Abstract
Russia has over 1.2 million HIV infections and Europe’s highest HIV incidence. Although its HIV epidemic is intertwined with high alcohol consumption rates, the interaction between alcohol use and HIV care in Russia is understudied. 586 HIV-positive persons were recruited using social network methods in St. Petersburg. 59% of males—and 45% of females—drank regularly, and 30% of alcohol users reported binge drinking (males: ≥5; females ≥4 drinks) in the past week. Alcohol use was associated with lower HIV care engagement and having detectable viral load. Multivariate analyses showed that any alcohol consumption, number of alcohol drinks consumed, and having a binge drinking day in the past week were associated with male gender, use of illicit drugs, drug injection, smaller social network size, lower social supports, being unmarried, and reporting condomless intercourse with non-main partners. Interventions to improve HIV care in Russia must comprehensively address the use of alcohol and substances that interfere with care engagement.
Keywords: HIV epidemic, alcohol use and misuse, Russia, people living with HIV/AIDS, HIV care engagement, HIV treatment
Introduction
Although there have been global declines in HIV incidence, Russia’s epidemic continues to expand. Over 1.2 million HIV infections have been officially recorded in Russia, and it is one of four countries that account for the greatest number of new HIV infections in the world (Russian Federal AIDS Center, 2017; UNAIDS, 2018). Russia’s HIV incidence rate has consistently remained at 10% over the past decade (UNAIDS, 2018), in sharp contrast to other European countries that have seen declines. Seventy percent of all people living with HIV in Eastern Europe and Central Asia are in Russia, and almost two-thirds of Europe’s incident HIV cases in 2017 were recorded in the country (Cousins, 2017; UNAIDS, 2018). HIV increases over recent decades were not unique to Russia and occurred almost simultaneously in Ukraine, Belarus, and Moldova. However, Russia’s HIV incidence of 71.1 cases per 100,000 population in 2017 (Russian Federal AIDS Center, 2017) exceeds that observed in neighboring post-Soviet countries by 2- to 3.5-fold, and is the highest in Europe.
HIV treatment coverage in Russia is very poor. Only about one-third of persons living with HIV infection (PLH) in Russia—or approximately half of those diagnosed as HIV-positive—receive antiretroviral therapy (ART; (Russian Federal AIDS Center, 2017; UNAIDS, 2018). Over 20% of persons who initiate ART in Russia later discontinue the therapy. With only 27% of PLH in Russia believed to be virally suppressed (UNAIDS, 2018), Russia’s still-expanding HIV epidemic is fueled by transmission by persons with unsuppressed viral load.
Russia is also among the countries with exceptionally high levels of alcohol consumption as measured by liters of alcohol per capita of drinking-age population. According to a recent World Bank (2016) report, annual consumption of alcohol in Russia was 11.7 liters per person, one of the world’s highest levels. National levels of alcohol consumption in recent years have moderated to some extent, and alcohol-related mortality in Russia declined from approximately 33% in 2004 to approximately 22% in 2016 (WHO, 2004; WHO, 2018). These reductions have been associated with increased life expectancy, particularly among men (GBD Russia Collaborators, 2018). However, rates of distilled alcohol beverage consumption in Russia remain much higher than those in Europe overall.
Heavy drinking (≥15 of drinks per week among males; ≥8 of drinks per week among females) is associated with adverse HIV/AIDS outcomes. Alcohol intoxication increases behavioral risk of sexual HIV transmission and is associated with greater likelihood of engaging in unprotected sex (Brown et al., 2016), including in Russia (Lan et al., 2014; 2017). Intoxication also increases the likelihood of needle sharing among HIV-positive persons who inject drugs (PIDs) in Russia (Krupitsky et al., 2005). Chronic heavy drinking is linked with life chaos, lower socioeconomic status, social disadvantage, and poor mental health and well-being, factors that further increase behavioral risks of HIV transmission. Among persons living with HIV infection, alcohol use is associated with HIV stigma and with poorer HIV care engagement and medical appointment attendance, with lower ART adherence, and with having unsuppressed viral load and lower CD4 cell counts (Lunze et al., 2017; Samet, 2003). Finally, alcohol exerts negative effects on the central nervous system (for a review, see Silverstein & Kumar, 2014). Given Russia’s expanding HIV epidemic, poor HIV care indicators, and high prevalence of problem drinking, research is needed to better understand factors associated with alcohol abuse among PLH in Russia.
International bodies have long called attention to the stigma, marginalization, and difficult life circumstances confronting PLH in Russia (Human Rights Watch, 2004). However, relatively few studies have systematically examined the psychosocial coping of HIV-infected persons in the country, including relationships between alcohol use and HIV medical care engagement. Amirkhanian et al. (2003) recruited a community sample of 470 PLH in St. Petersburg, mostly current or former drug users. Participants completed assessments of their psychological characteristics, discrimination experiences, and behavior practices. Clinical depression, anxiety, and poor perceived social supports were prevalent, and reports of discrimination in employment, housing, and health care access were widely reported. A similar community sample of nearly 500 PLH in St. Petersburg, studied after ART became available, established that psychosocial distress indicators were high even following the advent of more effective HIV treatment (Amirkhanian et al., 2010). Further, a community sample of 241 PLH in St. Petersburg completed measures of HIV medical care attendance and ART adherence, and provided blood specimens that were tested for HIV viral load (Amirkhanian et al., 2018). Twenty-six percent of participants had no recent HIV medical care visits, 26% had suboptimal recent ART adherence, and fewer than half had undetectable viral load. Frequency of alcohol use in the past week consistently predicted poor care engagement, nonadherence, and having detectable viral load. Drug injection, being unmarried, being younger, lower medication-taking self-efficacy, and lower state anxiety were also associated with adverse HIV health outcomes.
