%; Counseling = not applicable.
Percent abstinent.
Two studies [47,50] provided the percentage of participants who continued to smoke after the intervention. The pooled rate of abstinent people after the intervention was 21% (95% CI: 11% to 31%). Two studies [53,59] evaluated a cardiovascular risk factors education and found no difference in the number of participants who continued smoking 6–12 months after the intervention (p>0.3). One implementation study with different participants assessed before and after implementation [45] reported similar rates of abstinence in both populations (80%; 95% CI: 75% to 84% vs 78%; 95% CI: 73% to 82%).
Reduction in the number of cigarettes.
Five studies reported on this outcome. In 1 comparative study, there was no statistically significant difference between groups (Table 3) [37]. Among 3 uncontrolled trials, 1 study reported that only 2 of the 8 participants who were current tobacco users reduced the number of cigarettes smoked a day, but the mean number of cigarettes smoked was not provided [42,43]. At 12 months, Naranjo and colleagues reported a reduction in smoking cigarettes of 30% or greater in 55 of 152 participants and a reduction of 50% in cigarette consumption in 29 of 152 participants [49,50]. Karlson et al. reported a significant reduction in the number of cigarettes smoked before and after the intervention for those participants who continued smoking (MD −9.0, 95% CI: −13.8 to −4.1) [47]. For 1 implementation study with different participants assessed before and after the intervention, the mean (± SD) number of cigarettes smoked per day was similar in the post-implementation and pre-implementation periods (12 ± 7 vs. 14 ± 8, respectively) [45].
Knowledge about benefits of smoking cessation.
Three studies reported on this outcome. John and colleagues reported mean scores at 6 months on the Heart Disease Fact Questionnaire-Rheumatoid Arthritis (HDFQ-RA), a 13-item questionnaire [46]. The mean (± SD) score in the intervention group, which participated in 2.5-hour education meetings weekly for 8 weeks, was 10.2 ± 2.4, while that of the control group, which was a waiting-list delayed-intervention arm, was 9.1 ± 2.75 (MD: 1.1; 95% CI: 0.13 to 2.1). Tekkatte et al. reported similar scores for participants’ knowledge regarding the importance of smoking on arthritis across 3 groups: current, past, and never-tobacco users (visual analog scale [VAS] mean ± SD: 7.1 ± 2.4, 7.7 ± 1.9, and 7.4 ± 2.2, respectively) [52]. In an implementation study (i.e., one with different participants before and after the intervention) asking participants whether there is a link between RA and smoking, the percentage of adequate responses increased from 5.2% to 25.9%. When participants in this study were asked whether smoking can lessen the effectiveness of RA treatment, the percentage of adequate responses rose from 4.2% to 48.5% [45].
Disease measures.
Two studies reported data on disease measures. Karlsson et al. compared patients who quit smoking with the intervention versus those who continued smoking at 2 years [47]. The mean pain score of patients who quit smoking improved from 37.5 to 3.0; whereas the mean pain score of patients who continued smoking improved by much less (from 60 to 40; MD: −37.0; 95% CI: −64.7 to −9.3). The global health assessment score decreased from 37.5 to 3.5 in patients who quit smoking, compared to 56.5 to 29.0 in patients who continued smoking (MD: −25.5; 95% CI: −51.9 to 0.91). The mean Health Assessment Questionnaire score improved from 0.57 to 0.0 in patients who quit versus 0.75 to 0.26 in patients who continued smoking (MD: −0.26; 95% CI: −0.84 to 0.32). Similarly, Khan et al. compared participants who quit smoking with participants who continued to smoke. The mean Disease Activity Score-28 for Rheumatoid Arthritis with C-Reactive Protein (DAS28-CRP) was higher in patients who continued smoking than in patients who quit smoking (4.9 vs 2.9, respectively; MD: −2.0; 95% CI: −2.3 to −1.7).
Outcomes of interventions targeting providers
Referrals to smoking cessation services.
Four studies provided rates of referral to smoking cessation services before and after implementation in different populations for each period evaluated (S4 Table). In all studies, the pooled rate of referrals increased from pre- to post-implementation (5% to 70% at 3–6 months).
Smoking status.
Two implementation studies with different participants assessed an intervention for providers before and after implementation [39,41]. The pooled rate of current tobacco users was lower in the post-implementation population at 6 months (15%; 95% CI: 13% to 18%) than in the pre-implementation population (20%; 95% CI: 18% to 21%) (S4 Table).
Number of cigarettes smoked.
For one implementation study with different participants assessed before and after intervention and unspecified follow-up, the mean (± SD) number of cigarettes smoked per day was similar in the post-implementation period and the pre-implementation period (7 ± 4 vs. 8 ± 5, respectively) [41].
