Figures
Abstract
Introduction
Globally, persons with HIV infection, depression and substance use disorders have a higher smoking prevalence and smoke more heavily than other populations. These associations have not been explored among Russian smokers with HIV infection and substance use disorders. The purpose of this study was to examine the relationship between the presence of depressive symptoms and smoking outcomes in an HIV-positive cohort of Russian smokers with a history of substance use disorders (alcohol and/or drug use disorders).
Methods
We performed a cross-sectional secondary data analysis of a cohort of HIV-positive regular smokers with a history of substance use disorders recruited in St. Petersburg, Russia in 2012–2015. The primary outcome was heavy smoking, defined as smoking > 20 cigarettes per day. Nicotine dependence (moderate-very high) was a secondary outcome. The main independent variable was a high level of depressive symptoms in the past 7 days (defined as CES-D > = 24). We used multivariable logistic regression to examine associations between depressive symptoms and the outcomes, controlling for age, sex, education, income, running out of money for housing/food, injection drug use, and alcohol use measured by the AUDIT.
Results
Among 309 regular smokers, 79 participants (25.6%) had high levels of depressive symptoms, and 65 participants (21.0%) were heavy smokers. High levels of depressive symptoms were not significantly associated with heavy smoking (adjusted odds ratio [aOR] 1.50, 95% CI 0.78–2.89) or with moderate-very high levels of nicotine dependence (aOR 1.35, 95% CI 0.75–2.41).
Citation: Lasser KE, Lunze K, Cheng DM, Blokhina E, Walley AY, Tindle HA, et al. (2018) Depression and smoking characteristics among HIV-positive smokers in Russia: A cross-sectional study. PLoS ONE 13(2): e0189207. https://doi.org/10.1371/journal.pone.0189207
Editor: Zhicheng Carl Lin, Harvard Medical School, UNITED STATES
Received: April 12, 2017; Accepted: November 21, 2017; Published: February 6, 2018
Copyright: © 2018 Lasser et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The project described was supported by grants R01DA032082 from the National Institute on Drug Abuse, U01AA020780, U01AA021989, U24AA020778, U24AA020779 from the National Institute on Alcohol Abuse and Alcoholism, and RSG-14-034-01-CPPB from the American Cancer Society. The work was facilitated by the Providence/Boston Center for AIDS Research (P30AI042853, PI: Susan Cu-Vin). Efforts of three co-authors, Drs. Walley, Lunze, and Samet were partially supported by the CFAR, as Drs. Walley and Lunze are faculty members of the Substance Use Research Core (SURC) and Dr. Samet is a co-director of the SURC. The funding source had no role in the study design, the data collection, analysis and interpretation of the data; in the writing of this manuscript or decision to submit this article for publication.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The Russian Federation (Russia) is a country with among the highest proportion of its population smoking: 12.3% of women and 38.2% of men are daily smokers [1]. Russia also has among the highest per capita cigarette consumption globally [1,2]. This tobacco burden is a significant contributor to low life expectancy; Russia has the third lowest life expectancy at birth among Organisation for Economic Co-operation and Development (OECD) countries, at 70.7 years [3]. Quit ratios (i.e., the percentage of persons who ever smoked daily who no longer smoke) in Russia are low, at less than 20% [4]. In order to improve health outcomes, smoking cessation interventions in Russia need to target populations at highest risk of smoking and who may have the most difficulty quitting due to high levels of nicotine dependence and/or the heaviest smoking. Such populations include persons with HIV infection, [5] depression [6,7], and/or substance use disorders [8].
