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Correlates of varenicline adherence among smokers with HIV and its association with smoking cessation
Addictive Behaviors 102 (2020) 106151
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Short Communication
Correlates of varenicline adherence among smokers with HIV and its association with smoking cessation
T
Mackenzie Hosie Quinna, Anna-Marika Bauera, Alex Flittera, Su Fen Lubitza, Rebecca L. Asharea, ⁎ Morgan Thompsona, Frank Leoneb, Robert Grossc,d, Robert Schnolla, a
Department of Psychiatry, University of Pennsylvania, PA, United States Pulmonary, Allergy, & Critical Care Division, University of Pennsylvania, PA, United States c Department of Medicine/Division of Infectious Diseases, University of Pennsylvania, PA, United States d Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, PA, United States b
H I GH L IG H T S
rate for adherent participants was 35% • Quit participants had less depression, anxiety, and insomnia. • Adherent • Adherent participants were older and had lower baseline creatinine.
A R T I C LE I N FO
A B S T R A C T
Keywords: Adherence Smoking cessation Varenicline HIV Depression Anxiety
Introduction: With medical advances, the life expectancy of people living with HIV/AIDS (PLWHA) has improved; however, tobacco use remains a prominent risk for mortality. Although studies have examined the efficacy of varenicline for treating smoking among PLWHA, the relationship between varenicline adherence and cessation and correlates of varenicline adherence remain under-studied. Methods: We conducted secondary analyses from a randomized placebo-controlled trial of varenicline for smoking among PLWHA, using data from participants who received varenicline (N = 89). The relationship between varenicline adherence (based on pill count) and end-of-treatment smoking cessation was assessed, as were correlates of varenicline adherence. Results: Those who were abstinent took an average of 137.1 pills (SD = 39.3), or 83% of pills prescribed, vs. 105.3 pills (SD = 64.1), or 64%, for those who were smoking (OR = 1.01, 95% CI: 1.001–1.021, p = 0.03); 52/ 89 (58%) participants were adherent based on taking ≥80% of pills. The quit rate for adherent participants was 35% (18/52) vs. 19% (7/37) for non-adherent participants. Adherent participants were older, smoked fewer cigarettes each day, started smoking at an older age, and had lower baseline creatinine vs. non-adherent participants (p < 0.05). There was a significant time-by-group interaction effect for anxiety (F[1,72] = 6.24, p = 0.02), depression (F[1,72] = 4.2, p = 0.04), and insomnia (F[1,72] = 7.73, p = 0.007), indicating that adherent participants had less depression, anxiety, and insomnia during the initial weeks of treatment, vs. nonadherent participants. Conclusions: Our findings underscore the importance of varenicline adherence for determining cessation and highlight the role of early changes in anxiety, depression, and insomnia determining varenicline adherence.
1. Introduction The life expectancy for people living with HIV/AIDS (PLWHA) has greatly improved over the past decade with advances in the effectiveness of antiretroviral therapies (ART) (Wandeler, Johnson, & Egger,
⁎
2016). At the same time, the importance of addressing modifiable risk factors for HIV mortality such as tobacco use, has also steadily grown (Althoff, 2016; Petoumenos & Law, 2016). Now, more life-years are lost among PLWHA to tobacco smoking than to the virus itself (Helleberg et al., 2012), and tobacco cessation among PLWHA represents one of
Corresponding author. E-mail address: [email protected] (R. Schnoll).
https://doi.org/10.1016/j.addbeh.2019.106151 Received 19 June 2019; Received in revised form 30 September 2019; Accepted 30 September 2019 Available online 22 October 2019 0306-4603/ © 2019 Elsevier Ltd. All rights reserved.
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smoking cessation counseling sessions at Weeks 0, 1, 3, 5, 7, and 9, inperson or by telephone (Lerman et al., 2015; Schnoll et al., 2010, 2015), which included a target quit day at week 1.
the most significant ways to substantially improve life-expectancy in this population (Reddy et al., 2016). Regrettably, upwards of 40% of the population with HIV/AIDS are smokers (Raposeiras-Roubín, AbuAssi, & Iñiguez-Romo, 2017), which greatly exceeds the prevalence of smoking in the general population (Mdodo et al., 2015; Norris, Schiller, & Clarke, 2018). Varenicline is an effective treatment for tobacco dependence (Cahill, Stevens, Perera, & Lancaster, 2013) among smokers with and without medical and psychiatric comorbidities (Anthenelli et al., 2016; Price et al., 2017). Two recent placebo-controlled trials – one in France (Mercié et al., 2018) and one by our group (Ashare, Thompson, Serrano, Leone, & Metzger, 2018) – found that varenicline is safe and effective for treating tobacco use among PLWHA, but yields quit rates that are substantially lower than those reported in the general population (Gonzales et al., 2006). Varenicline adherence – defined as self-reported use of ≥80% of prescribed pills – rarely exceeds 64% in any sample (Peng et al., 2017) and non-adherence significantly reduces the likelihood of cessation (Catz et al., 2011). Few studies, however, have examined predictors of adherence in any smoking populations to develop interventions to increase varenicline adherence (Pacek, McClernon, & Bosworth, 2017). The limited literature indicates that lower varenicline adherence is associated with female gender (O’Malley et al., 2018), younger age (Browning et al., 2016; Peng et al., 2017), less education (Peng et al., 2017), non-white race (Browning et al., 2016; Peng et al., 2017), higher nicotine dependence (Balmford, Borland, Hammond, & Cummings, 2010), and greater side-effects (Harrison-Woolrych & Ashton, 2010). However, little is known about other variables such as depression, anxiety, nicotine craving or withdrawal, or common varenicline-related side effects as correlates of varenicline adherence. Thus, to help guide interventions to address varenicline adherence, this study examined the relationship between varenicline adherence and cessation among smokers with HIV and examined correlates of varenicline adherence, including demographic variables, factors associated with HIV, depression and anxiety, varenicline side effects, and smokingrelated characteristics.
