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. Author manuscript; available in PMC: 2019 Jul 11.
Published in final edited form as: AIDS Behav. 2016 Aug;20(8):1754–1776. doi: 10.1007/s10461-016-1346-5

A Systematic Review of Published Respondent-Driven Sampling Surveys Collecting Behavioral and Biologic Data

Lisa G Johnston 1,2, Avi J Hakim 3, Samantha Dittrich 3, Janet Burnett 3, Evelyn Kim 3, Richard G White 4
PMCID: PMC6620785  NIHMSID: NIHMS1035886  PMID: 26992395

Abstract

Reporting key details of respondent-driven sampling (RDS) survey implementation and analysis is essential for assessing the quality of RDS surveys. RDS is both a recruitment and analytic method and, as such, it is important to adequately describe both aspects in publications. We extracted data from peer-reviewed literature published through September, 2013 that reported collected biological specimens using RDS. We identified 151 eligible peer-reviewed articles describing 222 surveys conducted in seven regions throughout the world. Most published surveys reported basic implementation information such as survey city, country, year, population sampled, interview method, and final sample size. However, many surveys did not report essential methodological and analytical information for assessing RDS survey quality, including number of recruitment sites, seeds at start and end, maximum number of waves, and whether data were adjusted for network size. Understanding the quality of data collection and analysis in RDS is useful for effectively planning public health service delivery and funding priorities.

Keywords: HIV/AIDS, Key populations, Respondent driven sampling, RDS, Biological and behavioral surveillance

Introduction

The first respondent-driven sampling (RDS) surveys to assess HIV prevalence in addition to risk behaviors were conducted in 2004 [1–3]. Since then hundreds of surveys have been conducted worldwide to capture data from populations considered at higher risk for HIV exposure, including people who use and/or inject drugs (PWUD, PWID), men who have sex with men (MSM), female sex workers (FSW), and other populations considered “hard-to-reach” due to stigma and the practice of illegal behaviors [4–6]. Over the past decade RDS has been widely used, with the endorsement of organizations such as the US Centers for Disease Control and Prevention, UNAIDS, WHO, Global Fund and others, to establish baseline and trend measurements of HIV and other infections prevalence, risk behaviors, and program impact through biological and behavioral surveys [6–9].

RDS is an important recruitment and analysis tools for sampling populations that have no sampling frames and that are linked through social networks. Beginning with a set number of participants, “seeds”, selected purposefully by the research team from the target population, RDS builds a sample through the passing of a coupon from one peer to another. Using a limited number of coupons for each participant limits overrepresentation of those with a higher number of ties to others in the population network. Coupons also limit participants having to provide personal information about their recruits and allows researchers to monitor the recruitment process. Providing ‘incentives’ for those participating in and for recruiting peers into the survey helps ensure ongoing participation and recruitment. Ideally, this process results in long recruitment chains made up of numerous “waves” of recruits [10, 11]. As recruitment chains lengthen, the structure of the sample becomes less dependent on the purposefully selected seeds and increasingly similar to the population being sampled. Once the sample is gathered, statistical adjustments for differential network sizes and recruitment effort are used to produce estimates representative of the sampled population’s network [10–14].

RDS is premised upon several assumptions, most importantly, random walk models [10]. Briefly, these assumptions include (1) reciprocal ties between respondents (i.e., know one another as members of the sampled population); (2) respondents are connected by a single network component; (3) sampling occurs with replacement; (4) respondents provide accurate personal network sizes (i.e., number of relatives, friends, and acquaintances they know from the sampled population); (5) peers are recruited randomly from the recruiter’s network; and, (6) each respondent can recruit at least one peer [11].

Methodologically appropriate RDS surveys are vital for developing national and international policies, guiding service delivery, informing budgets and dictating funding priorities. Quality reporting of data collected and analyzed using RDS methods allows users to assess their usefulness in decision making. However, there is ample potential for bias when using this method, many of which are related to implementation and analytical failures [15–20]. The allure of RDS as a more robust alternative to convenience snowball sampling methods has resulted in partial incorporation of RDS techniques (i.e., the use of coupons) while ignoring some of the more complex aspects which ensure the mitigation of chain referral-related biases [13]. Indeed, numerous published surveys report having used RDS, but present insufficient methodological and analytical information to support this assertion [21].

Building upon the STROBE RDS guidelines [22] which recommend improvements in the reporting of survey data, we extracted peer-reviewed literature that reported using RDS for collecting biological(HIV and other infections) and behavioral data through September, 2013. Specifically, we evaluate a set of general and RDS-specific survey indicators based on the STROBE RDS guidelines [21, 22] to describe the extent, consistency, and changes over time for planning, implementation, and analysis as reported in peer reviewed journals. In addition, we provide reasons why some published surveys were not included in the extraction and examples of surveys that reported using RDS when, in fact, they did not. We hope to build upon other efforts to increase accuracy in conducting RDS and to encourage more thorough and standardized reporting of RDS methods and analysis [4, 22] (Table 1).

Table 1.

