|Year : 2020 | Volume
| Issue : 5 | Page : 39-45
Size Estimation of high-risk groups for hiv infection in india based on data from national integrated bio-behavioral surveillance and targeted interventions
Elangovan Arumugam1, Boopathi Kangusamy2, Damodar Sahu3, Rajatashuvra Adhikary4, Pradeep Kumar5, Santhakumar Aridoss6
1 Scientist G, Computing and Information Science, HIV Surveillance, ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, India
2 Senior Technical Officer, HIV Surveillance, ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, India
3 Scientist E, Computing and Information Science, ICMR-National Institute of Medical Statistics, New Delhi, India
4 National Professional Officer, WHO India Country Office, New Delhi, India
5 Consultant, Strategic Information and Surveillance, National Aids Control Organization, Ministry of Health Family Welfare, Government of India, New Delhi, India
6 Scientist C, HIV Surveillance, ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, India
|Date of Submission||15-Oct-2019|
|Date of Decision||24-Jan-2020|
|Date of Acceptance||29-Feb-2020|
|Date of Web Publication||14-Apr-2020|
Dr. Santhakumar Aridoss
ICMR-National Institute of Epidemiology, R-127, 2nd Main Road, TNHB, Ayapakkam, Chennai - 600 077, Tamil Nadu
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Targeted interventions (TIs) are one of the most effective strategies to control HIV/AIDS transmission, especially among the high-risk groups (HRGs). Implementation of HIV/AIDS control strategies relies heavily on estimation of the size of HRG population. Size estimation for key populations such as female sex workers (FSWs), men who have sex with men (MSM), and injecting drug users (IDUs) is a crucial component of national HIV strategic planning. Objective: The objective of this study was to estimate the size of FSWs, MSM, and IDUs in various states of India. Methods: The program multiplier method was used to estimate the size of FSWs, MSM, and IDUs across the country using two distinct but overlapping data sources – Integrated Bio-Behavioral Surveillance and TI program from the same geographical area at the same time period. Results: In India, as on 2018–2019, there were nearly 18.2 lakhs estimated FSWs accounting to 0.53% among female population aged 15–49 years, with a highest in West Bengal (4.5 lakhs); 5.7 lakhs estimated MSM accounting to 0.16% among male population aged 15–49 years, with a highest in Gujarat (0.7 lakh); and 3.9 lakhs estimated IDUs accounting to 0.11% among male population aged 15–49 years, with a highest in Uttar Pradesh (0.5 lakh). Conclusions: The current size estimates on HRGs will support the development of projections and estimations of the HIV epidemic at national and state levels. These estimates also help in framing national guidelines such as HIV strategic planning, program design, allocation of resources, prioritizing the interventions, and monitoring and evaluation.
Keywords: Female sex workers, injecting drug user, India, men who have sex with men, program multiplier method, size estimation
|How to cite this article:|
Arumugam E, Kangusamy B, Sahu D, Adhikary R, Kumar P, Aridoss S. Size Estimation of high-risk groups for hiv infection in india based on data from national integrated bio-behavioral surveillance and targeted interventions. Indian J Public Health 2020;64, Suppl S1:39-45
|How to cite this URL:|
Arumugam E, Kangusamy B, Sahu D, Adhikary R, Kumar P, Aridoss S. Size Estimation of high-risk groups for hiv infection in india based on data from national integrated bio-behavioral surveillance and targeted interventions. Indian J Public Health [serial online] 2020 [cited 2020 Jun 3];64, Suppl S1:39-45. Available from: http://www.ijph.in/text.asp?2020/64/5/39/282415
| Introduction|| |
Estimating the size of female sex workers (FSWs), men who have sex with men (MSM), and injecting drug users (IDUs) is very important for planning and implementing HIV/STI prevention programs. It is crucial for planning, allocation of resources, epidemic projections, program management, and priority of interventions. Accurate information about the size of high-risk group (HRG) population is essential for funding, resource allocations, program monitoring, evaluation, and initiating policy changes. Globally, different types of mapping and size estimation methods are used, namely census, enumeration, population surveys, capture-recapture method,,, multiplier method,, and network scale-up method. All these methods have their own merits and limitations while estimating the HRG population who are working at different geographical locations. As the HRGs are either hidden or hard-to-reach or both, applying direct methods such as census, enumeration for mapping, and size estimation is not feasible and cost-effective, whereas applying indirect methods, such as multiplier method or capture-recapture method, needs sampling frame development. To develop sampling frame for HRG, data from multiple sources are required and it is at times difficult to obtain or not available., The Working Group of UNAIDS assessed the important methods of size estimation, and few studies have compared them for reliability.,, Unfortunately, there is no single approach or a gold standard method available to estimate the size of HRGs.