Heterogeneity
No statistically significant differences were observed in the pooled smoking cessation rates when studies were grouped by design (controlled trials: 11%; 95% CI: 1% to 26%; I2 = 76% vs uncontrolled studies: 30%; 95% CI: 11% to 53%; I2 = 94%; p = 0.12). In addition, no differences in cessation rates were observed between studies with different follow-up periods (≤ 6 months: 23%; 95% CI: 4% to 51%; I2 = 98% vs > 6 months: 21%; 95% CI: 3% to 46%; I2 = 76%; p = 0.98). Finally, the type of intervention used also did not significantly affect smoking cessation rates (with follow-up sessions/contact: 22%; 95% CI: 7% to 41%; I2 = 72% vs without follow-up sessions/contact: 23%; 95% CI: 3% to 54%; I2 = 98%; p = 0.96).
Removing studies that included patients with other rheumatic diseases did not influence the direction or the magnitude of the smoking cessation rate (22%; 95% CI: 2% to 52%; I2 = 97.6%). However, removing studies with a high risk of confounding bias resulted in a lower proportion of patients quitting smoking (17%; 95% CI: 6% to 31%; I2 = 85%).
Reporting biases
There was no evidence of small-study effects (Egger test p = 0.69) in the funnel plot for the primary outcome assessed (S2 Fig).
Certainty of evidence
The summary of findings table is shown as S5 Table. The evidence for the main outcome, self-reported cessation rates, was of moderate quality due to limitations in study design and inconsistency of results. Similarly, the evidence for the effects of smoking cessation strategies versus control was of moderate quality due to imprecision and the estimate derived from one small study.
Discussion
We identified published reports of smoking cessation strategies for patients with RA and synthesized their data on smoking cessation outcomes, referrals to quit services, knowledge about the benefits of cessation, and the benefits of smoking cessation on health outcomes. We found that the success rates of people with RA quitting smoking varied according to strategy used, follow-up duration, and quality of the studies with a pooled cessation rate of 22%. Furthermore, the majority of studies were not randomized trials and several lacked adequate comparator groups, therefore being subject to bias. When high-risk of bias studies were removed, the cessation rates were even lower (17%). This indicates that an optimal and more active strategy for this specific population is yet to be established and tested with a more rigorous methodology.
The most frequent strategy used in the studies included in this review was education with advice to quit smoking delivered in the context of cardiovascular risk. This type of interventions do not conform to current smoking cessation guidelines, in which counseling treatments and medications are recommended [62]. In our review, fewer than half of the studies (41%) reported use of telephone calls, emails, and counseling sessions to provide follow-up support. Included studies reporting on interventions that included counselling and/or NRT achieved greater cessation rates. This is similar to the evidence observed from the general population showing that extended counseling with pharmacotherapy enhances sustained abstinence relative to brief, time-limited approaches. Although for patients with RA, there may need to be some tailoring to cover the specific needs of these patients. Prior studies have explored the specific problems that tobacco interventions need to address in patients with RA [63–66]. Patients have expressed the need for counseling that highlights the relationship between smoking and RA to understand better the short- and long-term complications. They also wanted to learn how to replace smoking for alternatives when smoking was used as a coping mechanism for the frustrations of living with the disease or as a distraction from pain, in particular when most patients with active or severe disease find it difficult to exercise and use it as an alternative distraction. Finally, they prefer alternatives that help them overcome feeling unsupported and isolated from other patients with RA.
One noteworthy finding is that the included controlled trials reported similar cessation rates between groups in 3 different clinical scenarios: (1) comparing education and advice combined with NRT plus counseling versus the same elements without the counseling sessions; (2) comparing a nurse-administered program to evaluate cardiovascular risk factors and other comorbidities combined with sending a report to providers versus a sham program; and (3) education and advice given in the rheumatology clinic versus a general practice of the patient’s choice. The study with the first scenario explored the specific problems that tobacco interventions need to address in patients with RA [37,57]. All participants (intervention and control group) were offered cessation support if they wanted to quit and NRT. They also were counseled to highlight the relationship between smoking and RA to understand better the short- and long-term complications. However, participants in the intervention group were instructed on how to replace smoking for alternatives when smoking was used as a coping mechanism for the frustrations of living with the disease or as a distraction from pain, in particular when most patients with active or severe disease find it difficult to exercise and use it as an alternative distraction. They also received access to a support website to help them overcome feeling unsupported and isolated from other patients with RA. Although the smoking cessation rates in the intervention group were higher compared to the control group, the difference did not reach statistical significance. Both groups achieved smoking cessation rates higher than those rates reported for the general population when no intervention is received [67]. This finding supports previous expert opinion that even a brief general advice intervention not necessarily tailored to specific barriers can increase smoking cessation rates and that more complex or intensive interventions add only small benefits compared to standard care of care. This hypothesis will need to be further tested given that the pooled rate of smoking cessation from all the included studies was lower than the published average cessation rates for minimal interventions in the general population (22% vs 50–60% [68]). Some qualitative studies have explored barriers to and facilitators of smoking cessation in rheumatology clinics. This evidence suggests that psychosocial factors (e.g., patients’ feeling that smoking is ‘the one thing they still have control over’ while dealing with the burden of rheumatic disease), judgmental cessation support from rheumatology staff, lack of investment from providers (e.g., every provider recommending quitting without providing a solution), and lack of cessation education resources are the main barriers to quitting for patients with RA [69]. Patients’ readiness to quit and use of NRT were the most common facilitators of smoking cessation, and visible health effects and the cost of cigarettes were found to be the factors with the most influence on the desire to quit [63,69].