Methodological and analytical data from extraction of published articles

Location of study,
citation		 Year of study Population Pre-survey
assessment		 Sites (num) Interview
methoda 		Seeds at start (num) Final
seeds (num)		 Primary incentive of valueb Secondary incentive of valueb Target
sample
size		 Final
sample
size		 Max. number of waves Data collection duration, weeks Data
adjustedc
Africa
Kenya, Kisumu [23] 200S FSW Yes 1 ACASI 15 NR 4.00 1.25 480 481 6 12 Yes
Nigeria, Abuja [24] 2010 MSM NR NR ACASI/SA NR NR NR NR NR 194 8 NR Yes
Nigeria, Cross River [25] 2007 MSM NR 1 IA 10 10 4.00 NR 293 293 8 NR Yes
Nigeria, Cross River [26] 2010 PWID NR >1 IA 10 10 4.00 4.00 266 273 9 8 Yes
Nigeria, Federal Capital Territory [26] 2010 PWID NR >1 IA 10 10 4.00 4.00 266 271 13 8 Yes
Nigeria, Ibadan [27] 2006 MSM NR NR IA 38 38 4.00 NR NR 1125e NR 12 Yes
Nigeria, Ibadan [24] 2010 MSM NR NR SA ACASI/SA NR NR NR NR NR 210 8 NR Yes
Nigeria, Kaduna [26] 2010 PWID NR >1 IA 10 10 4.00 4.00 266 196 8 8 Yes
Nigeria, Kano [26] 2010 PWID NR >1 IA 10 10 4.00 4.00 266 270 12 8 Yes
Nigeria, Kano [25] 2007 MSM NR 1 IA 10 10 4.00 NR 293 315 9 NR Yes
Nigeria, Lagos [25] 2007 MSM NR 1 IA 10 14 4.00 NR 293 297 10 NR Yes
Nigeria, Lagos [26] 2010 PWID NR >1 IA 10 10 4.00 4.00 266 191 9 8 Yes
Nigeria, Lagos [27] 2006 MSM NR NR IA 38 38 4.00 NR NR 1125e NR 12 Yes
Nigeria, Lagos [24] 2010 MSM NR NR SA ACASI/SA NR NR NR NR NR 308 8 NR Yes
Nigeria, Oyo [26] 2010 PWID NR >1 IA 10 10 4.00 4.00 266 273 8 8 Yes
Mauritius [28] 2009 PWID Yes 2 IA 6 6 7.00 3.50 500 511 13 12 Yes
Mauritius [29] 2010 FSW NR 2 IA 5 5 17.50 7.00 NR 299 8 2 Yes
Somalia, Hargeisa, Somaliland [30] 2008 FSW Yes 1 IA w/HAPI 6 NR 4.00 3.00 146 237 NR 8 Yes
South Africa, Durban [31] 2011 MSM Yes 1 SA 4 15 5.00 and 5.00 voucher 5.00 and 5.00 voucher 200 81 11 17 NR
South Africa, Johannesburg [31] 2011 MSM Yes 1 IA or SA 5 14 5.00 and 5.00 voucher 5.00 and 5.00 voucher 200 204 15 21 NR
South Africa, Soweto [32] 2008 MSM NR 1 IA 15 15 NR NR NR 378 NR 30 Yes
South Africa, W. Cape Province [33, 34, 35] 2006 Heterosexualmen Yes 1 IA 8 20 8.00 phone voucher 2.70 phone voucher 430 421 13 15 Yes
South Africa, W. Cape Province [36] 2008 Heterosexualmen Yes 1 IA 19 19 8.50 phone voucher 2.85 phone voucher 430 423 20 12 Yes
South Africa, W. Cape Province [37] 2007 Young women Yes 1 SA 5 5 8.00 make-up voucher 2.50 270 259 12 NR Yes
South Africa, W. Cape Province [38] 2011 Heterosexual women Yes 1 ACASI 15 15 7.50 grocery voucher 7.50 grocery voucher 756 845 19 17 (weekend only) Yes
Sudan, Khartoum [39] 200S FSW NR 1 IA NR NR 10.00 10.00 NR 321 NR 8 Yes
Tanzania, Zanzibar [40, 41] 2007 MSM Yes 1 IA 10 10 3.00 1.50 500 509 10 12 Yes
Uganda, Kampala [42] 2008–09 MSM Yes 1 ACASI 8 14 3.00 1.00 600 300 11 44 Yes
Eastern Mediterranean
Egypt, Cairo [43] 2006 PWID Yes 1 IA 28 NR 7.00 5.30 406 413 NR 12 Yes
Iran, Kerman [44] 2010 FSW NR 1 IA 8 12 4.00 2.00 NR 177 NR 16 Yes
Lebanon, Beirut [45, 46] 2007–08 FSW NR NR NR NR NR 6.60 2.00 NR 135 NR NR Yes
Lebanon, Beirut [45] 2007–08 MSM NR NR NR NR NR 6.60 2.00 NR 101 NR NR Yes
Lebanon, Beirut [45, 47] 2007–08 PWID NR NR NR NR NR 6.60 2.00 NR 81 NR NR Yes
Libya, Tripoli [48] 2010 PWID Yes 1 IA 7 7 20.00 9.00 NR 328 10 8 Yes
Libya, Tripoli [49] 2010 MSM Yes 1 IA NR 14 NR NR NR 227 15 NR Yes
Libya, Tripoli [49] 2010 FSW Yes 1 IA NR 13 NR NR 314 69 10 20 Yes
Morocco, Agadir [50] 2010–11 MSM NR NR IA NR 10 7.00 3.50 NR 323 12 12 Yes
Morocco, Marrakesh [50] 2010–11 MSM NR NR SA NR 8 7.00 3.50 NR 346 23 12 Yes
Palestine, East Jerusalem [51] 2010 PWID Yes 1 IA NR 7 NR NR NR 199 12 NR Yes
Europe
Albania, Tirana [52] 2005 PWID Yes 3 IA 15 15 12.00 7.00 NR 225 NR 8 Yes
Albania, Tirana [53] 2008 MSM NR 1 IA 12 NR 10.00 5.00 NR 189 NR 5 Yes
Croatia, Zagreb [54, 55, 56] 2006 MSM NR 1 SA 8 10 18.00 9.00 400 360 13 14 Yes
Croatia, Zagreb [54] 2012 MSM Yes 1 SA 10 15 None 9.60 370 402 13 19 Yes
England, Bristol [57, 58] 2006 PWID NR NR CASI 7 7 15.00 10.00 NR 299 17 6 Yes
England, Bristol [57] 2009 PWID NR NR NR 6 6 NR NR NR 292 NR NR Yes
Estonia, Kohtla Jarve [59, 60] 2005 PWID NR NR SA NR NR NR NR NR 100 NR NR No
Estonia, Tallinn [61–63] 2007 PWID Yes 1 SA 5 5 10.00 food voucher 5.00 food voucher NR 350 16 7 No
Estonia, Tallinn [63, 64] 2005–06 FSW Yes 1;; other SA 6 43 10.00 shop voucher 11.00 shop voucher NR 227 8 28 Yes
Estonia, Tallinn [60, 65] 2005 PWID NR NR SA 6 NR NR NR NR 350 8 NR Yes
Estonia, Tallinn [62] 2009 PWID NR NR SA 6 6 10.00 food voucher 5.00 food voucher NR 327 NR NR No
Kazakhstan, Almaty [66] 2010 MSM Yes NR IA 4 4 10.00 2.50 400 400 NR 16 Yes
Moldova, Balti [67] 2009–10 FSW NR NR IA 5 5 7.00 5.00 350 359 6 16 Yes
Moldova, Balti [68] 2007–08 PWID Yes NR IA NR NR Items, cash (value NR) NR NR 350e NR NR No
Moldova, Balti [69] 2010 MSM Yes NR IA 5 5 8.30 5.80 250 209 6 20 Yes
Moldova, Chisinau [68] 2007–08 PWID Yes NR IA NR NR Items, cash (value NR) NR NR 350e NR NR No
Moldova, Chisinau [67] 2009–10 FSW NR NR IA 5 5 16.00 12.00 350 299 6 16 Yes
Moldova, Chisinau [69] 2010 MSM Yes NR IA 8 8 8.30 5.80 250 188 6 20 Yes
Moldova, Tiraspol [68] 2007–08 PWID Yes NR IA NR NR Items, cash (value NR) NR NR 350e NR NR No
Montenegro, Podgorica [70] 2008 PWID NR 1 SA 5 NR 20.80 7.00 NR 322 NR 12 Yes
Montenegro, Podgorica [71] 2005 PWID NR 1 ACASI NR NR 13.00 6.00 NR 328 NR 5 Yes
Russia, Ivanovo [72] 2010 PWID NR NR IA 11 11 Items, food (value NR) Items, food (value NR) NR 300 NR 24 No
Russia, Novosibirsk [72] 2010 PWID NR NR IA 10 10 Items, food (value NR) Item, food (value NR) NR 293 NR 24 No
Russia, St. Petersburg [73–75] 2005–08 PWUD/PWID NR NR CASI 48; 108 156 10.00 (items) items (value NR) NR 631; 689 14 124 No
Russia, St. Petersburg [74, 75] 2005–06 PWID NR NR CASI 35 NR 10.00 (items) NR NR 387 NR 56 No
Serbia, Belgrade [76] 2010 Youth Yes NR IA 8 8 13.00 6.00 371d 270 NR 8 Yes
Serbia, Belgrade [71] 2005 PWID NR 1 ACASI NR NR 13.00 6.00 NR 432 NR 6 Yes
Serbia, Kragujevac [76] 2010 Youth Yes NR IA 4 4 13.00 6.00 370d 141 NR 8 Yes
Ukraine, Poltava [77] 2011 PWID NR NR SA 4 4 3.00 2.00 NR 200 NR NR Yes
Ukraine, Khmelnitsky [77] 2011 PWID NR NR SA 7 7 3.00 2.00 NR 200 NR NR Yes
Ukraine, Dnipropetrovsk [77] 2011 PWID NR NR SA 6 6 3.00 2.00 NR 113 NR NR Yes
