Population Size Estimation for Key Populations in Mongolia. Dr. Tobi J Saidel Dr. Bulbul Aumakhan. May

Transcription

1 Population Size Estimation for Key Populations in Mongolia Dr. Tobi J Saidel Dr. Bulbul Aumakhan May

2 Contents Tables... 3 Introduction... 4 Background on Mongolian HIV Epidemic... 4 Data Gaps... 4 Key Populations Context... 5 Objectives... 5 Context of the Size Estimation Exercise... 5 Direct Estimation Using the Multiplier Method... 6 Assumptions of the Multiplier Method... 7 Service-based Multipliers... 7 Unique Object Multipliers... 7 Process of Developing Local and National Size Estimates... 9 Results MSM MSM in Ulaanbataar MSM in Darkhan and Orkhon National Level Female Sex Workers FSWs in Ulaanbataar FSWs in Remaining Provinces National Level Estimate Discussion Limitations How to use these size estimates for program planning FSWs MSM Recommendations for future size estimation activities A strategic framework is required Conducting an initial assessment Using data from a limited number of locations to obtain national estimates

3 Tables Table 1 Number of Unique Objects Distributed by Survey Site and Target Population... 9 Table 2 Summary of data, size estimates and population proportions for all multipliers for MSM in UB Table 3 Proportion of People Who were Part of the Source 1 Who were Recruited into the Survey: Table 4 Assessing the Biases in the Multiplier Data Table 5 Summary of Data, Size Estimates and Population Proportions for All Multipliers for MSM in Darkhan and Orkhon Table 6 National Level Extrapolated Estimates for MSM in Mongolia using the IBBS Multiplier Table 7 Summary of Data, Size Estimates and Population Proportions for All Multipliers for FSW in UB Table 8 Proportion of people included in both source 1 and source Table 9 Summary of Data, Size Estimates and Population Proportions for All Multipliers for FSW in Darkhan, Dornod, Knuvsgul and Orkhon Table 1110 Plausible Ranges of Size Estimates for Darkhan, Dornod, Khuvsgul and Orkhon Table 11 National Level Extrapolated Estimates for FSWs in Mongolia using the IBBS Multiplier

4 Introduction Background on Mongolian HIV Epidemic Mongolia is a low prevalence country with less than 0.1% of the general population estimated to be infected with HIV. The country has a population of 3 million with approximately 50% residing in the capital city of Ulaanbataar (UB). There is limited information about the size of key populations (KP) at risk of HIV in most parts of the country. Among reported AIDS cases, the ratio of infected males to females is more than 4 to 1, and a large proportion of infected males are men who have sex with men (MSM). Given the small size of the total population, and the nomadic lifestyle outside the capital city, it is assumed that the largest concentration of (MSM), and of female sex workers (FSW) is in UB. There is not a lot of information about where FSW are concentrated outside of UB, however, interventions have started to expand to a few locations where sex workers have been identified and where community based organizations exist, including Dornod, a province with 71,000 people where the level of STIs is high and there are NGOs working with female sex workers. Dornod also borders with China and Russia. There is no evidence of a group of vulnerable people who inject drugs but the proportion of infections related to MSM has been increasing over time. Data Gaps To date there have been scattered efforts to conduct size estimates, but there is no definitive approach that has been adopted by the NCCD (National Center for Communicable Disease), nor is there a report of population size estimates available from previous efforts. There was a large study of risk and vulnerability conducted in 2006 which had, as one of its specific objectives, the determination of the number and location of high risk population groups in Mongolia[1]. Such studies are thought to provide minimum estimates of lifetime prevalence of behaviors such as male to male sex, engaging in commercial sex, and recreational drug injection, but not current numbers of people reachable by programs. In 2014 Mongolia used the SPECTRUM model to estimate the number of people living with HIV in the country. Conducting this exercise requires estimates of sizes of key populations as input. Since there were no reliable estimates available, they used general assumptions (i.e. that 1% of the total male population between age 15 and 49 years are MSM, 10% of remaining men are clients of sex workers and 0.8% of women between age 15 and 49 are sex workers). These values represent the typical range in other Asian countries. Mongolia requires estimates of the size of key populations at risk of HIV to help provide a more indepth understanding of the HIV epidemic and its potential, and to help define resource needs and set program targets and priorities. Size estimates are also needed for evaluating the reach and coverage of the national program, and for improving HIV estimates and projections. Ideally, both local 4

