1. Introduction
Human immunodeficiency virus (HIV) prevalence in India is highly heterogeneous the disease burden, vulnerabilities, and risk behaviors vary across different populations and geographies. Female sex workers, men having sex with men, people who inject drugs (PWID), and transgender are considered high-risk groups (HRG) for HIV infection. In India , the initial HIV epidemic was concentrated in the states of South and North-eastern India , with most of the transmission occurring through the heterosexual route.[ 1 ] While the HIV epidemic in the Southern states is considered to have matured, the North-eastern states have shown a considerable increase in annual new HIV infections and HIV incidence since 2015.[ 2–5 ] This is attributed to the widely prevalent injecting drug use in the region, given its proximity to Myanmar.[ 6 ]
The estimated size of PWID in India was about 0.39 millions in 2019,[ 7 ] and recent reports suggest a higher incidence rate among PWID in the North and North-eastern regions than other HRGS.[ 4 ] The HIV prevalence among PWID in 2015 was 9.9%, and Uttar Pradesh had the highest prevalence at 29%.[ 2 ] In 2017, HIV prevalence among PWID was 6.26 %, and Mizoram had the highest prevalence of 19.8%.[ 8 ] In 2020, the North-eastern states reported the highest estimated adult HIV prevalence , with Mizoram recording 2.37%, followed by Nagaland (1.44%) and Manipur (1.15%).[ 5 ] Recent district-level HIV estimates also indicate that all districts with an adult HIV prevalence of 1% or more were concentrated in the North-eastern states.[ 9 ] These reports suggest that injecting drug use contributes significantly to the increase in HIV prevalence , thus placing injecting drug users (PWID) among India ’s third-largest vulnerable HIV population.
The transmission might occur at a much higher rate among PWID with risk behaviors resulting in confined disease transmission and clusters of high prevalence. Lack of awareness or adequate knowledge of HIV/acquired immuno-deficiency syndrome (AIDS), sexually transmitted diseases (STI) symptoms, unsafe injecting behaviors, and inconsistent condom usage are commonly associated with higher infection risk.[ 10 ] Among PWID, the structure and extent of sexual and injecting risk behaviors vary, resulting in subpopulations such as PWID with risk behaviors and PWID with safe practices. Identifying the heterogeneity of risk behaviors based on the geographical regions will be insightful in understanding the transmission dynamics and the underlying disease confinement. Such profiling will pave the way for improvised decentralized targeted interventions with profound effects on interventional outcomes. The integrated biological and behavioral surveillance (IBBS) conducted among HRGs in 2014 to 2015 serves as a significant source of behavioral data for PWID to date. With its behavioral components, the IBBS presents the actuality of risk profile and disease confinement at different geographical locations within India that affects the dynamics of HIV prevalence and transmission among PWID. This study aims to stratify the PWID population based on their risk behaviors in each state and determine the HIV prevalence for each stratum.
2. Methods
2.1. Study Design
This study analyses the behavioral and HIV prevalence data of the National IBBS. IBBS is a cross-sectional study conducted in 2014 to 2015 among PWID across 53 domains in 29 states of India .[ 2 ] A domain was the basic study unit in IBBS for which the bio-behavioral estimates were generated. The domains were contiguous administrative geographical units, which were usually a single district (independent domain) or, at times, a group of districts (composite domain). The sample size was fixed at 400 for each domain. The respondents were recruited based on probability-based cluster sampling; by employing conventional cluster sampling and time-location cluster sampling methods.
2.2. Participants
The inclusion criteria were men aged 15 years or more who had used any psychotropic (addictive/mind-altering) substance or drug for recreational or nonmedical reasons through injections at least once in the last 3 months. Women who inject drugs were not included in the study.
2.3. Data and Sample Collection
Informed consent was obtained from all participants, and a pre-validated standard questionnaire was used to collect the socio-demographic and behavioral data. For PWID, data on basic demographics, injecting and sexual behaviors, partner types, knowledge of STI, HIV, antiretroviral therapy (ART), and details on violence episodes were collected. Blood samples were collected from all consented respondents. IBBS followed the unlinked anonymous testing (UAT) approach and the standard 2-tests protocol for HIV testing. The detailed methodology for sample size calculation, data and sample collection is reported elsewhere.[ 2 ]
2.4. Issue of interest
The behavioral data considered for analysis were Self-reported symptoms of STIs; injecting practices such as sharing needles/syringes; condom usage with regular female partners; condom usage with sexual partners (other than regular female partners) such as paid or casual female partners or male or transgender partners and knowledge or misconceptions about HIV/AIDS.