Ukraine, Cherkasy [77] 2011 PWID NR NR SA 3 3 3.00 2.00 NR 175 NR NR Yes
Ukraine, Donetsk [77] 2011 PWID NR NR SA 6 6 3.00 2.00 NR 400 NR NR Yes
Ukraine, Kharkov [77] 2011 PWID NR NR SA 5 5 3.00 2.00 NR 175 NR NR Yes
Ukraine, Kherson [77] 2011 PWID NR NR SA 4 4 3.00 2.00 NR 225 NR NR Yes
Ukraine, Kirovograd [77] 2011 PWID NR NR SA 4 4 3.00 2.00 NR 175 NR NR Yes
Ukraine, Kyiv [77] 2011 PWID NR NR SA 8 8 3.00 2.00 NR 400 NR NR Yes
Ukraine, Lugansk [77] 2011 PWID NR NR SA 6 6 3.00 2.00 NR 200 NR NR Yes
Ukraine, Lutsk [77] 2011 PWID NR NR SA 4 4 3.00 2.00 NR 175 NR NR Yes
Ukraine, Lviv [77] 2011 PWID NR NR SA 7 7 3.00 2.00 NR 175 NR NR Yes
Ukraine, Mykolaiv [77] 2011 PWID NR NR SA 6 6 3.00 2.00 NR 260 NR NR Yes
Ukraine, Odesa [77] 2011 PWID NR NR SA 6 6 3.00 2.00 NR 400 NR NR Yes
Ukraine, Simferopol [77] 2011 PWID NR NR SA 5 5 3.00 2.00 NR 265 NR NR Yes
Ukraine, Sumy [77] 2011 PWID NR NR SA 5 5 3.00 2.00 NR 173 NR NR Yes
Latin America and Caribbean
Argentina, Buenos Aires [78, 79] 2009 MSM NR NR SA web-based 16 16 NR NR NR 500 NR NR No
Brazil, Belo Horizonte [80–82] 2009 MSM Yes NR IA 6 NR 10.00 6.67 350 NR NR NR Yes
Brazil, Belo Horizonte [17, 83] 2008–09 FSW NR NR ACASI 5–10 NR Misc. (value NR) 4.00 300 289 NR 52 No
Brazil, Brasilia [80–82] 2009 MSM Yes NR IA 6 NR 10.00 6.67 350 NR NR NR Yes
Brazil, Brasilia [17, 83] 2008–09 FSW NR NR ACASI 5–10 NR Misc. (value NR) 4.00 300 308 NR 52 No
Brazil, Campinas [84] 2005–06 MSM NR NR ACASI 10 30 NR NR NR 658 NR 52 Yes
Brazil, Campo Grande [80–82] 2009 MSM Yes NR IA 6 NR 10.00 6.67 350 NR NR NR Yes
Brazil, Campo Grande [17, 83] 2008–09 FSW NR NR ACASI 5–10 NR Misc. (value NR) 4.00 150 147 NR 52 No
Brazil, Curitiba [80–83] 2009 MSM Yes NR IA 6 NR 10.00 6.67 350 NR NR NR Yes
Brazil, Curitiba [17, 83] 2008–09 FSW NR NR ACASI 5–10 NR Misc. (value NR) 4.00 200 201 NR 52 No
Brazil, Fortaleza [85] 2008 Transvestite Yes NR IA 6 NR 6.00 food voucher 3.00 NR 304 NR 16 Yes
Brazil, Fortaleza [86] 2005 MSM Yes 2 IA 10 10 5.00 5.00 400 406 NR NR Yes
Brazil, Itajai [80–82] 2009 MSM Yes NR IA 6 NR 10.00 6.67 350 NR NR NR Yes
Brazil, Itajai [17, 83] 2008–09 FSW NR NR ACASI 5–10 NR Misc. (value NR) 4.00 100 90 NR 52 No
Brazil, Manaus [80–82] 2009 MSM Yes NR IA 6 NR 10.00 6.67 350 NR NR NR Yes
Brazil, Manaus [17, 83] 2008–09 FSW NR NR ACASI 5–10 NR Misc. (value NR) 4.00 200 199 NR 52 No
Brazil, Recife [80–82] 2009 MSM Yes NR IA 6 NR 10.00 6.67 350 NR NR NR Yes
Brazil, Recife [17, 83] 2008–09 FSW NR NR ACASI 5–10 NR Misc. (value NR) 4.00 200 237 NR 52 No
Brazil, Rio de Janeiro [80–82] 2009 MSM Yes NR IA 6 NR 10.00 6.67 350 NR NR NR Yes
Brazil, Rio de Janeiro [17, 83] 2008–09 FSW NR NR ACASI 5–10 NR Misc. (value NR) 4.00 600 601 NR 52 No
Brazil, Salvador [80–82] 2009 MSM Yes NR IA 6 NR 10.00 6.67 350 NR NR NR Yes
Brazil, Salvador [17, 83] 2008–09 FSW NR NR ACASI 5–10 NR Misc. (value NR) 4.00 300 260 NR 52 No
Brazil, Santos [80–82] 2009 MSM Yes NR IA 6 NR 10.00 6.67 350 NR NR NR Yes
Brazil, Santos [17, 83] 2008–09 FSW NR NR ACASI 5–10 NR Misc. (value NR) 4.00 150 191 NR 52 No
Dominican Rep., Barahona [87] 2008 MSM Yes NR IA 8 NR 9.00 3.00 300 281 12 8 Yes
Dominican Rep., La Altagracia [87] 2008 MSM Yes NR IA 7 NR 9.00 3.00 300 270 11 8 Yes
Dominican Rep., Santiago [87] 2008 MSM Yes NR IA 6 NR 9.00 3.00 300 327 13 8 Yes
Dominican Rep., Santo Domingo [87] 2008 MSM Yes NR IA 7 NR 9.00 3.00 500 510 15 8 Yes
El Salvador, San Miguel [88, 89] 2008 MSM Yes NR CASI w/interviewer 5 5 4.00 as items 2.70 (items) 200 195 19 (2 cities) 16 Yes
El Salvador, San Salvador [89] 2008 FSW NR 1 CASI w/interviewer 10 10 Items (value NR) Items (value NR) NR 787e NR NR No
El Salvador, San Salvador [88, 89] 2008 MSM Yes NR CASI w/interviewer 11 11 4.00 as items 2.70 (items) 600 596 19 (2 cities) 16 Yes
El Salvador, Sonsonate [89] 2008 FSW NR 1 CASI w/interviewer 5 5 Items (value NR) Items (value NR) NR 787e NR NR No
Honduras, Comayagua [90] 2006 FSW Yes 1 ACASI 5 5 Purse (value < 2.00) Items (value 3.50) 200 182 11 8 Yes
Honduras, La Ceiba [90] 2006 FSW Yes 1 ACASI 7 7 200 211 7 8 Yes
Honduras, San Pedro Sula [90] 2006 FSW Yes 1 ACASI 7 7 200 198 13 8 Yes
Honduras, Tegucigalpa [90] 2006 FSW Yes 1 ACASI 5 5 200 204 7 10 Yes
Peru, Lima [91] 2012 Transwoman Yes 6 SA 8 11 7.00 NR 420 450 NR 16 Yes
North America
Mexico, Juarez [92–96] 2005 PWID NR 1 SA NR NR NR NR NR 204 NR 8 Yes
Mexico, Juarez [97] 2005 PWID NR 1 SA 9 17 20.00 5.00 200 197 8 2 Yes
Mexico, Tijuana [92–96] 2005 PWID NR 3 SA 15 15 10.00 5.00 200 207 8 8 Yes
Mexico, Tijuana [92–102] 2006–07 PWID NR NR SA 32 NR 20.00 NR NR 1056 NR 52 Yes
USA, Appalachia [103] NR PWUD NR NR NR NR NR 50.00 10.00 NR 503 NR NR No
USA, Atlanta [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 561 NR NR Yes
USA, Baltimore [104] 2002–04 Youth PWID NR NR ACASI, w/w/out interviewer NR NR NR NR NR 736 NR 88 No
USA, Baltimore [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 722 NR NR Yes
USA, Baltimore [105] 2006 PWID NR NR CAPI 20 20 20.00 10.00 NR 670 NR 20 Yes
USA, Boston [106, 107] 2008 MSM NR 2 SA 8 21 50.00 10.00 NR 197 NR 24 No
USA, Boston [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 475 NR NR Yes
USA, Chicago [104] 2002–04 Youth PWID NR NR ACASI, w/w/out interviewer NR NR NR NR NR 586 NR 88 No
USA, Chicago [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 542 NR NR Yes
USA, Dallas [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 570 NR NR Yes
USA, Denver [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 532 NR NR Yes
USA, Detroit [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 545 NR NR Yes
USA, Ft. Lauderdale [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 384 NR NR Yes
USA, Houston [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 596 NR NR Yes
USA, Houston [108] 2006–07 High risk heterosexuals Yes 1 CAPI NR NR 40.00 10.00 750 939 NR 36 Yes
USA, Las Vegas [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 334 NR NR Yes
USA, Los Angeles [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 602 NR NR Yes
USA, Los Angeles [75, 109–111] 2005–06 PWUD/PWID/MSM NR 1 ACASI 25 25 50.00 20.00 NR 426 21 (both phases) 52 Yes
USA, Miami [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 607 NR NR Yes