As of 2016, approximately 1 million people live with HIV infection in Russia [9,10]. While there is a paucity of data regarding smoking prevalence among HIV-positive Russians, a recent study [11] showed that 60% of HIV-infected Russian women reported cigarette smoking in the past month. Nearly half of recent smokers were moderate or heavy smokers. Smoking among persons living with HIV/AIDS is associated with increased morbidity and mortality [12]. In addition to causing increased cardiovascular and cancer-related mortality, smoking impairs efficacy of some antiretroviral medications [13], and may increase susceptibility to opportunistic infections [14]. Kaleta et al recently evaluated patterns of nicotine dependence among Russian adults [15]. However, that study did not analyze data on HIV infection, substance use or depression. A second study in nine former Soviet republics, now independent states, showed that high levels of psychological distress have a significant association with higher levels of nicotine dependence [16]. While prior studies have documented an association between high levels of nicotine dependence and depressive symptoms among HIV-positive patients in France [17], and an association between depression and smoking among HIV-positive patients in the United States [18,19], we are unaware of studies that have examined the association between depression and smoking among HIV-positive smokers in Russia. Hypothesizing an association between depression and smoking is reasonable. According to Munafo and Araya [20], depression may cause people to smoke as a form of self-medication of depressive symptoms. Alternatively, smoking may produce an increased risk of depression via changes in neurotransmitter pathways following chronic exposure to tobacco smoke [20].
Our aim was to examine the relationship between depressive symptoms and heavy smoking in an HIV-positive cohort of Russians with a history of substance use disorders (alcohol and/or drug use disorders). We hypothesized that high levels of depressive symptoms in the past 7 days, as measured by the Center for Epidemiologic Studies-Depression Scale (CES-D) [21,22], would be associated with a higher odds of reporting heavy smoking, as compared to those without high levels of depressive symptoms. We also hypothesized that high levels of depressive symptoms would be associated with high levels of nicotine dependence, as measured by the Fagerström Test for Nicotine Dependence [23,24].
Methods
Study design and data sources
We performed a cross-sectional secondary analysis of data from the Russia ARCH (Alcohol Research Collaboration on HIV/AIDS) cohort recruited in St. Petersburg, Russia. Russia ARCH was an observational prospective cohort study conducted to assess the longitudinal association between alcohol consumption and biomarkers of microbial translocation and inflammation/altered coagulation [25]. The ARCH cohort included 351 participants recruited between November 2012 and June 2015 from clinical HIV and addiction care sites, non-clinical sites, and via snowball recruitment in St. Petersburg. Eligibility criteria for enrollment in the Russia ARCH study were: 1) age 18–70 years; 2) HIV infection; 3) having two contacts to enable follow-up; 4) residence within 100 km of St. Petersburg, Russia; 5) being ART-naïve (i.e., never having been on ART) and 6) having a telephone. Participants were excluded if they were not fluent in Russian or had a cognitive impairment that precluded them from providing informed consent. All participants provided informed consent in writing.
Participants were interviewed at baseline, 6, 12, 18, and 24 months post baseline. Of note, a subset (n = 254) of Russia ARCH participants participated in the ZINC trial, a double blind randomized placebo controlled trial of zinc supplementation among HIV-positive heavy drinkers. The present secondary data analysis was limited to Russia ARCH participants who completed a baseline assessment and were regular smokers (n = 309), defined as those who smoked at least one cigarette per day [26–28].
Measures
Trained research staff conducted face-to-face study interviews, collecting data on demographics (age, sex), socioeconomic status (education level and individual income), food insecurity [29] (a nine-item Household Food Insecurity Access Scale), social support [30] (a five-item scale developed for the National Institute on Drug Abuse: Seek, Test, Treat and Retain Initiative [31]), years since first HIV positive test, HIV transmission risk factors, BMI, injection drug use in the past 30 days [32,33], and past 12-month alcohol dependence as measured by a score of 13 or more for women, and 15 or more for men on the AUDIT (Alcohol Use Disorders Identification test) [34].
Independent variables
The main independent variable was a high level of depressive symptoms in the past 7 days. During the research assessment, participants self-administered the twenty-item CES-D.[21,22] We defined a high level of depressive symptoms as a CES-D score of ≥ 24, using a higher cutoff of the CES-D than the more commonly used cutoff of 16 [21]. We chose to use a higher cutoff, as false positive results on the order of 15% to 20% have resulted from use of a cut-off point of 16, leading some researchers to suggest that a higher cut-off point be used [35,36]. This is relevant because CES-D scores may be elevated in HIV-positive patients, as some symptoms of advanced HIV infection are similar to those of depression. We also performed sensitivity analyses using a CES-D cutoff of 16.