Given prior research implicating alcohol use as a contributor to poor medical care engagement, the present study sought to more closely examine patterns of drinking and identify factors associated with problem alcohol use in a large and diverse community sample of PLH in St. Petersburg, Russia’s second largest city. The aims of the study were to: (1) characterize participants and assess their levels of recent HIV medical care attendance, ART adherence, and viral load based on laboratory testing of blood samples; (2) describe patterns of alcohol use and psychosocial characteristics in the community sample of PLH; (3) determine associations between alcohol use, medical care engagement, and HIV viral suppression; and (4) identify factors that predict drinking alcohol, number of drinks consumed, and binge drinking among HIV-infected persons in Russia.
Methods
The study was undertaken during 2014–2015 and followed a protocol approved by the IRBs of the Medical College of Wisconsin, Milwaukee, USA and Botkin Hospital for Infectious Diseases, St. Petersburg. These data were collected as part of the baseline assessment of participants in ‘‘Stimulus,’’ a randomized controlled trial of a network-level intervention designed to increase HIV medical care engagement among PLH in the community.
Settings and Participant Recruitment
The study enrolled a sample of PLH using a social network recruitment strategy designed to reach HIV-positive individuals by means of their social connections with other PLH in the community. The recruitment of each network began when study staff identified an HIV-positive “seed” to serve as an initial access point to a network. Seeds were recruited from PLH self-support groups, needle exchange sites, medical service settings, and online forums for HIV-positive persons, as well as through community announcements and recommendations made by key informants. We recruited seeds so as to represent persons who inject drugs (PIDs), men who have sex with men (MSM), and heterosexual men and women. Seed eligibility criteria were being HIV-positive, verified by study testing, and also: (1) reporting either not having an HIV medical care visit in the past 6 months or—if prescribed ART—taking medication doses on <90% of days in the past month; (2) being age 18 or older; and (3) having one or more friends known to also be HIV-positive. After their recruitment, consented seeds were asked to provide the first names of these friends and to give each of them an invitation packet with study information and a request that the recipient contact the study office. Interested and eligible friends were recruited into the study as members of the network of the seed who named them. Seeds unable to recruit HIV-positive friends were not excluded from the sample. Friends of seeds were eligible if they were HIV-positive (confirmed by study testing) and were age 18 or older regardless of their HIV medical care attendance or adherence. The circle of friends surrounding a seed constituted the first “ring” of network members. When first-ring members entered the study, they—in turn—invited the participation of their own HIV-positive friends. This constituted the second and final ring of each network.
The study sample consisted of 105 networks of PLH composed of a total of 586 unique individuals (mean=5.6 participants per network, range=1 to 44, median=2), 341 males and 245 females. With respect to the HIV risk exposure groups, the sample consisted of 117 participants (including 33 seeds) in the group characterized by heterosexual exposure; 54 participants (including 28 seeds) in the group of MSM; and 415 participants (including 44 seeds) were in the PID group. Networks were typically composed of persons who shared the same HIV exposure history. HIV exposure heterogeneity within a network was uncommon, except for a small proportion of networks that originated with a seed reporting heterosexual exposure that also included some PID members.
Assessment Measures
All participants provided informed consent, individually completed assessment measures during a single assessment visit to the study office, and received an incentive payment equivalent to approximately 15 USD. The visit included individually-administered assessment measures in the Russian language and also biospecimen collection.
Demographic characteristics and HIV treatment history included age, gender, education, relationship status, length of serostatus knowledge, HIV exposure history, and HIV treatment and ART history.
HIV care attendance, appointment keeping, and viral suppression.
To measure care attendance, participants were asked how many HIV-related medical treatment visits they had in the past year. They were then asked how many appointments were scheduled in the past six months, and how many were missed. Persons who reported no HIV medical care visits during the past six months were defined as being out of care. Following completion of the behavioral measures, study nurses performed blood draws of participants. Samples were analyzed by the Botkin Hospital laboratory to verify participants’ HIV-positive serostatus and to measure CD4+ count and viral load. Undetectable viral load was defined as <75 copies/mL, a cutoff based on laboratory test sensitivity at the time of the study.
Alcohol use, drug use, and needle sharing behavior.
Because alcohol use in Russia is common and widespread, drinking over a one-week time frame was assessed. We measured drinking in both the past week and also in a typical week in the event that alcohol use in the past week differed from regular patterns. Alcohol users were defined as participants who reported drinking in either the past week or in a typical week; participants indicated how many alcohol drinks they consumed in the past week and a typical week. In addition, and to measure episodes of recent binge drinking, male participants specified the number of days in the past week when they consumed ≥5, and females ≥4, drinks. These thresholds represent the standard definition for drinking binges (NIAAA, 2004).