USA, Nassau [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 529 NR NR Yes
USA, New Haven [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 534 NR NR Yes
USA, Las Cruces [97] 2005 PWID NR 1 SA NR NR NR NR NR 100 NR 8 No
USA, New York City [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 508 NR NR Yes
USA, New York City [112] 2009 PWID Yes NR SA NR 3 NR NR NR 488 NR 8 Yes
USA, New York City [113–118] 2006–07 High risk heterosexuals Yes NR SA 8 NR 30.00 11.00 NR 850 NR NR No
USA, New York City [1, 119, 120] 2004 PWUD NR NR NR NR NR 20.00 10.00 NR 448 NR NR Yes
USA, Newark [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 440 NR NR Yes
USA, Norfolk [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 499 NR NR Yes
USA, Oakland [121] 2011–12 High risk/HIV pos. African American Yes 4 SA 48 NR 10.00 gift card Varied NR 243 NR 52 Yes
USA, Philadelphia [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 539 NR NR Yes
USA, San Diego [122] 2009–10 PWID NR NR ACASI NR NR NR 10.00 NR 510 NR 64 No
USA, San Diego [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 539 NR NR Yes
USA, San Francisco [123] 2007–08 MSM Yes 1 CAPI 10 10 40.00 10.00 NR 256 18 24 Yes
USA, San Francisco [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 581 NR NR Yes
USA, San Juan [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 573 NR NR Yes
USA, Seattle [124] 2009 PWID NR 1 IA 6 6 40.00 10.00 NR 497 16 16 Yes
USA, Seattle [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 400 NR NR Yes
USA, St. Louis [6] 2005–06 PWID NR NR SA 8–10 NR 25.00 10.00 NR 525 NR NR Yes
USA, Wash. DC [125] 2006–07 High risk heterosexuals Yes 1 SA NR NR 35.00 10.00 NR 750 NR 44 Yes
USA, Wash. DC [126] 2009 PWID NR 1 SA NR NR 30.00 10.00 NR 553 NR 16 Yes
USA, Texas, El Paso [97] 2006 PWID NR 1 SA NR NR NR NR NR 155 NR 24 Yes
South East Asia
Bangladesh, Dhaka [127] 2006 MSM Yes 1 SA 5 8 2.14 1.43 530 531 9 11 Yes
India, Bishenpur District, Manipur [128] 2006 PWID NR NR SA NR NR NR NR 400 420 NR NR Yes
India, Chennai [129] 2008 MSM Yes 2 SA NR 19 6.00 None NR 721e 3 8 No
India, Churachandpur District, Manipur [128] 2006 PWID NR NR SA NR NR NR NR 400 419 NR NR Yes
India, Coimbatore [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Dimapur District, Nagaland [130] 2006 FSW NR NR SA 10 10 NR NR 400 426 11 8 No
India, Dindigul [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Erode [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Goa [131, 132] 2005 FSW Yes NR SA 59 59 2.50 1.50 318 326 6 52 Yes
India, Kanyakumari [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Madurai [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Mumbai and Thane Districts [128] 2006 PWID NR NR SA NR NR NR NR 400 376 NR NR Yes
India, Nagapattinam [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Nilgiris [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Perambalur [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Phek District, Nagaland [128] 2006 PWID NR NR SA NR NR NR NR 400 440 NR NR Yes
India, Pudukkottai [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Ramanathapuram [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Salem [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Sivaganga [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Thanjavur [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Tiruchy [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Tirunelveli [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Thiruvarur [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Tuticorin [129] 2008 MSM Yes 1 SA NR 19 6.00 None NR 721e 3 8 No
India, Wokha District, Nagaland [128] 2006 PWID NR NR SA NR NR NR NR 400 420 NR NR Yes
Pakistan, Abbottabad [133] 2007 MTSW Yes 2 IA NR NR NR NR NR 103 NR 8 No
Pakistan, Abbottabad [133] 2007 FSW Yes 2 IA NR NR NR NR NR 109 NR 8 No
Pakistan, Lahore [133] 2007 FSW Yes 3 SA 3 NR NR NR 726 730 5 12 Yes
Pakistan, Rawalpindi [133] 2007 MTSW Yes 2 IA NR NR NR NR NR 812 NR 8 No
Pakistan, Rawalpindi [133] 2007 FSW Yes 2 IA NR NR NR NR NR 431 NR 8 No
Thailand, Bangkok [134] 2007 FSW NR 3 ACASI, with/w/out interviewer 15 15 11.80 1.50 NR 707 3 12 Yes
Western Pacific
China, Beijing [135] 2009 MSM NR 1 ACASI 7 7 4.50 3.00 NR 500 NR 8 No
China, Beijing [3] 2004 MSM NR 1 SA 1 1 None 2.10 NR 325 15+ 14 Yes
China, Beijing [3, 136] 2005 MSM NR 1 SA 10 10 None 2.10 NR 427 15+ 16 Yes
China, Beijing [3] 2006 MSM NR 1 SA 8 8 None 2.10 NR 540 15+ 14 Yes
China, Beijing [137] 2009 MSM NR 1 CAPI 7 8 5.00 3.20 NR 501 13 8 Yes
China, Chongqing [138] 2009 MSM NR NR CASI 7 7 4.50 3.00 NR 503 14 12 Yes
China, Guangdong [139] 200S PWID Yes 1 SA 6 7 7.50 3.00 238 290 11 16 Yes
China, Guangdong [140] NR FSW NR 1 IA or CASI 4 4 NR NR NR 320 16 NR Yes
China, Guangzhou [141] 2008 MSM NR 1 SA 13 13 5.00, gift/cash 1.50 NR 379 14 16 Yes
China, Jinan [142, 143] 2007 MSM Yes 1 SA 9 9 None NR 428 428 NR 20 Yes
China, Jinan [142, 143] 2008 MSM Yes 1 SA 5 5 None NR 500 500 NR 12 Yes
China, Jinan [144] 2008 FSW Yes 1 SA 7 7 7.30 2.90 NR 363 25 24 Yes
China, Jinan [144] 2009 FSW Yes 1 SA 4 4 7.30 2.90 NR 432 21 20 Yes
China, Liuzhou [145, 146] 2009–10 FSW Yes 1 SA 7 8 14.00 7.00 380 583 20 13 Yes
China, Nanjing [147] NR MSM NR 1 NR 9 9 4.00 phone card NA NR 416 NR NR No
China, Shandong [148] 2007–08 Money boys NR NR SA 16 NR NR NR 120 118 NR NR No
Indonesia, Bandung [149] 2007 PWID NR NR SA 8 NR NR 4.00 250 250 NR 16 No
Indonesia, Surabaya [149] 2007 PWID NR NR SA 8 NR NR 4.00 250 250 NR 16 No
Vietnam, Cam Ranh [150] 2005 MSM NR 1 NR 2 NR 1.90 0.95 300 295 5 12 No
Vietnam, Dien Khanh [150] 2005 MSM NR 1 NR 2 NR 1.90 0.95 300 295 5 12 No
Vietnam, Hai Phong [150] 2004 FSW NR NR SA 20 25 3.00 1.00 200 215 NR 12 Yes
Vietnam, Ho Chi Minh City [150] 2004 FSW NR NR SA 20 24 4.00 1.50 400 413 NR 12 Yes
Vietnam, Nha Trang [150] 2005 MSM NR 1 NR 2 NR 1.90 0.95 300 295 5 12 No
Vietnam, Ninh Hoa [150] 2005 MSM NR 1 NR 2 NR 1.90 0.95 300 295 5 12 No
Vietnam, Van Ninh [150] 2005 MSM NR 1 NR 2 NR 1.90 0.95 300 295 5 12 No
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NR not reported, N/A not applicable, IA interviewer administered, SA self-administered, CASI computer assisted structured interview, ACASI computer assisted structured interview