Outcomes
The primary outcome was heavy smoking, defined as smoking > 20 cigarettes per day. While previous studies have defined heavy smoking as ≥ 20 cigarettes per day [37], our data only included the following categories of cigarettes per day: ≤10, 11–20, 21–30, and ≥ 31. A secondary outcome was a score of ≥ 5 on the six-item Fagerström Test for Nicotine Dependence, indicating moderate-very high dependence [23,24]. A score of 0–2 corresponds to very low nicotine dependence, 3–4 corresponds to low dependence, 5 corresponds to moderate dependence, 6–7 high dependence, and 8–10 very high dependence [38].
Statistical analysis
We performed descriptive and multivariable regression analyses. In the descriptive analyses, we used chi-square test statistics to examine differences in proportions when analyzing categorical variables. We used two-sample t tests when analyzing continuous variables. Multivariable logistic regression analyses assessed associations between depressive symptoms and the outcomes (heavy smoking and moderate-very high nicotine dependence). The Hosmer-Lemeshow test was used to evaluate the goodness of fit of the logistic regression models. Based on knowledge from prior literature and clinical knowledge, we included the following potential covariates and confounders in the model: age, sex, education, income, running out of money for food/housing, injection drug use and alcohol dependence [39,40]. We did not expect zinc supplementation (received by some participants as part of the nested ZINC trial) to confound the relationships of interest; however, we assessed this in preliminary analyses. Among those enrolled in the ZINC trial, we compared outcomes for the current study by intervention arm and found no significant differences between randomized groups. As an exploratory analysis of effect modification, we included an interaction term between sex and depressive symptoms, given that men and women with depressive symptoms may have different smoking patterns. We evaluated possible multicollinearity by calculating Spearman correlation coefficients for each pair of independent variables and covariates prior to regression modeling. We verified that no pair of variables had a correlation greater than 0.40. Next, we calculated the variance inflation factor (VIF) for all terms in the regression models to further detect potential multicollinearity. We used a criterion of VIF>10 to indicate collinearity. The largest observed VIF was 1.09, suggesting multicollinearity was likely not an issue in the regression models. For hypothesis testing, we used two-tailed tests and a significance level of 0.05. Two-sided 95% confidence intervals were obtained for regression parameters of interest.
All analyses were performed using SAS 9.3 (Cary, North Carolina, USA). Institutional Review Boards of Boston University Medical Campus and First St. Petersburg Pavlov State Medical University approved the study protocol for the Russia ARCH study.
Results
The analytic sample for the primary analysis was comprised of 309 regular smokers. Of these, 65 participants (21.0%) were heavy smokers (Table 1). The following variables appeared to differ between those with and without heavy smoking: sex, education level, running out of money for housing/food, physical component score, and injection drug use.
Twenty-seven percent of participants with depressive symptoms were heavy smokers (n = 21), relative to one-fifth of participants without high levels of depressive symptoms (n = 44). Seventy percent of participants with high levels of depressive symptoms had moderate-very high nicotine dependence (n = 55), relative to 59% of participants without high levels of depressive symptoms (n = 136). In multivariable logistic regression analyses, the presence of high levels of depressive symptoms was not significantly associated with either heavy smoking (adjusted odds ratio [aOR] 1.50, 95% CI 0.78–2.89; Table 2) or with moderate-very high levels of nicotine dependence (aOR 1.35, 95% CI 0.75–2.41; Table 3). In terms of covariates, female sex, higher levels of education, and alcohol dependence appeared to be associated with a lower odds of heavy smoking, while injection drug use in the past 30 days and running out of money for housing/food appeared to be associated with an increased odds of heavy smoking. In these models, the interaction term between sex and depression was not significant. In sensitivity analyses using a CES-D cutoff of 16, these findings did not change for models examining heavy smoking (S1 Appendix Table, Associated factors of heavy smoking among HIV-positive smokers in Russia.). However, the presence of high levels of depressive symptoms, based on the lower CES-D cutoff, was significantly associated with moderate-very high levels of nicotine dependence (aOR 1.71, 95% CI 1.04–2.80; S2 Appendix Table, Associated factors of moderate-very high levels of nicotine dependence among HIV-positive smokers in Russia).