Participants indicated whether or not they used any kind of illicit drug in the past month. Participants reporting illicit drug use specified the number of times in the past month when they used marijuana, hallucinogens, ecstasy, heroin, illicit use of prescription medications, and other drugs. Participants indicated whether they ever injected drugs, and— if so—their frequency of injection. The past 30-day time frame for measuring substance use was based on the National Institute of Drug Abuse (NIDA) Risk Behavior Assessment (RBA) previously found valid and reliable (Needle et al., 1995).
Scales of psychosocial well-being.
Participants were administered three scales assessing psychosocial well-being. The 24-item Social Provisions Scale (SPS; Cutrona, 1989) measures the adequacy and quality of the respondents’ perceived social supports (scale range 24–96, Chronbach’s alpha=0.92, current sample). The 20-item Beck Hopelessness Scale (BHS; Beck et al, 1974) has been widely used to measure lack of hope, pessimism over the future, and depression (scale range 0–20, alpha=0.87, current sample). Finally, the State version of the 20-item State Anxiety Inventory Scale (STAI; Spielberger, 1983) assesses the individual’s level of presently-experienced anxiety (scale range 20–80, alpha=0.92, current sample).
Sexual transmission risk behavior.
Participants reported on their number of different- and same-sex partners in the past year. Participants then reported whether or not they engaged in condomless intercourse with non-main partners in the past 3 months.
Statistical Methods
Participant characteristics were summarized by frequencies and percent values for categorical variables and by means and standard deviations for continuous variables. These data were prepared for the sample overall, as well as broken down by gender and also broken down for participants in the PID, MSM, and heterosexual risk exposure groups (Table 1). Analyses then compared participants in the full sample who had a medical care visit or had no care visit in the past 6 months (care engaged or non-engaged) and those with undetectable viral load (<75 copies/ml) versus detectable viral load (≥75 copies/ml) on alcohol use variables. The number of alcohol drinks consumed in the past week and number of binge drinking days in the past week were compared between the care engagement and viral load groups using single predictor Poisson regressions fitted with generalized estimating equations (GEE) to control for possible dependence within social network. The proportion of participants in the care engaged/not engaged and virally suppressed/not suppressed groups who drank alcohol and engaged in binge drinking in the past week were compared with single predictor logistic regression fitted with GEE to control for possible dependence within social network (Table 2). Multiple logistic and Poisson regression analyses were used to analyze factors associated with alcohol consumption, number of alcohol drinks consumed, and binge drinking in the past week (Table 3). R v3.3 was used for all data analyses (R Core Team, 2013).
Table 1.
Participant Characteristics, HIV Care Indicators, and Alcohol Use Characteristics in the Sample1
| Variables | Total (n=586) | Sex |
HIV Risk Exposure Group |
|||||
|---|---|---|---|---|---|---|---|---|
| Females (n=245) | Males (n=341) | Heterosexual (n=117) | MSM (n=54) | PID (n=415) | ||||
| HIV Risk Exposure Groups | ||||||||
| Heterosexual, percent (n) | 20.0% (117) | 39.2% (96) | 6.2% (21) | |||||
| MSM, percent (n) | 9.2% (54) | 0.0% (0) | 15.8% (54) | |||||
| PID, percent (n) | 70.8% (415) | 60.8% (149) | 78.0% (266) | |||||
| Participant Characteristics | ||||||||
| Demographics | ||||||||
| Age, mean | 34.8 | 33.9 | 35.5 | 33.3 | 32.9 | 35.5 | ||
| Married, percent (n) | 19.8% (116) | 24.5% (60) | 16.4% (56) | 24.8% (29) | 0% (0) | 21% (87) | ||
| Male, percent (n) | 58.2% (341) | 17.9% (21) | 100% (54) | 64.1% (266) | ||||
| Education, percent (n) | ||||||||
| Completed middle school | 9.4% (55) | 8.2% (20) | 10.3% (35) | 4.3% (5) | 1.9% (1) | 11.8% (49) | ||
