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In using regularly collected or existing surveillance data to characterize engagement in human immunodeficiency virus (HIV) services among marginalized populations, differences in sampling methods may produce different pictures of the target population and may therefore result in different priorities for response.
The objective of this study was to use existing data to evaluate the sample distribution of eight studies of female sex workers (FSW) and men who have sex with men (MSM), who were recruited using different sampling approaches in two locations within Sub-Saharan Africa: Manzini, Swaziland and Yaoundé, Cameroon.
MSM and FSW participants were recruited using either respondent-driven sampling (RDS) or venue-based snowball sampling. Recruitment took place between 2011 and 2016. Participants at each study site were administered a face-to-face survey to assess sociodemographics, along with the prevalence of self-reported HIV status, frequency of HIV testing, stigma, and other HIV-related characteristics. Crude and RDS-adjusted prevalence estimates were calculated. Crude prevalence estimates from the venue-based snowball samples were compared with the overlap of the RDS-adjusted prevalence estimates, between both FSW and MSM in Cameroon and Swaziland.
RDS samples tended to be younger (MSM aged 18-21 years in Swaziland: 47.6% [139/310] in RDS vs 24.3% [42/173] in Snowball, in Cameroon: 47.9% [99/306] in RDS vs 20.1% [52/259] in Snowball; FSW aged 18-21 years in Swaziland 42.5% [82/325] in RDS vs 8.0% [20/249] in Snowball; in Cameroon 15.6% [75/576] in RDS vs 8.1% [25/306] in Snowball). They were less educated (MSM: primary school completed or less in Swaziland 42.6% [109/310] in RDS vs 4.0% [7/173] in Snowball, in Cameroon 46.2% [138/306] in RDS vs 14.3% [37/259] in Snowball; FSW: primary school completed or less in Swaziland 86.6% [281/325] in RDS vs 23.9% [59/247] in Snowball, in Cameroon 87.4% [520/576] in RDS vs 77.5% [238/307] in Snowball) than the snowball samples. In addition, RDS samples indicated lower exposure to HIV prevention information, less knowledge about HIV prevention, limited access to HIV prevention tools such as condoms, and less-reported frequency of sexually transmitted infections (STI) and HIV testing as compared with the venue-based samples. Findings pertaining to the level of disclosure of sexual practices and sexual practice–related stigma were mixed.
Samples generated by RDS and venue-based snowball sampling produced significantly different prevalence estimates of several important characteristics. These findings are tempered by limitations to the application of both approaches in practice. Ultimately, these findings provide further context for understanding existing surveillance data and how differences in methods of sampling can influence both the type of individuals captured and whether or not these individuals are representative of the larger target population. These data highlight the need to consider how program coverage estimates of marginalized populations are determined when characterizing the level of unmet need.
Although probability sampling is often preferred over nonprobability sampling for ensuring representativeness, there is no gold standard method for measuring the coverage of health services for marginalized populations and, consequently, the magnitude of the gaps in service coverage. This gap in characterization of health service needs is true for a number of different health conditions, including, especially, in the realm of human immunodeficiency virus (HIV). HIV key populations include gay men and other men who have sex with men (MSM), female sex workers (FSW), people who inject drugs, and transgender people. Despite much recent progress that has been made toward reducing the incidence of HIV globally, these key populations continue to bear a significant proportion of the HIV burden across high-, middle-, and low-income countries [
To get a sense of the disproportionate burden of HIV faced by these key populations, the adult prevalence (aged 15-49 years) of HIV, in Cameroon, for example, is 4.5%, with an estimated prevalence of 37% among MSM according to a respondent-driven sampling (RDS) study, and 24% among FSW in a meta-analysis [
Whereas key populations remain hard to sample in many settings, multiple recruitment strategies have been developed to improve sampling as a means of characterizing current gaps in service coverage [
Two of the most commonly used strategies for sampling hard-to-reach populations are snowball sampling and respondent-driven sampling (RDS) [