Discussion
Among HIV-positive smokers enrolled in the Russia ARCH study, we were unable to detect an association between depressive symptoms and heavy smoking or moderate-very high nicotine dependence. Our findings are consistent with a US study by Webb et al., which found that among a clinic-based sample of HIV-positive patients, depression was not associated with heavy smoking [18]. However, others in the US have found that depression is associated with higher nicotine dependence and smoking prevalence among HIV-positive individuals [17,41].
It is possible that social norms regarding smoking in Russia could explain our findings. Social norms in Russia support ongoing tobacco use; smoking cigarettes is the norm, and quitting has not become normative. Smoking remains largely socially accepted, providing little extrinsic motivation for persons with or without depressive symptoms to quit smoking. Tobacco control policies in Russia may also have affected our findings. Russia has begun to implement key mandates of the WHO Framework Convention on Tobacco Control [42], such as regular monitoring of tobacco use and tobacco prevention efforts, public smoking bans, mass-media campaigns about the dangers of tobacco use, and bans on tobacco advertising [43]. However, few of these measures have targeted HIV-positive populations, who have high rates of comorbid substance use disorders.
The prevalence of substance use disorders was high among our study population, with over half of all participants having alcohol dependence or recent injection drug use. Individuals with mental disorders such as depression may use alcohol and/or drugs as a form of self-medication.[44] Thus, it is possible that we did not observe the expected association between depression and heavy smoking because participants were self-medicating their depression with alcohol and/or drug use. In addition, as in many addiction treatment systems globally, there is little attention paid to addressing tobacco use in Russia’s Narcology (i.e., addiction care) system when substance use disorders are the primary concern.[43] Thus, heavy smoking may not be limited to those individuals with depression among persons with addictions.
In addition, the price of cigarettes remains low in Russia. Ross et al have attributed the low cigarette prices to insufficient tobacco tax policies [45]. Tobacco taxes in Russia are far below the 67 to 80% rate recommended by the World Bank to reduce tobacco use [45]. This again provides little disincentive for cigarette purchasing to smokers, with or without mental health problems.
Robust tobacco control measures in the US have reduced smoking rates to an extent that remaining US smokers tend to be those with vulnerabilities that may hinder quitting, such as lower socioeconomic status, mental health conditions, or substance use disorders [46]. The apparent lack of association between depressive symptoms and heavy smoking in this Russian cohort suggests that in order to decrease the tobacco burden among HIV-positive smokers, evidence-based tobacco control strategies need to be targeted to all HIV-positive smokers, regardless of comorbid depression. Given that people receiving antiretroviral therapy living with HIV who smoke lose more years of life expectancy to their tobacco use than to their HIV infection, tobacco control measures should target this population [47]. Such measures include tobacco taxation [48] and cessation support. Finally, tobacco control measures might also target men and smokers with low socioeconomic status, as we observed that these groups were more likely to be heavy smokers and to have higher levels of nicotine dependence.
Our study has several limitations. First, our analyses may have been underpowered to detect an association between depressive symptoms and heavy smoking. In post-hoc power calculations, based on the assumption that 19% of the participants without depressive symptoms reported heavy smoking (observed at study entry), our study would have approximately 80% power to detect an odds ratio as small as 2.4. Thus, it is likely that the study did not have high power to detect the observed magnitude of association between depressive symptoms and heavy smoking. We also did not have data on biological differences in the rate of nicotine metabolism among participants; participants who were fast metabolizers of nicotine may have smoked more heavily [49].
In conclusion, we were unable to detect an association between depressive symptoms and heavy smoking or moderate-very high nicotine dependence in this cohort of HIV-positive Russian smokers. Future research investigating these associations in larger population samples is warranted. Among people with HIV and substance use, who have high smoking prevalence overall, and a sizable minority of whom are heavy smokers with high levels of nicotine dependence, health policies that implement a broad anti-smoking approach are warranted irrespective of depressive symptoms.