| Completed high school | 32.9% (193) | 29.4% (72) | 35.5% (121) | 27.4% (32) | 29.6% (16) | 34.9% (145) | ||
| Completed technical school | 33.6% (197) | 35.5% (87) | 32.3% (110) | 26.5% (31) | 13.0% (7) | 38.3% (159) | ||
| Had some university | 8.4% (49) | 9.4% (23) | 7.6% (26) | 7.7% (9) | 14.8% (8) | 7.7% (32) | ||
| Completed university | 15.7% (92) | 17.6% (43) | 14.4% (49) | 34.2% (40) | 40.7% (22) | 7.2% (30) | ||
| Presently in school, percent | 5.0% (29) | 6.9% (17) | 3.5% (12) | 8.5% (10) | 9.3% (5) | 3.4% (14) | ||
| Presently employed, percent | 65.7% (385) | 55.9% (137) | 72.7% (248) | 69.2% (81) | 90.7% (49) | 61.4% (255) | ||
| Living in a stable place, percent | 94.7% (555) | 97.6% (239) | 92.7% (316) | 94.9% (111) | 94.4% (51) | 94.7% (393) | ||
| Sexual orientation, percent (n) | ||||||||
| Exclusively or primarily heterosexual | 86.0% (504) | 89.8% (220) | 83.3% (284) | 94.9% (111) | 1.9% (1) | 94.5% (392) | ||
| Bisexual | 6.1% (36) | 9.0% (22) | 4.1% (14) | 5.1% (6) | 22.2% (12) | 4.3% (18) | ||
| Exclusively or primarily homosexual | 7.9% (46) | 1.2% (3) | 12.6% (43) | 0% (0) | 75.9% (41) | 1.2% (5) | ||
| Psychosocial Wellbeing Scales | ||||||||
| State-Trait Anxiety Inventory (STAI), mean | 37.20 | 37.14 | 37.25 | 34.54 | 38.33 | 37.81 | ||
| Social Provisions Scale (SPS), mean | 82.51 | 83.88 | 81.52 | 85.06 | 82.56 | 81.78 | ||
| Beck Hopelessness Scale (BHS), mean | 4.72 | 4.36 | 4.97 | 3.67 | 4.72 | 5.01 | ||
| HIV Care Indicators | ||||||||
| Engaged in HIV care, percent (n/n) | 80.7% (465/576) | 83.5% (202/242) | 78.7% (263/334) | 90.4% (104/115) | 86.8% (46/53) | 77.2% (315/408) | ||
| Achieved undetectable HIV viral load, percent (n/n) | 50.0% (289/577) | 55.4% (133/240) | 46.3% (156/337) | 54.3% (63/116) | 54.7% (29/53) | 48.3% (197/408) | ||
| Alcohol Use Characteristics | ||||||||
| Any alcohol use either in the past or typical week, percent (n) | 53.2% (312) | 45.3% (111) | 58.9% (201) | 47% (55) | 74.1% (40) | 52.3% (217) | ||
| Among alcohol users, mean number of alcohol drinks past week | 10.1 | 5.7 | 12.5 | 1.79 | 7.48 | 6.09 | ||
| Among alcohol users, mean number of binge drinking days past week | .72 | .46 | .87 | 0.25 | 0.7 | 0.84 | ||
| Alcohol users who report 1 or more binge days past week, percent (n) | 27.9% (87) | 21.6% (24) | 31.3% (63) | 6.8% (8) | 22.2% (12) | 16.1% (67) | ||
| Drugs Use Characteristics | ||||||||
| Use of any illicit drugs, past month, percent (n) | 29.7% (174) | 20.8% (51) | 36.1% (123) | 9.4% (11) | 14.8% (8) | 37.3% (155) | ||
| Any drug injection, past month, percent (n) | 19.5% (114) | 14.7% (36) | 22.9% (78) | 0% (0) | 0% (0) | 27.5% (114) | ||
| Sexual HIV Transmission Risk Indicator | ||||||||
| Condomless intercourse with a nonmain partner, past 3 months, percent (n/n) | 9.2% (53/575) | 6.0% (14/235) | 11.5% (39/340) | 3.5% (4/114) | 20.8% (11/53) | 9.3% (38/408) | ||
Sample sizes vary slightly due to a small number of cases with missing data for several variables.
Table 2.
Bivariate Associations of Alcohol Use and HIV Care Indicators1
| Variables | HIV care engagement | |||
|---|---|---|---|---|
| Total (n=307) | Engaged (n=223) | Non-engaged (n=84) | p | |
| Any drinks in the past week, percent | 85.0% | 82.5% | 91.7% | .0418 |
| # of drinks in the past week, mean | 9.97 | 8.52 | 13.85 | .0961 |
| Any binge drinking days in the past week, percent | 27.4% | 25.6% | 32.1% | .2273 |
| # of binge drinking days in the past week, mean | .71 | .61 | .96 | .1316 |
| Undetectable HIV viral load | ||||
| Total (n=309) | Undetectable VL (n=143) | Detectable VL (n=166) | p | |
| Any drinks in the past week, percent | 85.1% | 85.3% | 84.9% | .9240 |
| # of drinks in the past week, mean | 10.07 | 7.15 | 12.59 | .0050 |
| Any binge drinking days in the past week, percent | 28.2% | 24.5% | 31.3% | .1421 |
| # of binge drinking days in the past week, mean | .73 | .50 | .92 | .0380 |
Sample size = 312 alcohol users but sample sizes vary slightly due to a small number of cases with missing data for several variables.
Table 3.
