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Indian Journal of Community Medicine: Official Publication of Indian Association of Preventive & Social Medicine logoLink to Indian Journal of Community Medicine: Official Publication of Indian Association of Preventive & Social Medicine
. 2025 Nov 10;50(Suppl 3):S436–S446. doi: 10.4103/ijcm.ijcm_877_24

Unraveling the Intricacies: Integrated and Enhanced Surveillance and Epidemiology for HIV, Syphilis, and Related Co-Infections Under the National AIDS and STD Control Programme in India

Pradeep Kumar 1,✉, Subrata Biswas 1, Nidhi Priyam 1, Lalit Singh Kharayat 1, Vishal Deo 2, Himanshu K Chaturvedi 2, Chinmoyee Das 1
PMCID: PMC12815312  PMID: 41561706

Abstract

Background:

Integrated and Enhanced Surveillance and Epidemiology (IESE) framework is a key surveillance tool for monitoring the level and trends of the human immunodeficiency virus (HIV) and related co-infections in the country.

Objectives:

This paper describes the level and trends of the human immunodeficiency virus (HIV) epidemic in the country, along with the sero-prevalence of the related infections based on the various surveillance surveys and disease burden activities undertaken under the IESE framework.

Materials and Methods:

HIV sentinel surveillance (HSS) monitors the prevalence of HIV, Syphilis, hepatitis B, and hepatitis C in eight population groups through periodic facility-based surveys. The collected data is then used in estimating the HIV burden using a globally used modeling tool. Here, we present the results from the latest round of HSS and HIV disease burden estimation to highlight epidemic status by location and population.

Results:

In 2023, adult HIV prevalence was 0.20% nationally, with a 44% drop in annual new infections and a 79% drop in acquired immune deficiency syndrome (AIDS)-related deaths since 2010. The incidence-prevalence ratio was 2.69, and the incidence-mortality ratio was 1.15 in 2023. The proportion of people living with HIV (PLHIV) aged 50+ among total PLHIV increased from 17% in 2000 to 37% in 2023. Two states and 29 districts had over 1% prevalence, with rising new infections in six states/union territories: Tripura, Arunachal Pradesh, Meghalaya, Punjab, Dadra and Nagar Haveli and Daman and Diu, and Assam. HIV prevalence ranged from 0.89% among migrants to 9.03% among people who inject drugs (PWID). Among HIV-positive individuals, syphilis sero-positivity was between 1% and 1.84%.

Conclusion:

The HIV/acquired immune deficiency syndrome (AIDS) epidemic in India remains low nationally, with new infections and AIDS-related deaths declining faster than global averages. However, certain states and key populations are experiencing a rising epidemic, potentially due to injecting drug use and casual heterosexual risk behavior. The high prevalence of co-infections necessitates integrated care to reduce morbidity and mortality among those infected.

Keywords: Epidemiology, HIV infections, People who inject drugs, substance use, sentinel surveillance

INTRODUCTION

Forty-three years ago, on June 5, 1981, the Centres for Disease Control’s (CDC) Morbidity and Mortality Weekly Report described clusters of Pneumocystis pneumonia and Kaposi sarcoma in previously healthy gay men.[1] Following the reporting of these rare diseases by the CDC bulletin, similar cases were reported from other parts of the world. Initially, the cause of this pandemic was unclear. Subsequently, a virus later named as human immunodeficiency virus (HIV) was determined to be the cause of immune deficiency in infected individuals, leading to various opportunistic infections such as Pneumocystis pneumonia.[2,3]

By 1985, the pandemic presence in Asia was confirmed.[4] Recognizing the potential public health threat of HIV, the Government of India initiated sero-surveillance for HIV in 1985 and reported the presence of HIV infection in India in April 1986.[5] Subsequently, sero-surveillance was expanded to collect samples from men and women belonging to the high-risk and vulnerable groups. To ensure monitoring the trends of the HIV infection in various population groups systematically, HIV sentinel surveillance (HSS) was first piloted in 1994 at 55 sites under National AIDS and STD Control Programme-Phase 1 (NACP I) and formalized with the implementation of the first nationwide round in 1998 at 176 sites.[6]

Over the years, annual HSS, later becoming biennial, has been supplemented by behavioral and bio-behavioral surveys in 2001, 2006, 2009, 2014–2015, and 2019.[7,8,9,10] Nonetheless, HSS remains the primary tool for tracking HIV levels and trends across various populations under the NACP. Estimates of the HIV burden are made following each HSS round to quantify the epidemic’s scale and direction.[11]

At the beginning of NACP-V, a comprehensive framework for integrated and enhanced surveillance and epidemiology (IESE) was established. This framework included the addition of hepatitis B and hepatitis C as additional biomarkers in HSS.[12] During 2021, HSS samples were tested for hepatitis B and hepatitis C across all eight population groups in collaboration with the National Programme for Surveillance of Viral Hepatitis (NPSVH). Epidemiological surveillance was further improved by strengthening program monitoring data, with a focus on recording and reporting individual-level data through an information technology (IT) -enabled information management system (IMS) called ‘SOCH’.

In this review, we describe the current epidemiology of the HIV/AIDS epidemic in India in terms of magnitude, direction, transmission route, and prevalence of related infections based on evidence generated through the IESE framework and routine program monitoring system of NACP.

MATERIALS AND METHODS

The methodology for HSS and HIV-burden estimation under the IESE framework of NACP is detailed elsewhere.[13,14,15,16,17] Briefly, HSS is conducted biennially in eight groups: pregnant women, long-distance truckers, migrants, prisoners, female sex workers (FSW), men who have sex with men (MSM), hijra/transgender (H/TG) people, and people who inject drugs (PWID). For pregnant women, migrants, and truckers, data is collected through consecutive sampling after obtaining written informed consent. For other groups, random sampling from intervention lists is used, followed by data collection with consent.

From pregnant women and prisoners, 400 serum samples per site are tested for HIV, syphilis, hepatitis B, and hepatitis C. For other groups, blood samples are collected using the dried blood spot method at 250 samples per site, tested for HIV, hepatitis B, and hepatitis C. This paper presents the burden of HIV and the prevalence of co-infections as reported by NACO and NPSVH.[13,14,15,16]

The IT-enabled IMS (SOCH) of NACP tracks a person’s data over time and at different service facilities. Newly detected HIV-positive cases are assessed on transmission routes (RoT), receive counseling on various other aspects, and are immediately started on anti-retroviral therapy as per the ‘Test and Treat’ policy. All services, including prevention, testing, and treatment, are recorded in SOCH. We used the self-reported pooled RoT data from newly detected HIV-positives, as recorded in SOCH, to describe HIV transmission dynamics by states/UT.