Supporting information
S1 Appendix Table. Associated factors of heavy smoking among HIV-positive smokers in Russia.
https://doi.org/10.1371/journal.pone.0189207.s001
(DOCX)
S2 Appendix Table. Associated factors of moderate-very high levels of nicotine dependence among HIV-positive smokers in Russia.
https://doi.org/10.1371/journal.pone.0189207.s002
(DOCX)
Acknowledgments
We would like to acknowledge Sally Bendiks and Ve Truong for their assistance with manuscript preparation, and Maxim D. Shrayer for commenting on earlier drafts of the manuscript.
References
- 1. GBD 2015 Tobacco Collaborators. Smoking prevalence and attributable disease burden in 195 countries and territories, 1990–2015: a systematic analysis from the Global Burden of Disease Study 2015. Lancet 2017 May 13;389(10082):1885–1906. pmid:28390697
- 2.
Institute for Health Metrics and Evaluation. Tobacco Burden Visualization. 2016; http://vizhub.healthdata.org/tobacco/. Accessed July 22, 2016.
- 3.
OECD. "Life Expectancy" in OECD Factbook 2015–2016: Economic, Environmental and Social Statistics. Paris, France: OECD Publishing; 2016.
- 4. Giovino GA, Mirza SA, Samet JM, Gupta PC, Jarvis MJ, Bhala N, et al. Tobacco use in 3 billion individuals from 16 countries: an analysis of nationally representative cross-sectional household surveys. Lancet 2012 Aug 18;380(9842):668–679. pmid:22901888
- 5. Reynolds NR. Cigarette smoking and HIV: more evidence for action. AIDS Educ Prev 2009 Jun;21(3 Suppl):106–121. pmid:19537958
- 6. Gfroerer J, Dube SR, King BA, Garrett BE, Babb S, McAfee T. Vital Signs: Current Cigarette Smoking Among Adults Aged greater than or equal to 18 Years with Mental Illness- United States, 2009–2011. Morb Mortal Weekly Rep 2013;62(5).
- 7. Lasser K, Boyd JW, Woolhandler S, Himmelstein DU, McCormick D, Bor DH. Smoking and mental illness: A population-based prevalence study. JAMA 2000;284(20):2606–2610. pmid:11086367
- 8. Cook BL, Wayne GF, Kafali EN, Liu Z, Shu C, Flores M. Trends in smoking among adults with mental illness and association between mental health treatment and smoking cessation. JAMA 2014 Jan 8;311(2):172–182. pmid:24399556
- 9.
UNAIDS. Report on the global AIDS epidemic. 2008. http://www.unaids.org/sites/default/files/media_asset/jc1510_2008globalreport_en_0.pdf. Accessed May 2, 2017.
- 10.
Ministry of Health. HIV infection in Russia. In: Federal Research Center for HIV/AIDS Prevention and Teratment. http://aids-centr.perm.ru/%D0%A1%D1%82%D0%B0%D1%82%D0%B8%D1%81%D1%82%D0%B8%D0%BA%D0%B0/%D0%92%D0%98%D0%A7/%D0%A1%D0%9F%D0%98%D0%94-%D0%B2-%D0%A0%D0%BE%D1%81%D1%81%D0%B8%D0%B8. Accessed May 2, 2017.
- 11. Brown JL, Winhusen T, DiClemente RJ, Sales JM, Rose ES, Safonova P, et al. The association between cigarette smoking, virologic suppression, and CD4+ lymphocyte count in HIV-Infected Russian women. AIDS care JID—8915313 OTO—NOTNLM 0512.
- 12. Lifson AR, Neuhaus J, Arribas JR, van den Berg-Wolf M, Labriola AM, Read TR, et al. Smoking-related health risks among persons with HIV in the Strategies for Management of Antiretroviral Therapy clinical trial. Am J Public Health 2010 Oct;100(10):1896–1903. pmid:20724677
- 13. Feldman JG, Minkoff H, Schneider MF, Gange SJ, Cohen M, Watts DH, et al. Association of cigarette smoking with HIV prognosis among women in the HAART era: a report from the women’s interagency HIV study. Am J Public Health 2006;96(6):1060–1065. pmid:16670229
- 14. Ompad DC, Fuller CM, Chan CA, Frye V, Vlahov D, Galea S. Correlates of illicit methadone use in New York City: a cross-sectional study. BMC Public Health 2008 Oct 28;8:375. pmid:18957116
- 15. Kaleta D, Polanska K, Korytkowski P, Usidame B, Bak-Romaniszyn L. Patterns of nicotine dependence in four Eastern European countries. BMC Public Health 2015 Nov 28;15:1189-015-2537-0.