5 estimates in places where program activities have been focused as well as national estimates are needed. Key Populations Context Male to male sex in Mongolia is still a largely hidden activity, with MSM mostly meeting privately to engage in casual sex, and avoiding gathering in public places. Overall awareness about men having sex with men is low. At the time of this exercise there were three NGOs working with MSM: Together Center, Youth for Health, and Support Center. They work mostly in UB but in recent years have been expanding to other cities. Sex work is an unrecognized profession and includes a range of very high to very low end sex workers. On the low end, sex work is thought to be on the rise in recent years because of economic difficulties in the country. Several NGOs work with sex workers including Perfect Ladies in UB and the Mongolian Red Cross. Aside from UB, NGOs working with FSW exist in Darkhan, Orkhon, and Selenge provinces. There are also a number of government run district health center based drop-in centers for sex workers managed by the National Center for Communicable Disease (NCCD) in UB and a drop-in center in Khuvsgul managed by the Khuvsgul branch of the Mongolian Red Cross branch Objectives To develop population size estimates for MSM and FSWs to use for 1) global fund application, 2) to measure coverage in places where intervention programs are running and 3) to improve availability of size estimates for SPECTRUM and other modelling exercises. Context of the Size Estimation Exercise Developing national estimates involves a multi-step process, which starts with collection of data in selected local areas, and then uses those data to extrapolate to the national level. Locations with local data collection will have more precise results for local use. To reach a national number, an extrapolation algorithm is required which relies on assumptions about the similarity between the places with and without local estimates. When local data are combined to build national level estimates, the national estimates are less precise (i.e. they have wider confidence intervals), but are generally good enough for national level purposes. For national estimation purposes, sites for local size estimation should be selected in a way that will allow for robust national estimates to be produced. Countries typically collect local size estimation data in the places where programs exist or are planned. This is logical, because the bulk of resources should be used to support local programs. However, applying these data at the national level can easily result in distorted estimates of key population sizes. It is therefore helpful to use a stratified approach when selecting locations for local size estimation. The stratified approach can help ensure that while the bulk of the resources are used to obtain data from locations in the highest risk strata, 5

6 there will at least be some data for the lower risk strata to use for extrapolation purposes. Stratified site selection for local size estimation requires that data to use as selection criteria be available ahead of time, which can be used to categorize locations into expected high, medium and low categories. In the case of Mongolia, by the time the size estimation activity was planned, the sites for direct data collection had already been selected, and they covered only areas where key populations had been identified, and where NGOs and CBOs were working. It was not possible to select locations for data collection using a stratified approach. There was also no scope for additional data collection, so it was understood that the ability to obtain national size estimates was limited. Methodology To develop direct local estimates, the country took advantage of planned IBBS surveys for MSM in 3 sites and FSWs in 5 sites. When IBBS surveys are conducted, it is relatively straightforward and lowcost to build-in data collection for the Multiplier Method (reference UNAIDS guide), which can be used to obtain size estimates for the population being surveyed, as long as certain assumptions are met. IBBS surveys were planned among MSM and FSWs in UB and a few other provinces (MSM in Darkhan and Orkhon, and FSW in Darkhan, Dornod, Orkhon and Knuvsgul). The methodology to be used for MSM was Respondent Driven Sampling and for FSWs it was Time Location Sampling (TLS). A protocol was developed for including multipliers in the planned IBBS surveys to obtain direct local estimates in those cities. Direct Estimation Using the Multiplier Method The multiplier method of population size estimation uses two overlapping data sources for the same population. Data source 1 - is a count or listing of unique key population (KP) members who accessed a particular service or received a particular object during a specific period of time (e.g. last one month or last six months). Data source 2 - is the percentage of the population that reports having accessed the service or received the object described by data source one during the same specified time period (as measured in representative sample of the same population). When all assumptions are met, a simple formula can be used to calculate the estimated size of the population. Estimated size = Source ONE / Source TWO 6

