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Review Article  
 
Accumulation of Biological and Behavioral Data of Female 
Sex Workers Using Respondent-Driven Sampling Around 
the World: Systematic Review 
 
Mihir Bhatta1,*, Agniva Majumdar1, Piyali Ghosh1 , Sitikantha Banerjee2, Debjit 
Chakraborty1, Subrata Biswas1, Srijan Sahoo1, Shanta Dutta1 
1 ICMR-National Institute of Cholera and Enteric Diseases, Kolkata - 700010, India 
2 AIIMS, Jammu, Jammu and Kashmir 180001, India 
 
* Correspondence: mihirbhatta@gmail.com. Phone: +919051821957 
 
Received 27 Feb 2024; Accepted 01 August 2024; Published 19 Dec  2024 
 

ABSTRACT 

Background: Respondent-Driven Sampling (RDS) is generally used to study hidden or 
hard-to-reach populations. The objective of the present work is to describe the initiation, 
implementation, and complications that arise during RDS of female sex workers (FSWs) 
around the world. 
Method: Behavioural and biological data of FSWs collected through RDS was mined from 
peer-reviewed articles, published during 2010-2022. Review protocol was developed and 
registered in the PROSPERO (registration number CRD42022346470) and published 
separately. 
Results: It was found that most of the RDS (69 articles, globally) were largely successful in 
the recruitment of FSWs, with varying response rates. 
Conclusion: Present outcomes supports the application of RDS in surveillance for any such 
population by providing a minimal set of parameters of testing procedures (methodology) 
including methods to evaluate the quality also.  

Keywords: Respondent-driven Sampling (RDS); Female sex workers (FSW); Sampling; RDS 
implementation; Hidden population; Hard to reach population  

INTRODUCTION 

The Respondent-driven Sampling (RDS) method is a non-probability sampling method that 
approximates probability sample design, allowing for extrapolating results to the target 
population. Studying communities that are hidden or difficult to reach has certain limits, which 
are typically addressed by this approach.1 In the early 1990s, the phrase "hard-to-reach 
population" was coined in the field of public health to describe poor socioeconomic and low 
literacy populations, ethnic minorities, and those, who are not successfully reached by the 
health workers through various healthcare initiatives.  

The phrase "hidden population" was created by social science researchers to refer to the 
population with an inadequate sampling frame. This may occur either they belong to an 
unorthodox occupational group (female sex workers) or closed social groups (men who have 

 
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sex with men), or have unsocial behaviours (injecting drug users), or very small population 
size belong to an ununorthodox occupational group (transgenders), among others.1 

RDS was originally developed by Heckathorn2 as a chain-reference sampling method that 
traces data from one individual to another by relational succession. Stochastic Markov chain 
modeling, snowball sampling, and the theory of biased networks (homophily model) are all 
combined in this method1. The idea that every individual, regardless of the size of the network, 
is indirectly connected to every other individual through around six intermediaries is another 
notion the authors rely on to explain the "small world" phenomena. In the event that this 
premise is accurate, any randomly selected individual can be reached in the sixth wave of a 
reference chain, including the most socially isolated individuals2. Additional sociological 
theories—particularly behavioral theories—provide additional justifications for the "small 
world" phenomena. RDS has been used in a number of countries to gather data from 
populations at higher risk of HIV exposure, including female sex workers (FSW), men sex with 
men (MSM), injecting drug users (PWUD, PWID), and other individuals deemed “hard-to-
reach” because of social stigma and engagement in socially unaccepted behavior.1,3 With the 
guidance of agencies like the US Centers for Disease Control and Prevention, UNAIDS, the 
Global Fund, WHO and others, this sampling technique has been used over the years to 
generate data on the baseline and trend analysis for prevalence estimation, study on risk 
behaviors, and the impact of the program on HIV and other sexually transmitted infections 
through biological and behavioral assessments.3 