a

Unless otherwise stated, ACASI and CASI are self-administered

b

All figures are in US Dollar

c

Adjusted is when at least one of the published articles analyzed frequency data using either the reciprocity model based estimator (RDS I) (Salganik and Heckathorn [13]), dual component estimator (RDSI DC) (Heckathorn [12]), probability-based estimator (RDSII) (Volz and Heckathorn, [14]) or successive sampling estimator (Gile [11]). In the cases data were reported as “weighted” but neither cited an appropriate reference nor an appropriate software (RDSAT, RDS Analyst and in some cases Stata 11 [Schonlaau and Liebau, 2012-RDSI Only]), R and Matlab, they were considered not providing sufficient information to determine correct analysis

d

Target sample size combined for Serbia (2010; Belgrade and Kragujevac)

e

Final sample size combined for Nigeria (2006; Ibadan and Lagos), Moldova (2007–08; Balti, Chisinau and Tiraspol), El Salvador (2008; San Salvador and Sonsonate), and India (2008;Chennai, Coimbatore, Dindigul, Erode, Kanyakumari, Madurai, Nagapattinam, Nilgiri, Perambalur, Pudukkottai, Ramanathapuram, Salem, Sivaganga, Thanjavur, Tiruchy, Tirunelveli,Thiruvarur, and Tuticorin)