Predictors of Any Alcohol Consumption, the Number of Alcohol Drinks, and Having Any Binge Drinking Days in the Past Week
| Variables | 95% Confidence Interval | p | |
|---|---|---|---|
| Logistic Regression for Any Alcohol Consumption in the Past Week1 | |||
| Adjusted Odds Ratio | |||
| Male gender | 1.45 | 1.02–2.07 | .04 |
| Used any illicit drug in the past month | 2.11 | 1.26–3.55 | .005 |
| Network size | .96 | .93–.99 | .01 |
| Used any injected drug in the past month | 2.54 | 1.42–4.54 | .002 |
| Poisson Regression for the Number of Alcohol Drinks in the Past Week2 | |||
| Incidence Rate Ratio | |||
| Male gender | 2.38 | 1.63–3.48 | <.0001 |
| Used any illicit drug in the past month | 2.66 | 1.79–3.95 | <.0001 |
| Network size | .97 | .96–.98 | <.0001 |
| Social Provisions Scale (SPS) | .97 | .95–.99 | .007 |
| Married | .48 | .30–.76 | .002 |
| Logistic Regression for Having Any Binge Drinking Days in the Past Week1 | |||
| Adjusted Odds Ratio | |||
| Male gender | 1.85 | 1.11–3.07 | .02 |
| Used any illicit drug in the past month | 3.01 | 1.76–5.17 | .0001 |
| Network size | .96 | .94–.98 | .0001 |
| Condomless intercourse with a non-main partner in the past 3 months | 2.49 | 1.25–4.98 | .01 |
All p-values were calculated using logistic regression fitted by GEE to account for clustering within social networks. Stepwise backward variable selection was used to find the final parsimonious model
All p-values were calculated using multiple Poisson regression fitted by GEE to account for clustering within social networks. Stepwise backward variable selection was used to find the final parsimonious model
Results
Participant Characteristics and Indicators of Psychosocial Distress
Table 1 reports on characteristics of the overall sample, the sample broken down by participant gender, and the sample also broken down based on participant risk exposure group (heterosexual, MSM, and PID). As shown in Table 1, participants were approximately 35 years old. One-quarter of females and 16% of males were married. Three-fourths of participants had less than a university level education, and one out of four either completed or was presently attending university. Five percent of participants reported having no stable place to live. With respect to sexual orientation, 83% of men in the overall sample described themselves as being exclusively or primarily heterosexual, 4% bisexual, and 13% exclusively or primarily homosexual. Sixty-two percent of males attributed their HIV exposure to injection drug use, 22% to heterosexual exposure, and 16% to male same-sex activities. Ninety percent of females reported heterosexual orientation, 9% bisexual, and 1% homosexual. Fifty-six percent of females reported an HIV exposure history of drug injection and 44% of heterosexual HIV risk.
Participants’ mean score on the Beck Hopelessness Scale was 4.72. BHS scores of nine or greater have been shown to predict elevated risk for committing suicide (Beck et al., 1990), and 15% (n=88) of participants (17% of males, n=58; 12% of females, n=30) in the study sample scored at or above this cutoff. Participants’ mean score on the State-Trait Anxiety Inventory was 37.20. STAI scores of 40 or greater are indicative of elevated anxiety (Spielberger et al., 1970; Tendais et al., 2014). Thirty-seven percent (n=217) of study participants (38% of males, n=129; 36% of females, n=88) had STAI scores at or above this cutoff. The mean score of participants on the Social Provisions Scale was 82.51, with higher scores signifying greater adequacy of perceived social supports. A mean SPS score of 69 was found in a sample of HIV-positive persons seeking mental health services in an era before effective treatment was available (Kelly et al., 1993). In the present study, 12% (n=71) of study participants had SPS scores of 68 or lower (14% of males, n=48; 9% of females, n=23). Thus, while mean scores on the BHS, STAI, and SPS in the overall sample were indicative of only moderate distress, substantial proportions of participants exceeded cutoffs for clinical distress in the domains assessed by these psychosocial measures. The three exposure groups generally had scores similar to one another on the psychosocial scale measures.
HIV Care Engagement Indicators in the Sample
Table 1 also shows indicators of HIV care engagement found in the study sample. Eighty-one percent of participants reported that they had an HIV medical care visit in the past six months while 19% were out of care. Fifty percent of participants had undetectable HIV viral load and 50% had detectable viral load based on laboratory testing of study biospecimens. The proportion of males and the proportion of PIDs who were out of care and who had detectable viral load were the highest among all HIV exposure groups.
Use of Any Drugs
Thirty percent of participants reported illicit drug use in the past month, 36% of males and 22% of females. Among the 174 participants who reported illicit drug use in the past month, 66% (n=114) injected a drug of some kind, 59% (n=102) used heroin, 44% (n=77) used marijuana; 21% (n=36) used ecstasy; 5% (n=9) reported illicit use of prescribed medications; and 1% (n=2) used hallucinogens; 21% (n=4) reported the use of other drugs.
Sexual Transmission Risk Behavior
Approximately 9% of participants reported that they had condomless anal or vaginal intercourse with a partner other than their main partner in the past 3 months, including 6% of females and 12% of males. Twenty-one percent of PLH MSM reported recent condomless intercourse with a non-main partner, twice the proportion found for other HIV exposure groups.
Alcohol Use Characteristics in the Sample
As shown in Table 1, 53% of the sample (n=312) said they used alcohol in the past week or in a typical week. This included 59% of men and 45% of women. Among alcohol users, the mean number of drinks consumed in the past week was 10.1 (12.5 drinks for males and 5.7 drinks for females). Among participants who reported any alcohol use in the past week, 28% reported binge drinking a mean of 0.72 times in that period. Thirty-one percent of men who used alcohol reported binge drinking in the past week (mean=0.87 binge days) and 22% of women who used alcohol reported binge drinking in the past week (mean=0.46 binge episodes).
