Next Article in Journal
Interactions with Arsenic: Mechanisms of Toxicity and Cellular Resistance in Eukaryotic Microorganisms
Previous Article in Journal
The Relationship between the Restorative Perception of the Environment and the Physiological and Psychological Effects of Different Types of Forests on University Students
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 18
Issue 22
10.3390/ijerph182212225
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Predictors of Adherence to Smoking Cessation Medications among Current and Ex-Smokers in Australia: Findings from a National Cross-Sectional Survey

by
Amanual Getnet Mersha
1,*,
Michelle Kennedy
1,2,
Parivash Eftekhari
1,2 and
Gillian Sandra Gould
3
1
School of Medicine and Public Health, The University of Newcastle, University Drive, Callaghan, NSW 2308, Australia
2
Hunter Medical Research Institute, Lot 1, Kookaburra Circuit, New Lambton Heights, NSW 2305, Australia
3
Faculty of Health, Southern Cross University, Coffs Harbour, NSW 2450, Australia
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(22), 12225; https://doi.org/10.3390/ijerph182212225
Submission received: 19 October 2021 / Revised: 11 November 2021 / Accepted: 19 November 2021 / Published: 21 November 2021
(This article belongs to the Section Public Health Statistics and Risk Assessment)
Browse Figure
Versions Notes

Abstract

:
Background: Adherence to smoking cessation medications (SCMs) improve the rate of successful quitting. This study aimed to evaluate the level of adherence to SCMs and associated factors among smokers and ex-smokers in Australia. Method: A cross-sectional study using an online survey was conducted in Australia. Descriptive statistics were used to present the overall characteristics of participants. Cross-tabulation with Pearson’s chi-square test was performed to evaluate the possible associations between factors. To explore barriers and facilitators of adherence to SCMs, logistic regressions were conducted. Results: Among 201 participants, 57 (28.4%) were found to be adherent to SCMs. The odds of being adherent were found to be higher among participants with good social support (AOR = 3.28, 95% CI of 2.30–6.27). Participants who did not experience anxiety symptoms had higher odds of being adherent to SCMs as compared to smokers who had anxiety symptoms (AOR = 4.41, 95% CI of 3.64–14.68). Having previous experience of using SCMs improved adherence four-fold (AOR = 3.87, 95% CI of 1.11–13.44). Level of nicotine dependence showed a direct association with adherence (AOR = 3.53, 95% CI of 1.40–8.95). Not relapsing while on the medications improved adherence (AOR = 2.88, 95% CI of 1.21–6.88). Conclusion: In a study of smokers and ex-smokers in Australia, the self-reported level of adherence to SCMs was found to be low. Adherence was associated with social, psychological, and medication-related factors. Smoking cessation interventions are recommended to include strategies that can address medication adherence.

1. Background

During the last few decades, smoking cessation programs and interventions have become an urgent need due to the global increase in health hazards from smoking [1]. Pharmacological interventions for tobacco dependence are both safe and effective [2]. Clinical guidelines recommend the use of smoking cessation medications such as nicotine replacement therapies (NRT), varenicline, or bupropion [3,4]. Participants using smoking cessation medications have been found to be 1.5 to 2 times more likely to quit smoking than those using behavioral therapy alone [5].
The rate of successful smoking cessation is higher among individuals who have consistent and adherent smoking cessation medication use [6]. A meta-analysis was conducted and published by the authors of this study in 2021 to evaluate the effect of adherence on the success of smoking cessation. The study indicated that adherence to NRT doubles the rate of successful smoking cessation [7]. The biochemically verified rate of smoking cessation was higher among individuals who were adherent to bupropion therapy as compared to non-adherent counterparts [8]. Similarly, adherence to varenicline was associated with a twofold increase in quit rates [9].
A systematic review incorporating studies conducted mainly in the USA and UK demonstrated the importance of multiple personal, social, and environmental factors [10]. The level of nicotine dependence, withdrawal symptoms, perceptions about medications and quitting, alcohol use, stress, depression, and social support were found to be associated with adherence to smoking cessation medications [10]. Smokers’ beliefs about nicotine and medications, adverse effects of the medicine, and attending more counseling sessions were associated with adherence to NRT and bupropion [11]. There are conflicting findings regarding the association between the intensity of nicotine dependence and adherence to smoking cessation medications. Some studies found better adherence among individuals with a higher level of nicotine dependence [12,13,14,15], whereas other studies reported that higher dependence reduced the rate of adherence to smoking cessation medications [16,17,18].
In Australia, there has been a significant downward trend of smoking prevalence from 24% in 1991 to 12.2% in 2016 [19]. Absolute reductions in smoking rates have largely been due to a decrease in smoking initiation. For example, the proportion of young adults aged 18–24 who never smoked increased from 58% to 80% between 2001 and 2019 [19]. The rate of ex-smokers improved only by 2% between 1991 and 2019 [19]. This could be partly explained by the under-utilization of smoking cessation supports including medications as well as a reduction in uptake of smoking due to tobacco control policies. However, relatively few comprehensive approaches are being implemented in Australia within smoking cessation services compared with other countries [20].
To the best of our knowledge, the level of adherence and factors associated with adherence to smoking cessation medications have not been investigated in Australia. Understanding the barriers and facilitators of adherence to smoking cessation medications is crucial for the development of comprehensive and effective interventions that can improve the success of smoking cessation.

2. Methods

A national cross-sectional study was conducted to assess adherence to smoking cessation medication use and its associated factors among adult smokers and recent ex-smokers in Australia from January 2021 to July 2021.

2.1. Sampling and Recruitment

The sample size was calculated using the single population proportion formula with the assumption of a confidence level of 95% and a margin of error of 5%, and the prevalence of smoking in Australia was taken as 11.6% in 2019 [21]. Thus, a minimum sample of 158 people would be required to detect a true association or effect between participants’ characteristics and adherence to smoking cessation medications.
A 61-item online survey was administered through a consumer panel provider, Pureprofile Pty Ltd (https://www.pureprofile.com/ (accessed on 16 April 2021). Pureprofile was contracted to distribute the survey invitation. Individuals who were registered to the survey provider and reported smoking were contacted and provided with the screening questions. Eligible participants were then provided with the survey link. The online survey was designed and disseminated using Qualtrics software (Qualtrics, Provo, UT, USA). Adults (aged 18 years old and above) who were current smokers or recent ex-smokers and quit smoking in the last 12 months by using smoking cessation medications (NRT, varenicline, bupropion) during their most recent quit attempt were invited for participation. Participants were reimbursed with 15 AUD in cash for their time through the online survey provider, Pureprofile.