Methods

Literature Search

We examined peer-reviewed literature published in physical or on-line journals that reported using RDS and were either accessible through September, 2013, or were identified from a previously conducted search [22]. Searches were conducted using MEDLINE (1997–2013), EMBASE (1997–2013), and Global Health (1997–2013). Search terms included “respondent driven”, “respondent-driven” or “RDS”. The original extraction included surveys in any country, in any language, and among any survey population that reported using RDS (n = 4562). Articles excluded in the initial extraction were those that were duplicates (n = 2360), irrelevant (e.g., protocols, presentations, flyers, etc.; n = 1716) and either reviews, opinion pieces, editorials, commentaries and papers strictly addressing RDS methodology (n = 44, i.e., those not intending to report population based estimates). This resulted in a total of 442 articles and abstracts. We further refined our search by eliminating abstracts (n = 58) and publications that were either duplicated (n = 3), non-English (n = 40), without biological data (n = 167), or claimed to, but did not, use RDS (n = 23). When there were a number of publications for a single survey, all related publications were reviewed to update the extraction sheet. This resulted in 151 articles representing 222 surveys (Fig. 1).

Fig. 1.

Fig. 1

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RDS extraction process

Categorizing Documents and Extraction

We selected and extracted key data from 151 journal articles and entered them into a master table in Excel® into rows specific to the survey(s) described. Journal data entered into the table were organized into seven sub-tables based on WHO categorizations of regions: Africa, Eastern Mediterranean (EM), Europe, Latin America and the Caribbean (LAC), North America, South-East Asia (SEA), and Western Pacific. We extracted information considered essential for assessing RDS-specific survey quality as reported in Malekinejad et al. [4], Montalegre et al. [5] and White et al. [21, 22]. The indicators reviewed included those informing survey design and implementation and analysis. Indicators informing survey design and implementation are the survey year, eligibility criteria, specimen type collected for biological testing, whether pre-survey research was conducted, number of recruitment sites, interview method, number of seeds at the start and end (and whether seeds were added or failed during data collection) of the survey, amount or type of primary and secondary incentives (USD), calculated target and final sample size, design effect used for sample size calculation, maximum number of waves, duration of data collection (in weeks), and maximum number of coupons distributed to each recruiter. Indicators informing analysis are whether equilibrium or convergence was assessed, whether data were adjusted for network size, software used, and the citation and estimator used for adjustment. The rationale for selecting these indicators, including their usefulness in any survey versus specifically for RDS surveys, are provided in Table 2.

Table 2.

Reporting of essential information to interpret survey quality, number, percentage, median and range (2004–2012) and rationale for reporting

Indicators Percent of publications reporting information Values of reported information Rational for reporting
N (222) % Median (range)
Year of survey 219 99 - Useful for any survey to identify how current data are, to plan future surveys and to compare data from other surveys
Eligibility criteria (minimum of behavior description)c 222 97 - Useful for any survey to determine the denominator being measured, to know measurement for the construction of the social network question needed for RDS analysis. Provides readers with possible criteria to use in different populations and settings; allows for comparison of data across countries
Type of specimen collected for biological testingc 193 87 - Useful for any survey. Informs readers about the types of testing being conducted in different populations and settings
Pre-survey research conducted 88 40 - Useful for any survey. Informs readers about the survey planning process, especially whether attempts were made to assess the underlying network structure of the population
Number of recruitment sites per survey area 95 43 1 (1–6) Especially useful in RDS as it alerts readers to the possible violation of the network being one complete component if participants at each site are not connected; informs readers about the possible clustering (or diffusion) of the sample
Interview method 210 94 - Useful for any survey. Provides information about the level of confidentiality in the survey (i.e., ACASI may provide more confidentiality) and informs readers about the different types of methods used for interviewing hidden populations in RDS surveys
Number of seeds at the start of the survey 62 28 7.5 (1–48) Specifically useful for RDS surveys. Informs readers about whether many seeds were added during data collection and the number of seeds in relation to the sample size and number of waves (too many seeds may result in too few waves needed to reduce seed dependence, adding too many seeds may be an indication that the population is not well networked); provides parameters for readers about seeds needed in different populations and settings
Whether seeds were added during data collection 17 8 5 (1–37)
Whether seeds failed during data collection 14 6 4 (1–24)
Number of seeds by the end of the survey 121 54 10 (1–156)
Amount or type of primary incentive (USD) 186 84 3 (1.9–5)a Specifically useful for RDS surveys. Provides readers with parameters about amounts used in different populations and settings; provides an indication of potential bias during recruitment (if incentives are too high, more people may enroll who are not eligible)
Having no primary incentive 6 3 -
Amount or type of secondary incentive (USD) 177 80 10 (0.95–20)b
Having no secondary incentive 18 8 -
Calculated target sample size 89 40 300 (100–756) Useful for any survey. Indicates if an original sample size was calculated and if that sample size was reached in order to ensure sufficient power and confidence for data analysis. Provides readers with parameters about sample sizes used in different populations and settings. Specific for RDS surveys: combining multiple survey sites is often a violation of the network being one network component
Final sample size 212 95 325 (100–1056)c
Final sample size for multiple cities combined 28 12 -
Design effect used for sample size calculationd 50 22 2 (1.3–3) Design effects, currently recommended to be at least 2 [16, 151, 152], are important for calculating a sufficient sample size to account for RDS not being a traditional random sample
Maximum number of waves 95 43 9 (3–21) Specific to RDS surveys: useful to assess seed dependence
Duration of data collection (in weeks) 139 63 12 (2–124) Informs readers of the time needed to gather samples of different sizes from different populations and settings; alerts readers of unusual recruitment lengths that may impact representativeness of the sample
Maximum number of coupons distributed to each recruiterd 163 73 3 (2–7) Specific to RDS surveys: The number of coupons used are normally three [7], but some surveys have used more or fewer. Analysis does not account for branching induced by the number of coupons provided to each participant so fewer coupons, when possible, is useful to mimic a random walk process
Whether equilibrium or convergence was assessedd 44 20 Specific to RDS surveys: Informs readers of seed dependence and is a diagnostic to assess bias
Whether data were adjusted for network size 157 70 - Specific to RDS surveys: Informs readers of the extent to which RDS was fully utilized, resulting in the ability to assess whether the survey may represent the network of the population from which the sample was gathered
Software used to adjust datad 162 73 - Specific to RDS surveys: There are limited software packages available for analyzing RDS data. Analyzing RDS data in more popular, preexisting software (i.e., STATA, SPSS) may not eliminate RDS specific biases
Citation for adjustmentd 59 26 - Specific to RDS surveys: Given the evolvement of the estimators for the analysis of RDS data, this is useful for providing information about the assumptions supporting the adjustments
Heckathorn [10, 153]d 19 32 -
Salganik and Heckathorn [12]d 28 47 -
Heckathorn [11]d 10 17 -
Volz and Heckathorn [13]d 7 12 -
Gile [154]d 4 7 -
Estimator used for adjustmentd 10 4 -
Whether seeds were discarded during analysis 31 14 - Some studies either did not collect data from seeds or did not include seeds data the analysis, which could likely result in the sample having addition seeds (analysis would assume wave 1 participants are seeds) thereby potentially impacting seed dependence and biasing the final estimate
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a