Associations Between Alcohol Use and HIV Care Indicators
Table 2 shows the results of the bivariate associations between indicators of alcohol use with HIV care engagement and viral suppression. Participants who were out of care were significantly more likely to report alcohol use in the past week (p<.05) than participants who attended care appointments in the past 6 months. They also tended to consume larger amounts of alcohol in the same timeframe (p<.10). Participants with undetectable viral load reported consuming fewer alcohol drinks in the past week (p=.005) and also had fewer binge drinking days in the past week (p<.04) than those who had detectable viral load.
Factors Associated with Alcohol Consumption, Number of Alcohol Drinks Consumed, and Binge Drinking in the Past Week
Table 3 presents the results of regression analyses to identify factors associated with the key alcohol consumption indicators. Logistic regression analysis found that any alcohol use in the past week was positively associated with male gender (p<.04), use of any illicit drug in the past month (p<.005), and use of any injected drug in the past month (p<.002), while negatively associated with the size of the participant’s social network (p=.01).
Poisson regression analysis showed that greater number of alcohol drinks consumed in the past week was associated with male gender (p<.0001) and with use of any illicit drug in the past month (p<.0001), as well as with smaller social network size (p<.0001), poorer social supports as measured by the SPS (p=.007), and being unmarried (p=.002).
Logistic regression was also used to identify factors associated with having any binge drinking days in the past week. Having drinking binges was associated with using any illicit drug in the past month (p=.0001), smaller social network size (p=.0001), engaging in condomless intercourse with a non-main partner in the past 3 months (p<.01), and male gender (p<.02).
Finally, the effect of the HIV exposure group (heterosexual, MSM, PID) on the same alcohol consumption indicators was tested controlling for significant predictors reported in Table 3. The exposure group was not significant in any of the models.
Discussion
In contrast to leveling or declining HIV incidence in many world areas, HIV rates in Russia continue to increase and particularly affect young men and women. Russia’s HIV epidemic has been under-recognized on the global stage, and people living with HIV/AIDS in Russia continue to experience adverse health outcomes, experience stigma and social isolation, and experience difficulties accessing care or receiving timely and sufficient medical care for the disease.
Earlier reports showed a dire picture of the lives of HIV-positive people in Russia, with very high levels of depression, poor supports, poor treatment by family members, and discrimination. In the present study, a larger proportion of PLH were engaged in HIV care and received ART, and one-half were virally suppressed. In addition, fewer participants overall reported the extreme levels of depression, anxiety, and poor social support found in studies undertaken in the past (Amirkhanian et al., 2003; 2010). This may be due to differences in sampling methods, the effect of time elapsed since previous research, or the fact that greater proportions of PLH are engaged in care than in the past. Nonetheless, substantial proportions of PLH exceeded cutoffs for clinical levels of hopelessness, state anxiety, and lack of social support.
This study highlights ways in which HIV/AIDS care and alcohol use are associated in Russia. Prior research has sought to reduce high-risk sexual behavior among PLH in Russia by intervening to reduce levels of problem drinking (Samet et al., 2015). By contrast, the present study—carried out with a large community social network sample of PLH—showed that alcohol use among PLH in Russia is common and has a negative effect on HIV care engagement and attainment of virologic suppression. A large body of literature has described effects of alcohol use in multiple HIV/AIDS contexts that range from elevated sexual risk behaviors to poor medical care engagement. Bivariate analyses in the present study confirmed that indicators of alcohol use are associated with poorer HIV care appointment attendance and failing to achieve undetectable viral load, a pattern consistent with findings of prior research with PLH in Russia (Amirkhanian et al., 2018; Samet et al., 2003) and in the West (Howe et al., 2014).
The present research additionally sought to examine factors associated with alcohol use and problem drinking among HIV-positive persons in Russia, a question that has received little attention. Regression analyses showed that any drinking, as well as the number of alcohol drinks consumed and binge drinking in the past week, were consistently associated with the use of illicit drugs and drug injection in the past month, having a smaller social network, and being of male gender. Lower social supports and being unmarried were associated with number of alcohol drinks consumed in the past week, and condomless intercourse with a non-main partner was also associated with recent binge drinking. These findings underscore the need for comprehensive services approaches for engaging PLH in health care, combining attention to both alcohol and other substance use, as well as counseling in sexual behavior risk reduction. Alcohol and drug use contexts and social support factors must be taken into account when interventions are designed and when target populations are identified.
Elevated alcohol use is linked with life stressors, life chaos and instability, as well as diseases caused by excessive drinking. In Russia, levels of alcohol use are among the highest in the world. Alcohol use is also a key factor contributing to high mortality, particularly among men. In the current study, male PLH reported consistently higher levels in all alcohol use indicators including drinking, amount of alcohol drinks consumed per week, and binge drinking episodes.
Although alcohol use among PLH was the focus of this study, structural and social barriers also affect access to HIV care in Russia. Low ART coverage in Russia can be linked to the country’s highly centralized HIV care provision, policies of initiating antiretroviral therapy only following decline in CD4+ counts rather than immediately upon diagnosis, insufficient budgets for HIV medications and care delivery systems, limited integration of HIV care with substance use treatment, high levels of stigma and homonegative policies that may deter medical care engagement, and a lack of effectively functioning nongovernmental organizations (NGOs) able to directly reach communities most impacted by HIV infection (Hamers & Downs, 2003; Kelly & Amirkhanian, 2003; Wirtz et al., 2016). In addition to structural barriers, studies by Kuznetsova et al. (2016) and Kiriazova et al. (2017) have shown that dissatisfaction with quality of care services, stigma and reports of negative attitudes held by some provider staff, and concerns over confidentiality exert a detrimental effect on HIV medical care engagement in Russia.
This study has several limitations. As in any cross-sectional study, associations were identified but causal inferences between variables cannot be conclusively established. Participant responses concerning sensitive or stigmatized behaviors are susceptible to social desirability bias, including with respect to sexual behavior, alcohol and drug use, and HIV medical care visit attendance. Future research must employ data collection methods that depend less on self-reports. However, the present study’s measurement of viral suppression through biospecimen testing—and the identification of predictors of biologically-measured viral load—lend confidence to the findings. The present research used a cutoff of <75 copies/mL to designate undetectable viral load. Recent research (Rodger et al., 2016) indicates that sexual transmission is very low even when viral load is as high as 200 copies/mL. Therefore, prevention benefits are found even with higher viral load cutoffs than we employed. Finally, the study employed a social network recruitment strategy, which may not have yielded a representative community sample of PLH.
With the Russia’s alcohol use among the highest in the world, public health programs designed to curb HIV epidemic must also address factors that enable the epidemic. Alcohol use and other substance abuse influence multiple aspects of HIV prevention and care, and merit further attention in Russia and other areas of the world.
Acknowledgments
This research was supported by grants R01-MH098729, R01-MH113555, and P30-MH52776 from the US National Institute of Mental Health and grant 17-56-30026 from Russian Foundation for Basic Research. The authors have no conflicts of interest to declare. The authors extend their appreciation to Anastasia Amirkhanian, Anastasia Meylakhs, Dmitry Mescheryakov, Dmitry Pirogov, Larisa Glyzhina, Maria Donskaya, and Rudolph Amirkhanian.
References
- Amirkhanian YA, Kelly JA, DiFranceisco WJ, Kuznetsova AV, Tarima SS et al. (2018). Predictors of HIV care engagement, antiretroviral medication adherence, and viral suppression among people living with HIV infection in St. Petersburg, Russia. AIDS and Behavior, 22, 791–799. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Amirkhanian YA, Kelly JA, Kuznetsova AV, DiFranceisco WJ, Musatov VB, & Pirogov DG (2010). People living with HIV in HAART-era Russia: Transmission risk behavior prevalence, antiretroviral medication-taking, and psychosocial distress. AIDS and Behavior, 15, 767–777. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Amirkhanian YA, Kelly JA, & McAuliffe TL (2003). Psychosocial needs, mental health, and HIV transmission risk behavior among people living with HIV/AIDS in St. Petersburg, Russia. AIDS, 17, 2367–2374. [DOI] [PubMed] [Google Scholar]
- Beck AT, Brown G, Berchick RJ Stewart BL, & Steer RA (1990). Relationship between hopelessness and ultimate suicide: A replication with psychiatric outpatients. American Journal of Psychiatry, 147, 190–195. [DOI] [PubMed] [Google Scholar]
- Beck AT, Weissman A, Lester D, & Trexler L (1974). The measurement of pessimism: the hopelessness scale. Journal of Consulting and Clinical Psychology, 43, 861–865. [DOI] [PubMed] [Google Scholar]
- Brown JL, Gause NK, & Northern N (2016). The association between alcohol and sexual risk behaviors among college students: A review. Current Addiction Reports, 3, 349–355. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cousins S (2017). Number of new HIV infections is rising in Russia. BMJ, 358, j3543. [DOI] [PubMed] [Google Scholar]
- Cutrona CE (1989). Ratings of social support by adolescents and adult informants: Degree of correspondence and prediction of depression symptoms. Journal of Personality and Social Psychology, 57, 723–730. [DOI] [PubMed] [Google Scholar]
- GBD 2016 Russia Collaborators. (2018). The burden of disease in Russia from 1980 to 2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet, 392 (10153), 1138–1146. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hamers FF & Downs AM (2003). HIV in Central and Eastern Europe. Lancet, 361, 1035–1044. [DOI] [PubMed] [Google Scholar]
- Howe CJ, Cole SR, Napravnik S, Kaufman JS, Adimora AA et al. (2014). The role of at-risk alcohol/drug use and treatment in appointment attendance and virologic suppression among HIV+ African Americans. AIDS Research and Human Retroviruses, 30, 233–240. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Human Rights Watch (April 28, 2004). Lessons not learned: Human rights abuses and HIV/AIDS in the Russian Federation Available at: https://www.hrw.org/report/2004/04/27/lessons-not-learned/human-rights-abuses-and-hiv/aids-russian-federation (accessed April 11, 2019).
- Kelly JA, & Amirkhanian YA (2003). The newest epidemic: HIV/AIDS in Central and Eastern Europe. International Journal of STD and AIDS, 14, 361–373. [DOI] [PubMed] [Google Scholar]
- Kelly JA, Murphy DA, Bahr GR, Kalichman SC, Morgan MG et al. (1993). Outcome of cognitive-behavioral and support group brief therapies for depressed, HIV-infected persons. American Journal of Psychiatry, 150, 1679–1686. [DOI] [PubMed] [Google Scholar]
- Kiriazova T, Lunze K, Raj A, Bushara N, Blokhina E, Krupitsky E et al. (2017). “It is easier for me to shoot up”: Stigma, abandonment, and why HIV-positive drug users in Russia fail to link to HIV care. AIDS Care, 29, 559–563. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Krupitsky EM, Horton NJ, Williams EC, Lioznov D, Kuznetsova M, Zvartau E et al. (2005). Alcohol use and HIV risk behaviors among HIV-infected hospitalized patients in St. Petersburg, Russia. Drug and Alcohol Dependence, 79, 251–256. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kuznetsova AV, Meylakhs AY, Amirkhanian YA, Kelly JA, Yakovlev AA et al. (2016). Barriers and facilitators of HIV care engagement: Results of a qualitative study in St. Petersburg, Russia. AIDS and Behavior, 20, 2433–2443. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lan C-W, Scott-Sheldon LAJ, Carey KB, Johnson BT, & Carey MP (2017). Prevalence of alcohol use, sexual risk behaviors, and HIV among Russians in high-risk settings: A systematic review and meta-analysis. International Journal of Behavioral Medicine, 24, 180–190. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lan C-W, Scott-Sheldon LAJ, Carey KB, Johnson BT, & Carey MP (2014). Alcohol and sexual risk reduction interventions among people living in Russia: A systematic review and meta-analysis. AIDS and Behavior, 18, 1835–1846. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lunze K, Lioznov D, Cheng DM, Nikitin RV, Coleman SM, Bridden C et al. (2017). HIV stigma and unhealthy alcohol use among people living with HIV in Russia. AIDS and Behavior, 21, 2609–2617. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Needle R, Fisher DG, Weatherby N, Chitwood D, Brown B et al. (1995). Reliability of self-reported HIV risk behaviors of drug users. Psychology of Addictive Behavior, 9, 242–250. [Google Scholar]
- NIAAA. NIAAA Council approves definition of binge drinking. National Institute of Alcohol Abuse and Alcoholism (NIAAA) Newsletter 2004; 3: 3. [Google Scholar]
- R Core Team (2013). R: A language and environment for statistical computing R Foundation for Statistical Computing, Vienna, Austria: URL: http://www.R-project.org/. [Google Scholar]
- Rodger AJ, Cambiano V, Bruun T, Vernazza P, Collins S, Van Lunzen J, Lundgren J (2016). Sexual activity without condoms and risk of HIV transmission in serodifferent couples when the HIV-positive partner is using suppressive antiretroviral therapy. Journal of the American Medical Association, 316(2), 171–181. 10.1001/jama.2016.5148. [DOI] [PubMed] [Google Scholar]
- Russian Federal AIDS Center. HIV infection in Russian Federation in 2017 Moscow: Russian Federal AIDS Center; 2017. [Google Scholar]
- Samet JH, Horton NJ, Traphagen ET, et al. (2003). Alcohol consumption and HIV disease progression: Are they related? Alcoholism Clinical and Experimental Research, 27, 862–867. [DOI] [PubMed] [Google Scholar]
- Samet JH, Raj A, Cheng DM, Blokhina E, Bridden C et al. (2015). HERMITAGE – A randomized controlled trial to reduce sexually transmitted infections and HIV risk behaviors among HIV-infected Russian drinkers. Addiction, 110, 80–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Silverstein PS, & Kumar A (2014). HIV-1 and alcohol: Interactions in the central nervous system. Alcoholism Clinical and Experimental Research, 38, 604–610. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Spielberger CD State-Trait Anxiety Inventory (Form 4) (1983). Palo Alto, CA: Consulting Psychologists Press. [Google Scholar]
- Spielberger CD, Gorsuch RL, & Lushene RE (1970). STAI: Manual for the State-Trait Anxiety Inventory Palo Alto, CA: Consulting Psychologists Press. [Google Scholar]
- Tendais I, Costa R, Conde A, & Figueredo B (2014). Screening for depression and anxiety disorders from pregnancy to post-partum with the EPDS and STAI. Spanish Journal of Psychology, 17, e7, 1–9. [DOI] [PubMed] [Google Scholar]
- UNAIDS. UNAIDS Data 2018 Geneva: UNAIDS; 2018. [Google Scholar]
- Wirtz AL, Zelaya CE, Latkin C, Peryshkina A, Galai N et al. (2016). The HIV care continuum among men who have sex with men in Moscow, Russia: A cross-sectional study of infection awareness and engagement in care. Sexually Transmitted Infections, 92, 161–167. [DOI] [PMC free article] [PubMed] [Google Scholar]
- World Bank (2016). Total alcohol consumption per capita Available at: https://data.worldbank.org/indicator/SH.ALC.PCAP.LI?locations=RU (accessed April 11, 2019).
- World Health Organization. Global status report on alcohol and health 2018 Geneva: World Health Organization; 2018. [Google Scholar]
- World Health Organization. WHO global status report on Alcohol 2004. European region: Country profiles: The Russian Federation Geneva: World Health Organization; 2004. [Google Scholar]