Under each behavioral variable, PWID were grouped as those reporting risk behaviors and those who did not. The respondents who had self-reported having at least 1 of the following 3 STIs, genital ulcer/sore, urethral discharge or genital warts, in the last 12 months were considered to have STI symptoms. PWID were considered to have adequate awareness and knowledge of HIV/AIDs if they had given correct answers to all questions on awareness, mode of transmission and HIV preventive measures included in the survey. PWID were considered to follow unsafe injecting practices if he had shared needles or syringes in any injecting episodes in the last 3 months of the survey. PWID were considered to have consistent condom usage if he had consistently used condoms during the sex act in the last 12 months of the survey with his sexual partners.
2.5. Statistical Analysis
HIV prevalence among PWID with and without risk behaviors was determined and compared based on the state weight under each category. A Chi-square test was done to establish an association between the risk behaviors and HIV prevalence in each state. Missing data on risk behaviors were not included in the analysis. The spatial mapping was done using Q-GIS software (Version 3.12). All statistical analysis was done using SPSS software (Version 26.0 Armonk, NY, IBM Corp. 2019).
2.6. Ethical Review
All respondents were informed about the details of the survey and the voluntary nature of the participation. Written informed consent was obtained through an informed consent form (ICF). The institutional ethical committee approved the study of ICMR - National Institute of Epidemiology, Chennai. The approval number is NIE/IHEC/20138/04.
3. Results
All valid samples of consented PWID were included in the analysis, accounting for 19,902 respondents with a median age of 30. The highest HIV prevalence was recorded in Uttar Pradesh (29.6%), followed by national capital territory (NCT) of Delhi (22.4%), Madhya Pradesh (15.7%), Uttarakhand (13.2%) and Manipur (12.1%). Chandigarh, West Bengal, and Mizoram recorded a prevalence of over 10%, while it ranged between 7% and 10% in Punjab, Haryana, and Chhattisgarh (Table 1 , Fig. 1A ).
Table 1 -
State-wise distribution and proportion of PWID based on risk characteristics.
State
n
Age median (IQR)
HIV prevalence (%)Have any STI symptoms (%)
Inadequate HIV knowledge (%)
Unsafe injecting (%)
Inconsistent condom usage RFP (%)
Inconsistent condom usage OP (%)
Andhra Pradesh
768
30 (26‐34)
3.1
17.7
32.4
18.0
86.8
48.3
Arunachal Pradesh
397
25 (22‐28)
0.0
4.7
72.8
23.9
82.7
65.4
Assam
805
28 (24‐31)
2.3
4.4
64.3
22.2
90.1
52.1
Bihar
288
28 (23‐34)
2.2
19.1
72.6
18.8
92.7
70.1
Chandigarh
401
30 (28‐36)
11.8
17.0
33.7
13.7
84.7
52.2
Chhattisgarh
764
26 (23‐31)
7.4
19.4
54.8
18.6
76.5
38.3
Goa
380
28 (23‐33)
1.4
19.0
84.5
60.3
52.1
65.5
Gujarat
394
35 (29‐40)
2.7
39.4
85.3
65.0
53.8
30.1
Haryana
1437
29 (25‐35)
8.0
27.3
73.3
34.8
84.8
60.4
Himachal Pradesh
403
27 (23‐34)
3.8
14.7
71.2
54.3
82.5
71.8
Jammu Kashmir
359
30 (26‐38)
0.4
27.3
72.1
32.6
91.5
76.5
Jharkhand
393
28 (23‐35)
0.7
2.5
70.7
8.9
82.6
41.5
Karnataka
364
27 (22‐33)
1.1
17.2
49.5
50.0