The IESE framework of NACP uses data from the program and epidemic monitoring to estimate HIV burden with the UNAIDS-recommended Spectrum tool. Spectrum is employed by 170 countries to assess HIV’s impact. The procedures and methodologies are elaborated in detail elsewhere.[17,18,19] In brief, this model estimates incidence trends based on demographics, treatment coverage, and HIV prevalence data. It calculates yearly size estimates for people living with HIV (PLHIV) and predicts the need for anti-retroviral (ARV) treatment to prevent vertical transmission among women living with HIV (WLHIV) aged 15–49. The latest HIV Estimates 2023 round using Spectrum 6.33 outlines India’s current and future HIV/AIDS epidemic trends, including aging aspects.

The 2023 State-specific model for HIV estimations disaggregated results by districts, treating each as a sub-epidemic.[20,21] The general population’s epidemic curve was modeled using HSS data from antenatal clinics and routine pregnant women testing, while the high-risk group’s curve used data from HSS, integrated biological and behavioral surveys, and targeted interventions. The Estimation and Projection Package Classic Platform was used for curve fitting. District-wise estimates from the Spectrum results were used to calculate relative burden and applied to the State HIV Estimations 2023.

Nine government public health institutes implement activities under the IESE framework. Institutes present their methodology and ethical considerations to ethics committees for approval before each HSS round. Data collection begins after local ethics committee approval. The HIV burden exercise uses aggregated, de-identified data from each surveillance round. The ethics committee of participating institutes approved the HSS 2021 protocol.

The descriptions of the indicators used in this paper are given below[22,23]

  1. Adult HIV prevalence: The percentage of adults (population aged 15–49) who are infected with HIV.

  2. HIV population: The total number of people who are alive and infected with HIV.

  3. Number of new HIV infections: The total number of new HIV infections each year.

  4. Incidence per 1,000: The number of new infections of all ages divided by the total uninfected population expressed in thousands.

  5. AIDS deaths: The annual number of deaths due to AIDS.

  6. AIDS mortality per thousand: The number of AIDS deaths per 1,000 population.

  7. HIV age distribution: The number of infected people, by age and sex.

  8. Incidence-prevalence ratio (IPR): Number of new infections occurring per year in a population per 100 persons living with HIV in that same population.

  9. Incidence-mortality ratio (IMR): Ratio of the number of people who become HIV infected per year to the number of all-cause mortality people among those already infected who die (from any cause) per year.

  10. Self-reported route of transmission: Proportional distribution of newly detected HIV-infected persons in various self-reported route of transmission categories.

  11. HIV prevalence by population group: The percentage of respondents in the given surveillance population groups who were infected with HIV.

  12. Syphilis sero-positivity by population group: The percentage of respondents in the given surveillance population groups who were infected with syphilis based on rapid plasma-based regimen titer of > 1:8.

Findings

HIV disease burden: Magnitude and directions

By the end of 2023, an estimated 2.54 million people were living with HIV (PLHIV) in India, with an adult prevalence rate of 0.20%. New HIV infections per 1,000 uninfected population were 0.05, totaling 68.45 thousand new cases in 2023—a 44.23% decrease since 2010 [Figure 1]. AIDS-related deaths per 1,000 population were at 0.03, with a 79.26% decline in annual AIDS-related deaths since 2010. The IPR dropped from 4.34 per 100 PLHIV in 2010 to 2.69 in 2023, while the IMR increased from 0.68 in 2010 to 1.15 in 2023. The proportion of HIV-positive individuals aged 50+ rose from 17% in 2000 to 37% in 2023, and is expected to surpass 50% by 2030, including nearly 25% aged 60+ [Figure 2].

Figure 1.

Figure 1

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HIV transition metrics, India, 1990–2030, (a) HIV incidence rate per 1,000 population, 1990-2030, (b) Reductions in annual new HIV infections, 2010-2030, (c) Incidence-prevalence ratio, 1990-2030, (d) AIDS-related mortality per 1,000 population, 1990-2030, (e) Reductions in annual AIDS-related deaths, 2010-2030, (f) Incidence-mortality ratio, 1990-2030

Figure 2.

Figure 2

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Age distribution of HIV-infected patients, 1990–2030

HIV disease burden: By States and districts

In 2023, Mizoram had the highest adult HIV prevalence at 2.73%, followed by Nagaland (1.37%) and Manipur (0.87%) [Table 1, Figure 3]. States with prevalences over 0.40% included Andhra Pradesh (0.62%), Telangana (0.44%), Meghalaya (0.43%), Karnataka (0.42%), and Punjab (0.42%). Other states exceeding the national prevalence of 0.20% were Tripura, Delhi, Goa, Maharashtra, Chandigarh, Arunachal Pradesh, Haryana, and Tamil Nadu. Maharashtra (390,000), Andhra Pradesh (320,000), and Karnataka (280,000) led in total cases. Uttar Pradesh (197,000), Tamil Nadu (169,000), Telangana (158,000), Bihar (156,000), Gujarat (120,000), and Punjab (106,000) each had over 100,000 PLHIV.

Table 1.

State/UT-wise HIV prevalence, incidence, mortality, PLHIV Size, IPR, and IMR, 2023