Avahan, the India AIDS Initiative, implemented a large-scale intervention to prevent HIV in India, and as a part of it, two rounds of estimation exercise of the size of HRGs were carried out, in high HIV prevalence states during 2005–2009, using multiple methods of size estimation.
In 2016, a mid-term appraisal concluded that methods, tools, and related guidelines for Mapping and Population Size Estimation (MPSE) need to be upgraded. This recommendation was reiterated in the Expert Consultation on HIV Surveillance and Estimations held in India. Further, the need to regularly update MPSE in order to facilitate strategic planning of the AIDS response costing, monitoring, reporting, and evaluation was emphasized in India's National Strategic Plan (2017–2024).
Since India has a long history of both managing TI programs and having quality data from a robust HIV surveillance system, multiplier method can be considered as one of the best tools for estimating the size of HRGs at national and subnational levels in India. In this study, we estimated the size of FSWs, MSM, and IDUs in various states of India.
| Materials and Methods|| |
The National AIDS Control Programme (NACP) recommended developing an interim working estimate for the country on priority basis using the methods adopted during the planning stage of NACP-III. For this, program multiplier method (PMM) was used to estimate the size of HRGs using data from Integrated Biological and Behavioral Surveillance (IBBS) conducted during 2014–2015 and targeted interventions (TIs) during the same period.
Program multiplier method
PMM is globally a well-accepted method,,,,, in the recent past, particularly after the publication of the last size estimation guideline by UNAIDS/WHO in 2011. However, this method requires good quality program data ( first source) as well as appropriate questions inserted into a good quality, representative survey with a well-designed survey instrument (second source). More specifically, in case of program multiplier, the first source could be a count or listing from program data (e.g., number of FSWs who visited the program STI clinic in the last month). The second source could be a representative survey among members of the population whose size is being estimated (e.g., IBBS). In the survey, each respondent could be asked whether they received the specific service.
One of the major challenges in implementing the PMM correctly is finding data for the two sources that correspond with one other. First, the population definitions must be clear and consistent. Second, the time reference period must be the same in both data sources. Third, the age range of the populations to be compared must be similar. Finally, the catchment area for the services or institutions must be clear and should ideally be the same as that covered in the subpopulation survey from which multipliers are derived.
Data source 1: Targeted intervention programs
TIs for HRGs offer a package of services (i.e., provision of risk reduction measures such as information, condoms, and treatment for STIs), which varies for each HRGs. It also aims to empower them and to enable improved negotiations and health-seeking behavior. Creating an enabling environment and community mobilization are the key programmatic strategies of TI. The total number of HRGs being serviced by all the TIs in India obtained from National AIDS Control Organization (NACO) is given in [Table 1].
|Table 1: Registered active population of female sex workers, men who have sex with men, and injecting drug users at targeted intervention (as on March 2015) and their participation in Integrated Biological and Behavioral Surveillance, 2014-2015|
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Data source 2: Integrated biological and behavioral surveillance
Over the past three decades, HIV-related surveillance in India has evolved significantly. In that line, NACO implemented the National IBBS during 2014–2015 among HRGs. The National IBBS, community-based cross-sectional survey design, using probability-based sampling, was the largest bio-behavioral study of its kind in the world. It was implemented among FSWs, MSM, and IDUs across 187 domains in 30 states/union territories (UTs) of India. Here, a “domain” is defined as a geographical unit consisting of either a single district or a combination of adjacent districts for which the bio-behavioral estimate was generated for a specific risk group.
Process of size estimation by program multiplier method
Parameters used for population size estimation
- Number of FSWs/MSM/IDUs registered with Non-Government Organizations (NGOs) s as on March 2015
- Registration with any NGOs
- Receipt of any service from the peer educator/outreach workers of the NGO/program.
Calculation of the estimated size, based on the program multiplier, is mathematically very simple. The size is derived by multiplying “number of members of a given population who attend a specific program service” of a specific geographical area over a defined period of time by “inverse of the proportion of the same population group who responded (in the survey) that they attended the same service” within the same geographic area during the same period of time.
- n is the absolute number of HRG members who accessed a specific service within a geographic area during a defined period of time
- p is the proportion of members of key populations who reported to have accessed the same service within the same geographical area during the same time period in a representative survey.
This method was used to estimate the size of FSWs from 28 states, MSM from 24 states, and IDUs from 28 states. The estimation was done for the year 2014–2015 and projected only until the current year (2018–2019) as any projection will be reliable/valid only for a short period, preferably up to a maximum of 5 years.