2.2. Measures

Participants’ socio-demographic data such as age in years, gender (male or female), geographic remoteness, region, marital status, level of education, and other characteristics were gathered. The age of the participants was collected as a continuous variable and categorized into two classes: as young and middle-aged adults (18–55 years old) and older adults (>55 years old). A previous study using this age group classification illustrated a significant difference in adherence between the age groups [22]. The Australian Statistical Geography Standard-Remoteness Area guideline was used to classify geographical remoteness by using participants’ postal codes [23]. The 2016 socioeconomic index for areas (SEIFA) created by the Australian Bureau of Statistics (ABS) to evaluate the relative socioeconomic disadvantage (IRSD) was determined using postal codes [24]. At an Australian national standard of 1000, a score of lower than 1000 indicates relatively greater socioeconomic disadvantage, and a score of >1000 was categorized as “nil/no” socioeconomic disadvantage [25]. The educational level was split into four categories: never attended formal education, completed primary education, completed secondary education, and completed college and above. Marital status was collected as married, never married or separated/divorced/widowed. Geographical remoteness was measured in five classes: major cities, inner regional, outer regional, remote, and very remote in Australia.
The level of nicotine dependence was assessed using the Fagerstrom Test for Nicotine Dependence (FTND). A high level of nicotine dependence is defined as a score of 6 and above out of a maximum score of 10 [26]. Social support during the quit attempt was evaluated using the Oslo social support scale (OSSS-3). The scale assesses the structural and functional level of social support using 3 questions with a possible score of 3 to 14. A score of 3 to 8, 9 to 11, and 12 to 14 indicates poor, moderate, and strong social support, respectively [27].
The type of medications used to assist smoking cessation were evaluated and reported. Medications could be prescribed by health care providers or bought over the counter. In Australia, most NRTs can be purchased over the counter; however, bupropion and varenicline are prescription-only medications listed in the pharmaceutical benefits scheme so individuals can obtain them at a subsidized rate.
Self-reported psychological symptoms were assessed using the depression, anxiety, and stress scale (DASS-21) [28]. This instrument was evaluated among individuals with substance use disorder and found to be sensitive and specific [29]. Each of the three DASS-21 scales contains seven items, divided into subscales with similar content. Scores for depression, anxiety, and stress are calculated by summing the scores for the relevant items. For each question, participants selected 0, 1, 2, or 3 if the stated symptom did not happen at all, happened some of the time, happened a considerable amount of the time, or most of the time, respectively [28].
To assess stress, a score of less than 11, 11 to 18, 19 to 26, 27 to 34, and more than 35 represents none, mild, moderate, severe, and extremely severe symptoms of stress, respectively. For anxiety, a score of less than 7, 7 to 9, 10 to 14, 15 to 19, and more than 20 indicates the presence of no, mild, moderate, severe, and extremely severe symptoms of anxiety, respectively. A score of less than 10, 10 to 12, between 13 to 20, 21 to 27, and more than 28 out of a maximum score of 42 represent no, mild, moderate, severe, and extremely severe depression, respectively [28].

2.3. Outcome Variables

There are no standard definition and assessment measures to evaluate adherence to smoking cessation medications to date [30]. Hence, previous studies used different definitions and measures such as daily use, pill count, and duration of medication use to measure adherence. the following definition was therefore used in this study to evaluate adherence.
Adherence was assessed using the self-reported duration and weekly consumption pattern of smoking cessation medications during participants’ most recent quit attempts. Participants were labeled as adherent if they used smoking cessation medications for at least 4 weeks and on average for 5 or more days per week. Combining duration and weekly usage patterns gives better information than evaluating the duration of treatment alone [31]. Moreover, previous studies have also used this operational definition to define adherence [22,32].
To provide a more detailed and informative report, we included a figure to illustrate adherence based on the number of days and duration of medication use measured in weeks (Figure 1).
Nonadherence was defined as medication use for fewer than 4 weeks or fewer than 5 days a week. If a participant used the medication for 4 weeks but fewer than 5 days a week on average, they were categorized as nonadherent. Likewise, if a participant used the medication for 5 days or more per week but for a total duration of less than 4 weeks, they were labeled nonadherent.

2.4. Data Analysis

Data were entered into and analyzed using Stata software (V16, Stata Corp LP, College Station, TX, USA). Descriptive statistics using frequency and percentages were used to present the overall characteristics of participants. Cross-tabulation with Pearson’s chi-square test was used to evaluate the possible association of various factors and adherence to smoking cessation medications.
Factors associated with adherence to smoking cessation medications were determined using logistic regression. Univariate analyses were conducted to evaluate the unadjusted effect of factors on adherence. Factors with a significant association from the univariate analyses were further included in the multivariable forward stepwise logistic regression. To declare a significant association, a p-value of 0.05 was used as a cut-off point, and the results were expressed by using an odds ratio (OR) with a 95% confidence interval (CI).

2.5. Ethics Approval

This study was approved by the University of Newcastle Human Research Ethics Committee, approval Number H-2021-0073. Participants were provided with an information sheet to inform them of the study objective and their rights. Participants were informed that participation was entirely voluntary. Volunteer participants signed an implied consent to start the survey. In case participants required emotional support after being exposed to the survey, contact details and links to some available free-of-charge services were provided to them in the information sheet and at the end of the survey.

3. Results

3.1. Socio-Demographic Characteristics of Participants

A total of 201 participants who used smoking cessation medications in the last 12 months for the purpose of quitting completed the survey and were included in the study. The median age of participants was 47 years. The majority of the participants were female (70.6%). Nearly half of the participants (47.8%) were from areas with greater socioeconomic disadvantage—a score of <1000, based on the Australian Bureau of Statistics (ABS) relative socioeconomic disadvantage (IRSD) score.
Half of the participants (53.2%) reported having good social support during their quit attempt; i.e., they scored 9 and above on the Oslo Social Support scale. A total of 63.2% participants reported drinking alcohol during their recent quit attempt; of these, 17.9% reported drinking alcohol-containing beverages at least 4 days a week and 27.4% reported drinking alcohol 2 to 3 times per week. The chi-square test illustrated a possible association between adherence to smoking cessation medications and various participant characteristics such as age, employment status, remoteness, alcohol use, social support, and psychological symptoms (Table 1).

3.2. Psychosocial Characteristics of Participants

The most frequently reported psychological symptoms experienced during a participants’ quit attempt were anxiety and stress, at 43.8% and 40.5%, respectively. Among the participants who had experienced anxiety, 13.4% and 11.9% participants experienced moderate and severe anxiety symptoms, respectively. Among the 81 participants who had experienced stress, 6% reported a moderate level of stress during their quit attempt. Moreover, 36.3% of participants reported depressive symptoms, and one in five of the participants (22.9%) who reported depressive symptoms experienced at least a moderate level of symptoms (Table 1).
To evaluate the association of the abovementioned psychological symptoms on medication adherence, further categorization into a binary classification was conducted for each of the three disorders. The classification was into groups of those participants with any symptoms and those who did not experience psychological symptoms.