Among those that reported a value (n = 166)

b

Among those that reported a value (n = 152)

c

Among those that reported a sample size for an individual city (n = 185)

d

Not presented in Table 1

Analysis

Frequencies were used to characterize the surveys and their contents. We conducted robust and logistic regressions of survey start year and pre-survey research, eligibility age, number of seeds at the start and end of survey, survey duration, final sample size, estimated design effect, length of longest recruitment chain, and adjustment of RDS to assess linear trends in the value (and for reporting having conducted pre-survey research or adjusted RDS data) of these indicators over time. Design effects were calculated for surveys that presented a point estimate for HIV prevalence, 95 % confidence intervals and the final sample size. The calculation for design effects consisted of dividing the widths of the confidence interval by two, dividing again by 1.96 (the standard normal value corresponding to a central area of 95 %), and squaring the final number.

Results

The identified published articles of RDS surveys were conducted in the following WHO regions: 21 from Africa (28 surveys), 12 from EM (11 surveys), 30 from Europe (44 surveys), 17 from LAC (37 surveys), 41 from North America (45 surveys), 12 from SEA (32 surveys), and 18 from the Western Pacific (25 surveys). Extracted surveys included 85 among PWID, 78 among MSM and 38 among FSW. Surveys of other groups included people who use and/or inject drugs (n = 2), male sex workers (n = 3), high-risk heterosexuals (n = 7), transgender (n = 2), and youth (n = 3). The remaining surveys were of mixed groups such as youth PWID (n = 2), people who use and/ or inject drugs together (n = 1) and MSM who use and/or inject drugs (n = 1).

Assessing Reports of Survey Quality

Survey data extracted from published articles included in this review were used to assess whether RDS recruitment and analysis were conducted, but the details provided for these surveys varied across articles. For instance, all published surveys reported basic implementation information such as the city, country and the population sampled, and 99 % reported the survey year (Table 2). Over 90 % of surveys reported the interview technique (e.g., face-to-face questionnaire, computer-assisted self-interviews, etc.) (94 %), final sample size (95 %) and at least the behavioral component of the eligibility criteria (97 %). Eighty-four percent reported the primary and 80 % reported the secondary incentive amounts or types, and 73 % reported the maximum number of coupons given to each recruiter. Sixty-three percent reported the data collection duration, 40 % reported whether pre-survey research was conducted, 43 % reported the number of recruitment sites used and the maximum number of waves, 40 % reported the target sample size, 22 % reported the design effect used for calculating the sample size. For those surveys that presented both calculated and final sample sizes (n = 77, 35 %), the median percentage difference was 1.0 (range 0.2–1.6). There was no significant difference in this measure over time by population or among all populations combined.

Seventy percent reported whether data were adjusted for network size, 73 % reported the type of software used to adjust data (74 % of which used RDS Analysis Tool [RDSAT]) and 26 % cited the statistical adjustment, among which 47 % cited Salganik and Heckathorn [12], 32 % cited Heckathorn [10] and/or 2002, 17 % Heckathorn [11], 12 % Volz and Heckathorn [13] and 7 % Gile [14, 154]. Only 20 % of surveys reported whether equilibrium or convergence was assessed and 4 % reported which estimator was used for their statistical adjustment. Thirty-one surveys (14 %) specifically reported discarding seeds from their analysis.

Design Effects for HIV

Of the 222 publications reviewed, 185 reported HIV prevalence point estimates above 0, 136 included 95 % confidence bounds, and 210 reported final sample sizes. Ninety-five surveys (42.7 %) included all three elements to enable calculation of the estimated design effect for HIV prevalence. Four (4.2 %) had a design effect less than 1.0, 28 (29.5 %) had a design effect of 1.0, 46 (48.4 %) had a design effect of 2. The remaining design effects were as high as 5.9, indicating that a larger sample size was needed to estimate HIV prevalence.

Assessing Changes over Time

In assessing changes over time (Table 3), we found significant decreases in values for eligibility age, final number of seeds, and final sample size (p \ 0.01, for all) and significant increases in the reporting of pre-survey research and values of design effects to calculate the target sample size (p \ 0.01). There were no significant changes in values for survey duration even when adjusting for target population and final sample sizes. Nor were there significant changes by year for survey duration, length of longest recruitment chain and reporting of having adjusted RDS data.

Table 3.

Annual rate of change over time (2004–2012) based on robust regression of values and reported information

Variable Change (95 % CI) p value
Reporting having conducted pre-survey research 0.04 (0.01, 0.08) 0.01
Reporting having adjusted RDS data 0.03 (0.00, 0.6) 0.09
Lower eligibility age value −0.14 (−0.20, −0.07) 0.00
Number of seeds at start of survey −0.14 (−0.43, 0.14) 0.33
Number of seeds at end of survey −0.31 (−1.86, −0.66) 0.00
Survey durations 0.15 (−0.35, 0.66) 0.55
Final sample sizes −29.14 (−39.56, −8.73) 0.00
Sizes of estimated design effects 0.07 (0.02, 0.12) 0.01
Length of longest recruitment chain 0.29 (−0.39, 0.97) 0.40
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Discussion

Reporting on details of survey design, implementation, and analysis is essential for assessing the quality of RDS surveys and findings. It is important to adequately describe both the methodological and analytical aspects of RDS in any publication. The majority of surveys reported the most essential information such as survey city, country, year, population sampled, interview method, and final sample size. Given that all surveys reported collecting biological specimens, it is surprising that 13 % did not provide information about specimen collection and testing methods. Gaps in reporting RDS methodological and analytical information made it difficult to assess survey quality and the strength of results. RDS does not work in all situations and challenges in meeting assumptions should be described. For instance, only 43 % of surveys reported the maximum number of waves and 20 % reported assessment of equilibrium or convergence, information needed to assess potential biases. Among those surveys reporting their maximum number of waves, some reported having only a maximum of three waves, indicating that the survey results were likely biased by the non-randomly selected seeds.