Another important agenda for future research is to find the best choice of outcome measures in this population. Apart from smoking abstinence, the number of cigarettes consumed per day for participants who continued smoking after receiving the intervention and improvement in knowledge about the benefits of smoking cessation after participants received the intervention have also been reported. It is unknown if these outcomes would translate in benefits in disease outcomes. To date only one study has showed improvement in pain, disability, and disease activity scores for patients who quit smoking using structured counseling every 4 weeks over 2 years compared to those who continued to smoke [47]. Future studies focusing on smoking cessation interventions in patients with RA should ensure that disease outcomes are measured given the highly clinical relevance of the measures and considering that many tobacco users with RA report smoking to cope with disease symptoms. To our knowledge, no other reviews have attempted to synthesize the effects of smoking cessation interventions specifically for patients with RA. One Cochrane review considered studies in patients with chronic autoimmune inflammatory joint diseases, but only 2 studies (also included in this review) could be synthesized narratively with no attempt to combine results of the individual studies [70].
Our study has limitations, including a lack of comparative evidence for most strategies reported and large heterogeneity in the populations, definitions of cessation, settings, methodologies used, and points of assessment, which restricted our ability to synthesize the effects. Moreover, most of the meta-analyses performed showed substantial inconsistency scores reflecting substantial clinical, methodological, and statistical heterogeneity. Nonetheless, the trends observed in our results seem to indicate that interventions that include counseling or another type of follow-up method (rather than a 1-time interaction) achieve greater cessation rates. In addition, only limited evidence was reported regarding the effects of smoking cessation on disease-related outcomes, which as evidence by prior qualitative data, RA symptoms are one of the listed reasons for continuing smoking. Furthermore, none of the studies compared cessation rates according to RA treatment received, functional ability, or other medical characteristics that could be helpful to determine the subpopulations that could most benefit from cessation strategies. Another concern was the serious risk of bias observed in the majority of the nonrandomized studies (i.e., 10 out of 18 studies were uncontrolled trials assessing the same participants before and after the intervention and 5 were implementation studies with different participants assessed before and after the intervention). Most such studies did not account for confounding factors (e.g., socioeconomic status, previous smoking cessation attempts, mental health). Moreover, the cessation outcomes were biased given that all studies but one used participant self-reports, rather than biochemical methods, to verify smoking status. Although self-reported smoking cessation is a simple and convenient method, prior studies have shown that participants do not always accurately report their smoking status [71], which can yield inaccurate estimates of the proportion of people quitting. Therefore, the level of certainty of the evidence was considered moderate to very low. This was further exacerbated by the limited information reported in studies published as conference abstracts (10 out of 18). Finally, our results should be carefully interpreted. The cessation rates reported in the studies were substantially heterogeneous which may not only reflect the different population characteristics and cessation strategies used but could also be attributed to different implementation intensities of the interventions evaluated. Adequate research studies with more robust methodology still need to be conducted to identify the effectiveness of smoking cessation programs targeting patients with RA. One promising randomized controlled trial comparing the effect on disease activity of an intensive smoking cessation intervention versus standard of care is still in progress, and its results have not yet been reported [72]. Thus, it is imperative that further well-designed studies be conducted to confirm the results observed in this review.
In conclusion, there was substantial heterogeneity due to differences in patient characteristics, the interventions used, and their implementation structure. Only 3 studies were controlled clinical trials and smoking cessation rates were similar across controlled and uncontrolled trials. However, the rates observed were even lower when removing studies with high risk of bias and considering the likelihood of overestimation due to the self-reported nature of the primary outcome measure, our findings suggest that additional studies evaluating stronger interventions based on current smoking cessation guidelines are needed to facilitate smoking cessation in patients with RA.
Supporting information
S1 Fig. Traffic-light plots showing the risk of bias of individual studies.
https://doi.org/10.1371/journal.pone.0279065.s001
(DOCX)
S4 Table. Synthesis of results of interventions targeting providers.
https://doi.org/10.1371/journal.pone.0279065.s006
(DOCX)
Acknowledgments
Preliminary results of this study were presented to the European League Against Rheumatism. Citation: Lopez-Olivo MA, Volk RJ, Krause KJ, Suarez-Almazor ME.
A review of smoking cessation strategies and lung cancer screening practices in patients with rheumatoid arthritis. Ann Rheum Dis 2020;79 (supplement 1): 1422.
We acknowledge the literature searching and editorial support of the Research Medical Library at MD Anderson.
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