3.3. Adherence to and Pattern of Smoking Cessation Medications Use

Among the participants, 16.9% reported they used the medications for at least 8 weeks; 15.9% used them for 4 to 8 weeks; 28.9% used them for 2 to 4 weeks; 16.4% used them for 1 to 2 weeks; and the remaining 21.9% participants used the medication for less than 1 week. The average weekly usage patterns of smoking cessation medications showed that 40.8% of participants used the medications every day of the week; 14.4% used them for 5 to 6 days a week; 10.9% used the for 3 to 4 days per week; and 33.8% of participants used them for 2 or less days per week. As per our definition of adherence (usage of smoking cessation medications for a duration of at least 4 weeks with weekly usage for at least 5 days a week), 28.4% of participants were found to be adherent to smoking cessation medications (Figure 1).

3.4. Smoking, Quit Attempt, and Beliefs about Smoking Cessation Medications

Among the participants, 134 (66.6%) were current smokers and 67 (33.3%) were ex-smokers. Around 39% of the participants had a moderate to high level of nicotine dependence at the start of their quit attempt; i.e., a score of 5 and above on the Fagerstrom Test for Nicotine Dependence (FTND). The majority of participants (85.6%) had at least one previous quit attempt, of which 65.2% had used smoking cessation medications during their previous quit attempts. The vast majority (71.6%) of participants used NRT in their recent quit attempt, whereas varenicline and bupropion were used by 19.9% and 8.5% of participants, respectively. Around 72.6% and 54.7% of participants believed smoking cessation medications were safe and effective, respectively. Relapse during a quit attempt characterized by smoking for more than two days per month while on the smoking cessation medications was reported by 64.2% of the participants. Among the participants, 82% reported craving for a cigarette during their quit attempt. The presence of other smokers at home during their quit attempt was reported by 41.3% of the participants. The chi-square test showed a possible association between adherence to smoking cessation medications and the extent of nicotine dependence, previous use and type of smoking cessation medication, forgetfulness, and relapse of smoking (Table 2).

3.5. Factors Associated with Adherence to Smoking Cessation Medications

Thirteen factors from the univariate analyses showed significant associations with self-reported adherence to smoking cessation medications. The variables were further adjusted using multivariable analysis, as illustrated in Table 3. Five factors showed a significant association with adherence in the multivariable analysis.
The odds of being adherent to SCMs were reported to be higher among participants who reported good social support during their quit attempt (AOR = 3.28, 95% CI of 2.30–6.27, p-value 0.012). Although all the assessed psychological variables showed an association in the univariate analysis, only anxiety showed a significant association after adjustment. Participants who did not experience any anxiety symptoms had improved adherence by odds of more than four times compared to participants who had experienced anxiety symptoms (AOR = 4.41, 95% CI of 3.64–14.68, p-value 0.016).
Having experience of using smoking cessation medications in previous quit attempts improved adherence nearly four-fold (AOR = 3.87, 95% CI of 1.11–13.44, p-value 0.033). Furthermore, participants with moderate to high nicotine dependence were more adherent than those with low nicotine dependence (AOR = 3.53, 95% CI of 1.40–8.95, p-value 0.008). Relapse was negatively associated with medication adherence. Participants who did not relapse had improved adherence compared to those who reported relapse, characterized by smoking more than two days a month during their quit attempt (AOR = 2.88, 95% CI of 1.21–6.88, p-value 0.017) (Table 3).

4. Discussion

Overall, this study showed a low level of adherence to smoking cessation medications among smokers and ex-smokers. Adherence to smoking cessation medications was associated with the level of social support, the extent of nicotine dependence, anxiety, relapse, and experience of previous use of the smoking cessation medications. The finding from this study agrees with a systematic review conducted and published by the authors that indicated the importance of multiple social, psychological, and medication-related factors in regulating adherence to smoking cessation medication [10].
Three in 10 (28.4%) smokers and ex-smokers in Australia were found to be adherent to smoking cessation medications. In a 2021 meta-analysis that included studies conducted mainly in the USA and UK, 26% of participants were found to be adherent to NRT. The meta-analysis showed higher rates of quitting among participants who were adherent to the medications and recommended that smoking cessation programs should consider adherence as one of the core components of the intervention [7]. In a study conducted among Korean smokers, 28.2% of participants used Varenicline for the recommended duration [33]. Adherence was reported to be higher in randomized controlled trial participants, at 61% and 74% among users of NRT [7] and bupropion [8], respectively. RCT participants are likely to be more motivated to quit and obtain the necessary medication-related information, which may have contributed to this disagreement [7]. This could be due to various factors of smokers and health care providers, such as lack of time, lack of training, and motivation [34]. A study conducted in the UK showed disparities between the expressed needs of smokers and the health care providers’ beliefs regarding the support required for adherence to NRT [35]. There is also a substantial discrepancy in asking about smoking status and assisting smokers to quit which may have affected the rate of adherence to the medications [34,36]. Smoker-related factors associated with adherence to smoking cessation medications are discussed in the following paragraphs.
Experience of using smoking cessation medications during previous quit attempts was associated with higher rates of adherence. Participants who reported previous use of the medications were more likely to have a better motivation to quit and use the SCMs, which may have confounded this finding. Our finding is in line with findings from reviews [10,11] and original studies conducted in China [22] and the UK [37]. Having previous exposure to smoking cessation medications improves adherence by helping participants to establish a realistic expectation about the effectiveness of the medications. It also helps individuals to select the type of medication they are comfortable with and prepare themselves for possible medication side effects such as headache, skin irritation, insomnia, and others [11,12,38]. Lack of knowledge about the side effects and unrealistic expectations was associated with poor adherence [39].
A higher level of nicotine dependence is positively associated with adherence to smoking cessation medications. Mixed results have been reported in the literature regarding the relationship between nicotine dependence and adherence. The finding in this study is in line with reports from randomized controlled trials [14] and population-based studies [12,13,15] conducted in the USA that reported higher adherence among smokers with higher levels of nicotine dependence. This could be due to a difference in the perceived need for smoking support medications. On the contrary, other studies reported that higher dependence reduced the rate of adherence to smoking cessation medications [16,17,18].
Relapsing to smoking reduced the rate of adherence to smoking cessation medications. The association between relapse and adherence can be bidirectional; i.e., relapse can be both a cause and effect of non-adherence. The demonstrated effect of relapse on adherence in this study may have been influenced by reverse causality. Hence, this should be taken into account during interpretation of this finding. Studies that reported an inverse association between nicotine dependence and adherence reported lower rates of quitting among individuals with more nicotine dependence and argue smoking status has a mediating effect between adherence and nicotine dependence [17,40]. This may be due to fear of compound toxicity from the medication and the smoke [16]. Participants may also start to doubt themselves, and this may affect their self-efficacy, which could reduce their motivation to use the medication and quit smoking [41]. Continuing the medications promotes recovery from relapse and the attainment of abstinence [40].
A greater social support network is associated with better adherence to smoking cessation medications. A systematic review aimed at assessing factors associated with adherence to NRT indicated a positive impact of having social support during a quit attempt [10]. Findings from patients with chronic medical conditions such as diabetes [42] and heart disease [43] demonstrated that both structural and functional components of social support are vital to medication adherence. Social support may improve medication adherence directly, by having someone to remind the individual to take the medications appropriately, and indirectly, by motivating them to stay smoke-free [44] and improve their self-efficacy [45].
The relatively higher levels of psychological symptoms can be partially explained by the proportion of the male to female ratio in the data and the overlapping of the study with the COVID pandemic [46]. The 2020 Australian Institute of Health and Wellbeing (AIHW) report indicated that a higher proportion of females than males (22% compared with 18%) had experienced symptoms in the previous 12 month period [47]. Symptoms of depression during quit attempts were reported to be as high as 40% in other studies [48]. Moreover, this study included participants who had experienced a quit attempt in the previous 12 months, in which time the COVID pandemic imposed a significant mental health challenge [49]. The experience of anxiety symptoms during a quit attempt negatively affected the rate of adherence to smoking cessation medications. Participants with no symptoms of anxiety had a four-fold increase in the odds of adherence. Similarly, other studies have also indicated the detrimental effect of psychological symptoms during quit attempts on adherence to medications [10,11,50]. This effect could be mediated by a higher level of relapse among participants with mental illnesses as compared to individuals with no psychological symptoms (24% vs. 12%) [51,52]. Integrated interventions directed at smoking cessation and improving mental health have demonstrated an improved adherence to treatment and quitting [53].