2.3. Measures Prior to treatment, we assessed demographic (e.g., age, race, education, income), HIV-related (e.g., mode of transmission, viral load, creatinine) and smoking-related (e.g., current smoking rate, level of nicotine dependence, including heaviness of smoking index and timeto-first-cigarette) characteristics. HIV information was ascertained from the medical record and included creatinine to assess functional status, and type of ART, since we found that efavarinz was associated with nicotine metabolism (Ashare et al., 2018). At baseline and week 3, the Hospital Anxiety and Depression Scale (HADS) (Zigmond & Snaith, 1983) was used to assess current depression and anxiety, the brief Questionnaire of Smoking Urges (QSU-B) (Cox, Tiffany, & Christen, 2001) measured an individual’s urge to smoke, the Shiffman-Jarvik Withdrawal Form (SJWF) (Shiffman & Jarvik, 1976) measured withdrawal symptoms, and an established checklist tracked varenicline-related side effects (e.g., nausea, sleep problems, depressive symptoms, hostility) (Lerman et al., 2015; Price et al., 2017), which were rated from 0 (none) to 3 (severe) and summed to create a side-effects index total score and averaged to provide a mean side effect severity measure. Nausea and insomnia, which are common to varenicline, were assessed individually as well. Varenicline adherence was assessed at Weeks 0, 1, 3, 5, 7, 9, and 12 using the timeline follow-back method (Brown et al., 1998) and blisterpack collection as done previously (Crawford et al., 2018; Lerman et al., 2015). We assessed the total number of pills taken out of the total pills prescribed and computed an overall proportion of medication adherence (adherence defined by taking ≥80% of prescribed medication) (Pacek et al., 2017). If a discrepancy regarding the number of pills taken arose between blister-packs and what was reported during timeline follow-back, the amount in the blister-pack was recorded and used. Smoking behavior was assessed using the timeline follow-back procedure as done previously (Lerman et al., 2015; Schnoll et al., 2015) and cessation was determined using 7-day point-prevalence abstinence at Week 12 based on no self-reported tobacco use (not even a puff) during the 7 days preceding the assessment and exhaled carbon monoxide (CO) ≤ 8 ppm (Hughes et al., 2003; Subcommittee, 2002).
2. Methods We used data from a completed clinical trial (NCT01710137) that compared placebo to varenicline for tobacco use among PLWHA for the present analyses. The methods and results of this trial, which was approved by the University of Pennsylvania IRB and was conducted between October 2012 and June 2018, have been reported elsewhere (Ashare et al., 2018). 2.1. Participants
2.4. Analyses
We recruited participants through Penn medical clinics, media advertisements, and through a community-based HIV clinic. To be eligible, participants had to be ≥ age 18, had to have a confirmed HIV diagnosis and were receiving treatment with ART, and had to have HIV viral loads < 1000 copies/ml. Participants were excluded for a lifetime history of psychosis or a suicide attempt, self-reported current or planned pregnancy, self-reported current use of smoking cessation medications, and indications of unstable or untreated alcohol/substance abuse. Of the 179 participants who were eligible and randomized to the trial, this secondary analysis included only participants in the varenicline arm (N = 89).
We assessed the relationship between adherence and week 12 abstinence using logistic regression. We used ANOVA (for continuous measures) and chi-square tests (for categorical measures) to evaluate differences in demographic, disease, and smoking-related factors between adherent and non-adherent participants, which identified covariates for subsequent analyses. We used mixed ANOVA, with time (week 0 to week 3) as the repeated measure variable and adherent vs. non-adherent as the between group measure, to evaluate changes between week 0 and week 3 in anxiety, depression, craving, withdrawal, and side effects as correlates of varenicline adherence, controlling for covariates. We chose this timeframe since it takes 2–3 weeks for steady state varenicline to be reached and this is when most relapse to smoking will occur if it does, making this the timeframe that is most likely to be sensitive to change and effects on adherence. Lastly, to examine the unique contributions to predicting adherence, variables associated with adherence were included in a logistic regression model predicting varenicline adherence. For missing data, the participant was not included in the specific analysis.