In this study, we aimed to analyze and compare existing data collected from FSW and MSM using RDS and venue-based snowball sampling approaches in two locations within Sub-Saharan Africa: Manzini, Swaziland and Yaoundé, Cameroon. We compared these samples by a number of characteristics to determine the impact of sampling methodology on inference about these populations. In particular, we examined the prevalence of sociodemographics, self-reported HIV status, frequency of HIV testing, level of experienced stigma, and other HIV-related characteristics. In comparing these characteristics across different samples, we hoped to gain a greater understanding of how sampling methodology influences the type of individuals captured and whether differences in sampling methodology result in differences in underlying target population being measured. Due to the ability of RDS to generate large numbers of recruitment waves [
These analyses represent post hoc analyses of data collected by the study investigators. Data were initially collected for HIV bio-behavioral surveillance and population size estimation of MSM and FSW populations in multiple cities in Swaziland [
In Manzini, Swaziland, we recruited 326 MSM and 325 FSW participants using RDS between July and December 2011. A total of 173 MSM and 249 FSW were recruited through snowball sampling through outreach at
We assessed participants’ level of sexual behavior–related stigma by asking them whether they had ever felt excluded from family gatherings or were afraid to seek health care because of same-sex practices or selling sex, for MSM and FSW, respectively. Disclosure of sexual practices was measured by asking participants whether they had ever told any member of their family or any health care worker that they have sex with other men or that they sell sex. Access to HIV prevention information was measured by asking MSM participants whether they had received any HIV prevention information on sex between men in the past 12 months. Among FSW, this was measured by asking whether participants received any HIV prevention information in the past 12 months. HIV-related knowledge was measured by asking all participants what type of sex carries the most sexual risk for HIV, and what type of lubricant is safest to use for vaginal or anal sex. In addition, participants were asked how difficult or easy it was to access condoms when they need them. Response options were dichotomized to “some access” or “easy access” and “difficult” or “little access.” Network size was captured through self-report. Question-and-answer responses were phrased in the same way across recruitment strategies and for both populations.
For MSM sexual risk behaviors, we assessed the participant’s reported number of male anal sex partners within the past 12 months and whether they had any condomless anal sex in the past 12 months. For FSW, we asked the participants to report their average number of clients per week. There were no comparable condom-use questions for FSW across the RDS and snowball studies. All participants were asked to report whether they had been tested for HIV or STI in the past 12 months. In addition, all participants were asked if they had ever been told by a doctor or health care provider that they have HIV.
In Yaoundé, Cameroon, 306 MSM were recruited through RDS from November 2015 to January 2016 and 576 FSW from December 2015 to February 2016. The snowball sampling studies accrued 259 MSM and 308 FSW at venues frequented by these groups between April and May 2013. Similar to Swaziland, venues were determined through key informant interviews and then leveraged as a basis for snowball sampling of key populations.
Similar to the Swaziland studies, all participants in Cameroon were administered a questionnaire that included questions about sociodemographics, self-identified sexual orientation (for MSM), sexual behavior–related stigma, disclosure of sex work to health care providers (FSW), whether any family members knew that they had sex with men (for MSM), and HIV/STI testing and diagnoses. Network size was captured through self-report. Question-and-answer responses, again, were phrased in the same way across recruitment strategy and for both populations. However, information regarding access to relevant HIV prevention information, HIV-related knowledge, ease in accessing condoms, and sexual risk behaviors were either not available or not comparable across recruitment studies and were not included in these analyses.
Detailed description of study methods.