7 Assumptions of the Multiplier Method The multiplier method relies on several assumptions: Assumption 1 The two sources of data used in the multiplier must be independent of one another Assumption 2 The count or number from source 1 is accurate and includes no duplicates Assumption 3 The key population incuded in source 1 should be defined in the same way as the populaton included in source 2 Assumption 4 The survey used for source 2 reaches a representative sample of the key population of interest A combination of service-based and unique object multipliers were used. Multiplier questions to be added to the IBBS questionnaire for the service-based multipliers were formulated on the basis of discussions with NGOs working with MSM and FSWs in the locations where the survey would take place. This required travelling to the different sites, discussing with project administrators, and reviewing potential data sources to ensure that there would be sufficient overlap and consistency between the two data sources (IBBS survey and listings from the NGOs). Service-based Multipliers The service-based multipliers used for the MSM survey included: # tested for HIV in the last 12 months at specific sites # attending Ms. Beauty social event in December 2013 # participating in the MSM Forum held in November, 2013 # enrolled in the HBV, HCV, HIV and syphilis research study funded by the Government of Japan at the Together NGO center ( Japanese study) The service-based multipliers for the FSW survey included # using services at specific drop-in centers during the four month period between Sept 1 and Dec 31, 2013 # tested for HIV or STIs at specific sites during the four month period between Sept 1 and Dec 31, 2013 Unique Object Multipliers For unique object multipliers, MSM and FSW community members and NGOs were consulted and customized objects were designed. These included a special handkerchief with an AIDS red ribbon symbol attached for MSM and neck scarves with red ribbon symbols for FSWs. See pictures below: 7

8 Unique object distributed among MSM Unique object distributed among FSWs 8

9 Unique objects for MSM were distributed through four MSM NGOs, survey staff, NCCD clinical providers, and other people with ties to community. Unique objects for FSW were distributed through outreach workers and staff of 5 drop-in centers in UB, outreach workers of Perfect Ladies NGO in UB and other NGOs in provincial survey sites. Individuals distributing unique objects were encouraged to distribute as widely as possible, at different locations/venues at different times, and not to distribute more than 10 at any one place. The aim was to distribute unique objects to around 10% of MSM and 10% of FSWs. Assuming that roughly 1% of adult males between years of age are MSM and 0.8% of females in the same age group are FSW, the desired number to be distributed was 420 for FSWs and 280 for MSM. The actual number distributed by site is shown in Table 1. Table 1 Number of Unique Objects Distributed by Survey Site and Target Population Survey site FSW MSM Given out Distributed Returned Given out Distributed Returned Ulaanbaatar Orkhon Darkhan Dornod NA NA NA Khuvsgul NA NA NA Total A question about whether the respondent had received the unique object in the last 3 months was included in the survey. Process of Developing Local and National Size Estimates Step 1 - Once data collection is complete, calculate point estimates with upper and lower bounds for each multiplier Step 2 - Assess bias of both data sources (source 1 and source 2) and the implications for over or under estimation Step 3 - Finalize plausible ranges for local size estimates for each group at each site. Step 4 - Develop strata for extrapolation purposes (post-hoc) Step 5 - Decide which data to use for extrapolation within each stratum Step 6 - Sum estimates across all strata to arrive at national estimate For the estimation of MSM population size percentages of the survey sample that reported having accessed the service or received the unique object were converted into RDS-weighted population proportions and bootstrap estimated confidence intervals were constructed to obtain upper and lower bounds for each multiplier. 9

10 No weighted population proportions were estimated from FSW survey as sampling and enumeration data from time location sampling that would have allowed calculation of sampling weights and construction of confidence intervals were not available. Hence, confidence intervals were obtained as per simple random sampling strategy. Analyses were conducted in STATA statistical software (STATA/IC v. 12.1, Stata Corporation, College Station, Texas, USA). 10