2.2. Procedures Participants provided written informed consent. Varenicline was provided for 12 weeks based on U.S. Food and Drug Administration dosing guidelines: Day 1-Day 3 (0.5 mg once daily); Day 4–7 (0.5 mg twice daily); and Day 8-Day 84 (1.0 mg twice daily). All participants were offered six standardized, Public Health Service guideline-based 2
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Table 1 Baseline demographic, smoking-related, and disease-related characteristics for the sample by adherence. Variable
Non-Adherent (N = 37) N (%) or M (SD)
Adherent (N = 52) N (%) or M (SD)
Total (N = 89) N (%) or M (SD)
Demographic variables Race (% African American) Sex (% Male) Education (% High School Grad or less) Annual Household Income (< 20 K) Age (Range: 21–70 years) BMI (Range: 18.8–58.2) # Alcohol Drinks in Past 7 Days (Range: 0–21)
29 (80.6) 26 (70.3) 19 (51.4) 19 (51.4) 45.4 (9.5) 28.3 (7.1) 1.2 (2.4)
40 (78.4) 38 (73.1) 27 (51.9) 36 (69.2) 51.0 (9.9) 27.0 (7.2) 3.1 (6.1)
69 (79.3) 64 (71.9) 46 (51.7) 55 (61.8) 48.7 (10.1)a 27.5 (7.2) 2.3 (5.0)
Smoking-related variables % High HSI % TTFC within 5 min Cigarettes/Day in Past 24 h (Range: 1–40) Breath CO, ppm (Range: 1–60) Number of Years Smoking (Range: 6–56) # Times Quit Smoking for > 24 Hours (Range: 0–500) Age Started Smoking (Range: 9–40)
11 (29.7) 15 (40.5) 13.0 (7.9) 15.7 (10.3) 30.7 (8.5) 3.6 (3.8) 14.8 (3.3)
11 (21.2) 22 (42.3) 9.9 (5.2) 14.5 (10.5) 32.5 (12.1) 12.5 (69.1) 17.5 (6.1)
22 (24.7) 37 (41.6) 11.2 (6.6)a 15.0 (10.4) 31.8 (10.8) 8.8 (52.9) 16.4 (5.3)a
Disease-related characteristics % of ART Prescribed in Past 2 Weeks Taken (Range: 79–100) % Undetectable Viral Load (< 50 copies/ml) CD4 + cells/mm3 (Range: 218–1932) % Acquired HIV via Sex % ART regimen containing efavirenz Estimated creatinine clearance (mL/min)
99 (0.03) 29 (78.4) 714.2 (262.2) 30 (81.1) 6 (16.2) 107.7 (39.2)
99 (0.04) 46 (89.5) 753.5 (371.7) 43 (82.7) 8 (15.7) 103.5 (38.9)
99 (0.03) 75 (84.3) 737.2 (329.5) 73 (82) 14 (15.9) 105.6 (39.0)a
Note. BMI = Body Mass Index; CO = Carbon Monoxide; HSI = Heaviness of Smoking Index; TTFC = Time to First Cigarette. adifference, p < 0.05
3. Results
anxiety symptoms (OR = 1.36, 95% CI: 1.09–1.69, p = 0.007), and changes in insomnia (OR = 3.28, 95% CI: 1.04–10.33, p = 0.042) remained significant. Analyses were re-run controlling for smoking status at week 3 and the results were unchanged. Also, analyses were rerun for the placebo arm, where adherence was 76%, but the rate of adherence in the placebo arm was not associated with smoking cessation or the correlates assessed here.
3.1. Sample characteristics Table 1 shows the characteristics of the participants, overall and separately for those who were adherent (58%) vs. non-adherent (42%). Of note, 72% of the sample was male, 79% were Black, smoked 13.5 cigarettes per day, and 84% of the sample had undetectable viral loads. As shown in Table 1, adherent participants were significantly older, smoked fewer cigarettes each day, started smoking at an older age, and had lower baseline creatinine compared to non-adherent participants (p’s < 0.05). These variables were included as covariates in all subsequent analyses.
4. Discussion The objective of this study was to describe the relationship between varenicline adherence and varenicline efficacy and, in turn, to examine correlates of varenicline adherence among PLWHA. Few studies have assessed the role of factors beyond demographic characteristics and side effects as correlates of varenicline adherence (Anthenelli et al., 2016; Cahill et al., 2013) and, thus, the literature on targets of adherence interventions is relatively under-developed. The results from the present analyses underscore the importance of varenicline adherence in determining cessation outcomes. While we cannot disentangle medication effects on mood and insomnia from mood and insomnia effects on adherence, the results also highlight that anxiety, depression, and insomnia may be potential targets of interventions to boost adherence among PLWHA trying to quit smoking with varenicline. First, 42% of the sample reported suboptimal varenicline adherence, which converges with previous studies with smokers in the general population (Liberman et al., 2013) and with smokers with HIV (Browning et al., 2016). Further, the sizable impact of adherence on cessation is similar to past studies with the general population of smokers and with smokers with HIV (Browning et al., 2016; Liberman et al., 2013). Thus, suboptimal adherence is a major barrier to quitting smoking with varenicline, which necessitates the development of interventions to address. Second, older age, smoking fewer cigarettes per day, becoming a regular smoker later in life, and lower baseline creatinine were associated with greater adherence. These findings are consistent with those of past studies that examined correlates of tobacco treatment adherence among PLWHA (Browning et al., 2016; de Dios, Stanton, Cano, LloydRichardson, & Niaura, 2015; McQueen, Shacham, Sumner, & Overton, 2014; Shelley et al., 2015) and can be useful in targeting interventions
3.2. Association of varenicline adherence with smoking cessation Participants were prescribed 165 pills and those who had quit smoking took an average of 137.1 pills (SD = 39.3), or 83% of pills prescribed, compared to 105.3 pills (SD = 64.1), or 64%, for those who had not quit smoking (OR = 1.01, 95% CI: 1.001–1.021, p = 0.03). At Week 12, 28.1% of participants had quit smoking. The quit rate for adherent participants was 35%, compared to 19% for non-adherent participants. 3.3. Correlates of varenicline adherence Changes in craving, withdrawal, and total and mean side effects from week 0 to week 3 were not significantly different between adherent and non-adherent participants (all p’s > 0.05). In contrast, we found a significant time-by-group interaction effect for anxiety (F [1,72] = 6.24, p = 0.02) and depressive (F[1,72] = 4.2, p = 0.044) symptoms. Likewise, we found a significant time-by-group interaction effect for insomnia (F[1,72] = 7.73, p = 0.007). As shown in Fig. 1, compared to non-adherent participants, adherent participants showed a decrease in symptoms of anxiety, depression, and insomnia from week 0 to week 3. Lastly, when covariates and anxiety and depression symptoms, and insomnia (all variables associated in univariate analysis with adherence) were entered into a logistic regression model, baseline creatinine (OR = 0.97, 95% CI: 0.96–0.99, p = 0.006), changes in 3
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Fig. 1. Changes in Anxiety and Depression Symptoms and Insomnia between Adherent and Non-Adherent Participants.