State/UT Adult (15–49 years) HIV prevalence (in %) PLHIV size HIV Incidence per 1,000 uninfected population New HIV infections Decline in annual new HIV infections between 2010 to 2023 (%) IPR AIDS-related mortality per 100,000 population Annual AIDS-related Deaths Decline in annual AIDS-related deaths between 2010 and 2023 (%) IMR
A&N Islands 0.16 (0.08–0.33) 513 (266–1,031) 0.05 (0.02–0.22) 20 (8–87) 53.49 3.90 2.46 (0.75–7.00) 10 (3–28) 44.44 1.67
Andhra Pradesh 0.62 (0.53–0.74) 320,222 (28,2756–368,070) 0.07 (0.04–0.11) 3,509 (2,219–5,753) 76.08 1.10 10.04 (7.15–14.89) 5,311 (3,783–7,876) 86.26 0.41
Arunachal Pradesh 0.25 (0.21–0.32) 2,465 (2,073–3,101) 0.23 (0.18–0.33) 359 (278–502) -469.84 14.56 1.40 (1.10–1.87) 22 (17–29) -214.29 12.82
Assam 0.13 (0.11–0.16) 32,031 (27,018–39,735) 0.06 (0.04–0.09) 2,021 (1,260–3,277) -22.11 6.31 0.95 (0.69–1.45) 337 (243–513) 34.94 3.95
Bihar 0.16 (0.14–0.20) 155,646 (132,714–196,039) 0.07 (0.05–0.10) 8,271 (6,096–12,456) 9.50 5.31 1.47 (1.14–1.90) 1,838 (1,435–2,386) 57.50 2.71
Chandigarh 0.26 (0.17–0.39) 2,596 (1,744–3,831) 0.07 (0.03–0.23) 81 (40–278) 53.18 3.12 1.43 (0.78–3.04) 17 (9–37) 50.00 2.45
Chhattisgarh 0.16 (0.16–0.18) 40,169 (37,653–44,526) 0.04 (0.03–0.05) 1,252 (862–1,583) 60.81 3.12 3.29 (2.53–4.45) 984 (756–1,332) 65.12 0.93
DNH&DD 0.18 (0.13–0.25) 1,752 (1,351–2,426) 0.07 (0.04–0.13) 89 (51–154) -67.92 5.08 0.97 (0.66–1.76) 12 (8–21) 79.31 4.94
Delhi 0.31 (0.25–0.38) 58,869 (47,456–71,876) 0.13 (0.09–0.19) 2,710 (1,811–3,991) 21.83 4.60 4.87 (2.13–9.15) 1,026 (449–1,929) -13.37 2.01
Goa 0.29 (0.26–0.40) 4,474 (3,962–5,864) 0.06 (0.04–0.11) 96 (67–175) 4.95 2.15 2.12 (1.52–3.64) 33 (24–57) 93.24 1.60
Gujarat 0.19 (0.17–0.22) 120,312 (110,101–138,915) 0.04 (0.03–0.06) 2,671 (1,796–4,490) 56.86 2.22 1.13 (0.77–1.68) 800 (547–1,194) 66.44 1.37
Himachal Pradesh 0.11 (0.09–0.13) 7,883 (6,626–9,024) 0.03 (0.02–0.04) 209 (144–292) 61.01 2.65 0.87 (0.62–1.21) 64 (46–90) 84.58 1.49
Haryana 0.23 (0.19–0.28) 56,578 (46,186–67,190) 0.10 (0.07–0.13) 2,898 (1,995–3,875) 24.26 5.12 1.02 (0.69–1.44) 305 (208–431) 79.36 3.74
Jharkhand 0.07 (0.06–0.09) 23,794 (18,731–28,455) 0.02 (0.01–0.03) 744 (510–1,145) 57.94 3.13 0.50 (0.30–0.79) 194 (115–309) 64.79 1.85
Jammu & Kashmir Ladakh 0.06 (0.04–0.08) 6,689 (4,846–9,128) 0.01 (0.01–0.03) 175 (90–340) 75.11 2.62 1.01 (0.48–1.96) 137 (64–264) 52.92 0.96
Karnataka 0.42 (0.36–0.48) 280,497 (246,258–314,661) 0.05 (0.03–0.06) 3,175 (2,104–4,026) 70.19 1.13 8.32 (5.71–11.69) 5,599 (3,842–7,865) 81.68 0.37
Kerala 0.07 (0.06–0.09) 24,416 (20,994–30,911) 0.01 (0.01–0.02) 325 (211–775) 74.39 1.33 0.81 (0.53–1.39) 288 (190–495) 71.54 0.63
Meghalaya 0.43 (0.37–0.55) 9,501 (7,992–12,088) 0.28 (0.20–0.45) 928 (646–1,470) -124.70 9.77 1.58 (1.13–2.34) 52 (38–78) 29.73 10.31
Maharashtra 0.29 (0.24–0.35) 389,629 (341,587–450,958) 0.04 (0.02–0.06) 4,443 (2,786–7,233) 66.69 1.14 5.94 (3.93–9.39) 7,462 (4,939–11,783) 78.59 0.40
Manipur 0.87 (0.73–1.03) 24,472 (20,978–28,363) 0.22 (0.11–0.32) 686 (356–1,030) 43.91 2.80 15.32 (10.71–21.29) 490 (342–681) 68.63 1.08
Madhya Pradesh 0.10 (0.07–0.14) 70,170 (52,556–101,535) 0.02 (0.01–0.03) 1,515 (883–2,678) 68.46 2.16 1.26 (0.88–1.82) 1,082 (751–1,562) 79.63 0.87
Mizoram 2.73 (2.18–3.32) 25,294 (20,441–31,097) 1.02 (0.70–1.44) 1,226 (840–1,727) 17.33 4.85 10.81 (6.88–16.20) 133 (84–199) 77.72 4.13
Nagaland 1.37 (1.30–1.47) 23,086 (21,764–25,191) 0.46 (0.39–0.56) 1,008 (857–1,231) 37.08 4.37 9.68 (7.58–12.98) 214 (167–287) 76.22 2.86
Odisha 0.12 (0.11–0.14) 47,508 (42,917–53,422) 0.02 (0.02–0.03) 1,135 (874–1,509) 70.17 2.39 2.07 (1.63–2.82) 955 (748–1,299) 64.79 0.79
Puducherry 0.18 (0.07–0.35) 2,793 (1,640–4,803) 0.05 (0.005–0.19) 89 (8–311) 12.75 3.19 3.19 (1.26–8.46) 52 (21–137) 73.87 1.19
Punjab 0.42 (0.40–0.44) 105,791 (98,034–119,596) 0.30 (0.26–0.36) 9,103 (8,050–10,944) -116.69 8.60 1.85 (1.30–2.81) 566 (398–857) -41.50 6.54
Rajasthan 0.12 (0.09–0.16) 82,454 (63,709–107,966) 0.04 (0.02–0.07) 3,355 (1,512–5,929) 23.07 4.07 0.55 (0.36–0.91) 442 (291–731) 75.15 2.80
Sikkim 0.11 (0.06–0.18) 605 (330–984) 0.04 (0.02–0.11) 28 (10–77) 22.22 4.63 0.76 (0.30–1.65) 5 (2–11) -25.00 3.11
Tamil Nadu 0.20 (0.17–0.25) 168,893 (143,378–200,222) 0.02 (0.01–0.05) 1,762 (1,007–3,768) 69.01 1.04 2.44 (1.66–3.69) 1,867 (1,267–2,827) 84.70 0.48
Telangana 0.44 (0.35–0.57) 158,164 (133,921–193,201) 0.08 (0.04–0.15) 2,962 (1,548–5,673) 57.98 1.87 7.44 (4.11–13.13) 2,816 (1,557–4,971) 81.69 0.68
Tripura 0.37 (0.33–0.44) 10,126 (8,766–12,112) 0.32 (0.27–0.42) 1,330 (1,093–1,722) -524.41 13.13 1.08 (0.80–1.48) 44 (33–61) -300.00 17.50
Uttarakhand 0.13 (0.09–0.17) 12,301 (9,308–15,872) 0.04 (0.03–0.06) 479 (327–734) 55.36 3.89 1.60 (0.86–2.91) 184 (99–335) 39.07 1.68
Uttar Pradesh 0.10 (0.07–0.14) 197,451 (137,546–258,777) 0.03 (0.02–0.06) 7,841 (4,124–14,955) 31.94 3.97 0.78 (0.40–1.50) 1,816 (939–3,508) 75.14 2.06
West Bengal 0.08 (0.08–0.10) 77,220 (70,552–88,518) 0.02 (0.02–0.03) 1,956 (1,509–2,766) 54.73 2.53 0.72 (0.53–1.03) 709 (524–1,015) 85.28 1.44
India 0.20 (0.17–0.25) 2,544,364 (216,7577–303,8032) 0.05 (0.03–0.08) 68,451 (45,853–107,075) 44.23 2.69 2.61 (1.77–3.95) 35,866 (24,322–54,404) 79.26 1.15
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Figure 3.