Detailed sampling methodsa | Eligibility criteriab | |||
Seeds were recruited from local MSM- or FSW-affiliated communicated-based organizations (although not all seeds were members. Each participant was given three coupons to distribute. Sample size calculations for the initial data collection were powered based on the ability to estimate local HIV prevalence of MSM and FSW, and the required sample sizes were achieved. | Possess valid recruitment coupon | |||
MSM | 4 of 5 seeds successfully accrued 326 men through up to 14 waves. A few participants (n=16) could not be traded back to valid sees and were excluded post-hoc. | Report receptive or insertive anal sex with another man for the last 12 months | ||
FSW | 10 of 14 seeds successfully recruited 325 FSW through up to 7 waves | Report exchanging sex for money, favors, or goods in last 12 months | ||
MSM (November 2015 - January 2016) | 2 of 4 MSM seeds successfully accrued 306 men through up to 12 waves | Report receptive or insertive anal sex with another man for the last 12 months | ||
FSW (December 2015 - February 2016) | 2 of 3 seeds successfully recruited 576 FSW through up to 22 waves | Report more than half income in the past 12 months from sex work | ||
Participants were recruited through snowball sampling at “hotspot” venues. MSM and FSW venues were visited by study staff, and participants were approached at each of the venues to provide informed written consent and completed the interviewer-administered survey. In many locations, venue managers or key community leaders acted as key informants and assisted staff in identifying MSM and FSW and facilitating introductions. | ||||
A modified version of the PLACE method [ |
||||
MSM | 173 MSM were recruited. 7 unique venues in Manzini/Matsapha were mapped and 2 were sampled from. | Report receptive or insertive anal sex with another man for the last 12 months | ||
FSW | 249 FSW were recruited. 12 venues were mapped and 2 were sampled from. | Report exchanging sex for money, favors, or goods in last 12 months | ||
MSM | 259 MSM were recruited. 4 unique venues were mapped and 3 were sampled from. | Report receptive or insertive anal sex with another man for the last 12 months | ||
FSW | 308 FSW were recruited. 15 venues were both mapped and sampled from. | Report more than half income in the past 12 months from sex work |
aAll surveys were conducted using face-to-face interviews in private locations.
bAll participants had to be at least 18 years of age.
cRecruiters were additionally compensated the equivalent of up to US $2 for each participant they recruited into the study.
dParticipants were reimbursed for their time and travel to the study site dependent on distance traveled to and from the study site.
eParticipants were reimbursed for the average cost of a meal and transportation (~US $5).
Data collection was approved by the Swaziland Ministry of Health and Scientific Ethics and the Cameroon National Ethics Committee. All studies were also approved by the institutional review board of the Johns Hopkins School of Public Health.
Crude prevalence estimates were generated for each sample; additionally, 95% CIs were generated for RDS samples. For the RDS samples, RDS-adjusted prevalence estimates (RDS-I estimator) and 95% CIs were created using Respondent-Driven Sampling Analysis Tool (RDSAT Version 7.0, Cornell University, Ithaca, New York). To assess whether or not it was likely that RDS samples had become independent from seed selection, we reported the wave number at which equilibrium was reached. Equilibrium was measured by calculating the cumulative sample proportions of a variable at each wave. When the proportions came within 2% of the final sample proportions at a particular wave, and did not fluctuate more than 2% during the sampling of additional waves, then that wave was considered the
In Manzini, Swaziland, MSM who were recruited using venue-based snowball sampling in 2014, as compared with those recruited through RDS in 2011, tended to be older than 26 years, less likely to belong to the youngest age group (18-21 years), more highly educated, more likely to be employed, less likely to be a student, and less likely to be single/never married (
FSW in Manzini, Swaziland, who were recruited using venue-based snowball sampling in 2014 as compared with RDS in 2011 were older and more highly educated (
Among MSM in Yaoundé, Cameroon, compared with those recruited by RDS in 2015-2016, those recruited using venue-based snowball sampling in 2013 tended to be less likely to belong to the youngest (18-21 year) age group, were less likely to have completed only up to a primary school education, were more likely to have completed secondary school/high school, were more likely to be employed, and were less likely to be a student (
For FSW in Yaoundé, Cameroon, those who were recruited using snowball sampling in 2013 as compared with RDS in 2015-2016 were less likely to belong to the youngest age group (18-21 years), were more likely to be married, were less likely to have completed only a primary school education, and were more likely to have completed secondary school or high school (
Crude and respondent-driven sampling (RDS)-adjusted prevalence estimates from men who have sex with men (MSM) recruited using snowball sampling and RDS methods in Manzini, Swaziland.