changes in adherence. It is also unclear if changes in depression, anxiety, and insomnia are side effects of the medication or symptoms of withdrawal (Ashare et al., 2018). Prior research highlights how the measures used to assess withdrawal symptoms and side effects overlap, which hinders our ability to distinguish one from the other (Ashare et al., 2018). It is also possible that a third factor may be influencing adherence and these side effects, like smoking cessation success, rather than these side effects affecting adherence to varenicline. This is consistent with recent studies, which found improvement in psychological well-being following smoking cessation (Rodríguez-Cano et al., 2018; Shahab, Andrew, & West, 2014; Stepankova et al., 2016; Taylor et al., 2014).
to increase adherence to sub-groups most in need of formal support. Non-consistent results may be from comparing a sample of smokers with HIV to past findings with non-HIV samples. Lastly, we identified three factors that were prospectively associated with varenicline adherence in the sample: a reduction in depression, anxiety, and insomnia. These reactions may be a consequence of changes in smoking behavior or may be side effects of greater varenicline use. These results suggest that managing these reactions to cessation or the medication may serve as useful intervention targets to promote greater varenicline adherence in this population. Since depression occurs twice as frequently in smokers and three times as frequently in PLWHA (Hitsman et al., 2013; Weinberger et al., 2017), methods to address depression within an adherence intervention may be particularly important. Likewise, anxiety is a common, yet less studied, reaction to quitting smoking that can trigger a relapse to smoking and insomnia is a common side effect of varenicline (Leventhal & Zvolensky, 2015; Short et al., 2017). Our results indicate that anxiety, depression, and insomnia may also be important for driving varenicline adherence and should be targeted by an adherence intervention.
4.2. Conclusions This is one of the first studies to prospectively evaluate early changes in common experiences following cessation using varenicline that may influence adherence to varenicline. The results can offer information useful for targeting adherence interventions to those at-risk and for developing the content of such interventions. Unfortunately, there have been very few studies that have designed and tested formal interventions to address tobacco use medication adherence among PLWHA. The results from this study can help move that field forward so that in the coming years we have a better understanding of effective methods to increase varenicline adherence so that we can capitalize on its effectiveness and improve the lives of PLWHA.
4.1. Limitations These results should be considered in the context of study limitations. First, the sub-sample of those in the varenicline treatment arm was relatively small and may have resulted in analyses that were underpowered. Yet, we still found a strong effect of adherence. Second, the inclusion and exclusion criteria used to control for potential confounding variables may limit the generalizability of the results. Indeed, the sample may not be representative of the population of PWLHA who are regular smokers and caution should be exercised when generalizing results to the overall population of smokers with HIV. It is important to recognize the limitation of using self-reported pill counts to measure adherence. Self-report data is not always accurate; however, the selfreported number of pills taken was checked against the blister packs. Another limitation is that we cannot determine causality from the results, meaning that it is plausible that these changes in anxiety, depression, and insomnia led to changes in adherence or were effects of
Role of Funding This work was supported by National Institutes of Health grants K24 DA045244 and R01 DA033681 and support from the Penn Center for AIDS Research (P30 AI 045008) and the Penn Mental Health AIDS Research Center (P30 MH 097488). Conflicts of interest Dr. Schnoll receives medication and placebo free from Pfizer and 4
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has provided consultation to Pfizer. Dr. Schnoll has provided consultation to GlaxoSmithKline and CuraLeaf. Dr. Gross serves on a Data and Safety Monitoring Board for a Pfizer drug unrelated to HIV or smoking.
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Adherence to varenicline in the COMPASS smoking cessation intervention trial. Nicotine & Tobacco Research, 13(5), 361–368. Cox, L. S., Tiffany, S. T., & Christen, A. G. (2001). Evaluation of the brief questionnaire of smoking urges (QSU-brief) in laboratory and clinical settings. Nicotine & Tobacco Research, 3(1), 7–16. Crawford, G., Jao, N., Peng, A. R., Leone, F., Kalhan, R., Tyndale, R. F., ... Schnoll, R. (2018). The association between self-reported varenicline adherence and varenicline blood levels in a sample of cancer patients receiving treatment for tobacco dependence. Addictive Behaviors Reports, 8, 46–50. https://doi.org/10.1016/j.abrep.2018. 06.006. de Dios, M. A., Stanton, C. A., Cano, M.