Figure 3

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India map showing district-wise HIV prevalence and PLHIV size, 2023, (a) HIV prevalence (State), (b) PHIV size (State), (c) HIV prevalence (District), (d) PLHIV size (District)

Mizoram (1.02) had the highest HIV incidence per 1,000 uninfected people, followed by Nagaland (0.46), and Tripura (0.32). Between 2010 and 2023, all states/UTs, except six-Tripura, Arunachal Pradesh, Meghalaya, Punjab, Dadra and Nagar Haveli and Daman and Diu (DNH and DD), and Assam-saw a decline in new HIV infections, with Andhra Pradesh leading at 76.1%. In 2023, the highest IPR was in Arunachal Pradesh (14.56), followed by Tripura (13.13) and Meghalaya (9.77).

In 2023, AIDS-related mortality per 1,000 was highest in Manipur (0.15), followed by Mizoram (0.11) and Andhra Pradesh (0.10). Most states saw a decrease in annual AIDS-related deaths from 2010 to 2023, except for Delhi, Sikkim, Punjab, Arunachal Pradesh, and Tripura. The highest IMR was in Tripura (17.5), followed by Arunachal Pradesh (12.82), Meghalaya (10.31), Punjab (6.54), and DNH and DD (4.94).

Twenty-nine districts had an adult HIV prevalence of 1% or more, whereas 263 districts had less than 0.10% [Figure 3]. A total of 165 districts had over 5,000 PLHIV, with 5 districts exceeding 25,000 and 43 districts between 10,000 and 25,000 PLHIV. There were 265 districts with fewer than 1,000 PLHIV. In summary, 192 districts in 22 states had either an adult prevalence of >1% prevalence or over 5,000 PLHIV. Additionally, 161 districts in 29 states/UT had either a 0.40% to <1% prevalence or 2,500 to <5,000 PLHIV. Further, 183 districts had a 0.20% - <0.40% prevalence or 1,000 - <2,500 PLHIV, while 226 districts had below 0.20% prevalence or fewer than 1,000 PLHIV.

HIV Transmission Routes

In 2023–2024, SOCH recorded transmission routes for 1,56,325 HIV-positive cases [Table 2]. Nationally, 74% of transmissions were through heterosexual contact: casual partners (32%), regular partners (27%), and commercial partners (15%). Infected needle/syringe sharing accounted for HIV transmission in 12% of newly detected cases [Figure 4].

Table 2.

State/UT-wise route of transmission as self-reported by newly diagnosed HIV-infected people, 2023

State/UT %
 Total newly diagnosed HIV- positive cases
Heterosexual: Commercial partner Heterosexual: Casual/non-commercial, non-regular partner Heterosexual (Regular partner/spouse) Homosexua/Bi sexual Through blood and Blood products Through infected syringes and Needles Parent to Child Not specified/Unknown
ANI 2 49 17 12 2 2 0 15 41
Andhra Pradesh 16 42 36 2 0 0 2 2 13,513
Arunachal Pradesh 1 7 6 4 1 77 0 3 489
Assam 7 9 9 7 1 64 1 2 6,472
Bihar 29 27 30 4 0 1 4 4 7,287
Chandigarh 8 57 11 7 1 14 2 1 673
Chha⍰sgarh 16 41 28 3 1 3 4 4 1,900
Delhi 9 35 19 6 3 24 1 3 2,509
DNH&DD 9 44 28 6 0 0 6 8 89
Goa 11 39 31 13 0 1 2 2 260
Gujarat 18 40 27 5 1 0 3 5 7,702
Haryana 10 29 20 3 2 21 2 13 6,033
Himachal Pradesh 16 38 16 5 1 17 1 5 564
J&K and Ladakh 5 49 16 6 4 8 4 9 278
Jharkhand 25 27 28 7 2 0 4 6 1,625
Karnataka 17 38 35 3 1 1 3 3 13,387
Kerala 17 28 16 24 0 7 1 6 1,235
Madhya Pradesh 16 35 32 3 1 5 4 5 6,220
Maharashtra 21 33 31 5 1 0 4 4 17,017
Manipur 11 28 32 5 0 17 5 2 709
Meghalaya 3 52 28 3 0 8 3 3 1,347
Mizoram 2 24 40 3 0 27 2 1 2,109
Nagaland 1 49 35 1 0 8 3 4 1,581
Odisha 23 36 20 10 2 1 4 3 3,245
Puducherry 13 27 39 9 0 1 1 11 159
Punjab 5 16 13 1 0 61 1 3 12,308
Rajasthan 12 32 30 2 1 6 5 12 7,048
Sikkim 24 17 19 28 0 0 0 12 58
Tamil Nadu 11 47 27 9 0 0 2 3 7,016
Telangana 13 44 33 4 1 0 3 2 8,985
Tripura 6 6 8 3 1 76 1 0 1,569
Uttar Pradesh 17 29 27 3 3 9 4 8 14,851
Uttarakhand 13 28 21 3 1 27 3 2 1,439
West Bengal 23 27 20 18 2 3 2 4 6,607
India 15 32 27 5 1 12 3 5 1,56,325
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Figure 4.