Characteristics | Snowball | RDS (N=310) | |||
n (%) | n (%) | Adjusted % |
Equilibrium wavea | ||
18-21 | 42 (24.3) | 139 (44.8) | 47.6 (39.2-57.0) | 5 | |
22-25 | 54 (31.2) | 94 (30.3) | 29.6 (22.4-36.9) | 5 | |
26+ | 77 (44.5) | 77 (24.8) | 22.8 (16.2-30.0) | 7 | |
Primary school or lower | 7 (4.0) | 109 (35.2) | 42.6 (33.3-51.4) | 4 | |
Secondary school/high school | 88 (50.9) | 133 (42.9) | 42.4 (34.5-50.5) | 9 | |
More than high school | 84 (45.1) | 68 (21.9) | 15.0 (10.0-21.0) | 4 | |
Unemployed | 61 (35.3) | 96 (32.3) | 29.2 (22.3-36.1) | 7 | |
Student | 28 (16.2) | 101 (34.0) | 40.0 (30.1-47.7) | 7 | |
Employed | 84 (48.6) | 100 (33.7) | 30.8 (23.8-41.0) | 7 | |
2 | |||||
Yes | 147 (90.7) | 295 (96.1) | 96.6 (93.0-99.4) | ||
No | 15 (9.3) | 12 (3.9) | 3.4 (0.6-7.0) | ||
Heterosexual | 2 (1.2) | 5 (1.6) | 3.0 (0.1-7.5) | 4 | |
Bisexual | 59 (35.1) | 112 (36.4) | 41.4 (33.6-49.5) | 7 | |
Gay/homosexual | 107 (63.7) | 191 (62.0) | 55.6 (47.3-63.1) | 7 | |
5 | |||||
Yes | 122 (73.5) | 83 (26.9) | 79.3 (74.2-84.8) | ||
No | 44 (26.5) | 226 (73.1) | 20.7 (15.2-25.8) | ||
7 | |||||
Yes | 69 (40.1) | 74 (24.0) | 20.4 (14.5-27.0) | ||
No | 103 (59.9) | 235 (76.1) | 79.6 (73.0-85.5) | ||
7 | |||||
Yes | 51 (29.7) | 93 (30.0) | 29.2 (23.2-36.4) | ||
No | 121 (70.4) | 217 (70.0) | 70.8 (63.6-76.8) | ||
5 | |||||
Access or easy access | 155 (90.1) | 249 (80.8) | 82.3 (76.2-87.6) | ||
Difficult or little access | 17 (9.9) | 59 (19.2) | 17.7 (12.4-23.8) | ||
6 | |||||
Yes | 56 (34.8) | 132 (49.3) | 49.3 (39.8-58.4) | ||
No | 105 (65.2) | 136 (50.7) | 50.7 (41.6-60.2) | ||
1 | 54 (32.5) | 143 (46.7) | 52.5 (43.8-59.8) | 8 | |
2-4 | 73 (44.0) | 133 (43.5) | 41.1 (34.1-49.0) | 6 | |
5 or more | 39 (23.5) | 30 (9.8) | 6.5 (3.3-10.1) | 3 | |
3 | |||||
Yes | 78 (45.3) | 40 (13.2) | 15.1 (9.8-21.2) | ||
No | 94 (54.7) | 263 (86.8) | 84.9 (78.8-90.2) | ||
9 | |||||
Yes | 121 (89.0) | 152 (52.9) | 52.1 (44.0-60.7) | ||
No | 15 (11.0) | 138 (47.1) | 47.9 (39.3-56.0) | ||
7 | |||||
Negative/don’t know/not tested | 135 (92.5) | 285 (94.4) | 94.4 (88.6-97.5) | ||
Positive | 11 (7.5) | 17 (5.6) | 5.6 (2.5-11.4) | ||
4 | |||||
Yes | 77 (44.5) | 166 (53.5) | 41.7 (36.1-50.3) | ||
No | 96 (55.5) | 144 (46.5) | 58.3 (49.7-63.9) | ||
4 | |||||
Yes | 37 (21.4) | 80 (25.8) | 28.5 (22.0-36.2) | ||
No | 136 (78.6) | 230 (74.2) | 71.5 (63.8-78.0) | ||
5 | |||||
Yes | 36 (21.3) | 94 (30.3) | 25.7 (20.4-32.4) | ||
No | 133 (78.7) | 216 (69.7) | 74.3 (67.6-79.6) | ||
9 | |||||
Yes | 42 (24.3) | 173 (56.4) | 60.4 (52.1-67.9) | ||
No | 131 (75.7) | 134 (43.6) | 39.6 (32.1-47.9) |
aRespondent-driven sampling (RDS) recruitment wave number at which equilibrium was reached for given variable.