Á., Lloyd-Richardson, E., & Niaura, R. (2015). The influence of social support on smoking cessation treatment adherence among HIV+ smokers. Nicotine & Tobacco Research, 18(5), 1126–1133. https://doi.org/10.1093/ ntr/ntv144. Gonzales, D., Rennard, S. I., Nides, M., Oncken, C., Azoulay, S., Billing, C. B., ... & Varenicline Phase 3 Study Group. (2006). Varenicline, an α4β2 nicotinic acetylcholine receptor partial agonist, vs sustained-release bupropion and placebo for smoking cessation: A randomized controlled trial. Jama, 296(1), 47–55. doi:10.1001/jama. 296.1.47. Harrison-Woolrych, M., & Ashton, J. (2010). Utilization of the smoking cessation medicine varenicline: An intensive post-marketing study in New Zealand. Pharmacoepidemiology and Drug Safety, 19(9), 949–953. Helleberg, M., Afzal, S., Kronborg, G., Larsen, C. S., Pedersen, G., Pedersen, C., ... Obel, N. (2012). Mortality attributable to smoking among HIV-1–infected individuals: A nationwide, population-based cohort study. Clinical Infectious Diseases, 56(5), 727–734. https://doi.org/10.1093/cid/cis933. Hitsman, B., Papandonatos, G. D., McChargue, D. E., DeMott, A., Herrera, M. J., Spring, B., ... Niaura, R. (2013). Past major depression and smoking cessation outcome: A systematic review and meta-analysis update. Addiction (Abingdon, England), 108(2), 294–306. https://doi.org/10.1111/add.12009. Hughes, J. R., Keely, J. P., Niaura, R. S., Ossip-Klein, D. J., Richmond, R. L., & Swan, G. E. (2003). Measures of abstinence in clinical trials: Issues and recommendations. Nicotine & Tobacco Research, 5(1), 13–25. https://doi.org/10.1093/ntr/5.1.13. Lerman, C., Schnoll, R. A., Hawk Jr, L. W., Cinciripini, P., George, T. P., Wileyto, E. P., ... & PGRN-PNAT Research Group. (2015). Use of the nicotine metabolite ratio as a genetically informed biomarker of response to nicotine patch or varenicline for smoking cessation: a randomised, double-blind placebo-controlled trial. The Lancet Respiratory Medicine, 3(2), 131–138. doi:10.1016/S2213-2600(14)70294-2. Leventhal, A. M., & Zvolensky, M. J. (2015). Anxiety, depression, and cigarette smoking: A transdiagnostic vulnerability framework to understanding emotion–smoking comorbidity. Psychological Bulletin, 141(1), 176. Liberman, J. N., Lichtenfeld, M. J., Galaznik, A., Mastey, V., Harnett, J., Zou, K. H., ...
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Short Communication
Correlates of varenicline adherence among smokers with HIV and its association with smoking cessation
T
Mackenzie Hosie Quinna, Anna-Marika Bauera, Alex Flittera, Su Fen Lubitza, Rebecca L. Asharea, ⁎ Morgan Thompsona, Frank Leoneb, Robert Grossc,d, Robert Schnolla, a
Department of Psychiatry, University of Pennsylvania, PA, United States Pulmonary, Allergy, & Critical Care Division, University of Pennsylvania, PA, United States c Department of Medicine/Division of Infectious Diseases, University of Pennsylvania, PA, United States d Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, PA, United States b
H I GH L IG H T S
rate for adherent participants was 35% • Quit participants had less depression, anxiety, and insomnia. • Adherent • Adherent participants were older and had lower baseline creatinine.
A R T I C LE I N FO
A B S T R A C T
Keywords: Adherence Smoking cessation Varenicline HIV Depression Anxiety
Introduction: With medical advances, the life expectancy of people living with HIV/AIDS (PLWHA) has improved; however, tobacco use remains a prominent risk for mortality. Although studies have examined the efficacy of varenicline for treating smoking among PLWHA, the relationship between varenicline adherence and cessation and correlates of varenicline adherence remain under-studied. Methods: We conducted secondary analyses from a randomized placebo-controlled trial of varenicline for smoking among PLWHA, using data from participants who received varenicline (N = 89). The relationship between varenicline adherence (based on pill count) and end-of-treatment smoking cessation was assessed, as were correlates of varenicline adherence. Results: Those who were abstinent took an average of 137.1 pills (SD = 39.3), or 83% of pills prescribed, vs. 105.3 pills (SD = 64.1), or 64%, for those who were smoking (OR = 1.01, 95% CI: 1.001–1.021, p = 0.03); 52/ 89 (58%) participants were adherent based on taking ≥80% of pills. The quit rate for adherent participants was 35% (18/52) vs. 19% (7/37) for non-adherent participants. Adherent participants were older, smoked fewer cigarettes each day, started smoking at an older age, and had lower baseline creatinine vs. non-adherent participants (p < 0.05). There was a significant time-by-group interaction effect for anxiety (F[1,72] = 6.24, p = 0.02), depression (F[1,72] = 4.2, p = 0.04), and insomnia (F[1,72] = 7.73, p = 0.007), indicating that adherent participants had less depression, anxiety, and insomnia during the initial weeks of treatment, vs. nonadherent participants. Conclusions: Our findings underscore the importance of varenicline adherence for determining cessation and highlight the role of early changes in anxiety, depression, and insomnia determining varenicline adherence.
1. Introduction The life expectancy for people living with HIV/AIDS (PLWHA) has greatly improved over the past decade with advances in the effectiveness of antiretroviral therapies (ART) (Wandeler, Johnson, & Egger,
⁎
2016). At the same time, the importance of addressing modifiable risk factors for HIV mortality such as tobacco use, has also steadily grown (Althoff, 2016; Petoumenos & Law, 2016). Now, more life-years are lost among PLWHA to tobacco smoking than to the virus itself (Helleberg et al., 2012), and tobacco cessation among PLWHA represents one of
Corresponding author. E-mail address: [email protected] (R. Schnoll).
https://doi.org/10.1016/j.addbeh.2019.106151 Received 19 June 2019; Received in revised form 30 September 2019; Accepted 30 September 2019 Available online 22 October 2019 0306-4603/ © 2019 Elsevier Ltd. All rights reserved.