5. Strength and Limitations of the Study

Although studies have indicated that adherence improves successful smoking cessation, there has been no study conducted to explore the factors associated with adherence in Australia until now. This study is the first to explore the barriers and facilitators of adherence to smoking cessation medications in Australia. It will provide a framework for further large-scale clinical trials and population-based studies.
This study has several limitations that should be noted. Most of the participants were urban residents, which could be due to differences in internet access. Participants of online surveys are more likely to be active users of technology and have exposure to smoking and medication-related information.
As the survey was an online survey and potential participants could open and close the survey link at any time, it is difficult to determine the exact response rate. However, the sociodemographic characteristics of participants in this study are comparable to the national data. In the 2019 report of the Australian Institute of Health and Welfare, the proportion of daily smokers was higher in middle-aged adults than older adults (18.4% in the age group between 40–49 years as compared to 11.9% among adults from 60–69 years old). The same data reported that around 31% of smokers completed college-level education and above [54]. According to the Australian national census, New South Wales (32%) and Victoria (24%) are the most populated regions in Australia [55]. Despite these limitations, this study illustrated essential factors affecting adherence to smoking cessation medications and provided new information in Australia.
The findings may have been influenced by limitations inherent to a cross-sectional study design, such as establishing a temporal relationship and recall bias, which may have affected reported adherence rates and associated factors. As this is a cross-sectional study, relapse may have a reverse causality effect on adherence; i.e., relapse can be a cause or effect of non-adherence. Future studies are recommended to control for reverse causality by including participants who did not relapse or by performing the analysis to the point of relapse. Additionally, the issue of polypharmacy in individuals with chronic medical conditions was not investigated, which may have been impacted adherence.

6. Conclusions and Recommendations

In conclusion, we found a low level of adherence to smoking cessation medications among smokers and ex-smokers in Australia. Medication adherence was associated with social, psychological, and medication-related factors, as well as the extent of nicotine dependence. Knowledge of how medications are used for smoking cessation is essential to guide further interventions. A further study with larger national representative data is recommended to achieve stronger evidence.

Author Contributions

Conceptualization, A.G.M.; methodology, A.G.M., P.E., M.K. and G.S.G.; formal analysis, A.G.M.; investigation, A.G.M., P.E., M.K. and G.S.G.; resources, A.G.M. and G.S.G.; writing—original draft preparation, A.G.M.; writing—revised version and editing, A.G.M., P.E., M.K. and G.S.G.; supervision, P.E., M.K. and G.S.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of the University of Newcastle (approval Number H-2021-0073).

Informed Consent Statement

Informed consent was obtained from all participants involved in the study.

Data Availability Statement

All relevant materials and data supporting the findings of this study are contained within the manuscript. However, if you need additional information, you can access the data from the corresponding author on responsible request.

Acknowledgments

A.G.M is supported by the University of Newcastle Vice-Chancellor’s Higher Degree by Research Training Scholarship. G.S.G is supported by a National Health and Medical Research Council Translating Research into Practice Fellowship. M.B. is supported by the National Health and Medical Research Council Early Career Research Fellowship. PE is supported by funding from the Australian Federal Government, department of health.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

FTNDFagerstrom Test for Nicotine Dependence
DASSDepression, Anxiety, and Stress Scale
NRTNicotine replacement therapy
OSSSOslo Social Support Scale
SCMSmoking cessation medication