90.0
70.5
Kerala
1113
31 (26‐40)
0.2
10.0
41.8
23.5
96.1
70.7
Madhya Pradesh
1175
28 (24‐35)
15.7
15.4
58.1
33.6
72.0
63.4
Maharashtra
383
31 (28‐36)
0.6
8.4
77.5
49.1
83.5
72.5
Manipur
1594
32 (27‐37)
12.1
11.3
38.0
25.6
83.0
53.4
Meghalaya
396
26 (23‐29)
3.2
2.7
77.0
33.1
77.2
68.7
Mizoram
1084
25 (23‐30)
10
7.2
39.8
37.7
89.7
74.9
Nagaland
1198
33 (28‐40)
3.2
18.5
66.6
21.6
80.4
63.4
NCT Of Delhi
790
30 (25‐35)
22.4
34.9
48.7
20.5
76.6
67.3
Orissa
391
27 (24‐33)
1.4
12.8
47.3
16.4
74.6
54.5
Punjab
1087
26 (23‐31)
9.6
23.1
65.8
34.5
79.6
54.4
Rajasthan
273
34 (28‐40)
1
15.1
72.9
19.4
97.3
62.8
Sikkim
385
24 (21‐28)
0.2
15.9
47.8
30.9
93.8
83.5
Tripura
286
28 (25‐33)
0.6
6.1
68.2
15.0
88.5
57.7
Uttar Pradesh
1587
35 (28‐40)
29.6
14.0
76.8
48.7
93.9
83.9
Uttarakhand
411
30 (26‐35)
13.2
40.7
60.3
23.8
96.8
73.1
West Bengal
596
30 (24‐37)
10.8
13.7
66.9
22.8
89.7
67.8
HIV = human immunodeficiency virus, NCT = national capital territory, OP = other partners including paid or casual female partners, male or transgender partners, PWID = people who inject drugs , RFP = regular female partners, STI = sexually transmitted infections.
Figure 1.: State-wise HIV prevalence of PWID (A), state-wise HIV prevalence of PWID based on risk characteristics; having at least one STI symptom (B), having inadequate knowledge of HIV knowledge (C), having unsafe injection practice (D), inconsistent condom usage with regular female partners (E) and inconsistent condom usage with other sexual partners (F). HIV = human immunodeficiency virus, PWID = people who inject drugs , STI = sexually transmitted diseases.
About 1-fourth to 2-fifths of PWID reported the presence of 1 or more STI symptoms, the highest in Uttarakhand, followed by Gujarat, NCT of Delhi, Haryana, Jammu & Kashmir, and Punjab. (Fig. 1B ). Accordingly, in most high prevalence states, a positive association was significantly established between the presence of STI symptoms and HIV prevalence . A reverse association was significant in Chandigarh and Chhattisgarh, whereas, irrespective of the presence or absence of STI symptoms, HIV prevalence was invariably high among PWID in Uttar Pradesh (Table 2 ).
Table 2 -
Stratified
HIV prevalence among PWID subgroups based on risk profile.
High risk behaviors
HIV prevalence (%)
Presence of any STI symptoms
Injecting practice
Aware of HIV transmission
Consistent condom usage (RFP)
Consistent condom usage (OP)
States
Yes
No
Safe
Unsafe
Yes
No
Yes
No
Yes
No
Andhra Pradesh
1.5
3.3
3.7
0.7
2.1
5.2
5.3
2.1
3.5
2.4
Arunachal Pradesh
0
0
0
0
0
0
0
0
0
0
Assam
2.8
2.3
2.4
2.2
2.4
2.3
2.8
1.5
0
0
Bihar
1.8
2.1
2.6
0
3.8
1.4
6.7
2.6
2.2
1.9
Chandigarh
2.9
13.5
*
11.8
10.9
9
17.0
*
4.3
7.1
3.9
6
Chhattisgarh
2.7
8.5
*
3.9
22.5
***
4.1
10.0
**
4.2
8.9
3.3
3.5
Goa
5.5
0.6
**
0.7
1.7
0
1.6
3.6
5
0
0
Gujarat
5.2
0.8
*
1.4
3.5
3.4
2.7
6.6
1.4
3.8
2.9
Haryana
12.8
6.1
***
6.9
9.8
9.1
7.5
8.4
9.3
15.6
8.7
**
Himachal Pradesh
0
4.4
7.1
0.9
***
1.7
4.5
0
1.1
1.4
5.3
Jammu & Kashmir
0
0.4
0
0.9
0
0.4
0
0.6
0
1.3
Jharkhand
0
0.8
0.6
2.9
0
1.1
0
1.2
0
0
Karnataka
1.6
1
2.2
0.5
2.2
0.6
0
0.7
2.6
1.1
Kerala
0
0.2
0
0.8
*
0
0.4
0
0.2
0
0.4
Madhya Pradesh
13.3
16.2
15
17