11 Results MSM MSM in Ulaanbataar Step 1 Calculated Point Estimates and Confidence Intervals for MSM in UB Using Different Multipliers Calculation of size estimates using all multipliers included in the IBBS survey of MSM in UB produced the results in Table 2. The range of size estimates calculated by all multipliers ranged from 320 to Table 2 Summary of data, size estimates and population proportions for all multipliers for MSM in UB NGO Source 1 (count) Source Size Estimate 2 (survey ) % Lower Upper Point Estimate Year Old Male Population Proportion Lower Upper Point Lower Upper 1 Together NGO testing Japanese Study Japanese Study corrected * 3 Ms Beauty MSM Forum Unique Object * Most plausible estimate Step 2 Assessment of Bias of Both Data Sources in UB Potential Sources of Bias in Data Source 1 1) The count of 374 from Together NGO testing site included duplicates (the degree of duplication is unknown) and resulted in an overestimate of the size. 11

12 2) In the Japanese study, unique identifier codes (UIC) were available which allowed the study team to assess the quality of the multiplier data. It was found that out of 86 participants who reported that they were enrolled in the Japanese study, only 50 matched on their UIC codes, suggesting that the Japanese study multiplier may have been too high. This could have resulted if respondents were confused between the study and other activities they were involved with at the Together NGO. An overestimate of the proportion of people who reported being enrolled in the Japanese study would have led to a potential underestimate in the size of the population. Potential Sources of Bias in Data Source 2 1) Table 3 suggests that the RDS survey may have captured a subset of the MSM population that did not represent the larger group (i.e. there is evidence that the survey sample was biased). This is evident because of the high degree of overlap between people included in both source 1 and 2. This type of overlap is unlikely to have happened randomly unless the population size is so small that a large proportion of people could be expected to be included in both sources. Barring that possibility, these overlaps suggest that a) the survey is biased, and b) the two data sources are not independent. Violation of the assumption of independence in this way can lead to an underestimate in the estimated size of the population 1. The potential bias is especially pronounced for the Ms. Beauty multiplier, where three quarters of the MSM who were at Ms. Beauty also participated in the survey. There is also a large degree of overlap in the MSM forum multiplier (60%). These overlaps are summarized in Table 3. Table 3 Proportion of People Who were Part of the Source 1 Who were Recruited into the Survey: # in Source 1 # included in survey who were also in Source 1 % included in both Source 1 and 2 Together NGO testing % Japanese Study % Ms Beauty % MSM Forum % Unique Object % Special Multiplier Unique Identifier Code 1 This type of bias is systematic and there is no way to correct for it through confidence intervals 12

13 An unusual feature of the Mongolia study was the availability of unique identifier codes available from both Source 1 and Source 2. The unique identifier codes were collected as part of the IBBS survey and the same codes were available from the Japanese study. Matching these numbers suggested that the proportion reporting participation in the Japanese study in the IBBS survey was higher than the reality. Using the actual data (matches on UIC) instead of the reported data resulted in a 46% reduction in the estimated size using that multiplier. The potential biases in the sources of the multiplier data can be seen in Table 4. Table 4 Assessing the Biases in the Multiplier Data Limitation Direction of Bias Violation of assumption of independence of sources 1 and 2 (large overlap) Questionable quality of the multiplier data Together NGO duplicates Participation in Japanese Study over-reported Unique object multiplier Underestimate Underestimate Underestimate Unknown quality Step 3 Finalization of Plausible Ranges for Local Size Estimates for UB Of the five multipliers, the Ms. Beauty and MSM forum were eliminated because they have the highest degree of overlap with the survey which suggests potential violation of the independence assumption and thus, estimates from those multipliers would be biased downward. The Japanese study multiplier which made use of the UIC was seen as being the most reliable. Once the data were corrected using the UIC multiplier, the point estimate was 1580 for UB with lower and upper limits of 1030 to Using the population size for males age in UB, this corresponds to a population based point prevalence of 0.5% (C.I. = 0.3% to 0.97%) for UB (see Table 2). 13