Crude and respondent-driven sampling (RDS)-adjusted prevalence estimates from female sex workers (FSW) recruited using snowball sampling and RDS methods in Manzini, Swaziland.
Characteristics | Snowball | RDS (N=325) | |||||||
n (%) | n (%) | Adjusted % |
Equilibrium wavea | ||||||
18-21 | 20 (8.0) | 82 (25.2) | 42.5 (31.6-49.6) | 6 | |||||
22-25 | 63 (25.3) | 85 (26.2) | 22.9 (20.5-32.1) | 4 | |||||
26+ | 166 (6.7) | 158 (48.6) | 34.6 (25.4-42.1) | 5 | |||||
Primary school or lower | 59 (23.9) | 281 (86.5) | 86.6 (78.7-92.3) | 3 | |||||
Secondary school/high school | 166 (67.2) | 40 (12.3) | 11.2 (6.9-17.8) | 3 | |||||
More than high school | 22 (8.9) | 4 (1.2) | 2.2 (0-5.7) | 1 | |||||
4 | |||||||||
Yes | 210 (85.4) | 285 (88.8) | 89.6 (86.4-95.4) | ||||||
No | 36 (14.6) | 36 (11.2) | 10.4 (4.6-13.6) | ||||||
3 | |||||||||
Yes | 211 (85.1) | 276 (86.0) | 84.0 (77.8-91.7) | ||||||
No | 37 (14.9) | 45 (14.0) | 16.0 (8.3-22.2) | ||||||
2 | |||||||||
Yes | 62 (25.1) | 34 (10.5) | 7.0 (4.4-11.5) | ||||||
No | 185 (74.9) | 290 (89.5) | 93.0 (88.5-95.6) | ||||||
5 | |||||||||
Yes | 73 (29.7) | 39 (12.1) | 10.4 (4.9-15.9) | ||||||
No | 173 (70.3) | 284 (87.9) | 89.6 (84.1-95.1) | ||||||
4 | |||||||||
Yes | 26 (10.6) | 23 (7.3) | 3.3 (1.8-6.2) | ||||||
No | 219 (89.4) | 293 (92.7) | 96.7 (93.8-98.2) | ||||||
4 | |||||||||
Access or easy access | 242 (98.0) | 266 (83.1) | 87.3 (82.3-90.9) | ||||||
Difficult or little access | 5 (2.0) | 54 (16.9) | 12.7 (9.1-17.7) | ||||||
0-3 | 55 (22.5) | 95 (29.9) | 36.3 (25.9-42.9) | 3 | |||||
4-9 | 104 (42.6) | 143 (45.0) | 46.4 (39.3-54.9) | 5 | |||||
10 or more | 85 (34.8) | 80 (25.2) | 17.3 (12.8-25.6) | 4 | |||||
4 | |||||||||
Yes | 145 (58.7) | 89 (27.5) | 24.9 (19.4-31.6) | ||||||
No | 102 (41.3) | 235 (72.5) | 75.1 (68.4-80.6) | ||||||
4 | |||||||||
Yes | 121 (83.4) | 92 (62.6) | 55.5 (45.9-80.1) | ||||||
No | 24 (16.6) | 55 (37.4) | 44.5 (19.9-54.1) | ||||||
5 | |||||||||
Negative/don’t know/not tested | 142 (61.7) | 146 (45.3) | 44.5 (38.5-53.2) | ||||||
Positive | 88 (38.3) | 176 (54.7) | 55.5 (46.8-61.5) | ||||||
3 | |||||||||
Yes | 45 (18.9) | 98 (30.2) | 24.8 (19.2-32.0) | ||||||
No | 203 (81.9) | 226 (69.8) | 75.2 (68.0-80.8) | ||||||
4 | |||||||||
Yes | 32 (13.0) | 124 (38.3) | 37.5 (28.9-43.2) | ||||||
No | 215 (87.0) | 200 (61.7) | 62.5 (56.8-71.1) | ||||||
5 | |||||||||
Yes | 20 (8.1) | 84 (25.9) | 14.6 (10.4-20.5) | ||||||
No | 226 (91.9) | 240 (74.1) | 85.4 (79.5-89.6) | ||||||
4 | |||||||||
Yes | 38 (15.3) | 143 (44.0) | 41.2 (32.9-47.9) | ||||||
No | 211 (84.7) | 182 (56.0) | 58.8 (52.1-67.1) |
aRespondent-driven sampling (RDS) recruitment wave number at which equilibrium was reached for given variable.