Figure 4

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State/UT-wise distribution of newly detected HIV-positive cases by self-reported route of transmission, 2023

In Bihar, 29% of new cases were from commercial partners, followed by Jharkhand (25%), Sikkim (24%), Odisha and West Bengal (23% each), and Maharashtra (21%). Casual partner transmission was high in Chandigarh (57%) and Meghalaya (52%), followed by Andaman and Nicobar Islands, Jammu and Kashmir, and Ladakh (49%). Regular partner infections were notable in Mizoram (40%), Puducherry (39%), and Andhra Pradesh (36%).

Transmission through a homosexual/bisexual partner was reported in Sikkim (28%), Kerala (24%), and West Bengal (18%). In Assam, Arunachal Pradesh, Tripura, and Punjab, 61–77% of new HIV cases in 2023–2024 were attributed to injecting drug use. Delhi, Haryana, Mizoram, and Uttarakhand reported 21% of cases linked to injecting drug use, while Chandigarh, Himachal Pradesh, and Manipur saw 14–17%.

HIV prevalence in sub-population

In 2021, HIV prevalence was 1.85% among FSW, 1.99% among prisoners, 3.26% among MSM, 3.78% among H/TG people, and 9.03% among PWID. For migrants and truckers, it was 0.89% and 1%, respectively [Figure 5]. HIV prevalence had remained high among PWID but declined nationally among FSW and MSM.

Figure 5.

Figure 5

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HIV prevalence (%) trend among various population groups

State/UT-wise, Meghalaya had the highest HIV prevalence among FSW at 10.92%, followed by Punjab (3.38%) and Karnataka (3.01%) [Supplementary Table 1]. Among MSM, Mizoram reported the highest prevalence at 12.80%, followed by Punjab (11.62%). For PWID, Mizoram had the highest prevalence at 32.08%, followed by Punjab (19.57%), Maharashtra (18.41%), Tripura (18.00%), Delhi (15.87%), Meghalaya (11.48%), and Assam (11.24%).

Supplementary Table 1.

State/UT-wise prevalence/sero-positivity of HIV and syphilis among various population groups, 2021