In a range of contexts the sampling technique is proven viability and success in attracting 
hidden populations of IDUs, resulting in a quick acquired of long and diverse recruitment 
chains. It has been used in a number of countries to gather biological and behavioral 
information from male who have sex with male (e.g., Bangladesh, Cambodia, Uganda, United 
States) and sex workers (e.g., Vietnam, India).3–4 RDS begins with a set number of 
participants, referred to as "seeds," who are selected from the target population, chosen with 
care by the surveillance team. Samples are created through peers giving coupons to one 
another.Each participant receives a limited quantity of coupons, which restricts the excessive 
contributions made by those with more connections to other members of the same network. 
These save participants from providing specific details on their recruits, allowing researchers 
to see the recruitment process in action.5 To guarantee continued involvement and appropriate 
recruitment, those who are taking part in the survey and enlisting peers are given "incentives." 
A protracted recruitment chain made up of numerous "waves" of recruits may emerge from 
this recruitment process. As the recruitment chains lengthen, the sample arrangement 
becomes less dependent on the purposefully chosen seeds.6 Following sample collection, 
statistical modifications are made to account for variations in network sizes and recruiting 
efforts, yielding data on estimates that are representative of the population's network.The fact 
that most 1-2,5 FSWs are mobile and frequently move between solicitation points, within and to 
other districts, towns, states, and provinces, which makes them difficult to reach. 7 Some ladies 
are difficult to locate as they engage in part-time sex work. Higher paid sex workers have the 
option to stay anonymous, such as those who use apps, the internet, or agencies to solicit. 
Through bio-behavioural surveillance efforts, FSWs in many countries have also been 
sampled using a probability sampling technique time location sampling (TLS). TLS, however, 
can only represent FSWs those are readily recognizable at visible places; as a result, it may 
omit potentially significant information from other kinds of FSWs. Since there hasn't been 
much experience with RDS yet, more testing is necessary to confirm this modality. The viability 

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of using this sampling technique to find hidden populations of FSWs in environments where 
access to FSWs and the organization of sex work are tightly regulated, and where there is 
minimal or no interaction between the target population and community resources, is not well 
understood.7 In this context, objective of the present article is to describe implementation and 
outcome of RDS practices to accumulate biological and behavioral informations of FSW 
population, around the world. 

METHOD 
Details of the methods for the present article is developed as a separate protocol registered 
in the PROSPERO (registration number CRD42022346470) and published in a peer reviewed 
journal.8 

Eligibility criteria 

A total of 438 articles  and abstracts are initially included after the initial search. Present search 
is further refined by removing abstracts and texts as duplicated (n = 177), without relevant 
protocols (n = 49), or claimed but not using RDS (n = 75) and review articles (n = 68). There 
were several publications found for a particular study and all related articles were reviewed 
and included on the previously prepared extraction sheet. This brings about 69 articles 
representing 76 different surveys included in the present work Figure 1, based on WHO 
classifications of regions.9 

 

Figure 1. Flow digram of RDS extraction  

 

Data extraction 

The data has been extracted from 69 full text (Table 1) published articles10-77 through Epi 
Info™ (Ver. 6.0) generated modified data extraction tool accordance to the STROBE-RDS 

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(Strengthening the Reporting of Observational Studies in Epidemiology for Respondent-
Driven Sampling) guideline. That was arranged into six sub-tables in Table-1 based on WHO 
classifications of regions:9 African Region (AFR), Region of the Americas (AMR), South-East 
Asian Region (SEAR), European Region (EUR), Mediterranean Region (EMR), and Western 
Pacific Region (WPR). The included reviewed indicators whose apprising design and 
implementation of the survey are observed and subjected to further analysis. Indicators 
informing survey design and implementation, year of publication, criteria for eligibility, 
specimen type/s collected for lab testing, pre-survey research was conducted or not, number 
and position of recruitment sites, methods of interview, the actual number of seeds at the start 
and at the end (and whether seeds were added or failed during data collection), amount and 
kind of primary and secondary incentives (in USD), calculated target and final sample size, 
design effect used in sample size calculation, the duration of data collection (in weeks), 
maximum number of waves, and maximum number of coupons distributed to each recruiter. 
Further analysis was carried out to find whether equilibrium or convergence was assessed or 
not, whether data were adjusted for network size, software used or not, etc. 