References

  1. Reitsma, M.B.; Kendrick, P.J.; Ababneh, E.; Abbafati, C.; Abbasi-Kangevari, M.; Abdoli, A.; Abedi, A.; Abhilash, E.S.; Abila, D.B.; Aboyans, V.; et al. Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: A systematic analysis from the Global Burden of Disease Study 2019. Lancet 2021, 397, 2337–2360. [Google Scholar] [CrossRef]
  2. Moore, D.; Aveyard, P.; Connock, M.; Wang, D.; Fry-Smith, A.; Barton, P. Effectiveness and safety of nicotine replacement therapy assisted reduction to stop smoking: Systematic review and meta-analysis. BMJ 2009, 338, b1024. [Google Scholar] [CrossRef] [Green Version]
  3. Siu, A.L. Behavioral and Pharmacotherapy Interventions for Tobacco Smoking Cessation in Adults, Including Pregnant Women: U.S. Preventive Services Task Force Recommendation Statement. Ann. Intern. Med. 2015, 163, 622–634. [Google Scholar] [CrossRef] [Green Version]
  4. Krist, A.H.; Davidson, K.W.; Mangione, C.M.; Barry, M.J.; Cabana, M.; Caughey, A.B.; Donahue, K.; Doubeni, C.A.; Epling, J.W.; Kubik, M.; et al. Interventions for Tobacco Smoking Cessation in Adults, Including Pregnant Persons: US Preventive Services Task Force Recommendation Statement. JAMA 2021, 325, 265–279. [Google Scholar]
  5. Hartmann-Boyce, J.; Chepkin, S.C.; Ye, W.; Bullen, C.; Lancaster, T. Nicotine replacement therapy versus control for smoking cessation. Cochrane Database Syst. Rev. 2018, 5, CD000146. [Google Scholar] [CrossRef] [PubMed]
  6. Hays, J.T.; Leischow, S.J.; Lawrence, D.; Lee, T.C. Adherence to treatment for tobacco dependence: Association with smoking abstinence and predictors of adherence. Nicotine Tob Res. 2010, 12, 574–581. [Google Scholar] [CrossRef]
  7. Mersha, A.G.; Eftekhari, P.; Bovill, M.; Tollosa, D.N.; Gould, G.S. Evaluating level of adherence to nicotine replacement therapy and its impact on smoking cessation: A systematic review and meta-analysis. Arch. Public Health 2021, 79, 26. [Google Scholar] [CrossRef]
  8. Leischow, S.J.; Muramoto, M.L.; Matthews, E.; Floden, L.L.; Grana, R.A. Adolescent Smoking Cessation With Bupropion: The Role of Adherence. Nicotine Tob. Res. 2016, 18, 1202–1205. [Google Scholar] [CrossRef] [Green Version]
  9. Catz, S.L.; Jack, L.M.; McClure, J.B.; Javitz, H.S.; Deprey, M.; Zbikowski, S.M.; McAfee, T.; Richards, J.; Swan, G.E. Adherence to varenicline in the COMPASS smoking cessation intervention trial. Nicotine Tob.Res. Off. J. Soc. Res. Nicotine Tob. 2011, 13, 361–368. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Mersha, A.G.; Gould, G.S.; Bovill, M.; Eftekhari, P. Barriers and Facilitators of Adherence to Nicotine Replacement Therapy: A Systematic Review and Analysis Using the Capability, Opportunity, Motivation, and Behaviour (COM-B) Model. Int. J. Environ. Res. Public Health 2020, 17, 8895. [Google Scholar] [CrossRef]
  11. Pacek, L.R.; McClernon, F.J.; Bosworth, H.B. Adherence to Pharmacological Smoking Cessation Interventions: A Literature Review and Synthesis of Correlates and Barriers. Nicotine Tob. Res. 2017, 20, 1163–1172. [Google Scholar] [CrossRef] [Green Version]
  12. Burns, E.K.; Levinson, A.H. Discontinuation of nicotine replacement therapy among smoking-cessation attempters. Am. J. Prev. Med. 2008, 34, 212–215. [Google Scholar] [CrossRef]
  13. Hood, N.E.; Ferketich, A.K.; Paskett, E.D.; Wewers, M.E. Treatment adherence in a lay health adviser intervention to treat tobacco dependence. Health Educ. Res. 2013, 28, 72–82. [Google Scholar] [CrossRef] [Green Version]
  14. Berg, C.J.; Ahluwalia, J.S.; Cropsey, K. Predictors of adherence to behavioral counseling and medication among female prisoners enrolled in a smoking cessation trial. J. Correct. Health Care 2013, 19, 236–247. [Google Scholar] [CrossRef] [PubMed]
  15. Kim, N.; McCarthy, D.E.; Loh, W.Y.; Cook, J.W.; Piper, M.E.; Schlam, T.R.; Baker, T.B. Predictors of adherence to nicotine replacement therapy: Machine learning evidence that perceived need predicts medication use. Drug Alcohol Depend. 2019, 205, 107668. [Google Scholar] [CrossRef]
  16. Alterman, A.I.; Gariti, P.; Cook, T.G.; Cnaan, A. Nicodermal patch adherence and its correlates. Drug Alcohol Depend. 1999, 53, 159–165. [Google Scholar] [CrossRef]
  17. Vaz, L.R.; Aveyard, P.; Cooper, S.; Leonardi-Bee, J.; Coleman, T. The Association Between Treatment Adherence to Nicotine Patches and Smoking Cessation in Pregnancy: A Secondary Analysis of a Randomized Controlled Trial. Nicotine Tob. Res. 2016, 18, 1952–1959. [Google Scholar] [CrossRef] [Green Version]
  18. Ben Taleb, Z.; Ward, K.D.; Asfar, T.; Bahelah, R.; Maziak, W. Predictors of adherence to pharmacological and behavioral treatment in a cessation trial among smokers in Aleppo, Syria. Drug Alcohol Depend. 2015, 153, 167–172. [Google Scholar] [CrossRef] [Green Version]
  19. Gong, M.; Dunbar, M.S.; Setodji, C.; Shadel, W.G. Zonnic®: A new player in an old field. Subst. Abuse Treat. Prev. Policy 2017, 12, 40. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Borland, R.; Li, L.; Driezen, P.; Wilson, N.; Hammond, D.; Thompson, M.E.; Mons, U.; Willemsen, M.C.; McNeill, A.; Thrasher, J.F. Cessation assistance reported by smokers in 15 countries participating in the International Tobacco Control (ITC) policy evaluation surveys. Addiction 2012, 107, 197–205. [Google Scholar] [CrossRef] [Green Version]
  21. Australian Institute of Health and Welfare: Tobacco Smoking. Available online: https://www.aihw.gov.au/reports/australias-health/tobacco-smoking (accessed on 21 July 2021).