State/UT HIV Syphilis
n % 95% CI n % 95% CI
Pregnant Women
    Andaman & Nicobar Islands 1553 0.06 0.00-0.19 1553 0.00 0.00-0.00
    Andhra Pradesh 15400 0.37 0.27-0.47 15400 0.02 0-0.04
    Arunachal Pradesh 2976 0.00 0.00-0.00 2976 0.00 0.00-0.00
    Assam 11544 0.11 0.05-0.17 11544 0.01 0-0.03
    Bihar 12299 0.24 0.15-0.32 12299 0.00 0.00-0.00
    Chandigarh 800 0.38 0-0.8
    Chhattisgarh 10635 0.17 0.09-0.25 10635 0.36 0.2.40.047
    Dadra & Nagar 400 0.25 0-0.74 400 0.00 0.00-0.00
    Daman & Diu 800 0.00 0.00-0.00 800 0.00 0.00-0.00
    Delhi 3920 0.41 0.21-0.61
    Goa 1200 0.25 0-0.53 1200 0.00 0.00-0.00
    Gujarat 13921 0.27 0.19-0.36 8721 0.13 0.05-0.2
    Haryana 7604 0.11 0.03-0.18 7604 0.01 0-0.04
    Himachal Pradesh 3600 0.06 0-0.13 3600 0.06 0-0.13
    Jammu & Kashmir 6193 0.02 0-0.05 6193 0.00 0.00-0.00
    Jharkhand 10340 0.13 0.06-0.19 10340 0.1 0.04-0.16
    Karnataka 24705 0.29 0.22-0.36 15200 0.01 0-0.02
    Kerala 4800 0.04 0-0.1 4800 0.00 0.00-0.00
    Madhya Pradesh 20436 0.09 0.05-0.13 20436 0.22 0.15-0.28
    Maharashtra 30379 0.25 0.2-0.31 23179 0.03 0.01-0.05
    Manipur 7023 0.33 0.19-0.46 7023 0.23 0.12-0.34
    Meghalaya 4668 0.58 0.36-0.8 4668 0.77 0.52-1.02
    Mizoram 3718 1.13 0.79-1.47 3718 0.35 0.160.054
    Nagaland 4647 1.61 1.25-1.98 4647 0.71 0.47-0.95
    Odisha 13200 0.21 0.13-0.29 13200 0.1 0.04-0.15
    Puducherry 800 0.00 0.00-0.00 800 0.13 0-0.37
    Punjab 8829 0.18 0.09-0.27 8829 0.08 0.02-0.14
    Rajasthan 13998 0.14 0.07-0.2 11200 0.03 0-0.06
    Sikkim 1979 0.05 0-0.15 1979 0.25 0.03-0.47
    Tamil Nadu 32714 0.17 0.13-0.22 32714 0.09 0.06-0.12
    Telangana 11200 0.16 0.09-0.23 11200 0.13 0.06-0.19
    Tripura 2400 0.38 0.13-0.62 2400 0.13 0-0.27
    Uttar Pradesh 33870 0.17 0.13-0.22 33870 0.02 0.01-.004
    Uttarakhand 6388 0.05 0.0-0.1 6388 0.00 0.00-0.00
    West Bengal 9951 0.08 0.02-014 9951 0.03 0-0.06
    India 3.38,890 0.22 0.21-0.24 3.09,467 0.1 0.09-0.11
Prisoners
    Andhra Pradesh 800 3.25 2.02–4.48 800 0.00 0.00-0.00
    Assam 798 2.01 1.03–2.98 798 0.5 0.01-0.99
    Bihar 1196 0.00 0.00–0.00 1196 0.00 0.00-0.00
    Chandigarh 404 3.47 1.68–5.25 404 0.99 0.02-1.96
    Chhattisgarh 717 0.00 0.00–0.00 717 0.98 0.26-1.70
    Delhi 735 2.45 1.33–3.57 735 0.41 0.00-0.87
    Gujarat 1600 0.88 0.42–1.33 1600 0.00 0.00-0.00
    Haryana 799 1.63 0.75–2.50 799 0.00 0.00-0.00
    Himachal Pradesh 400 0.25 0.00–0.74 400 0.00 0.00-0.00
    Jharkhand 684 0.00 0.00–0.00 684 0.29 0.00-0.70
    Karnataka 1200 0.67 0.21–1.13 800 0.00 0.00-0.00
    Kerala 400 0.5 0.00–1.19 400 0.00 0.00-0.00
    Madhya Pradesh 1200 1.58 0.88–2.29 1200 1.08 0.50-1.67
    Maharashtra 1600 1.13 0.61–1.64 1600 0.19 0.00-0.40
    Manipur 398 2.26 0.80–3.72 398 0.00 0.00-0.00
    Mizoram 400 26.00 21.70–30.30 400 0.00 0.00-0.00
    Nagaland 196 4.59 1.66–7.52 196 3.57 0.97-6.17
    Odisha 372 0.00 0.00–0.00 372 0.00 0.00-0.00
    Punjab 1202 7.49 6.00–8.98 1202 0.75 0.26-1.24
    Rajasthan 1200 0.67 0.21–1.13 800 0.13 0.00-0.37
    Tamil Nadu 1200 0.5 0.10–0.90 1200 0.08 0.00-0.25
    Telangana 400 2.5 0.97–4.03 400 2.25 0.80-3.70
    Tripura 400 1.00 0.02–1.98 400 0.5 0.00-1.19
    Uttar Pradesh 800 0.13 0.00–0.37 800 0.25 0.00-0.60
    Uttarakhand 400 0.00 0.00–0.00 400 0.00 0.00-0.00
    West Bengal 1194 0.84 0.32–1.35 1194 0.08 0.00-0.25
    India 20695 1.93 1.75–2.12 19895 0.34 0.26-0.42
FSW
    Andhra Pradesh 3,211 1.78 1.32–2.23
    Arunachal Pradesh 750 0.27 0.00–0.64
    Assam 2,967 1.65 1.19–2.11
    Bihar 999 0.62 0.12–1.11
    Chandigarh 751 0.8 0.16–1.44
    Chhattisgarh 1,249 1.92 1.16–2.68
    Delhi 1,001 0.81 0.25–1.36
    Goa 500 0.6 0.00–1.28
    Gujarat 2,692 1.34 0.90–1.77
    Haryana 1,275 1.33 0.70–1.96
    Himachal Pradesh 1,269 0.55 0.00–1.18
    J&K and Ladakh 250 0.4 0.19–0.90
    Jharkhand 1,750 0.55 0.19–0.90
    Karnataka 5,484 3.01 2.56–3.46
    Kerala 2,490 0.44 0.18–0.70
    Madhya Pradesh 2,263 0.75 0.40–1.11
    Maharashtra 5,001 2.54 2.10–2.98
    Manipur 1,499 1.13 0.60–1.67
    Meghalaya 705 10.92 8.62–13.22
    Mizoram 130 56.15 47.62–64.68
    Nagaland 250 2.00 0.26–3.74
    Odisha 2,749 0.65 0.35–0.96
    Puducherry 1,000 0.5 0.06–0.94
    Punjab 2,487 3.38 2.67–4.09
    Rajasthan 2,002 2.75 2.03–3.46
    Sikkim 239 0.00 0.00–0.00
    Tamil Nadu 5,970 1.52 1.21–1.84
    Telangana 3,750 1.81 1.39–2.24
    Tripura 1,000 2.9 1.86–3.94
    Uttar Pradesh 2,207 1.04 0.62–1.47
    Uttarakhand 750 0.42 0.00–0.88
    West Bengal 1,491 1.27 0.70–1.84
    India 60,131 1.85 1.75–1.96
MSM
    Andhra Pradesh 1,213 2.06 1.26–2.86
    Assam 747 3.61 2.78–4.95
    Bihar 246 0.41 0.00–1.20
    Chandigarh 249 1.61 0.04–3.17
    Chhattisgarh 499 4.01 2.29–5.73
    Delhi 502 2.59 1.20–3.98
    Goa 500 2.4 1.06–3.74
    Gujarat 1,975 4.61 3.68–5.53
    Haryana 1,001 6.89 5.32–8.46
    Himachal Pradesh 257 1.56 0.04–3.07
    Jharkhand 419 6.68 4.29–9.07
    Karnataka 1,955 2.81 2.08–3.55
    Kerala 2,000 0.35 0.09–0.61
    Madhya Pradesh 1,253 1.84 1.09–2.58
    Maharashtra 1,195 4.18 3.05–5.32
    Manipur 435 9.43 6.68–12.17
    Meghalaya 88 9.09 3.08–15.10
    Mizoram 250 12.8 8.66–16.94
    Nagaland 490 3.06 1.54–4.59
    Odisha 250 1.2 0.00–2.55
    Puducherry 750 0.00 0.00–0.00
    Punjab 749 11.62 9.32–13.91
    Rajasthan 250 6.4 3.37–9.43
    Tamil Nadu 3,482 2.07 1.60–2.54
    Telangana 974 2.67 1.66–3.68
    Uttar Pradesh 1,453 1.1 0.56–1.64
    Uttarakhand 224 2.68 0.56–4.79
    West Bengal 987 4.36 3.08–5.63
    India 24,393 3.26 3.03–3.48
IDU
    Andhra Pradesh 606 1.32 0.41–2.23
    Arunachal Pradesh 250 1.6 0.04–3.16
    Assam 587 11.24 8.69–13.80
    Bihar 490 2.86 1.38–4.33
    Chandigarh 250 2.8 0.76–4.84
    Chhattisgarh 750 7.2 5.35–9.05
    Delhi 750 15.87 13.25–18.48
    Goa 250 0.00 0.00–0.00
    Gujarat 250 2 0.26–3.74
    Haryana 1,007 9.24 7.45–11.02
    Himachal Pradesh 250 4.4 1.86–6.94
    J&K and Ladakh 1,009 0.5 0.06–0.93
    Karnataka 156 0.00 0.00–0.00
    Kerala 750 0.4 0.00–0.85
    Madhya Pradesh 1,013 2.96 1.92–4.01
    Maharashtra 201 18.41 13.05–23.77
    Manipur 3,246 8.84 7.87–9.82
    Meghalaya 418 11.48 8.43–14.54
    Mizoram 1,730 32.08 29.88–34.28
    Nagaland 2,650 2.53 1.93–3.13
    Odisha 1,000 1.9 1.05–2.75
    Punjab 3,280 19.57 18.22–20.93
    Sikkim 500 0.2 0.00–0.59
    Telangana 250 0.4 0.00–1.18
    Tripura 250 18 13.24–22.76
    Uttar Pradesh 3,891 5.45 4.74–6.16
    Uttarakhand 471 9.77 7.09–12.45
    West Bengal 500 7.4 5.11–9.69
    India 26,755 9.03 8.69–9.37
H/TG people
    Andhra Pradesh 217 4.61 1.82–7.40
    Chhattisgarh 250 6 3.06–8.94
    Delhi 500 3.6 1.97–5.23
    Gujarat 250 3.6 1.29–5.91
    Karnataka 500 3.2 1.66–4.74
    Kerala 716 0.56 0.01–1.10
    Maharashtra 250 6 3.06–8.94
    Odisha 604 1.49 0.52–2.46
    Rajasthan 250 3.6 1.29–5.91
    Tamil Nadu 250 4.8 2.15–7.45
    Telangana 150 4 0.86–7.14
    Uttar Pradesh 250 3.6 1.29–5.91
    West Bengal 492 9.15 6.60–11.69
    India 4,679 3.78 3.24–4.33
SMM
    Andhra Pradesh 750 0.93 0.25–1.62
    Assam 249 3.21 1.02–5.40
    Chandigarh 500 0.4 0.00–0.95
    Chhattisgarh 250 1.2 0.00–2.55
    Delhi 265 0.75 0.00–1.80
    Gujarat 750 0.13 0.00–0.39
    Himachal Pradesh 250 0.00 0.00–0.00
    Karnataka 500 0.2 0.00–0.59
    Kerala 500 0.00 0.00–0.00
    Madhya Pradesh 258 0.78 0.00–1.85
    Maharashtra 750 0.13 0.00–0.39
    Mizoram 250 4.8 2.15–7.45
    Odisha 250 1.6 0.04–3.16
    Puducherry 250 0.00 0.00–0.00
    Punjab 499 3.01 1.51–4.50
    Rajasthan 255 0.39 0.00–1.16
    Tamil Nadu 500 0.00 0.00–0.00
    Telangana 250 0.8 0.00–1.90
    Uttar Pradesh 750 0.67 0.08–1.25
    West Bengal 250 3.2 1.02–5.38
    India 8,276 0.89 0.69–1.10
LDT
    Andhra Pradesh 500 0.6 0.00–1.28
    Assam 472 2.12 0.82–3.42
    Chhattisgarh 500 2 0.77–3.23
    Delhi 250 0.8 0.00–1.90
    Gujarat 1,000 0.3 0.00–0.64
    Jharkhand 461 0.00 0.00–0.00
    Karnataka 250 1.2 0.00–2.55
    Kerala 250 1.2 0.00–2.55
    Madhya Pradesh 255 0.00 0.00–0.00
    Maharashtra 500 0.4 0.00–0.95
    Nagaland 249 1.2 0.00–2.56
    Odisha 250 2 0.26–3.74
    Punjab 257 2.33 0.49–4.18
    Rajasthan 249 0.00 0.00–0.00
    Tamil Nadu 500 0.6 0.00–1.28
    Telangana 500 0.8 0.02–1.58
    Uttar Pradesh 1,000 0.7 0.18–1.22
    Uttarakhand 238 2.1 0.28–3.92
    West Bengal 747 2.01 1.00–3.01
    India 8,428 1 0.78–1.21
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Coinfections