- 16. Roberts B, Gilmore A, Stickley A, Kizilova K, Prohoda V, Rotman D, et al. Prevalence and psychosocial determinants of nicotine dependence in nine countries of the former Soviet Union. Nicotine Tob Res 2013 Jan;15(1):271–276. pmid:22529221
- 17. Bénard A, Bonnet F, Tessier J, Fossoux H, Dupon M, Mercié P, et al. Tobacco addiction and HIV infection: toward the implementation of cessation programs. ANRS CO3 Aquitaine Cohort. AIDS Patient Care STDS 2007;21(7):458–468. pmid:17651027
- 18. Webb MS, Vanable PA, Carey MP, Blair DC. Cigarette smoking among HIV+ men and women: examining health, substance use, and psychosocial correlates across the smoking spectrum. J Behav Med 2007 Oct;30(5):371–383. pmid:17570050
- 19. Cropsey KL, FAU WJ, FAU MM, FAU CH, McCullumsmith CF, Lawrence S FAU—Raper James L, et al. Cigarette Smokers are Less Likely to Have Undetectable Viral Loads: Results From Four HIV Clinics. Journal of addiction medicine JID—101306759 1213.
- 20. Munafo MR, Araya R. Cigarette smoking and depression: a question of causation. Br J Psychiatry 2010 Jun;196(6):425–426. pmid:20513848
- 21. Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. Applied Psychological Measurement 1977;1:385–401.
- 22. Chishinga N, Kinyanda E, Weiss HA, Patel V, Ayles H, Seedat S. Validation of brief screening tools for depressive and alcohol use disorders among TB and HIV patients in primary care in Zambia. BMC Psychiatry 2011 May 4;11:75. pmid:21542929
- 23. Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO. The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict 1991 Sep;86(9):1119–1127. pmid:1932883
- 24. Meneses-Gaya IC, Zuardi AW, Loureiro SR, Crippa JA. Psychometric properties of the Fagerstrom Test for Nicotine Dependence. J Bras Pneumol 2009 Jan;35(1):73–82. pmid:19219334
- 25.
So-Armah K, Freiberg M, Cheng D, Patts G, Tracy R, Doyle M, et al. Longitudional association between alcohol use and inflammatory biomarkers. Poster Presentation at 2017 Conference on Retroviruses and Opportunistic Infections (CROI) 2017.
- 26.
Janus E, Cockram C, Fielding R, Hedley A, Ho P, Lam K, et al. Hong Kong cardiovascular risk factor prevalence study 1995–1996. 1996.
- 27. Abdullah AS, Fielding R, Hedley AJ. Patterns of cigarette smoking, alcohol use and other substance use among Chinese university students in Hong Kong. Am J Addict 2002 Summer;11(3):235–246. pmid:12202016
- 28. Zhou J, Abdullah AS, Pun VC, Huang D, Lu S, Luo S. Smoking status and cessation counseling practices among physicians, Guangxi, China, 2007. Prev Chronic Dis 2010 Jan;7(1):A15. pmid:20040230
- 29.
Coates J, Swindale A, Bilinsky P. Household Food Insecurity Access Scale (HFIAS) for Measurement of Food Access: Indicator Guide. 2007.
- 30. Fleishman JA, Sherbourne CD, Crystal S, Collins RL, Marshall GN, Kelly M, et al. Coping, conflictual social interactions, social support, and mood among HIV-infected persons. HCSUS Consortium. Am J Community Psychol 2000 Aug;28(4):421–453. pmid:10965385
- 31.
National Institute on Drug Abuse: Seek, Test, Treat and Retain Initiative. HIV/HCV/STI testing status and organizational testing practices questionnaire. 2013; http://www.drugabuse.gov/researchers/research-resources/data-harmonization-projects/seek-test-treat-retain/addressing-hiv-among-vulnerable-populations.