Preparation of map 

Topographical sheets of world map have been scanned, geo-referenced, and then digitized 
with the help of quantum geographic information system (Q-GIS), a free, open-sourced, cross-
platform desktop-based GIS application, written in C++, Python, QT languages and provides 
services like viewing, editing, analysis of geospatial data. Data based on the region has been 
entered to the newly prepared digitized map as the non-spatial data or attributes78. 

Case definition 

In the present study, female sex workers are considered as those who are females and born 
biologically same, and actively engaged in sailing sex in terms of money/ gift or both.79 

Data analysis 

Frequencies were used to describe the studies and their findings. The details of starting year 
of the study and pre-survey investigation, suitability of age, number of seeds at the beginning 
and end of the survey, duration of the survey, ultimate sample size, assessed design effect, 
the span of longest recruitment chain, and adjustment of RDS were reported accordingly. 
Thematic analysis was performed to identify the outcomes (challenges, reasons, and 
suggested interventions) common between studies.80-83 The evidence was synthesized 
manually and the conceptual model was presented in a tabular sheet. 

RESULTS 
The available previously published articles on RDS were belongs to the following WHO 
regions 8: 34 studies from the African Region (AFR), 11 studies from the Region of the 
Americas (AMR), 11 studies from the South-East Asian Region (SEAR), four from the 
European Region (EUR), 6 studies from the Eastern Mediterranean Region (EMR) and 9 
studies from the Western Pacific Region (WPR) (Figure 2). There were ten articles from India, 
nine articles from Brazil, six from Zimbabwe, five from China, four articles each from Togo, 
Swaziland, South Africa, and Papua-New Guinea; three articles each from Sudan and Kenya; 
two articles each from Uganda, Namibia, Iran, and Burkina Faso. The rest of the included 
countries had one article from each (Table 1). 

The key challenges faced by researchers while recruiting FSWs using RDS have been 
described here in detailed. The challenge reported by most of the studies is the poor response 
rate,84-86 which was measured in different ways by low coupon return rate and the considerable 
number of seeds being non-productive. As a result, the workers either did not complete 
recruiting the desired sample within the stipulated time, had to increase the duration of data 

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collection, or added new seeds when the data collection process was ongoing.79 The key 
reasons for poor response rate were found to be less mixing/ networking of FSWs and their 
high level of independence, external control of FSW restricted freedom of movement and 
perception of inadequate incentive and their mistrust towards researchers due to poor 
community involvement67. Moreover, accessibility, acceptability, and timing of RDS sites and 
social stigma also resulted from low coupon returns.80 Pre-surveillance formative research 
involving community representatives (community  representatives are often former or current 
sex workers themselves, who can understand and address the unique challenges faced by 
their community) was considered as crucial for assessing network characteristics and 
feasibility of RDS in the study setting. Other interventions for improving response included 
intensive rapport building, provision of services along with research, finalization of RDS site, 
and nature of incentive after discussion with study participants.81   

Selection bias could occur due to high homophily, i.e. tendency of participants to recruit others 
with similar characteristics.82 This could be minimized by weighted analysis and statistical 
modelling.83 RDS could miss a selected geographical pocket where seeds were non-
productive, and their characteristics could be significantly different from those who were 
selected.84 The use of primary and secondary incentives was likely to attract the FSWs with 
poor socioeconomic status, but unlikely to involve those with higher purchasing power.85 A 
meticulous seed selection could decrease such selection bias by recruiting FSWs from 
different geographical divides, social statuses and occupational differences.84 