14.6
16.5
10.3
14.9
13.9
11.7
Maharashtra
0
0.6
0.5
0.5
0
0.7
0
0.4
1.5
0
Manipur
8.3
12.6
11.8
13.2
13.8
9.4
*
16.7
7.7
**
8.2
10
Meghalaya
9.1
3.1
2.6
3.8
2.2
3.6
11.5
1.1*
7.3
2.2
Mizoram
31.6
8.3
***
10.5
9.3
11.5
7.7
*
6
10.3
5.6
15.0
*
Nagaland
4.5
2.9
3.1
3.9
5.3
2.1
**
2.3
3.1
1.2
4.7
*
NCT Of Delhi
30.1
18.3
***
19.6
32.7
***
11.6
33.6
***
6.7
21.4
***
8.1
30.9
***
Orissa
0
1.5
1.2
1.6
1.5
1.1
0
1
1
2.6
Punjab
15.9
7.8
***
11
7.2*
6.7
11.1
*
17.3
6.3
***
4.9
7.1
Rajasthan
0
1.3
1.4
0
2.7
0.5
0
1.1
4.8
0
Sikkim
0
0.3
0.4
0
0.5
0
0
0.6
0
0.6
Tripura
0
0.7
0.8
0
0
1
0
0
0
0
Uttar Pradesh
34.8
28.8
25.3
34.2
***
30.4
29.4
40
24.9
*
27.1
27.8
Uttarakhand
15.5
11.5
12.1
16.3
12.9
13.3
44.4
10.2
**
10.2
9
West Bengal
25.6
8.4
***
7
23.5
***
6.6
13.0
*
21.9
6.5
**
7.8
11.1
HIV = human immunodeficiency virus, NCT = national capital territory, OP = other partners including paid or casual female partners, male or transgender partners, PWID = people who inject drugs , RFP = regular female partners, STI = sexually transmitted diseases.
* Significantly differed at 5% level (P < .05).
** Significantly differed at 0.5% level (P < .005).
*** Significantly differed at 0.1% level (P < .001).
While the overall awareness of HIV among PWID in India was around 96%, nearly 10% of the PWID in Uttar Pradesh and Delhi were unaware of HIV. Nearly 1 to third or more PWID lacked adequate awareness of HIV/AIDS across India , with the highest proportions in Goa and Gujarat (Table 1 ). Higher the rate of unawareness higher was the rate of unsafe injecting practices in most states. HIV prevalence was predominantly higher among PWID with inadequate knowledge of HIV/AIDS, which was significant in Delhi, Haryana, Chandigarh, Punjab, West Bengal, Chhattisgarh, and Andhra Pradesh (Fig. 1C ).
Nearly 1 to third or more respondents reported unsafe injecting practices in Gujarat, Goa, Himachal Pradesh, Karnataka, Maharashtra, Uttar Pradesh, Mizoram, Haryana, Punjab, and Madhya Pradesh (Table 1 ). In most states, HIV prevalence was higher among PWID with unsafe injecting practices than those with safe injecting practices in most states (Table 2 , Fig. 1D ). The unsafe injecting practice was significantly associated with higher HIV prevalence in Uttar Pradesh, NCT of Delhi, West Bengal, Chhattisgarh, and Kerala. In contrast, a reverse association was observed in Punjab and Himachal Pradesh (Table 2 ).
Inconsistent condom usage with regular female partners was associated with slightly higher HIV prevalence in most states, which was significant in the NCT of Delhi (Fig. 1E ). In contrast, a significant reverse association between consistent condom usage with regular female partners and HIV prevalence was found in Meghalaya, Manipur, Punjab, West Bengal, Uttar Pradesh, and Uttarakhand (Table 2 ). Similarly, a reverse association was established between consistent condom usage with casual or paid sexual female partners and HIV prevalence in most states, which was significant in Haryana. Nevertheless, inconsistent condom usage with casual or paid partners was significantly associated with higher HIV prevalence in Delhi, Mizoram, and Nagaland (Table 2 , Fig. 1F ).