- 32. Weatherby N, Needle R, Cesar H, Booth R, McCoy C, Watters J, et al. Validity of self-reported drug use among injection drug users and crack smokers recruited through street outreach. Eval Program Plann 1994;17:347-347–355.
- 33. Needle R, Fisher DG, Weatherby N, Chitwood D, Brown B, Cesari H, et al. The reliability of self-reported HIV risk behaviors of drug users. Psychol Addict Behav 1995;9:242–250.
- 34.
Babor TF, Higgins-Biddle JC, Saunders JB, Monteiro MG. AUDIT The Alcohol Use Disorders Identification Test: Guidelines for Use in Primary Care. Second Edition ed.: World Health Organization; 2001.
- 35. Boyd JH, Weissman MM, Thompson WD, Myers JK. Screening for depression in a community sample. Understanding the discrepancies between depression symptom and diagnostic scales. Arch Gen Psychiatry 1982 Oct;39(10):1195–1200. pmid:7125849
- 36. Zich JM, Attkisson CC, Greenfield TK. Screening for depression in primary care clinics: the CES-D and the BDI. Int J Psychiatry Med 1990;20(3):259–277. pmid:2265888
- 37. Khaled SM, Bulloch AG, Williams JV, Lavorato DH, Patten SB. Major depression is a risk factor for shorter time to first cigarette irrespective of the number of cigarettes smoked per day: evidence from a National Population Health Survey. Nicotine Tob Res 2011 Nov;13(11):1059–1067. pmid:21832274
- 38.
National Institute on Drug Abuse. Fagerstrom score. https://cde.drugabuse.gov/instrument/d7c0b0f5-b865-e4de-e040-bb89ad43202b/module/f7cc1db9-2f13-70d5-e040-bb89ad4345a3/question/f7cc1db9-2f15-70d5-e040-bb89ad4345a3. Accessed July 17, 2017.
- 39. Hymowitz N, Cummings KM, Hyland A, Lynn WR, Pechacek TF, Hartwell TD. Predictors of smoking cessation in a cohort of adult smokers followed for five years. Tob Control 1997;6 Suppl 2:S57–62.
- 40. Caponnetto P, Polosa R. Common predictors of smoking cessation in clinical practice. Respir Med 2008;102(8):1182–1192. pmid:18586479
- 41. Burkhalter JE, Springer CM, Chhabra R, Ostroff JS, Rapkin BD. Tobacco use and readiness to quit smoking in low-income HIV-infected persons. Nicotine Tobacco Res 2005;7(4):511–522.
- 42.
World Health Organization. WHO Framework Convention on Tobacco Control. 2005; http://apps.who.int/iris/bitstream/10665/42811/1/9241591013.pdf?ua=1. Accessed 10/28, 2016.
- 43. Lunze K, Migliorini L. Tobacco control in the Russian Federation—a policy analysis. BMC Public Health 2013 Jan 23;13:64-2458-13-64.
- 44.
National Institute on Drug Abuse. Why do drug use disorders often co-occur with other mental illnesses. https://www.drugabuse.gov/publications/research-reports/comorbidity-addiction-other-mental-illnesses/why-do-drug-use-disorders-often-co-occur-other-men. Accessed Oct 5, 2017.
- 45.
Ross H, Shariff S, Gilmore A. Economics of tobacco taxation in Russia. Paris: International Union Against Tuberculosis and Lung Disease 2008:4–5.
- 46. Schroeder SA. Shattuck Lecture. We can do better—improving the health of the American people. N Engl J Med 2007 Sep 20;357(12):1221–1228. pmid:17881753
- 47. Helleberg M, Afzal S, Kronborg G, Larsen CS, Pedersen G, Pedersen C, et al. Mortality attributable to smoking among HIV-1-infected individuals: a nationwide, population-based cohort study. Clin Infect Dis 2013 Mar;56(5):727–734. pmid:23254417
- 48.
WHO. Tobacco Free Initiative: Taxation. 2016; http://www.who.int/tobacco/economics/taxation/en/.
- 49. Tyndale RF, Sellers EM. Variable CYP2A6-mediated nicotine metabolism alters smoking behavior and risk. Drug Metab Dispos 2001 Apr;29(4 Pt 2):548–552. pmid:11259349