Crude and respondent-driven sampling (RDS)-adjusted prevalence estimates from men who have sex with men (MSM) recruited using snowball sampling and RDS methods in Yaoundé, Cameroon.
Characteristics | Snowball | RDS (N=306) | |||
n (%) | n (%) | Adjusted % |
Equilibrium wavea | ||
18-21 | 52 (20.1) | 99 (32.4) | 47.9 (35.7-57.5) | 9 | |
22-25 | 101 (39.0) | 106 (34.6) | 32.6 (23.6-43.6) | 6 | |
26+ | 106 (40.9) | 101 (33.0) | 19.5 (13.6-27.6) | 9 | |
Primary school or lower | 37 (14.3) | 138 (45.1) | 46.2 (35.9-57.4) | 9 | |
Secondary school/high school | 112 (43.2) | 29 (9.5) | 15.3 (6.9-24.8) | 8 | |
More than high school | 110 (42.5) | 139 (45.4) | 38.5 (29.2-48.4) | 9 | |
Unemployed | 31 (12.5) | 51 (16.8) | 12.9 (7.4-20.6) | 7 | |
Student | 103 (41.5) | 159 (52.3) | 63.8 (53.3-73.3) | 8 | |
Employed | 114 (46.0) | 94 (30.9) | 23.4 (15.0-32.5) | 7 | |
4 | |||||
Yes | 7 (2.7) | 9 (2.9) | 1.0 (0.3-2.2) | ||
No | 251 (97.3) | 297 (97.1) | 99.0 (97.8-99.7) | ||
Heterosexual | 3 (1.2) | 1 (0.3) | 1.5 (0.0-4.1) | 4 | |
Bisexual | 158 (62.0) | 187 (61.9) | 59.4 (50.1-70.5) | 11 | |
Gay/homosexual | 94 (36.9) | 114 (37.8) | 39.2 (28.2-49.0) | 11 | |
11 | |||||
Yes | 188 (88.7) | 152 (67.6) | 55.9 (42.3-73.3) | ||
No | 24 (11.3) | 73 (32.4) | 44.1 (26.7-57.7) | ||
Negative | 212 (88.3) | 174 (59.8) | 60.6 (48.8-71.4) | 7 | |
Positive | 14 (5.8) | 65 (22.3) | 15.1 (8.6-24.5) | 10 | |
Not tested | 14 (5.8) | 52 (17.9) | 24.3 (14.2-34.7) | 11 | |
7 | |||||
Yes | 117 (45.5) | 182 (59.5) | 57.9 (48.1-67.7) | ||
No | 140 (54.5) | 124 (40.5) | 42.1 (32.3-51.9) | ||
6 | |||||
Yes | 10 (3.9) | 27 (8.8) | 7.4 (3.6-12.0) | ||
No | 248 (96.1) | 279 (91.2) | 92.6 (88.0-96.4) | ||
9 | |||||
Yes | 39 (15.1) | 26 (8.5) | 7.1 (3.6-11.9) | ||
No | 219 (84.9) | 280 (91.5) | 92.9 (88.1-96.4) | ||
7 | |||||
Yes | 116 (45.3) | 113 (36.9) | 32.6 (24.0-42.3) | ||
No | 140 (54.7) | 193 (63.1) | 67.4 (57.7-76.0) |
aRespondent-driven sampling (RDS) recruitment wave number at which equilibrium was reached for given variable.
Crude and respondent-driven sampling (RDS)-adjusted prevalence estimates from female sex workers (FSW) recruited using snowball sampling and RDS methods in Yaoundé, Cameroon.