  22. Lam, T.H.; Abdullah, A.S.; Chan, S.S.; Hedley, A.J. Adherence to nicotine replacement therapy versus quitting smoking among Chinese smokers: A preliminary investigation. Psychopharmacology 2005, 177, 400–408. [Google Scholar] [CrossRef]
  23. Combs, P.; Cohen, W.; Siddiqi, U.A.; Jeevanandam, V. An international survey: Tobacco smoking cessation strategies within left ventricular assist device centers. Int. J. Artif. Organs 2021, 44, 110–114. [Google Scholar] [CrossRef]
  24. Australian Bureau of Statistics (ABS) Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia. 2016. Available online: https://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/2033.0.55.0012016?OpenDocument (accessed on 3 October 2021).
  25. Ghosh, A.; Charlton, K.E.; Batterham, M.J. Socioeconomic disadvantage and its implications for population health planning of obesity and overweight, using cross-sectional data from general practices from a regional catchment in Australia. BMJ Open 2016, 6, e010405. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Heatherton, T.F.; Kozlowski, L.T.; Frecker, R.C.; Fagerström, K.O. The Fagerström Test for Nicotine Dependence: A revision of the Fagerström Tolerance Questionnaire. Br. J. Addict. 1991, 86, 1119–1127. [Google Scholar] [CrossRef] [PubMed]
  27. Kocalevent, R.-D.; Berg, L.; Beutel, M.E.; Hinz, A.; Zenger, M.; Härter, M.; Nater, U.; Brähler, E. Social support in the general population: Standardization of the Oslo social support scale (OSSS-3). BMC Psychol. 2018, 6, 31. [Google Scholar] [CrossRef] [PubMed]
  28. Ng, F.; Trauer, T.; Dodd, S.; Callaly, T.; Campbell, S.; Berk, M. The validity of the 21-item version of the Depression Anxiety Stress Scales as a routine clinical outcome measure. Acta Neuropsychiatr. 2007, 19, 304–310. [Google Scholar] [CrossRef] [PubMed]
  29. Beaufort, I.N.; De Weert-Van Oene, G.H.; Buwalda, V.A.J.; de Leeuw, J.R.J.; Goudriaan, A.E. The Depression, Anxiety and Stress Scale (DASS-21) as a Screener for Depression in Substance Use Disorder Inpatients: A Pilot Study. Eur. Addict. Res. 2017, 23, 260–268. [Google Scholar] [CrossRef]
  30. Mersha, A.G.; Eftekhari, P.; Bovill, M.; Tollosa, D.N.; Gould, G.S. Evaluating level of adherence to nicotine replacement therapy and its impact on smoking cessation: A protocol for systematic review and meta-analysis. BMJ Open 2020, 10, e039775. [Google Scholar] [CrossRef]
  31. Lam, W.Y.; Fresco, P. Medication Adherence Measures: An Overview. Biomed. Res. Int. 2015, 2015, 217047. [Google Scholar] [CrossRef] [Green Version]
  32. Hollands, G.J.; Sutton, S.; McDermott, M.S.; Marteau, T.M.; Aveyard, P. Adherence to and consumption of nicotine replacement therapy and the relationship with abstinence within a smoking cessation trial in primary care. Nicotine Tob Res. 2013, 15, 1537–1544. [Google Scholar] [CrossRef]
  33. Lee, J.-Y.; Kim, M.; Jun, H.-J.; Kang, M.; Park, A.; Oh, D.; Choi, Y.H.; Hwang, J.H. Adherence to Varenicline and Abstinence Rates for Quitting Smoking in a Private Health Promotion Center-Based Smoking Cessation Clinic. Tuberc. Respir. Dis. 2012, 72, 426–432. [Google Scholar] [CrossRef] [Green Version]
  34. Meijer, E.; Van der Kleij, R.M.J.J.; Chavannes, N.H. Facilitating smoking cessation in patients who smoke: A large-scale cross-sectional comparison of fourteen groups of healthcare providers. BMC Health Serv. Res. 2019, 19, 750. [Google Scholar] [CrossRef] [Green Version]
  35. McDaid, L.; Thomson, R.; Emery, J.; Coleman, T.; Cooper, S.; Phillips, L.; Naughton, F. Agreement and Disparities between Women and Stop-Smoking Specialists about How to Promote Adherence to Nicotine Replacement Therapy in Pregnancy. Int. J. Environ. Res. Public Health 2021, 18, 4673. [Google Scholar] [CrossRef] [PubMed]
  36. Gould, G.S.; Twyman, L.; Stevenson, L.; Gribbin, G.R.; Bonevski, B.; Palazzi, K.; Zeev, Y.B. What components of smoking cessation care during pregnancy are implemented by health providers? A systematic review and meta-analysis. BMJ Open 2019, 9, e026037. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Bowker, K.; Campbell, K.A.; Coleman, T.; Lewis, S.; Naughton, F.; Cooper, S. Understanding Pregnant Smokers’ Adherence to Nicotine Replacement Therapy During a Quit Attempt: A Qualitative Study. Nicotine Tob Res. 2016, 18, 906–912. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Harrison-Woolrych, M.; Ashton, J. Utilization of the smoking cessation medicine varenicline: An intensive post-marketing study in New Zealand. Pharmacoepidemiol. Drug Saf. 2010, 19, 949–953. [Google Scholar] [CrossRef] [PubMed]
  39. Kushnir, V.; Sproule, B.A.; Cunningham, J.A. Mailed distribution of free nicotine patches without behavioral support: Predictors of use and cessation. Addict. Behav. 2017, 67, 73–78. [Google Scholar] [CrossRef]
  40. Ferguson, S.G.; Gitchell, J.G.; Shiffman, S. Continuing to wear nicotine patches after smoking lapses promotes recovery of abstinence. Addiction 2012, 107, 1349–1353. [Google Scholar] [CrossRef]
  41. Velicer, W.F.; Diclemente, C.C.; Rossi, J.S.; Prochaska, J.O. Relapse situations and self-efficacy: An integrative model. Addict. Behav. 1990, 15, 271–283. [Google Scholar] [CrossRef]
  42. Gu, L.; Wu, S.; Zhao, S.; Zhou, H.; Zhang, S.; Gao, M.; Qu, Z.; Zhang, W.; Tian, D. Association of Social Support and Medication Adherence in Chinese Patients with Type 2 Diabetes Mellitus. Int. J. Environ. Res. Public Health 2017, 14, 1522. [Google Scholar] [CrossRef] [Green Version]