In 2023, syphilis sero-positivity was 0.10% among pregnant women and 0.34% among prisoners [Table 3]. Hepatitis B virus (HBV) rates ranged from 0.85% in pregnant women to 3.09% in PWID, while HCV rates ranged from 0.29% in pregnant women to 33.41% in PWID. HIV-syphilis co-infection was 0.004% in pregnant women and 0.019% in prisoners. HIV-HBV ranged from 0.002% in pregnant women to 0.62% in PWID, and HIV-HCV from 0.003% in pregnant women to 7.45% in PWID.

Table 3.

Prevalence/Sero-positivity of HIV and related infections among various population groups, 2021

Population Groups HIV
 Syphilis
 Hep B
 Hep C
 HIV-Syphilis
 HIV-Hep B
 HIV-Hep C
n % 95% CI n % 95% CI n % 95% CI n % 95% CI n % 95% CI n % 95% CI n % 95% CI
All respondents
Pregnant Women 338,890 0.22 0.21–0.24 309,467 0.10 0.09–0.11 309,467 0.004 0.002–0.006 338,633 0.002 0.001–0.004 338,883 0.003 0.001–0.005
Migrants 8,276 0.89 0.69–1.10 - - - - - - 8,268 0.05 0.00–0.09 8,268 0.05 0.00–0.09
Truckers 8,428 1.00 0.78–1.21 - - - - - - 8,420 0.05 0.00–0.09 8,420 0.10 0.03–0.16
FSW 60,131 1.85 1.75–1.96 - - - - - - 59,686 0.04 0.02–0.06 59,686 0.17 0.13–0.20
Prisoners 20,695 1.93 1.75–2.12 19,895 0.34 0.26–0.42 19,895 0.019 0.000–0.038 20,695 0.15 0.10–0.20 20,695 1.07 0.93–1.21
MSM 24,393 3.26 3.03–3.48 - - - - - - 24,338 0.16 0.11–0.21 24,338 0.19 0.13–0.24
H/TG people 4,679 3.78 3.24–4.33 - - - - - - 4,647 0.09 0.00–0.17 4,647 0.06 0.00–0.14
PWID 26,755 9.03 8.69–9.37 - - - - - - 26,639 0.62 0.53–0.71 26,638 7.45 7.14–7.77
HIV-positive respondents
Pregnant Women - - - 681 1.84 0.81–2.88 746 1.06 0.33–1.79 746 1.28 0.45–2.12 - - - - - - - - -
Migrants - - - - - - 74 5.41 0.25–10.56 74 5.41 0.25–10.56 - - - - - - - - -
Truckers - - - - - - 84 4.76 0.21–9.32 84 9.52 3.25–15.80 - - - - - - - - -
FSW - - - - - - 1,112 2.17 1.31–3.03 1,112 9.06 7.36–10.75 - - - - - - - - -
Prisoners - - - 384 1.00 0.02–1.98 399 7.75 5.13–10.37 399 55.25 50.38–60.12 - - - - - - - - -
MSM - - - - - - 786 4.79 3.31–6.28 786 5.80 4.17–7.43 - - - - - - - - -
H/TG people - - - - - - 177 2.30 0.07–4.53 177 1.72 0.00–3.66 - - - - - - - - -
PWID - - - - - - 2,416 6.84 5.82–7.83 2,416 82.23 80.63–83.69 - - - - - - - - -
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In HIV-positive pregnant women and prisoners, the sero-positivity rate for syphilis ranged from 1% to 1.84% [Table 3]. For hepatitis B among HIV-positive individuals, the sero-positivity rate ranged from 1.06% in pregnant women to 7.75% in prisoners. Regarding hepatitis C, the sero-positivity rate varied significantly, from 1.28% in pregnant women to 82.23% among PWID.