4. Discussion
The regions of higher HIV prevalence among PWID were confined to north and central India and a few states of East and North-East India . Accordingly, Uttar Pradesh, Delhi, Punjab, Manipur, and Nagaland have the highest estimated PWID.[ 7 ] The analysis shows that unsafe injecting practices were more prevalent in Western and Central India , whereas unsafe sexual behaviors were widespread among most states. PWID with unsafe injecting practices and sexual practices had a significantly higher prevalence. Higher HIV prevalence was also significantly associated with inadequate knowledge or misconceptions about HIV/AIDS. These behaviors cannot be generalized to all regions in India , and region-specific indicators need to be identified and addressed.
Awareness and adequate knowledge of HIV/AIDS are essential to HIV prevention and management.[ 11–13 ] PWID with inadequate knowledge or misconceptions about HIV/AIDS are more likely to be involved in high-risk behaviors. In India , 26.1% of the PWID had misconceptions about the transmission routes or HIV preventive measures, and 42.6% lacked comprehensive knowledge of HIV/AIDs. Information education and communication activities aim to create awareness of HIV/AIDS among all PWID in India , which is of utmost importance to prevent disease transmission. However, in India , only 58.2% of the PWID had received information education and communication services, representing a knowledge gap to be addressed for effective HIV management.[ 14 , 15 ] While educating the PWID on HIV prevention and management is essential, it is necessary not to foster unintended, false assumptions. For instance, evidence suggests that ART awareness led to misconceptions about considering HIV as a non-communicable or curable disease.[ 16 ]
Safe injecting practices prevent HIV transmission among PWID,[ 17 , 18 ] which, however, is compromised by various social-structural contextual factors such as social networks, peer pressure, fear of harassment, and inaccessibility to sterile needles or syringes.[ 19 , 20 ] Reports show that HIV-positive PWID follow specific strategies to reduce transmission risks, such as “being the last receiver,” sharing with HIV-positive PWID and washing the needles/syringes before sharing. These practices, however, increase their risk of acquiring other STIs, such as hepatitis C virus (HCV) and hepatitis B virus.[ 19 , 21 ]
Reports show that the HIV prevention interventions among PWID focus on propagating safer injection practices and emphasizing condom use with high-risk partners such as paid.[ 22 ] Among all HRGs, more than 50% of the participants have reported having consistent condom usage among all partner types other than regular partners, except PWID. Nationwide, the proportion of PWID with female partners is 80.2%, of which 15.9%, 29.2%, and 50.0% had consistent condom usage with regular, casual, and paid partners. While 37.4% of them had male/transgender partners, only 35.9% reported consistent condom usage.[ 2 ] A significant positive association between HIV prevalence and inconsistent condom usage in high prevalence states such as Delhi and Mizoram suggests the need to emphasize safe sexual practices in PWID interventions. Several studies have documented the unsafe sexual behaviors of PWID and transmission risk from PWID to their non-injecting partners, specifically the regular partners.[ 23 , 24 ]
Various factors affect condom usage with sexual partners of PWID, such as non-disclosure of risk behaviors due to fear of rejection, social stigma, or discrimination. In some cases, HIV sero-concordant and concordant HIV-negative relationships may significantly affect the consistent use of condoms. Nevertheless, condom usage reduces transmission risks as well as resistance to ART.[ 25 , 26 ] Drug intake during or before sex is often associated with unsafe sexual behavior due to the physiological impact the drug has on the drug user. Negotiating condom usage under the alcohol influence is difficult, posing a much higher risk of disease transmission. Hence, advocating consistent condom usage among PWID with all sexual partners and providing necessary psychological support and counseling ensures safe sexual practices in PWID.[ 22 ]
Untreated STIs increase HIV and other infection risks; therefore, HIV interventions in India include regular screening and treatment for STIs, despite which only 76.1% of PWID have heard of STIs against 96% of HIV/AIDs. A predominantly significant association between the presence of self-reported STI symptoms and high HIV prevalence calls for appropriate interventions. Recent studies emphasize the risk of HIV and HCV coinfection among PWID in India , as needle sharing is a predominant risk factor for HCV.[ 27 , 28 ] However, a cross-sectional study indicated that most HIV and HCV-infected PWID were unaware of their infection status and underlined the urgent need to rapidly identify and treat HIV and HCV-infected PWID in India .[ 27 ] Besides common factors such as personal hygiene, drug abuse and sexual encounters, studies also report an association between accessibility to interventions like needle syringe exchange programme and HCV prevalence.[ 29 ] The criminalization of drug use in India is considered to be a major barrier to accessing HIV interventions.[ 28 ] Identifying and addressing such programmatic gaps is crucial for successful harm reduction and drug reform efforts in high-burden states.