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smoking cessation counseling sessions at Weeks 0, 1, 3, 5, 7, and 9, inperson or by telephone (Lerman et al., 2015; Schnoll et al., 2010, 2015), which included a target quit day at week 1.
the most significant ways to substantially improve life-expectancy in this population (Reddy et al., 2016). Regrettably, upwards of 40% of the population with HIV/AIDS are smokers (Raposeiras-Roubín, AbuAssi, & Iñiguez-Romo, 2017), which greatly exceeds the prevalence of smoking in the general population (Mdodo et al., 2015; Norris, Schiller, & Clarke, 2018). Varenicline is an effective treatment for tobacco dependence (Cahill, Stevens, Perera, & Lancaster, 2013) among smokers with and without medical and psychiatric comorbidities (Anthenelli et al., 2016; Price et al., 2017). Two recent placebo-controlled trials – one in France (Mercié et al., 2018) and one by our group (Ashare, Thompson, Serrano, Leone, & Metzger, 2018) – found that varenicline is safe and effective for treating tobacco use among PLWHA, but yields quit rates that are substantially lower than those reported in the general population (Gonzales et al., 2006). Varenicline adherence – defined as self-reported use of ≥80% of prescribed pills – rarely exceeds 64% in any sample (Peng et al., 2017) and non-adherence significantly reduces the likelihood of cessation (Catz et al., 2011). Few studies, however, have examined predictors of adherence in any smoking populations to develop interventions to increase varenicline adherence (Pacek, McClernon, & Bosworth, 2017). The limited literature indicates that lower varenicline adherence is associated with female gender (O’Malley et al., 2018), younger age (Browning et al., 2016; Peng et al., 2017), less education (Peng et al., 2017), non-white race (Browning et al., 2016; Peng et al., 2017), higher nicotine dependence (Balmford, Borland, Hammond, & Cummings, 2010), and greater side-effects (Harrison-Woolrych & Ashton, 2010). However, little is known about other variables such as depression, anxiety, nicotine craving or withdrawal, or common varenicline-related side effects as correlates of varenicline adherence. Thus, to help guide interventions to address varenicline adherence, this study examined the relationship between varenicline adherence and cessation among smokers with HIV and examined correlates of varenicline adherence, including demographic variables, factors associated with HIV, depression and anxiety, varenicline side effects, and smokingrelated characteristics.
2.3. Measures Prior to treatment, we assessed demographic (e.g., age, race, education, income), HIV-related (e.g., mode of transmission, viral load, creatinine) and smoking-related (e.g., current smoking rate, level of nicotine dependence, including heaviness of smoking index and timeto-first-cigarette) characteristics. HIV information was ascertained from the medical record and included creatinine to assess functional status, and type of ART, since we found that efavarinz was associated with nicotine metabolism (Ashare et al., 2018). At baseline and week 3, the Hospital Anxiety and Depression Scale (HADS) (Zigmond & Snaith, 1983) was used to assess current depression and anxiety, the brief Questionnaire of Smoking Urges (QSU-B) (Cox, Tiffany, & Christen, 2001) measured an individual’s urge to smoke, the Shiffman-Jarvik Withdrawal Form (SJWF) (Shiffman & Jarvik, 1976) measured withdrawal symptoms, and an established checklist tracked varenicline-related side effects (e.g., nausea, sleep problems, depressive symptoms, hostility) (Lerman et al., 2015; Price et al., 2017), which were rated from 0 (none) to 3 (severe) and summed to create a side-effects index total score and averaged to provide a mean side effect severity measure. Nausea and insomnia, which are common to varenicline, were assessed individually as well. Varenicline adherence was assessed at Weeks 0, 1, 3, 5, 7, 9, and 12 using the timeline follow-back method (Brown et al., 1998) and blisterpack collection as done previously (Crawford et al., 2018; Lerman et al., 2015). We assessed the total number of pills taken out of the total pills prescribed and computed an overall proportion of medication adherence (adherence defined by taking ≥80% of prescribed medication) (Pacek et al., 2017). If a discrepancy regarding the number of pills taken arose between blister-packs and what was reported during timeline follow-back, the amount in the blister-pack was recorded and used. Smoking behavior was assessed using the timeline follow-back procedure as done previously (Lerman et al., 2015; Schnoll et al., 2015) and cessation was determined using 7-day point-prevalence abstinence at Week 12 based on no self-reported tobacco use (not even a puff) during the 7 days preceding the assessment and exhaled carbon monoxide (CO) ≤ 8 ppm (Hughes et al., 2003; Subcommittee, 2002).
2. Methods We used data from a completed clinical trial (NCT01710137) that compared placebo to varenicline for tobacco use among PLWHA for the present analyses. The methods and results of this trial, which was approved by the University of Pennsylvania IRB and was conducted between October 2012 and June 2018, have been reported elsewhere (Ashare et al., 2018). 2.1. Participants
2.4. Analyses
We recruited participants through Penn medical clinics, media advertisements, and through a community-based HIV clinic. To be eligible, participants had to be ≥ age 18, had to have a confirmed HIV diagnosis and were receiving treatment with ART, and had to have HIV viral loads < 1000 copies/ml. Participants were excluded for a lifetime history of psychosis or a suicide attempt, self-reported current or planned pregnancy, self-reported current use of smoking cessation medications, and indications of unstable or untreated alcohol/substance abuse. Of the 179 participants who were eligible and randomized to the trial, this secondary analysis included only participants in the varenicline arm (N = 89).