DISCUSSION

The Government of India is implementing NACP-phase V to address the HIV/AIDS epidemic. The goal of ending the HIV epidemic as a public health threat by 2030 remains a public health priority in India. This paper presents an update on the magnitude, trends, transmission routes, and co-infections by states/UT and various population groups at a critical stage of NACP.

Adult HIV prevalence in India is 0.20%, a quarter of the global average of 0.70% and similar to the Asia-Pacific rate.[24] Trends show declines in both prevalence and incidence. Since 2010, India has seen a 44% drop in annual new infections and a 79% reduction in AIDS-related deaths. Globally, new infections have fallen by 39% and AIDS-related deaths by 51% during the same period. The declining IPR and an IMR near 1 with over 70% ART coverage indicate that India’s epidemic response is ‘proceeding in the right way’ and the epidemic size will remain stable.[23] These indicators demonstrate continued national success.

However, challenges persist with diverse epidemics across geographies. In northeastern India, Mizoram (2.73%) and Nagaland (1.37%) have HIV prevalence rates comparable to regions like the Caribbean and Central Africa. Twenty-nine districts, mostly in northeastern states, have adult HIV prevalence of 1% or more, with one exception in Karnataka. In the Asia-Pacific region, only Papua New Guinea (1.0%) and Thailand (1.1%) have adult HIV prevalence above 1%.[24]

Northeastern Indian states are facing growing HIV challenges. Initially, only Manipur, Mizoram, and Nagaland were critical.[25] Now, Meghalaya and Tripura show higher HIV prevalence than national averages, with new infections in Tripura and Arunachal Pradesh increasing rapidly. Assam also has rising cases. These states report high IMR, indicating a rapidly growing epidemic driven by injecting drug use and multi-partner and higher-risk sexual behavior.[26] This mix of high-risk activities poses significant challenges for health programs by potentially directly linking high-risk groups to low-risk sexual partners.

HIV prevalence and related risk behaviors in northern, central, and eastern India are concerning. New HIV infections in Punjab nearly doubled from 2010 to 2023. The IMR in Punjab, Haryana, Chandigarh, Delhi, Rajasthan, Uttar Pradesh, and Bihar ranges between 2 to 6.5, indicating at least two new HIV infections per death among PLHIV. These regions report higher-risk sex among males, exceeding the national average.[26] While not as high in prevalence as northeastern India, similar risk patterns necessitate vigilance and sustained tailored interventions by NACP.

In India, the HIV epidemic is most severe among key populations: FSW, MSM, H/TG people, PWID, and prisoners, who have notably higher prevalence and incidence rates. Among PWID, prevalence has surged in some states: Assam (0.69% to 11.24%), Tripura (8.55% to 18%), Mizoram (19.8% to 32%), and Punjab (12% to 19.60%). Higher rates of hepatitis B and C are also noted in these groups.[16] Comprehensive targeted interventions are crucial to eliminating AIDS as a public health threat, given the intersection of diseases and associated behaviors.

Managing comorbidities must be integral to India’s HIV epidemic response. As ART coverage and survival rates rise, the aging population of PLHIV also grows.[27] In India, the proportion of PLHIV aged 50 + is projected to increase from 17% in 2000 to 37% in 2023, and 51% by 2030. HIV raises risks for cardiovascular, renal, neurocognitive, oncological, and osteoporotic diseases, especially in older individuals.[27,28] Sero-prevalence of syphilis, hepatitis B, and hepatitis C is also higher among those with HIV. Effective management of these conditions is crucial to reducing HIV/AIDS-related morbidity and mortality under NACP in India.

Our study is based on the periodic HSS and HIV burden estimation activities under the IESE framework of NACP. Both activities under the IESE framework have some limitations. HSS is a facility-based exercise and may have characteristics different from those of the probability-based surveys in household/community settings. Further, HSS has few sites only in the private sector for pregnant women and thus may not have adequate representation of a specific socio-economic group. The limitations of HIV burden exercise using the spectrum modeling tool have been detailed elsewhere.[23,29,30] In brief, disease burden modeling tools have inherent limitations. Factors such as the quantity of surveillance data and population-based surveys affect the uncertainty bounds. Additionally, validating incidence and mortality estimates is essential, but this has not yet been performed in the Indian context.

CONCLUSION

Our study, based on evidence generated through the IESE framework under NACP, offers an in-depth overview of the status of HIV and related co-infections across various states/UTs and population groups. It outlines the continued successful AIDS response in the country while advising caution due to the current epidemic levels and trends in certain states and population groups. Additionally, it underscores the interconnected role of injecting drug use and heterosexual casual sex as potential key drivers of the epidemic in many northern and northeastern states. The study also emphasizes the need for an integrated response to the epidemic, considering the aging population of PLHIV and the higher prevalence of related infections such as syphilis, hepatitis B, and hepatitis C among PLHIV.

Ethical approval

Nine government public health institutes implement activities under the IESE framework. Institutes present their methodology and ethical considerations to ethics committees for approval before each HSS round. Data collection begins after local ethics committee approval. The HIV burden exercise uses aggregated, de-identified data from each surveillance round. The ethics committee of participating institutes approved the HSS 2021 protocol.

Conflicts of interest

There are no conflicts of interest.

Acknowledgment(s)

Integrated and enhanced surveillance and epidemiology are conducted by the National AIDS Control Organization (NACO) under the leadership of the Technical Resource Group (TRG)- Surveillance and Epidemiology (S and E), the Technical Working Group (TWG)- S and E, and the Technical Sub-Group on HIV Burden Estimations. The implementation of various activities is carried out by Regional Institutes (S and E) and State AIDS Control Societies (SACS) under NACO›s leadership. We express our gratitude to the Chairs, Co-Chairs, and Members of TRG (S and E), TWG (S and E), and the Technical Sub-Group on HIV Burden Estimations for their guidance. Additionally, we acknowledge the efforts of the focal persons from Regional Institutes (S and E), project directors (SACS), and SI officers (SACS) in ensuring the timely execution of activities within the IESE framework. The views presented here are those of the authors and do not necessarily represent that of the NACO.

Funding Statement

Nil.

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