14 MSM in Darkhan and Orkhon Step 1 Calculated Point Estimates and Confidence Intervals for MSM in Darkhan and Orkhon Using Different Multipliers In Darkhan and Orkhon there was one service-based multiplier; portion of men tested through mobile VCT, plus the unique-object multiplier. The results are shown in Table 5. Table 5 Summary of Data, Size Estimates and Population Proportions for All Multipliers for MSM in Darkhan and Orkhon Multiplier City Source 1 (count) Source 2 (survey ) % Lower Upper Point Estimate Size Estimate Lower Upper Point Estimate % Population Proportion Lower Upper Darkhan % 28 Mobile VCT Darkhan corrected Orkhan Orkhon corrected % % % 0.40% -1.5 Unique Object Darkhan % Orkhon % Step 2 Assessment of Bias of Both Data Sources in Darkhan and Orkhon Source 1 consisted of a count of the number of MSM who had been tested at the mobile VCT. However, this multiplier may not have been very precise because some of the people tested and included in the count, may have come from nearby areas, outside Darkhan. In addition, the selfreported data on being tested at the mobile site had some inaccuracies, which were evident because of the availability of UIC codes. It was found that 11 respondents matched on the UIC code in both data sources, but only 7 of them reported having been tested at the mobile clinic. Among the 7 people who reported having been tested at the mobile clinic, only 2 had UIC codes that matched what was on the list. For Source 2, the main issue (for both Orkhon and Darkhan) was reliability of the RDS survey. The sample sizes reached in both places was very small (only 30 in each city). This is not a large enough sample to obtain a reliable estimate. In addition, field reports indicate that there were significant 14

15 difficulties in finding or reaching MSM in Darkhan and particularly, in Orkhon such that RDS methodology was not always closely followed, which adds to the data quality issues. Step 3 Finalization of Plausible Ranges for Local Size Estimates for Darkhan and Orkhon Given this combination of data quality issues, the numbers generated by the multiplier data in Darkhan must be seen as being very rough. The Upper limit on the mobile VCT size estimate is very skewed (7500). However, once the multiplier information is corrected using the known UIC codes, a more reasonable corrected value is possible. Using the corrected multiplier data for mobile VCT data for Darkhan, the estimated range is 0.28% to 0.97% with a point estimate of 0.48%. The data for Orkhon was considered unreliable, because of small sample sizes, negative confidence bounds, and reported problems with recruitment. The data are therefore not used for local estimate in Orkhon. National Level The approach for converting the local size estimates from UB and Darkhan into national size estimates was to develop high, medium and low strata. A number of criteria were used to create the strata including population size, STI rates and AIDS case rates. Provinces were ranked and sorted according to these criteria, and then divided as follows: Strata 1 UB by itself Strata 2 Darkhan, Khuvsgul, Dornod and Dornogobi Strata 3 All remaining provinces The population proportion from the multiplier in UB was used for Strata 1 and the population proportion for Darkhan was applied to all provinces in Strata 2. As it turned out, the population proportions and the ranges came out nearly equal for both places. In the absence of any other data, 50% of the UB and Darkhan value was applied to strata 3 to obtain crude national estimates Following these extrapolation rules, the final local estimates for UB and Darkhan, and a crude estimate for the national level can be found in Table 6. 15

16 Table 6 National Level Extrapolated Estimates for MSM in Mongolia using the IBBS Multiplier Point Estimate Range Population Range Proportion UB 1580 (1030 to 3386) 0.5% (0.3% to 0.97 %) Darkhan 128 (74 to 248) 0.48% (0.28% to 0.93%) National Level 3118 (1861, 6047) 0.4% (0.2% to 0.8%) Female Sex Workers The approach for local size estimation for FSWs in Mongolia was similar to that of MSM, in that a multiplier approach from IBBS surveys was used to obtain direct size estimates. However, the multiplier surveys used time location sampling. So they captured only venue-based sex-workers, i.e. those who work at hotels, saunas, massage parlors or on the street. Women who solicit clients through escort services or via phone, pimp or taxi driver were not included in the survey. So the FSW surveys captured a more narrow subset of FSWs (as compared to the MSM survey which used RDS, and captured a broader subset). FSWs in Ulaanbataar Step 1 Calculated Point Estimates and Confidence Intervals for FSWs in UB Using Different Multipliers Calculation of size estimates using all multipliers included in the IBBS survey of FSWs in UB produced the results in Table 7. 16