  43. Mondesir, F.L.; Carson, A.P.; Durant, R.W.; Lewis, M.W.; Safford, M.M.; Levitan, E.B. Association of functional and structural social support with medication adherence among individuals treated for coronary heart disease risk factors: Findings from the REasons for Geographic and Racial Differences in Stroke (REGARDS) study. PLoS ONE 2018, 13, e0198578. [Google Scholar] [CrossRef] [Green Version]
  44. Burns, R.J.; Rothman, A.J.; Fu, S.S.; Lindgren, B.; Joseph, A.M. The relation between social support and smoking cessation: Revisiting an established measure to improve prediction. Ann. Behav. Med. 2014, 47, 369–375. [Google Scholar] [CrossRef] [Green Version]
  45. Vyavaharkar, M.; Moneyham, L.; Tavakoli, A.; Phillips, K.D.; Murdaugh, C.; Jackson, K.; Meding, G. Social support, coping, and medication adherence among HIV-positive women with depression living in rural areas of the southeastern United States. AIDS Patient Care STDS 2007, 21, 667–680. [Google Scholar] [CrossRef] [PubMed]
  46. Santomauro, D.F.; Mantilla Herrera, A.M.; Shadid, J.; Zheng, P.; Ashbaugh, C.; Pigott, D.M.; Abbafati, C.; Adolph, C.; Amlag, J.O.; Aravkin, A.Y.; et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet 2021, 398, 1700–1712. [Google Scholar] [CrossRef]
  47. Osibogun, O.; Bursac, Z.; Maziak, W. E-Cigarette Use and Regular Cigarette Smoking among Youth: Population Assessment of Tobacco and Health Study (2013–2016). Am. J. Prev. Med. 2020, 58, 657–665. [Google Scholar] [CrossRef]
  48. Ojo-Fati, O.; Thomas, J.L.; Vogel, R.I.; Ogedegbe, O.; Jean-Louis, G.; Okuyemi, K.S. Predictors of Adherence to Nicotine Replacement Therapy (Nicotine Patch) Among Homeless Persons Enrolled in a Randomized Controlled Trial Targeting Smoking Cessation. J. Fam. Med. 2016, 3, 1079. [Google Scholar]
  49. Chidambaram, A.G.; Dennis, R.A.; Biko, D.M.; Hook, M.; Allen, J.; Rapp, J.B. Clinical and radiological characteristics of e-cigarette or vaping product use associated lung injury. Emerg. Radiol. 2020, 27, 495–501. [Google Scholar] [CrossRef] [PubMed]
  50. Handschin, J.; Hitsman, B.; Blazekovic, S.; Veluz-Wilkins, A.; Wileyto, E.P.; Leone, F.T.; Ashare, R.L.; Schnoll, R.A. Factors Associated with Adherence to Transdermal Nicotine Patches within a Smoking Cessation Effectiveness Trial. J. Smok. Cessat. 2018, 13, 33–43. [Google Scholar] [CrossRef]
  51. Zvolensky, M.J.; Bakhshaie, J.; Sheffer, C.; Perez, A.; Goodwin, R.D. Major depressive disorder and smoking relapse among adults in the United States: A 10-year, prospective investigation. Psychiatry Res. 2015, 226, 73–77. [Google Scholar] [CrossRef] [Green Version]
  52. McClave, A.K.; Dube, S.R.; Strine, T.W.; Kroenke, K.; Caraballo, R.S.; Mokdad, A.H. Associations between smoking cessation and anxiety and depression among U.S. adults. Addict. Behav. 2009, 34, 491–497. [Google Scholar] [CrossRef]
  53. OʼCleirigh, C.; Zvolensky, M.J.; Smits, J.A.J.; Labbe, A.K.; Coleman, J.N.; Wilner, J.G.; Stanton, A.M.; Gonzalez, A.; Garey, L.; Regenauer, K.S.; et al. Integrated Treatment for Smoking Cessation, Anxiety, and Depressed Mood in People Living With HIV: A Randomized Controlled Trial. J. Acquir. Immune Defic. Syndr. 2018, 79, 261–268. [Google Scholar] [CrossRef] [PubMed]
  54. AIHW. National Drug Strategy Household Survey 2019. Available online: https://www.aihw.gov.au/getmedia/7ebfd47a-9063-4ae0-b22f-1aeff56a30dc/aihw-phe-270-Chapter2-Tobacco.pdf.aspx (accessed on 20 August 2021).
  55. Australian Bureau of Statistics Census—2016 Report. Available online: https://www.abs.gov.au/census/find-census-data (accessed on 20 August 2021).
Ijerph 18 12225 g001 550
Figure 1. Adherence and smoking cessation medication usage patterns.
Figure 1. Adherence and smoking cessation medication usage patterns.
Ijerph 18 12225 g001
Table
Table 1. Psycho-social and demographic characteristics of participants (n = 201).
Table 1. Psycho-social and demographic characteristics of participants (n = 201).
Variables Adherence n = 201p-Value
No
Frequency (%)		Yes
Frequency (%)
AgeMedian—47 years old0.002 *
18–55100 (69.4%)26 (45.6%)
≥5644 (30.6%)31 (54.4%)
Gender 0.552
Female100 (69.4%)42 (73.7%)
Male44 (30.6%)15 (26.3%)
Education level
Completed primary or secondary school71 (49.3%)28 (49.1%)0.981
Completed college and above73 (50.7%)29 (50.9%)
Employment status 0.037 *
Employed89 (61.8%)26 (45.6%)
Unemployed55 (38.2%)31 (54.4%)
Marital status 0.066
Married60 (41.7%)22 (38.7%)
Never married48 (33.3%)12 (21%)
Divorced/Widowed/Separated36 (25%)23 (40.3%)
Remoteness 0.005 *
Major cities of Australia116 (80.6%)35 (61.4%)
Regional and remote Australia28 (19.4%)22 (38.6%)
Socioeconomic disadvantage (SEIFA) 0.808
High76 (52.8%)29 (50.9%)
Low68 (47.2%)28 (49.1)
State of residence 0.088
New South Wales59 (50%)10 (17.5%)
Victoria35 (24.3%)17 (29.8%)
Queensland19 (13.2%)15 (26.3%)
South Australia14 (9.7%)8 (14%)
Western Australia17 (11.81%)7 (12.3%)
Drinking alcohol 0.015 *
No59 (41%)15 (26.3%)
Yes85 (59%)42 (73.7%)
Social support
Good social support (OSSS-3 score ≥ 9)55 (38.2%)43 (75.4%)0.001 *
Poor social support (OSSS-3 score ≤ 8)89 (61.8%)14 (24.6%)
Stress 0.004 *
No stress (DASS score ≤ 18)76 (53.1%)43 (75.4%)
Had symptoms of stress(DASS score ≥ 19)67 (46.9%)14 (24.6%)
Anxiety 0.001 *
No anxiety (DASS score ≤ 9)64 (44.4%)49 (86%)