We assessed the relationship between adherence and week 12 abstinence using logistic regression. We used ANOVA (for continuous measures) and chi-square tests (for categorical measures) to evaluate differences in demographic, disease, and smoking-related factors between adherent and non-adherent participants, which identified covariates for subsequent analyses. We used mixed ANOVA, with time (week 0 to week 3) as the repeated measure variable and adherent vs. non-adherent as the between group measure, to evaluate changes between week 0 and week 3 in anxiety, depression, craving, withdrawal, and side effects as correlates of varenicline adherence, controlling for covariates. We chose this timeframe since it takes 2–3 weeks for steady state varenicline to be reached and this is when most relapse to smoking will occur if it does, making this the timeframe that is most likely to be sensitive to change and effects on adherence. Lastly, to examine the unique contributions to predicting adherence, variables associated with adherence were included in a logistic regression model predicting varenicline adherence. For missing data, the participant was not included in the specific analysis.
2.2. Procedures Participants provided written informed consent. Varenicline was provided for 12 weeks based on U.S. Food and Drug Administration dosing guidelines: Day 1-Day 3 (0.5 mg once daily); Day 4–7 (0.5 mg twice daily); and Day 8-Day 84 (1.0 mg twice daily). All participants were offered six standardized, Public Health Service guideline-based 2
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Table 1 Baseline demographic, smoking-related, and disease-related characteristics for the sample by adherence. Variable
Non-Adherent (N = 37) N (%) or M (SD)
Adherent (N = 52) N (%) or M (SD)
Total (N = 89) N (%) or M (SD)
Demographic variables Race (% African American) Sex (% Male) Education (% High School Grad or less) Annual Household Income (< 20 K) Age (Range: 21–70 years) BMI (Range: 18.8–58.2) # Alcohol Drinks in Past 7 Days (Range: 0–21)
29 (80.6) 26 (70.3) 19 (51.4) 19 (51.4) 45.4 (9.5) 28.3 (7.1) 1.2 (2.4)
40 (78.4) 38 (73.1) 27 (51.9) 36 (69.2) 51.0 (9.9) 27.0 (7.2) 3.1 (6.1)
69 (79.3) 64 (71.9) 46 (51.7) 55 (61.8) 48.7 (10.1)a 27.5 (7.2) 2.3 (5.0)
Smoking-related variables % High HSI % TTFC within 5 min Cigarettes/Day in Past 24 h (Range: 1–40) Breath CO, ppm (Range: 1–60) Number of Years Smoking (Range: 6–56) # Times Quit Smoking for > 24 Hours (Range: 0–500) Age Started Smoking (Range: 9–40)
11 (29.7) 15 (40.5) 13.0 (7.9) 15.7 (10.3) 30.7 (8.5) 3.6 (3.8) 14.8 (3.3)
11 (21.2) 22 (42.3) 9.9 (5.2) 14.5 (10.5) 32.5 (12.1) 12.5 (69.1) 17.5 (6.1)
22 (24.7) 37 (41.6) 11.2 (6.6)a 15.0 (10.4) 31.8 (10.8) 8.8 (52.9) 16.4 (5.3)a
Disease-related characteristics % of ART Prescribed in Past 2 Weeks Taken (Range: 79–100) % Undetectable Viral Load (< 50 copies/ml) CD4 + cells/mm3 (Range: 218–1932) % Acquired HIV via Sex % ART regimen containing efavirenz Estimated creatinine clearance (mL/min)
99 (0.03) 29 (78.4) 714.2 (262.2) 30 (81.1) 6 (16.2) 107.7 (39.2)
99 (0.04) 46 (89.5) 753.5 (371.7) 43 (82.7) 8 (15.7) 103.5 (38.9)
99 (0.03) 75 (84.3) 737.2 (329.5) 73 (82) 14 (15.9) 105.6 (39.0)a
Note. BMI = Body Mass Index; CO = Carbon Monoxide; HSI = Heaviness of Smoking Index; TTFC = Time to First Cigarette. adifference, p < 0.05
3. Results
anxiety symptoms (OR = 1.36, 95% CI: 1.09–1.69, p = 0.007), and changes in insomnia (OR = 3.28, 95% CI: 1.04–10.33, p = 0.042) remained significant. Analyses were re-run controlling for smoking status at week 3 and the results were unchanged. Also, analyses were rerun for the placebo arm, where adherence was 76%, but the rate of adherence in the placebo arm was not associated with smoking cessation or the correlates assessed here.
3.1. Sample characteristics Table 1 shows the characteristics of the participants, overall and separately for those who were adherent (58%) vs. non-adherent (42%). Of note, 72% of the sample was male, 79% were Black, smoked 13.5 cigarettes per day, and 84% of the sample had undetectable viral loads. As shown in Table 1, adherent participants were significantly older, smoked fewer cigarettes each day, started smoking at an older age, and had lower baseline creatinine compared to non-adherent participants (p’s < 0.05). These variables were included as covariates in all subsequent analyses.