17 Table 7 Summary of Data, Size Estimates and Population Proportions for All Multipliers for FSW in UB NGO 1 Perfect ladies 2 Unique Object 3 Drop in center Source 1 (count) Source 2 (survey ) Size Estimate Population Proportion % Lower Upper Point Estimate Lower Upper Point Lower Upper The size estimates for female ex workers was similar for all three multipliers ranging between 0.2% on the low end and 0.5% on the high end. The point estimates range between 878 and Step 2 Assessment of Bias in Both Data Sources in UB There is some concern about the independence between the Source 1 and Source 2 because of the high overlap (excluding Khuvsgul) from 21% to 77%, of women who were included in both data sources (see Table 8). Table 8 Proportion of people included in both source 1 and source 2 # in Source 1 # included in survey who were also in Source 1 % included in both Source 1 and 2 Drop in center in Ulaanbaatar % Ulaanbaatar Perfect Ladies % Darkhan Faith, Trust and Union (Itgel, Naidvar, Kholboo) % Orkhon - Tus dem % Khuvsgul -Red Cross operated drop in center % Dornod- Eruul bolomj % Dornod - LEOS % This is unlikely to have happened by chance alone. If the survey differentially included more women who had used the NGO services, or who had received a unique object, this could have caused the size estimate to be too low. Indeed, over the course of the survey, difficulties in finding and recruiting 17

18 FSW at the venues were reported. To reach the target sample size (n=357 in UB), a special sampling event (training activity) was organized by Perfect Ladies NGO in UB where 50 FSWs were recruited into the survey by invitation. So this is further evidence about the reasons for bias in the survey. Step 3 Finalization of Plausible Ranges for Local Size Estimates in UB After discussion, it was decided to use the range of 0.2% to 0.5% of the female population age as the most plausible direct size estimate from the multiplier data. This translates to a number between 786 and 1964 for UB. It is important to bear in mind however that this number represents only venue-based sex workers, so it is likely to underestimate the total number of sex workers in UB. FSWs in Remaining Provinces Step 1 Calculated Point Estimates and Confidence Intervals for FSWs in UB Using Different Multipliers Point estimates in Darkhan, Dornod, Khuvsgul and Orkhon were calculated in a similar fashion as in UB, using both the service-based and the unique object multipliers. The results are summarized in 18

19 Table 9 Summary of Data, Size Estimates and Population Proportions for All Multipliers for FSW in Darkhan, Dornod, Knuvsgul and Orkhon NGO Source 1 (count) Source 2 (survey ) % Lower Upper Point Estimate Size Estimate Lower Upper Point % Population Proportion Lower Darkhan "Itgel, Naidvar, Kholboo" Unique Object Dornod "Eruul bolomj" "LEOS" Unique Object Khuvsgul "Red Cross % NA NA NA NA NA NA NA NA Drop in" Unique Object Orkhon "Tus dem" Unique Object Step 2 Assessment of Bias in Both Data Sources in Darkhan, Dornod, Khuvsgul and Orkhon Upper The main issue with the IBBS surveys in these four cities is that the sample sizes were quite small. The planned sample sizes for these cities were 126, 88, 79, and 101 respectively. With such small sample sizes, it is difficult to obtain reliable information. However, the populations are relatively small in these cities, so even a small number of sex workers can account for a high proportion of the women. This is particularly of note in Orkhon, where the figures indicate that between 1-2% of women age are FSWs. It should be also noted that due to lack of sampling and enumeration data population size estimate and its bounds were not weighted which limits the interpretation of data. 19

20 Step 3 Finalization of Plausible Ranges for Local Size Estimates for Darkhan, Dornod, Khuvsgul and Orkhon The plausible ranges agreed upon for Darkhan, Dornod, Khuvsgul and Orkhon are indicated in Table 10. However, it should be noted that all of these cities had higher population proportions of females as FSWs than did UB, the capital city. That is likely due to the extreme differences in the size of the population of UB vs other cities in the country. Virtually half of the population lives in UB, and the rest of the urban centers are quite small. Table 10 Plausible Ranges of Size Estimates for Darkhan, Dornod, Khuvsgul and Orkhon Point Estimate Range Population Proportion Range Darkhan 154 (112, 196) 0.55% (0.4% to 0.7%) Dornod 170 (85, 297) 0.8% (0.4% to 1.4%) Khuvsgul 215 (107, 358) 0.6% (0.3% to 1%) Orkhon 365 (241, 576) 1.3% (0.9% to 2.1%) National Level Estimate The approach for obtaining the national estimate was to add up the numbers from the five cities where direct size estimates were available, and to assume that the proportion of FSWs in remaining provinces was half that of UB. This was an arbitrary decision, intended to provide a crude estimates for the country, because there were no data available upon which to base an extrapolation. However, given that the population proportions were higher outside of UB then inside UB, the more conservative option was to take 50% of the UB proportion rather than 50% of the proportion in one of the smaller provinces. 20