Characteristics | Snowball | RDS (N=576) | |||
n (%) | n (%) | Adjusted % |
Equilibrium waveb | ||
18-21 | 25 (8.1) | 75 (13.0) | 15.6 (10.3-19.0) | 12 | |
22-25 | 68 (22.1) | 129 (22.4) | 26.7 (21.8-33.1) | 15 | |
26+ | 215 (69.8) | 372 (64.6) | 57.7 (51.2-65.0) | 15 | |
Primary school or lower | 238 (77.5) | 520 (90.3) | 87.4 (81.8-92.2) | 12 | |
Secondary school/high school | 60 (19.5) | 22 (3.8) | 4.3 (2.4-7.1) | 8 | |
More than high school | 9 (2.9) | 34 (5.9) | 8.3 (4.0-13.1) | 13 | |
3 | |||||
Yes | 19 (6.3) | 17 (3.0) | 4.5 (1.9-7.6) | ||
No | 285 (93.8) | 559 (97.0) | 95.5 (92.4-98.1) | ||
14 | |||||
Yes | 182 (70.3) | 291 (70.0) | 65.2 (55.7-72.1) | ||
No | 77 (29.7) | 125 (30.1) | 34.8 (27.9-44.3) | ||
Negative | 254 (83.3) | 434 (76.3) | 76.8 (71.7-81.9) | 8 | |
Positive | 21 (6.9) | 75 (13.2) | 10.5 (6.5-14.7) | 9 | |
Not tested | 30 (9.8) | 60 (10.5) | 12.7 (8.9-16.7) | 10 | |
14 | |||||
Yes | 107 (34.7) | 85 (14.8) | 15.9 (12.1-21.7) | ||
No | 201 (65.3) | 491 (85.2) | 84.1 (78.3-87.9) | ||
6 | |||||
Yes | 5 (1.6) | 3 (0.5) | 0.1 (0.0-0.3) | ||
No | 300 (98.4) | 573 (99.5) | 99.9 (99.7-1.0) | ||
13 | |||||
Yes | 139 (45.3) | 87 (15.1) | 13.2 (10.2-17.7) | ||
No | 168 (54.7) | 489 (84.9) | 86.8 (82.3-89.8) | ||
9 | |||||
Yes | 176 (57.9) | 305 (53.0) | 56.4 (50.7-62.5) | ||
No | 128 (42.1) | 270 (47.0) | 43.6 (37.5-49.3) |
aRespondent-driven sampling (RDS) recruitment wave number at which equilibrium was reached for given variable.
In these secondary analyses, the Swaziland and Cameroon studies that recruited MSM and FSW using RDS and venue-based snowball sampling methods produced samples that had different compositions for key sociodemographic, stigma, and HIV-related characteristics. In particular, the RDS as compared with the venue-based snowball samples were younger, less educated, had reduced exposure to HIV prevention interventions, had limited access to HIV prevention tools such as condoms, and showed less-reported frequency of STI and HIV testing. MSM and FSW in Swaziland recruited using venue-based snowball sampling as compared with RDS reported larger numbers of sexual partners and sex work clients, respectively, suggesting larger sexual and personal network sizes for individuals in these samples.
In the context of health and HIV surveillance, our snowball sampling studies in Cameroon and Swaziland leveraging
These analyses are based on studies that were previously conducted for the objective of conducting HIV bio-behavioral surveillance of key populations, and the studies similarly had limited time and resources. As a result, there were some limitations to the application of both approaches in practice. For example, with the exception of FSW in Cameroon, only a small number of venues were mapped and sampled from. Of the sampled venues, the majority of participants were recruited from only a single venue (99.4% of MSM in Manzini, 99.2% of FSW in Manzini, 95.8% of MSM in Yaoundé). If a probability sample of individuals had been taken from either a random selection of venues or a set of venues with special attention to diversity of characteristics of venues (eg, large vs small and high-end vs low-end), coverage estimates may have been closer to the true underlying target population. Even then, those sampled at venues, regardless of the number or diversity of characteristics of venues, may not ever provide a representative estimate of all members of the larger target population. In addition, RDS equilibrium was reached late for some variables, suggesting that in some cases RDS may not have achieved the goal of reaching deeper into population networks, and some traits may still have been dependent on seed selection. Thus, our findings may have been different had these studies reached a greater number of venues or penetrated more deeply into social networks. In particular, the results from the venue-based snowball studies are likely very sensitive to whether program services reached the venue from which the vast majority of participants were recruited. Taking into account this caveat, the limited amount of time and resources available, and the similar objectives of each of the studies to conduct HIV bio-behavioral surveillance of MSM and FSW, RDS in this particular context appears to provide a very different estimate of program coverage when compared with venue-based snowball sampling.