While a significant positive association between risk behaviors and higher HIV prevalence has been observed in a few states, a significant reverse association could also be observed in most states, as discussed above. In a cross-sectional study design, the temporality or causality between the prevalence and associated risk behaviors cannot be established. Several behavioral studies have reported such reverse association, which could be explained by a former association, id est an infected person becomes more aware of HIV and opts for behavioral changes.
A systematic review of HIV among PWID in India indicated that needle sharing, injection frequency, early drug use, inconsistent condom use and having multiple partners are associated with HIV among PWID.[ 28 ] Studies also report various other individual and socio-structural contextual factors that influence the behavior and service utilization of PWID, namely the age, age at initiation of the drug use or duration of PWID behavior, stigma, violence victimization, and inaccessibility to interventional services.[ 30 , 31 ] The researchers highlight the need for high coverage and combined approaches for HIV prevention and recommend social and structural changes for effective outcomes.[ 32 ] Stratified, region-specific interventions based on geographic risk structure and combination approaches are recommended to prevent HIV transmission among PWID.
5. Conclusion
Ensuring adherence to safe injecting and safe sexual practices under the influence of drugs by PWID is a significant challenge for HIV prevention and control measures. Addressing this situation through intensified, decentralized, region-specific strategies with high coverage and outreach are recommended for optimal and effective program response.
6. Limitations
IBBS does not include any female PWID; hence the data analyzed in this paper are limited to male PWID alone. The variables selected for this analysis were chosen to identify the behavioral risk structure; therefore, the underlying socio-demographic factors, such as age, education, age at initiation of drug use or occupation, and violence victimization, were not considered. Further, owing to the cross-sectional study design, the temporality of the risk behaviors and HIV infection could not be established.
Acknowledgments
The authors wish to thank the project directors of state AIDS control societies for their support in completing the surveillance activities on time. The authors also thank the concerned referral laboratories, state surveillance team members, and sentinel site personnel. Our special thanks to Dr Sanjay Madhav Mehendale, Former Director, and Dr Manoj Murhekar, Director of ICMR—National Institute of Epidemiology, Chennai, for their immense contribution and technical inputs towards the successful completion of the HIV surveillance.
Author contributions
Conceptualization: Santhakumar Aridoss, Elangovan Arumugam.
Data curation: Santhakumar Aridoss, Joseph K. David.
Data Analysis and Spatial mapping: Nagaraj Jaganathasamy.
Formal analysis: Nagaraj Jaganathasamy.
Funding acquisition: Elangovan Arumugam.
Investigation: Santhakumar Aridoss, Joseph K. David, Nagaraj Jaganathasamy, Ganesh Balasubramanian, Shobini Rajan, Elangovan Arumugam.
Methodology: Santhakumar Aridoss, Pradeep Kumar, Elangovan Arumugam.
Project administration: Santhakumar Aridoss, Pradeep Kumar, Shobini Rajan, Elangovan Arumugam.
Resources: Santhakumar Aridoss, Manikandan Natesan, Elangovan Arumugam.
Software: Shobini Rajan.
Supervision: Santhakumar Aridoss, Joseph K. David, Ganesh Balasubramanian, Manikandan Natesan, Pradeep Kumar, Shobini Rajan, Elangovan Arumugam.
Validation: Santhakumar Aridoss, Nagaraj Jaganathasamy.
Visualization: Nagaraj Jaganathasamy, Padmapriya V.M.
Writing – original draft: Malathi Mathiyazhakan, Elangovan Arumugam.
Writing – review & editing: Santhakumar Aridoss, Pradeep Kumar, Shobini Rajan, Ganesh Balasubramanian, Manikandan Natesan, Padmapriya V.M, Elangovan Arumugam.
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