21 Table 11 National Level Extrapolated Estimates for FSWs in Mongolia using the IBBS Multiplier Point Estimate Range Population Proportion Range UB 1178 (786, 1964) 0.30% (0.2% to 0.5%) Darkhan 154 (112, 196) 0.55% (0.4% to 0.7%) Dornod 170 (85, 297) 0.8% (0.4% to 1.4%) Khuvsgul 215 (107, 358) 0.6% (0.3% to 1%) Orkhon 357 (247, 576) 1.3% (0.9% to 2.1%) National Level 2673 (1725, 4358) 0.32% (0.2% to 0.5%) The final extrapolated value at the national level for venue-based sex workers, using the multiplier method ranged from 0.2% to 0.5% of females age These proportions represent the most visible sex workers and do not include non-venue based sex workers such as high-end call girls or women who work through phone, pimp or taxi, unless those women also solicit at venues. Discussion The size estimation exercise in Mongolia was able to take advantage of planned IBBS surveys in locations where HIV programs were ongoing for FSWs and MSM, to obtain data for size estimation purposes. IBBS surveys were implemented in the capital city for both MSM and FSWs, and in 2 other cities for MSM and 4 other cities for FSWs. Limitations The IBBS surveys were done using Respondent Driven Sampling for MSM and Time Location Sampling for FSWs. The major limitations for FSWs were: The TLS sample captured venue based sex workers (including hotels, saunas, massage parlors and street-locations). However it missed a substantial portion of sex workers 21

22 who do not frequent these types of venues, or who go to them only infrequently. This includes sex workers who operate as call-girls or through pimps and taxi drivers. The validity and quality of data for service-based multipliers varied between programs. Varied and limited documentation, lack of standardized recording and registration of women using services by different programs made it challenging to link the program data with the data in central database (the Global Fund database) to compare, verify the records and de-duplicate if necessary. It is possible that service-based multipliers may not have been well-matched to the survey population if they were not limited to venue-based sex workers. Some of the multipliers referred to events that took place several months before the survey, so to the extent that there was turnover in the population of venue-based sex workers (people moving in and out over time), this would have added to the mismatch. There was evidence suggesting that the TLS survey may have been more likely to include sex workers who were likely to be included in the service-based multiplier than those who were not. This violates the assumption of independence and can result in underestimated size estimates. Sample sizes in the cities outside of UB were very small, so the confidence interval around the proportions used to calculate the size estimates in those places was very wide. It was not possible to perform cluster analysis on the TLS data, so the actual confidence intervals are most likely wider than what the calculations indicate. Data to inform an algorithm for national level extrapolation were missing because the survey was limited to a few cities where services were being provided. For MSM the limitations included: Lack of independence between the survey data and the multiplier data (similar to what was described for FSWs). Small sample sizes outside of UB (similar to what was described for FSWs) Data to inform national level algorithm missing (similar to what was described for FSWs) How to use these size estimates for program planning FSWs These estimates reflect current venue-based sex workers in the geographic areas covered by the survey. The numbers do not take the following factors into account: 22