Had symptoms of anxiety (DASS score ≥ 10)80 (55.6%)8 (14%)
Depression 0.001 *
No depression (DASS score ≤ 12)79 (54.9%)49 (86%)
Had symptoms of depression
(DASS score ≥ 13)		65 (45.1%)8 (14%)
DASS—Depression, Anxiety, and Stress Scale; OSSS—Oslo Social Support Scale; p-value—chi square test; *—significant p-value.
Table
Table 2. Smoking characteristics, quit attempts, and beliefs about SCMs among smokers and ex-smokers in Australia (n = 201).
Table 2. Smoking characteristics, quit attempts, and beliefs about SCMs among smokers and ex-smokers in Australia (n = 201).
VariablesAdherence n = 201p-Value
No
Frequency (%)		Yes
Frequency (%)
Level of nicotine dependence 0.002 *
Low dependence (FTND score ≤ 4)98 (68.1%)25 (43.9%)
Moderate to high dependence (FTND score ≥ 5)46 (31.9%)32 (56.1%)
Ever had quit attempt previously 0.060
Yes119 (82.6%)53 (93%)
No25 (17.6%)4 (7%)
Ever used SCMs during a previous quit attempt 0.003 *
Yes83 (69.7%)48 (90.6%)
No36 (30.3)5 (9.4%)
Type of SCM in the current quit attempt 0.027 *
NRT108 (75%)36 (63.2)
Varenicline22 (15.3%)18 (31.5%)
Bupropion14 (9.7%)3 (5.3%)
Ever forget to take the SCMs 0.016 *
Yes75 (52.1%)19 (33.3%)
No69 (47.9%)38 (66.7%)
Believe SCMs are safe 0.107
Yes100 (69.4%)46 (80.7%)
No44 (30.6%)11 (19.3%)
Believe SCMs are effective 0.570
Yes77 (53.5%)33 (57.9%)
No67 (46.5%)24 (42.11%)
Intentionally missed SCMs 0.026 *
Yes65 (45.1%)16 (28.1)
No79 (54.9%)41 (71.9%)
Skip SCMs when feeling better 0.063
Yes84 (58.3%)25 (43.9%)
No60 (41.7%)32 (56.1%)
Relaps 0.043 *
Yes99 (68.7%)30 (52.6%)
No45 (31.3%)27 (47.4%)
Experienced sleeping problems since quitting 0.420
Yes62 (43.1%)21 (36.8%)
No82 (56.9%)36 (63.2%)
Experienced eating problems since quitting 0.640
Yes68 (47.2%)29 (50.9%)
No76 (52.8%)28 (49.1%)
Do physical exercise during the quit attempt 0.140
Yes67 (46.5%)20 (35.1%)
No77 (53.5%)37 (64.9%)
Experienced craving since quitting 0.086
Yes114 (79.2%)51 (89.5%)
No30 (20.8%)6 (10.5%)
Others smoking inside living home 0.625
Yes61 (42.4%)22 (38.6%)
No83 (57.6%)35 (61.4%)
FTND—Fagerstrom Test for Nicotine Dependence; SCMs—smoking cessation medications; NRT—nicotine replacement therapy; p-value—chi-square test; *—Significant p-value.
Table
Table 3. Factors associated with self-reported adherence to SCMs among smokers and ex-smokers in Australia aged 18 and above (n = 201).
Table 3. Factors associated with self-reported adherence to SCMs among smokers and ex-smokers in Australia aged 18 and above (n = 201).
Variables Adherence n = 201COR (95% CI)AOR (95% CI)
No
Frequency (%)		Yes
Frequency (%)
Age
18–55100 (69.4%)26 (45.6%)11
≥5644 (30.6%)31 (54.4%)2.71 (1.44–5.08) **1.50 (0.47–4.78)
Employment status
Employed89 (61.8%)26 (45.6%)0.52 (0.29–0.96) *0.92 (0.34–2.49)
Unemployed55 (38.2%)31 (54.4%)11
Remoteness
Major cities of Australia116 (80.6%)35 (61.4%)11
Regional and remote Australia28 (19.4%)22 (38.6%)2.60 (1.33–5.11) **1.11 (0.42–2.89)
Alcohol drinking
No49 (34.1%)30 (52.6%)2.15 (1.15–4.02)*1.16 (0.45–2.98)
Yes95 (65.9%)27 (47.4%)11
Level of social support
Good social support 55 (38.2%)43 (75.4%)4.97 (2.49–9.91) **3.28 (2.30–6.27)*
Poor social support89 (61.8%)14 (24.6%)11
Level of stress
No symptom of stress76 (53.1%)43 (75.4%)2.70 (1.36–5.38) **0.57 (0.17–1.90)
Symptom of stress67 (46.9%)14 (24.6%)11
Level of anxiety
No symptom of anxiety64 (44.4%)49 (86%)7.66 (3.38–17.32) **4.41 (3.64–14.68)*
Symptom of anxiety80 (55.6%)8 (14%)11
Level of depression
No symptom of depression79 (54.9%)49 (86%)5.04 (2.23–11.39) **1.80 (0.46–7.07)
Symptom of depression65 (45.1%)8 (14%)11
Level of nicotine dependence
Low dependence98 (68.1%)25 (43.9%)11
Moderate to high dependence46 (31.9%)32 (56.1%)2.73 (1.45–5.11) **3.53 (1.40–8.95) **
Previous SCMs use
Yes83 (69.7%)48 (90.6%4.16(1.53–11.32) **3.87 (1.11–13.44) *
No36 (30.3)5 (9.4%)11
Type of SCM used
NRT108 (75%)36 (63.2)
Varenicline22 (15.3%)18 (31.5%)2.45 (1.18–5.08) *1.48 (0.53–4.13)
Bupropion14 (9.7%)3 (5.3%)0.64 (0.17–2.36)0.24 (0.39–1.49)
Ever forget to take the SCMs
Yes75 (52.1%)19 (33.3%)11
No69 (47.9%)38 (66.7%)2.17 (1.15–4.12)*1.67 (0.61–4.57)
Relapse
Yes99 (68.7%)30 (52.6%)11
No45 (31.3%)27 (47.4%)1.98 (1.05–3.71) *2.88 (1.21–6.88) *
* p-value <0.05; ** p-value <0.01; SCMs—smoking cessation medications; NRT—nicotine replacement therapy; COR—crude odds ratio; AOR—adjusted odds ratio.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Mersha, A.G.; Kennedy, M.; Eftekhari, P.; Gould, G.S. Predictors of Adherence to Smoking Cessation Medications among Current and Ex-Smokers in Australia: Findings from a National Cross-Sectional Survey. Int. J. Environ. Res. Public Health 2021, 18, 12225. https://doi.org/10.3390/ijerph182212225

AMA Style

Mersha AG, Kennedy M, Eftekhari P, Gould GS. Predictors of Adherence to Smoking Cessation Medications among Current and Ex-Smokers in Australia: Findings from a National Cross-Sectional Survey. International Journal of Environmental Research and Public Health. 2021; 18(22):12225. https://doi.org/10.3390/ijerph182212225

Chicago/Turabian Style

Mersha, Amanual Getnet, Michelle Kennedy, Parivash Eftekhari, and Gillian Sandra Gould. 2021. "Predictors of Adherence to Smoking Cessation Medications among Current and Ex-Smokers in Australia: Findings from a National Cross-Sectional Survey" International Journal of Environmental Research and Public Health 18, no. 22: 12225. https://doi.org/10.3390/ijerph182212225

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop