Globally, around 16 million people inject (illicit) drugs and 3 million of them are living with human immunodeficiency virus (HIV) infection. On average, 1 of every 10 new HIV infections worldwide is caused by injecting drugs using contaminated apparatus. People Who Inject Drugs (PWID) are at a high risk of HIV infection, and are increasingly targeted to prevent the spread of HIV [1]. In Nepal, the HIV epidemic among PWID is severe [2,3].
Nepal is recognized as a country facing a concentrated HIV epidemic. The National Centre for AIDS and STD Control (NCASC) has estimated that there were 39,281 people living with HIV (PLHIV) in Nepal in 2015, with adult HIV prevalence of 0.20% [4]. The National HIV and AIDS Strategy (2011–2016) identifies PWID as a key affected population (KAP) at high risk of HIV infection. Evidence from the various rounds of Integrated Biological and Behavioral Surveillance (IBBS) surveys conducted in Nepal suggests that HIV prevalence is still high among the PWID relative to other KAPs, such as female sex workers (FSW) and men who have sex with men (MSM), immigrants, and their spouses. PWID have regular female sexual partners, most of whom are at high risk of HIV infection because of unprotected sex with their regular (injecting) partner/husband [5,6]. Similarly, PWID also participate in high-risk behaviors of sharing needles/syringes between injecting partners, and reusing needles kept in public places. The crossover of (illicit) drug use with sex work has also been found to be major contributing factor to the spread of HIV to other at risk populations and their partners [7,8]. This overlap of risk behaviors puts the PWID at elevated risk of acquiring HIV and creates potential bridges for the onward transmission to other high-risk populations and their sexual partners. Studies have revealed that behavioral and sociodemographic factors are linked to HIV infection. Lifestyle variables, socioeconomic factors, and psychosocial propensity are elements that influence HIV preventive behaviors and HIV infections [9,10,11].
In Nepal, limited studies of behavioral and sociodemographic factors associated with HIV among PWID have been documented. Social and behavioral determinants affecting HIV prevalence among these groups are important and need to be assessed. With these view and importance, the present study aimed to investigate the prevalence of HIV and social and behavioral correlates of HIV infections among PWID in Nepal. This study used IBBS survey data to provide updated comprehensive assessment of the epidemic situation among the PWID, using 22 rounds of IBBS surveys.
This observational study retrospectively analyzed IBBS surveys of PWID in Nepal, conducted from 2002 to 2015. IBBS surveys were cross-sectional in design and were conducted in Kathmandu Valley, Pokhara Valley, eastern Terai (3 districts), and western-to-far-western Terai (7 districts). PWID were defined as
IBBS datasets for year and region were available as computer files comprising background characteristics, knowledge on HIV and AIDS, drug injecting practices, sexual behavior and access to HIV services. The independent variables selected were background characteristics (age, year, region, education, and marital status), drug injecting practices (age of first drug injection, duration of drug use and injecting drugs, used needle/syringe previously used by someone else in past week, used syringe/needle left in a public place in past week, and shared needle/syringe with someone after using in past week) and sexual behaviors (age at first intercourse, number of sexual partners, number of FSWs, consistent condom use with regular partner, FSW and nonregular female sex partner). The sex partners of the PWID were categorized as regular female sex partners, FSWs, and nonregular female sex partners. A ‘regular female sex partner’ is defined as spouse or any sexual partner living together with PWID. FSWs were defined as those who sell sex in exchange for cash, kind, or drugs. ‘Non-regular female sex partners’ were defined as those with whom the PWID was not married or living together. These may include girlfriends or other female friends with whom they have sexual relationship.
Statistical analyses were performed using the R program. Bivariate analyses were performed to estimate the association of demographic and behavioral variables with HIV prevalence using χ2 tests. Logistic regression [12,13] analyses were performed to determine variables associated with HIV infected proportion defined by combinations of the determinants, using the additive model:
In this model,
Data from a total of 7,073 PWID from 2002 to 2015 were included in the analysis, of whom 17.8% (1,257) tested HIV positive.
Associationbetween characteristics and HIV infection among people who inject (illicit) drugsCharacteristic Total (n = 7073) HIV infection (n = 1257,17.7%) Not HIV infected (n = 5816, 82.3%) χ2( P n (%) n (%) 947 (7) <0.001 2002 303 (4.3) 206 (68) 97 (32) 2003 645 (9.1) 187 (29) 458 (71) 2005 1245 (17.6) 364 (29.2) 881 (70.8) 2007 1245 (17.6) 217 (17.4) 1028 (82.6) 2009 1245 (17.6) 129 (10.4) 1116 (89.6) 2011 685 (9.7) 45 (6.6) 640 (93.4) 2012 660 (9.3) 44 (6.7) 616 (93.3) 2015 1045 (14.8) 65 (6.2) 980 (93.8) 342 (3) <0.001 Eastern 2100 (29.7) 376 (17.9) 1724 (82.1) Kathmandu 1883 (26.6) 575 (30.5) 1308 (69.5) Pokhara 1890 (26.7) 199 (10.5) 1691 (89.5) Western 1200 (17) 107 (8.9) 1093 (91.1) 358 (2) <0.001 18–20 years 1315 (18.6) 97 (7.4) 1218 (92.6) 20–29 years 3999 (56.5) 599 (15) 3400 (85) 30–39 years 1759 (24.9) 561 (31.9) 1198 (68.1) 82 (2) <0.001 No education 323 (4.6) 92 (28.5) 231 (71.5) Primary 1483 (21) 353 (23.8) 1130 (76.2) Secondary and above 5267 (74.5) 812 (15.4) 4455 (84.6) 137 (1) <0.001 Unmarried 4158 (58.8) 553 (13.3) 3605 (86.7) Married 2915 (41.2) 704 (24.2) 2211 (75.8)
Association between drug injecting behaviors and HIV prevalence among people who inject (illicit) drugsCharacteristics Total (n = 7073) HIV infected (n = 1257,17.7%) Not HIV infected (n = 5816, 82.3%) χ2( P n (%) n (%) 247 (2) <0.001 <20 years 3870 (54.7) 571 (14.8) 3299 (85.2) 20–29 years 2610 (36.9) 442 (16.9) 2168 (83.1) ≥30 years 593 (8.4) 244 (41.1) 349 (58.9) 407 (3) <0.001 <2 years 1863 (26.3) 152 (8.2) 1711 (91.8) 2–5 years 680 (9.6) 63 (9.3) 617 (90.7) 5–10 years 2730 (38.6) 463 (17) 2267 (83) >10 years 1800 (25.4) 579 (32.2) 1221 (67.8) 295 (3) <0.001 <2 years 3929 (55.5) 475 (12.1) 3454 (87.9) 2–5 years 593 (8.4) 93 (15.7) 500 (84.3) 5–10 years 1829 (25.9) 428 (23.4) 1401 (76.6) >10 years 722 (10.2) 261 (36.1) 461 (63.9) 196 (2) <0.001 Yes 5283 (74.7) 904 (17.1) 4379 (82.9) No 962 (13.6) 301 (31.3) 661 (68.7) Not used in past week 828 (11.7) 52 (6.3) 776 (93.7) 400 (2) <0.001 Yes 5274 (74.6) 824 (15.6) 4450 (84.4) No 968 (13.7) 381 (39.4) 587 (60.6) Not used in past week 831 (11.7) 52 (6.3) 779 (93.7) 173 (2) <0.001 Yes 5254 (74.3) 910 (17.3) 4344 (82.7) No 990 (14) 295 (29.8) 695 (70.2) Not used in past week 829 (11.7) 52 (6.3) 777 (93.7)
Age of first sexual intercourse, number of sexual partners, number of FSWs, consistent condom use with regular partners, FSWs and nonregular female sex partners were significantly associated with HIV infection, as shown in
Association between sexual behaviors and HIV prevalence among people who inject (illicit) drugsCharacteristics Total (n = 7073) HIV infection (n = 1257,17.7%) Not HIV infected (n = 5816, 82.3%) χ2( P n (%) n (%) 67 (3) <0.001 <20 years 5740 (81.2) 968 (16.9) 4772 (83.1) 20–29 years 1074 (15.2) 272 (25.3) 802 (74.7) >30 years 15 (0.2) 2 (13.3) 13 (86.7) Not had sex 244 (3.4) 15 (6.1) 229 (93.9) 171 (2) <0.001 One partner 2409 (34.1) 468 (19.4) 1941 (80.6) More than one partner 2530 (35.8) 261 (10.3) 2269 (89.7) Not had Sex 2134 (30.2) 528 (24.7) 1606 (75.3) 79 (2) <0.001 One partner 652 (9.2) 84 (12.9) 568 (87.1) More than one 1331 (18.8) 141 (10.6) 1190 (89.4) Not had Sex 5090 (72) 1032 (20.3) 4058 (79.7) 30 (2) O.001 Yes 1052 (14.9) 169 (16.1) 883 (83.9) No 1356 (19.2) 311 (22.9) 1045 (77.1) Not had Sex 4665 (66) 777 (16.7) 3888 (83.3) 86 (2) O.001 Yes 222 (3.1) 34 (15.3) 188 (84.7) No 1758 (24.9) 186 (10.6) 1572 (89.4) Not had Sex 5093 (72) 1037 (20.4) 4056 (79.6) 172 (2) <0.001 Yes 569 (8) 44 (7.7) 525 (92.3) No 1619 (22.9) 150 (9.3) 1469 (90.7) Not had Sex 4885 (69.1) 1063 (21.8) 3822 (78.2)
In bivariate analysis, all determinants were found to be significantly associated with prevalence of HIV infection. Therefore, these determinants were included for multivariate logistic regression analysis. In the multivariate logistic regression, year, region, age, education, duration of drug use was significantly associated with HIV infection. However, the study found an interaction between year and region and age and education. Therefore, year and region were combined. There were 22 levels in the year-region group factor depending on year and region. Age and education were also combined. The number of levels in the age group-education factor depends on the age and education of HIV infection. For HIV, we chose 7 levels of age group-education factor with 3 education level and 3 age groups (16–20 years, 21–30 years, and >30 years). However, PWID who had no education were merged in one group because of the small sample size.
In the logistic regression, year-region factor, age group-education factor, and duration of injecting drugs were significantly associated with HIV infection. The graph below shows results from fitting logistic model for HIV infection prevalence, with year-region factor, age group-education factor, and duration of injecting drugs as determinants. The model also highlights a substantial decrease on HIV infection prevalence from 2002 to 2015. Kathmandu Region had highest prevalence compared to other regions of Nepal. HIV infection prevalence increases with age. The prevalence was highest for those aged ≥30 years. Education was significantly associated with HIV infection as the highest prevalence was observed among the illiterate in all age groups. Moreover, the prevalence significantly increased with duration of injecting drugs. Higher prevalence was found among PWID who had a duration of injecting drugs >10 years.
IBBS surveys were analyzed to confirm that PWID in Nepal are indeed at elevated risk of HIV infection. The study found year-region factor, age group-education factor, and duration of injecting drugs were associated with HIV prevalence in a multivariate logistic regression. This study showed notable success in the prevention of HIV infection among PWID. HIV infection prevalence among PWID has significantly decreased from 2002 to 2015. Factors such as access to HTV infection intervention programs, needle sharing programs, safe drug injecting practices, safe sexual practices with partners, and increase knowledge of HIV had contributed to decrease in HIV infection prevalence in recent years. Data from the IBBS surveys and Nepal Demographic Health Survey (NDHS) surveys delineate major improvements in several factors that may have attributed to this decline in HIV infection among these groups [4,5,6,7,8, 12]. Moreover, the prevalence remains similar in the latest three IBBS surveys of 2011,2012, and 2015. Although, HIV infection prevalence in Nepal remains stagnant in recent years, PWID still remain the most vulnerable subpopulation compared to other KAPs with regard to HIV infections [4,5,6,7,8].
There was pronounced spatial variation of HIV infection in four zones of Nepal. High HIV prevalence was found in Kathmandu and the Eastern region compared with Western Region and Pokhara. The highest concentration of drug user population is confined in the Kathmandu Valley and in the locations along the Eastern Highway. According to mapping and size estimation among drugs users, around 4,341 to 4,758 drug users were living in Kathmandu [17,18]. In Kathmandu, PWID has been widespread in the shooting galleries that are typical common places for PWID to congregate, and these are found in clandestine locations that often provide opportunities for buying, renting, or borrowing needles/syringes and other items for injection. Higher prevalence in the Eastern Region may also be a consequence of cross-border issues with India. The open and porous border between these two neighbors has provided an ideal passage for smugglers for decades, and districts mostly in the Terai region of the country turned into a major transit points for peddling drugs.
HIV infection prevalence was positively associated with increasing age and low education status. A previous study has shown that not only are young PWID at increased risk of HIV, but also that those who use drugs in shooting galleries, or places with other and older PWID, are more likely to start injecting drugs early, and are at increased risk of HIV, partly because of the high prevalence among the older subgroup [19]. HIV infection was also associated with low education level. A study in Nepal among female drug users also shows similar results, i.e., low education is strongly associated with HIV infection prevalence [9]. Low or limited education relates to poor awareness about HIV or acquired immunodeficiency syndrome (AIDS), and contributes to their vulnerability. In Nepal, PWID often came from rural areas and have limited education, making them extremely vulnerable to drug involvement and unsafe sex. Their powerlessness and poor understanding of their own risk probably also make them more likely to engage in high-risk drug use and unsafe sexual practices [4,8].
Consistent with previous studies, injection behavior showed a strong association with HIV infections in this population [9,10,11]. HIV infection prevalence was higher among PWID who had a long duration of injecting drugs (>10 years). This association could be causal because the sharing of injection needles over long duration was associated strongly with HIV infection rates, and in a dose-dependent manner. Sharing needles or syringes and other equipment for injection is the most frequent drug-related risk behavior, which puts the PWID at risk of HIV transmission. In Nepal, the sharing practice is deeply rooted within the PWID social and cultural context. In addition, other frequently cited reasons for sharing include limited resources; lack of clean needles/syringes, and fears of arrest by law personnel, all of which reinforce reusing and sharing equipment [5,8].
PWID provide an effective epidemiological bridge for a wider epidemic, through unsafe sexual practices with their regular partners (wives) and with nonregular partners. Unprotected sex behavior also increased HIV risk among the PWID. The risk was significantly increased by sex with commercial or casual sex partners. In the present study, we found the number of sexual partners and the inconsistent use of condom with a regular partner were significantly associated with HIV infection in bivariate analysis. However, in multivariate analysis, no statistical association was found. HIV infection was higher among PWID who do not have sex or had one sexual partner in past year. This may be the result of PWID were aware of their status and limited their sexual intercourse with partners. Moreover, PWID perceive that the risk of HIV transmission is less likely through sexual contact than through sharing injection items, and were therefore less likely to adopt safe sex than safe injection practices. Similarly, HIV infection was higher among those consistently using a condom with their regular female sexual partners. The reason may be the PWID were using condoms to either prevent HIV transmission to their clients, or as self-protection from infection with HIV, or sexually transmitted diseases. In addition, previous studies among drug users have found safer sex practices strongly associated with HIV infection. A study in the United States found that self-reporting as HIV infected was the strongest factor associated with consistent condom use in the past 6 months [20]. Another study in Puerto Rico found that HIV-positive drug users were nearly five times more likely to use condoms during vaginal sex [21].
Consistent with earlier findings [22,23,24], the HIV risk behaviors include sharing injecting equipment and unprotected sex, and the risk of contamination from sharing practices and nonsanitized usage of injecting equipment is present in considerable proportions, albeit in varying degrees. Programs to target HIV infection prevention and treatment should be urgently developed and implemented for this population. Provision of clean needles and syringes will be useful for the prevention of HIV transmission through networks of PWID.
In conclusion, the decrease in HIV infection prevalence among the PWID gives the impression that HIV infection prevention interventions have been successful in Nepal. Data from the upcoming IBBS survey rounds are expected to provide more specific insights into the level of impact by interventions in the study sites. This study also guides policymakers on designing HIV and sexually transmitted infection intervention programs based on the risk factors.
The study has a few limitations. IBBS surveys are cross-sectional by their design and cannot provide evidence of causal relationships between the determinants and HIV infections. Moreover, this study covered only HIV infection and risk-related behaviors, while other issues related to HIV intervention programs and drug policies have not been discussed. Despite such limitations, the positive association between background characteristics and drug injecting practices that increase the risk of HIV infection have important implications.