As expected, RDS and venue-based snowball sampling differed in the characteristics of key populations recruited, and as expected, the group recruited by convenience sampling was more likely to be linked to a program, as indicated by the fact that the largest differences among samples were seen for services that participants may have received together (HIV prevention materials, improved HIV knowledge, recent HIV and STI testing). It may be especially important when using venue-based sampling to consider the types of venues used for recruitment and whether or not HIV-related services are also offered either at or through these venues. In sampling key populations, it is important to clarify early and often the intended purpose and use of the generated estimates—that is, who really is the target population and who are we really trying to capture with our surveys and surveillance strategies. For example, in the case of MSM, is the purpose of the study or surveillance system to capture all MSM regardless of degree of prevention or treatment need, the most at-risk MSM who are likely hidden and hard-to-reach, or well-connected MSM who already frequent hotspots and facilities that provide HIV services? Without a clear definition of the target population, it will be difficult to even begin to think about a method for sampling that population. Future studies should work to clearly define their target population and to evaluate, given time and cost constraints, which sampling methodology may be the best fit for different definitions of the target population.
As prioritization of resources and targeted interventions requires a representative estimation of service coverage [
In a previous analysis, the prevalence of HIV-related characteristics and other sample characteristics were examined across recruitment waves among MSM in Swaziland, Malawi, and Lesotho [
In addition, findings pertaining to the relationship of sampling method with level of sexual practice disclosure and sexual practice–related stigma were mixed. This could be because we were limited to using only a limited number of items to assess stigma and disclosure; namely, those items that were used consistently across the different studies. It could be because of structural- or community-level differences in stigma between the two countries and key population groups [
There are several limitations of this study to consider. First, comparisons across studies should be done with caution because our comparisons were limited to the subset of variables that were most similar across existing datasets. Our results may not be generalizable outside Swaziland and Cameroon, although findings may be comparable to other settings where stigma affecting key populations is prevalent. The venue-based snowball samples were smaller in terms of sample size, and recruitment was for a shorter period of time compared with RDS. In addition, RDS and venue-based snowball studies were conducted between 2 and 3 years apart, making these interpretations potentially subject to a period effect. If the source populations changed over time, then the differences in the sample compositions could be because of differing source populations as opposed to different sampling methodologies. However, there were no changes in HIV prevention policies or laws about sex work or same-sex practices during this time, making it unlikely that the composition of key populations in these two cities changed dramatically over this time frame. Further, RDS was conducted before snowball sampling in Swaziland but was conducted after snowball sampling in Cameroon. However, we cannot rule out the possibility of uncontrolled time-varying confounders or changes to the population-level prevalence of characteristics of MSM or FSW over time.
There remains a sustained and often growing burden of HIV between these two key populations [
female sex workers
human immunodeficiency virus
men who have sex with men
National Institutes of Health
sexually transmitted infections
respondent-driven sampling
This work was supported by the United States Agency for International Development (USAID), Cooperative Agreement #AID-OAA-A-12-00,058 to the Johns Hopkins Center for Communication Programs, and by the Measurement & Surveillance of HIV Epidemics (MeSH) Consortium funded by the Bill & Melinda Gates Foundation. In addition, this publication was made possible with help from the Johns Hopkins University Center for AIDS Research, a National Institutes of Health (NIH)-funded program (P30AI094189), which is supported by the following NIH Co-Funding and Participating Institutes and Centers: NIAID, NCI, NICHD, NHLBI, NIDA, NIMH, NIA, FIC, NIGMS, NIDDK, and OAR. We would like to thank all of the study participants for their time and efforts. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or other funding agencies.
None declared.