23 Turnover (women who move in and out of the sex worker population over time) Less frequent sex work (women who go to venues less often) Non-venue based sex workers (women who do not sell sex from venues) For program target-setting and modelling purposes the following factors should be considered and adjustments made as necessary These data can be used to reflect the number of FSWs who can be reached with services such as outreach or distribution of commodities (such as condoms), during a short period of time (e.g. during the course of a few weeks or a month). For services that are required on an intermittent basis (such as HIV and STI testing), the numbers of individuals to be reached over time should reflect turnover and frequency. If the program aims to reach non venue-based sex workers, an inflation factor is required to estimate the proportion of women who do not frequent venues. If the program is measuring annual coverage, an inflation factor should be applied to account for the total number of individuals who may benefit from the program over the course of a year, which will exceed the number that can be reached at any point in time. Likewise if the estimates are to be used for modelling HIV incidence, an inflation factor reflecting turnover should also be considered, to account for the total number of people who will potentially be at risk for acquiring and transmitting HIV throughout the course of the year. Ideally, adjustments for frequency, turnover, and less visible population members should be based on data specific to the local areas to which they are being applied. Triangulation of the estimates with other available data such as previous mapping data, program outreach data, and police records may be useful for helping to inform the adjustments. MSM For MSM, since RDS sampling was used, the size estimate covers a broader subset of the population, potentially including the less visible subset, and also those who engage in risky behavior less frequently. The same issues therefore do not apply. However, the possibility that those participating in the survey are more likely to be a more active subset of the population should be considered. Recommendations for future size estimation activities A strategic framework is required Data for population size estimates is produced from data that are generally collected for other purposes, such as mapping for program planning, or integrated bio-behavioral surveys for surveillance 23

24 or evaluation purposes. These activities are resource intensive, and they are usually implemented in a limited fashion that does not always meet all size estimation needs. For this reason, it is important for countries to have a strategic framework for size estimation that builds on existing data collection, complimenting it with additional data collection as needed, to provide a more complete picture. Conducting an initial assessment When planning HIV programs for key populations, it is important to be strategic about which data to collect where. The first step is to understand which populations are most at-risk and affected by the epidemic, and to identify the geographic locations with the largest concentrations of those populations. To achieve this, it is helpful to conduct an initial assessment in all areas of the country (i.e. at the province or district level) to pinpoint the locations where further action is needed. This assessment can be based on a review of existing information, plus key informant interviews or onsite visits that provide general information on: 1) The presence of FSWs and high risk MSM o If possible, it is very helpful to obtain rough estimates of size (e.g. < 100, , > 1000) to help categorize locations for further action 2) Predisposing characteristics of HIV epidemic potential, such as: o Large and/or growing number of STI infections o High numbers of reported AIDS cases or HIV infections o Anecdotal evidence of the presence of FSWs and MSM o Previous mapping data indicating concentrations of MSM and/or FSW o Predisposing factors for convergence of risk in the area o Transport hubs or halt points for truck drivers o Presence of mines o Presence of industrial hubs 3) Police records if available (e.g. # of FSWs) 4) Other (as appropriate) Once these data are compiled, areas can then be categorized for further action. The locations expected to have the largest concentrations of key populations should be prioritized for programs. These locations generally will require mapping data to help with program start-up. These mapping exercises can be used to estimate population sizes, which can be adjusted for factors like frequency, turnover and less visible portions of the population (as described above). Increasingly, mapping methods are being expanded to include internet and social media sites, as more and more key population members are using these sites to make contact with potential sexual partners. 24

25 In locations where the numbers of KPs are expected to be large (e.g. more than 1000), or where the prevalence of HIV is known to be high (e.g. greater than 5%), it can be useful to conduct surveys to measure the prevalence of HIV and risk behaviors. These surveys can also produce size estimates. However, in locations with small numbers of key populations (i.e. less than 500), such surveys are not likely to produce useful data. Using data from a limited number of locations to obtain national estimates Size estimation of key populations can be a chicken and egg proposition. This is because the estimates are needed to make national (and global) level decisions about resource allocations. But the data used to make those decisions is generally not reflective of the national situation, since it is usually based on data from only a few locations with the greatest epidemic potential. The solution to this problem is to use an iterative approach, collecting a light level of information from all areas of the country, to inform a stratified approach to more heavy data collection in selected areas. The stratified approach is needed so that the majority of resources can be directed toward locations with the biggest epidemic potential, while reserving a smaller amount of resources for locations with smaller epidemic potential. This combination of data from diverse epidemic zones is needed to provide robust national estimates that reflect the national situation, while at the same time providing precise data to the locations that need it the most. In Mongolia, data have already been collected in a few locations. But it is still not clear whether there are additional areas with epidemic potential. Data to inform national level extrapolation is also missing. Going forward, it will be helpful to invest some resources on conducting an initial assessment, to help the country plan in-depth data collection activities in a way that provides a more comprehensive picture of the situation throughout the country. 25