4. Discussion The objective of this study was to describe the relationship between varenicline adherence and varenicline efficacy and, in turn, to examine correlates of varenicline adherence among PLWHA. Few studies have assessed the role of factors beyond demographic characteristics and side effects as correlates of varenicline adherence (Anthenelli et al., 2016; Cahill et al., 2013) and, thus, the literature on targets of adherence interventions is relatively under-developed. The results from the present analyses underscore the importance of varenicline adherence in determining cessation outcomes. While we cannot disentangle medication effects on mood and insomnia from mood and insomnia effects on adherence, the results also highlight that anxiety, depression, and insomnia may be potential targets of interventions to boost adherence among PLWHA trying to quit smoking with varenicline. First, 42% of the sample reported suboptimal varenicline adherence, which converges with previous studies with smokers in the general population (Liberman et al., 2013) and with smokers with HIV (Browning et al., 2016). Further, the sizable impact of adherence on cessation is similar to past studies with the general population of smokers and with smokers with HIV (Browning et al., 2016; Liberman et al., 2013). Thus, suboptimal adherence is a major barrier to quitting smoking with varenicline, which necessitates the development of interventions to address. Second, older age, smoking fewer cigarettes per day, becoming a regular smoker later in life, and lower baseline creatinine were associated with greater adherence. These findings are consistent with those of past studies that examined correlates of tobacco treatment adherence among PLWHA (Browning et al., 2016; de Dios, Stanton, Cano, LloydRichardson, & Niaura, 2015; McQueen, Shacham, Sumner, & Overton, 2014; Shelley et al., 2015) and can be useful in targeting interventions
3.2. Association of varenicline adherence with smoking cessation Participants were prescribed 165 pills and those who had quit smoking took an average of 137.1 pills (SD = 39.3), or 83% of pills prescribed, compared to 105.3 pills (SD = 64.1), or 64%, for those who had not quit smoking (OR = 1.01, 95% CI: 1.001–1.021, p = 0.03). At Week 12, 28.1% of participants had quit smoking. The quit rate for adherent participants was 35%, compared to 19% for non-adherent participants. 3.3. Correlates of varenicline adherence Changes in craving, withdrawal, and total and mean side effects from week 0 to week 3 were not significantly different between adherent and non-adherent participants (all p’s > 0.05). In contrast, we found a significant time-by-group interaction effect for anxiety (F [1,72] = 6.24, p = 0.02) and depressive (F[1,72] = 4.2, p = 0.044) symptoms. Likewise, we found a significant time-by-group interaction effect for insomnia (F[1,72] = 7.73, p = 0.007). As shown in Fig. 1, compared to non-adherent participants, adherent participants showed a decrease in symptoms of anxiety, depression, and insomnia from week 0 to week 3. Lastly, when covariates and anxiety and depression symptoms, and insomnia (all variables associated in univariate analysis with adherence) were entered into a logistic regression model, baseline creatinine (OR = 0.97, 95% CI: 0.96–0.99, p = 0.006), changes in 3
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Fig. 1. Changes in Anxiety and Depression Symptoms and Insomnia between Adherent and Non-Adherent Participants.
changes in adherence. It is also unclear if changes in depression, anxiety, and insomnia are side effects of the medication or symptoms of withdrawal (Ashare et al., 2018). Prior research highlights how the measures used to assess withdrawal symptoms and side effects overlap, which hinders our ability to distinguish one from the other (Ashare et al., 2018). It is also possible that a third factor may be influencing adherence and these side effects, like smoking cessation success, rather than these side effects affecting adherence to varenicline. This is consistent with recent studies, which found improvement in psychological well-being following smoking cessation (Rodríguez-Cano et al., 2018; Shahab, Andrew, & West, 2014; Stepankova et al., 2016; Taylor et al., 2014).
to increase adherence to sub-groups most in need of formal support. Non-consistent results may be from comparing a sample of smokers with HIV to past findings with non-HIV samples. Lastly, we identified three factors that were prospectively associated with varenicline adherence in the sample: a reduction in depression, anxiety, and insomnia. These reactions may be a consequence of changes in smoking behavior or may be side effects of greater varenicline use. These results suggest that managing these reactions to cessation or the medication may serve as useful intervention targets to promote greater varenicline adherence in this population. Since depression occurs twice as frequently in smokers and three times as frequently in PLWHA (Hitsman et al., 2013; Weinberger et al., 2017), methods to address depression within an adherence intervention may be particularly important. Likewise, anxiety is a common, yet less studied, reaction to quitting smoking that can trigger a relapse to smoking and insomnia is a common side effect of varenicline (Leventhal & Zvolensky, 2015; Short et al., 2017). Our results indicate that anxiety, depression, and insomnia may also be important for driving varenicline adherence and should be targeted by an adherence intervention.
4.2. Conclusions This is one of the first studies to prospectively evaluate early changes in common experiences following cessation using varenicline that may influence adherence to varenicline. The results can offer information useful for targeting adherence interventions to those at-risk and for developing the content of such interventions. Unfortunately, there have been very few studies that have designed and tested formal interventions to address tobacco use medication adherence among PLWHA. The results from this study can help move that field forward so that in the coming years we have a better understanding of effective methods to increase varenicline adherence so that we can capitalize on its effectiveness and improve the lives of PLWHA.
4.1. Limitations These results should be considered in the context of study limitations. First, the sub-sample of those in the varenicline treatment arm was relatively small and may have resulted in analyses that were underpowered. Yet, we still found a strong effect of adherence. Second, the inclusion and exclusion criteria used to control for potential confounding variables may limit the generalizability of the results. Indeed, the sample may not be representative of the population of PWLHA who are regular smokers and caution should be exercised when generalizing results to the overall population of smokers with HIV. It is important to recognize the limitation of using self-reported pill counts to measure adherence. Self-report data is not always accurate; however, the selfreported number of pills taken was checked against the blister packs. Another limitation is that we cannot determine causality from the results, meaning that it is plausible that these changes in anxiety, depression, and insomnia led to changes in adherence or were effects of
Role of Funding This work was supported by National Institutes of Health grants K24 DA045244 and R01 DA033681 and support from the Penn Center for AIDS Research (P30 AI 045008) and the Penn Mental Health AIDS Research Center (P30 MH 097488). Conflicts of interest Dr. Schnoll receives medication and placebo free from Pfizer and 4
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has provided consultation to Pfizer. Dr. Schnoll has provided consultation to GlaxoSmithKline and CuraLeaf. Dr. Gross serves on a Data and Safety Monitoring Board for a Pfizer drug unrelated to HIV or smoking.
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