IBBS study was approved by NACO's Ethics Committee on July 26, 2013, Ref. No: T-11020/20/2008-NACO (R and D). Written informed consent was obtained from all the participants. Respondents were informed about their voluntary nature of participation and were given clear information regarding the risks and benefits of their participation. Consent process emphasized that the participation was voluntary, and they can withdraw from the IBBS at any point of time during the survey, which will not affect any services they used to receive from the NGOs or clinics. Participants' time spent during the survey was compensated.
| Results|| |
A total sample of 27,007 FSWs were considered for analysis across 73 domains in 28 states/UTs. The median age of FSWs across most states was between 28 and 30 years, and nationally, it was 30 years. The total number of MSM considered for analysis was 23,081 across 61 domains in 24 states/UTs. The median age of respondents was 28 years nationally and ranged between 24 and 30 years across different states. The total number of IDUs considered for analysis was 19,902 across 53 domains in 28 states. The median age of IDUs was 30 years nationally and ranged between 24 and 35 years across different states. The size was estimated for FSWs, MSM, and IDUs from the year 2015 to 2019 based on national IBBS and TI data.
In this study, the parameter “registered with NGO” is being used as a multiplier to estimate the size of high-risk population as it gives more reliable estimate when compared to the parameter “received condom from PE/ORW,” which may not be considered as most reliable because receiving condom may depend on the age, partner type, etc.
Assumption used for this estimation
- For nonsampled states/UTs, data were borrowed from neighboring states (sharing the border)
- TI programs are running only at some selected districts of state. These district figures were considered for the entire states for all purposes, and hence, a uniform distribution across the state was assumed
- 15–49 years' age group of female populations was considered for estimating the size of FSWs, and male population of the same age group was considered for estimating the size of MSM and IDUs
- Projected population for 15–49 years' age group (calculated by DemProj of Spectrum software) were considered for uniformity, compatibility, and comparability of the projection period (2015–2019)
- The same proportion of estimated HRGs among 15–49 years' age group calculated for the year 2014–2015 was continued for the remaining years.
Estimated number of female sex workers
Inclusion criteria: Women aged 15 years or more and those who engaged in consensual sex in exchange of money/payment in kind in the last 1 month.
In India, in 2019, an estimated number of nearly 1.82 million FSWs were living in the age group of 15–49 years and which was worked out to be 0.53%. West Bengal had the maximum number of FSWs contributing to nearly 25% of total estimated number of FSWs in the country, followed by Andhra Pradesh and Maharashtra [Table 2].
|Table 2: Size estimation of female sex workers (based on female sex workers registered with NGOs) for the years 2015-2019|
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Estimated number of men who have sex with men
Inclusion criteria: Men aged 15 years or more and those who had anal or oral sex with a male/hijra partner in the last 1 month.
In India, in 2019, an estimated number of nearly 0.56 million MSM were living in the age group of 15–49 years and which was worked out to be 0.16%. Gujarat had the maximum number of MSM contributing to nearly 13% of total MSM in the country, followed by Tamil Nadu and Maharashtra [Table 3].
|Table 3: Size estimation of men who have sex with men (based on men who have sex with men registered with NGOs) for the years 2015-2019|
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Estimated number of injecting drug users
Inclusion criteria: Men aged 15 years or more and those who used any psychotropic substance or drug for recreational or nonmedical reasons, through injections, at least once in the last 3 months.
In India, in 2019, an estimated number of nearly 3.9 lakh IDUs were living in the age group of 15–49 years. The maximum number of IDUs was estimated in Uttar Pradesh contributing to nearly 13% of total estimated number of IDUs in the country, followed by Delhi and Punjab [Table 4].
|Table 4: Size estimation of injecting drug users (based on injecting drug users registered with NGOs) for the years 2015-2019|
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Estimated number of high-risk groups (female sex workers, men who have sex with men, and injecting drug users)
In India, the total estimated size of all the three HRGs was around 28 lakhs in 2019. The maximum number of HRGs (4.6 lakh with 16.5%) was estimated in West Bengal, followed by Andhra Pradesh (3.2 lakh with 11.6%), Maharashtra (3.1 lakh with 11.3%), Karnataka (1.71 lakh with 6.2%), and Delhi (1.70 with 6.0%), respectively, in the year 2019. About half (51.6%) of the HRGs were living in West Bengal, Andhra Pradesh, Maharashtra, Karnataka, and Delhi. Among the estimated HRGs, 73.9% of them live in ten states/UTs.