Pre-survey research has become increasingly cited as being essential to conducting RDS surveys [7, 15, 22, 155], and more publications over time were found to provide information about having conducted some pre-survey research. Because RDS samples a social network, pre-survey research is imperative to understand the underlying network structure of the sampled population. If the sampled network is fragmented or has isolated sub-groups, the chances of sampling more than one network are higher, possibly resulting in unstable estimates [15]. Furthermore, pre-survey research data can help investigators plan survey logistics (i.e., types and number of seeds) and encourage participation by learning about which survey procedures are most acceptable to the target population [7]. We recommend that all surveys using RDS conduct pre-survey research to evaluate social networks, as well as to assess the feasibility of using RDS in a particular population.

Although 70 % of surveys reported whether data were adjusted for network size and 73 % reported the software used to adjust those data, few cited the adjustment procedure and even fewer reported the estimator used. There are currently at least five different estimators for adjusting RDS data [156]. Given that many of the reviewed articles were written before the existence of some estimators, it is understandable that earlier publications did not cite the estimator used for analysis. Forthcoming publications should cite the estimator since knowing this information will allow readers to know how adjustments were made, if they were made properly, and the assumptions supporting those adjustments.

Several publications reported discarding seeds from analysis. While it has been written that “seeds are eliminated from analysis” [13, 153], this is not to say that seeds should be manually eliminated from a dataset. The RDS-I and RDS-II estimators [11–13, 153] use a matrix of recruits and recruiters whereby data from the recruits are necessary for calculating inclusion probabilities used to derive final estimates. Even though the seeds do not technically show up in the probability matrix since they were never recruited by their peers, their data are nonetheless necessary for establishing the placement of the seeds’ recruits in the matrix. We recommend that seeds remain in the dataset during all analyses and that the final reported sample includes the seeds.

We found an increase over time in surveys reporting design effects, an element in the sample size calculations to account for RDS not being a simple random sample. Although recent publications have found that design effects of 3 or 4 would be optimal, in most situations, a design effect of 2 is often recommended [16, 151, 152]. Because operational constraints, such as limited financial resources, often preclude large sample sizes for some RDS surveys, using a design effect greater than 2 may result in unfeasibly large sample sizes. Post-hoc design effects on key variables can help determine if sample sizes were large enough for the analysis and inform sample size calculations for follow-up surveys of the same population. As such it is useful for publications to include point estimates, 95 % confidence intervals and final sample sizes to allow for the post hoc estimation of design effects.

Equilibrium or convergence was reported in only 20 % of the articles reviewed. Equilibrium, the term most often used when referring to RDS surveys, measures the progression of waves to determine when the proportion for a characteristic approaches and remains stable in relation to the final sample statistic [10]. Convergence, a more sensitive measurement, measures the progression of enrolling subjects to determine when the proportion for a characteristic approaches and remains stable in relation to the adjusted estimate [15]. Nevertheless, the assessment of either equilibrium or convergence is useful for determining seed dependence, a typical bias found in chain referral sampling methods, and should be reported for publications reporting population estimates from RDS surveys [22].

While most surveys reported a minimum eligibility age of 18 years (n = 150), we found the minimum age decreased over time. Collecting HIV and other biological and behavioral data from younger key populations is important given they are disproportionately affected by HIV worldwide and are comprising a high percentage of new HIV infections [9, 157].

Our review has limitations. As in any systematic review, we are restricted by the completeness of our publication search and whether investigators published their surveys in peer-reviewed journals. Furthermore, we only included surveys that collected biological data leaving room for further evaluation of those surveys that reported using RDS and did not collect biological data. The number of peer-reviewed articles of RDS surveys is far fewer than the actual number of surveys conducted. Key data were missing from articles, an important finding in itself which supports the need to uniformly report results from RDS surveys, which limited the scope of our analyses and introduced uncertainty into some of our other findings [22]. We excluded articles clearly stating they either used RDS ‘recruitment’ only or did not fulfill necessary features of the method; however, we may have included some surveys that did not incorporate all RDS methodological and analytical features, given their incomplete reporting. In those instances, we classified the surveys as using RDS and included them in the extraction. Several of the 23 articles claiming to use RDS, but did not, reported using a ‘modified’ or ‘mixed methods’ RDS. However, they did not provide conclusive evidence such as the collection and use of personal network size data, recruitment ties (who recruited whom), coupon quotas, and multiple recruitment waves. In several extracted publications, significant limitations were reported, including unprepared staff, numerous ineligible persons trying to participate, closing or moving survey sites during data collection, overly high (possible indication of enrollment of ineligible participants) or low incentives, overcrowding at the interview site, failure to recruit important population subgroups (i.e., females in PWID surveys, older MSM), incorrect or no social network question, and early survey termination due to finances or community disturbances [3, 34, 41, 55, 86, 127, 158]. Presenting key limitations is useful for interpreting findings and should be included in all publications presenting data from RDS surveys.

The majority of published surveys were from North American and Europe; it would be useful to see more publications of RDS survey results from other regions. Not only could experiences from these different settings help researchers improve survey methods and analysis, but the results themselves could help policy makers, donors, and service providers to improve responses to HIV and other infection risk. Future publications of biological and behavioral surveys using RDS should provide a minimum set of parameters in order for readers to assess specific methodological, analytical and testing procedures, and to make determinations of the overall quality of these surveys.

Acknowledgments

We would like to thank Kate Orroth for conducting the literature search for the STROBE-RDS Guidelines and allowing us to use it for this analysis.

Funding This Project has been supported in part by the President’s Emergency Plan for AIDS Relief (PEPFAR) through the Centers for Disease Control and Prevention (CDC). RGW is funded the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement that is also part of the EDCTP2 programme supported by the European Union (MR/J005088/1, G0802414), the Bill and Melinda Gates Foundation (TB Modelling and Analysis Consortium: OPP1084276, and SA Modelling for Policy: #OPP1110334) and UNITAID (4214-LSHTM-Sept15; PO #8477-0-600).

Footnotes

The findings and conclusions in this paper are those of the authors and do not necessarily represent the official position of the US Centers for Disease Control and Prevention.

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