West Bengal had the maximum number of FSWs and ranked first in all HRGs, Gujarat had the maximum number of MSM and ranked seventh overall, and Utter Pradesh had the highest number of IDUs and ranked eighth overall. The maximum number of HRG was heavily dependent on number of FSWs in the state/UT. The top six ranks of FSWs and the overall top six ranks were the same.
| Discussion|| |
The use of general population surveys to estimate the size of key populations by addition of direct questions about high-risk behaviors has been limited. Independent census and enumeration exercises are relatively straightforward but are expensive. Further, household-based sampling does not reach locations where people either solicit or engage in high-risk behaviors and thus tend to underestimate the prevalence of people engaging in high-risk behaviors. The network scaleup method is a relatively novel method that can produce population size estimates at the national and state levels. Although it is a largescale populationbased survey, it still requires some adjusting factor owing to low social visibility of HRGs.
As mentioned earlier, with the available data sources such as the listing data and the IBBS data, PMM holds to be more appropriate and cost-effective method to estimate the size of the HRG populations.
In India, the PMM was used as part of the Integrated Bio-Behavioral Assessment implemented under the Avahan Project in 2006–2007. This method was used for estimation of the size of FSWs in five districts in Maharashtra, five districts in Tamil Nadu, and three districts in Andhra Pradesh. For MSM, the same method was used in four districts in Andhra Pradesh, three districts in Tamil Nadu, and one district in Maharashtra., This PMM estimated the size of FSWs, MSM, and IDUs for all states including the nonsampled states, and it was useful for program implementation across the country.
During 2008–2009, 17 states were covered under the mapping and size estimation exercise (Assam, Bihar, Chhattisgarh, Goa, Gujarat, Jharkhand, Madhya Pradesh, Maharashtra, Manipur, Meghalaya, Nagaland, Odisha, Punjab, Rajasthan, Uttar Pradesh, Tripura, and Jammu and Kashmir). This mapping exercise was followed by a revalidation exercise conducted by the International Institute for Population Sciences, Mumbai. Over the next 2 years, few more states were covered through this direct mapping approach. Finally, the sizes from the mapping exercise across 21 states were aggregated as the national size estimates, arriving at a total of 868,000 FSWs, 412,000 MSM, and 177,000 IDUs. These estimates are used as current official estimates under NACP. While the abovementioned direct size estimates focused largely on urban and periurban areas, size estimates for rural areas were also available as a part of the mapping activities under the Link Worker Scheme (LWS). Under the LWS, the overall size estimates for HRGs were available for 54 districts across 18 states (Karnataka, Madhya Pradesh, Tamil Nadu, Andhra Pradesh, West Bengal, Nagaland, Manipur, Mizoram, Tripura, Kerala, Goa, Maharashtra, Gujarat, Bihar, Uttar Pradesh, Odisha, Rajasthan, and Chhattisgarh), and the estimates were 90,300 FSWs, 12,000 MSM, and 7900 IDUs. These estimations were carried out latest by 2012–2013 and may not be applicable for the current scenario, and hence, this current estimate becomes our need of the hour.
Although IBBS is considered as the latest behavioral survey, it has been 5 years since now. Hence, the estimation needs to be revised periodically whenever the new set of data becomes available. Further, for nonsampled states, the value of multiplier was borrowed from their neighboring states, and hence, the estimated size may differ for those states. There would be difficulty in choosing a multiplier due to TI/NGO operating services in overlapping areas. Migration and double counting are some of the major problems for any survey, which are not addressed. Similarly, due to stigma, avoidance of program services for fear of being identified might have resulted in less NGO registration.
| Conclusions|| |
Implementation of HIV/AIDS control strategies relies heavily on estimation of the size of HRG population. The current size estimates on HRGs will support the development of projections and estimations of the HIV epidemic at national and states levels in India. These estimates will also be helpful in framing appropriate national HIV strategic planning and designing, resource allocation, prioritizing the interventions, and monitoring and evaluation of the program.
The authors wish to thank the Project Directors of all the State AIDS Control Societies and Regional Institutes for their support in completing the surveillance activities in a timely manner. The authors also express their gratitude to the concerned Referral Laboratories, State Surveillance Team members, and sentinel site personnel. Our special thanks to Dr. Sanjay Madhav Mehendale, former Additional Director General, Indian Council of Medical Research, New Delhi; Prof. DCS Reddy, Former Professor and Head, Department of PSM, Banaras Hindu University, Varanasi; Dr. Arvind Pandey, Former Director, ICMR-National Institute of Medical Statistics, New Delhi; and Prof. Shashi Kant, Professor and Head, Department of Community Medicine, AIIMS, New Delhi, for their immense contribution and technical inputs toward establishing a robust HIV surveillance system in India.
Financial support and sponsorship
The first author received funding from the National AIDS Control Organization for conducting the IBBS, especially in seven southern states of India. Permission also received for authorship and publication of this article.
Conflicts of interest
There are no conflicts of interes.
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[Table 1], [Table 2], [Table 3], [Table 4]