Research had revealed that known HIV-infected persons could have a high probability of 
refusing to attend RDS sites and get tested for HIV, compared to their counterparts, as a 
result, RDS could under-estimate prevalence of HIV.85  On the other hand, inaccurate 
reporting of network size could result in a biased sampling weight, which could adversely affect 
the outcome.86 Scientists are suggested that the accuracy of reporting network size could be 
checked by collecting network size-related data both during the main interview and in the 
follow-up interview (while collecting secondary incentives), and conducting test-retest 
reliability of this estimate.87 

Among the 69 previously published articles, 64 articles are found to be reported HIV 
prevalence point estimations above zero, and one article selected was not on HCV instead of 
HIV. 32 articles (46.37 %) included all three components to facilitate the design of calculated 
design effect for HIV prevalence. 6 articles (8.7 %) conveyed the design effect less than 1.0, 
22 articles (31.9 %) carried the design effect of 1.0 and 4 articles (5.8 %) carried the design 
effect of more than 1.0., which directed that larger sample size is always required to calculate 
correct HIV prevalence. As mentioned by many researchers, it was of utmost importance to 
carry out a detailed formative evaluation of network characteristics and occupational 
differentials of this high-risk group before deciding whether RDS was feasible or not. After 
deciding to introduce RDS, the next step should be thorough rapport building (through key 
informant interview or consultation meeting or both) with the community, and selection of seed 
and venue (location and timing) through proper guidance from community representatives. 

 

 

  

 

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Table 1. Methodological and analytical data extracted from previously published articles. 
 

Author Year Country Pre-survey 
assessment 

No. 
of 

sites 

Interview 
method 

Initial 
seeds 

Final 
seeds 

Primary 
incentives 

($) 

Secondary 
incentives 

($) 

Target 
sample 

size 

Final 
sample 

size 

Maximum 
no. of 
waves 

Duration 
of data 

collection 
(week) 

Data 
adjusted 

Lowest 
age 

limits 
(FSW) 

Inconsistent 
condom 
use (%) 

African Region (AFR) 

Abdelrahim et 
al. 2010 Sudan NR 1 IA NR NR 10 10 NR 321 NR 8 Yes NR NR 

Vandenhoudt, 
et al 2013 Kenya Yes 1 ACASI 15 NR 4 1.25 480 481 6 12 Yes NR NR 

Johnston, et 
al. 2013 Mauritius NR 2 IA 5 5 17.5 7 NR 299 8 2 Yes NR NR 

Fonner et al. 2014 Swaziland Yes 1 IA 3 9 3 2.5 324 325 NR 20 NR 18 17.84 

Baral et al. 2014 Swaziland Yes 1 IA NR NR NR NR NR 328 NR 12 No 18 61.7 

Yam et al. 2014 Swaziland Yes 1 IA NR NR NR NR NR 325 NR 12 Yes 16 NR 

Musyoki et al. 2015 Kenya Yes 1 NR 6 NR 2 2 600 596 10 8 Yes 18 35.4 

Schwitters et 
al. 2015 Uganda Yes 1 ACASI 4 NR 4 1.25 1500 1501 25 36 Yes 15 40 

Mtetwa et al. 2015 Zimbabwe Yes 3 IA 22 22 5 2 NR 836 6 48 NR 18 NR 

Holland  2016 Burkina Faso NR 2 IA 10 10 3 4 700 694 NR 28 Yes 18 NR 

Quaife et al. 2016 South Africa Yes 1 IA 7 10 3.14 1.26 200 200 NR 24 No 16 NR 

Holland et al. 2016 Togo NR 2 IA 10 10 101 10 700 679 NR 28 Yes 18 NR 

Hargreaves et 
al. 2016 Zimbabwe NR 14 CASI 4 9 NR NR 2800 2800 5 20 Yes 18 NR 

Ouedraogo et 
al. 2017 Burkina Faso Yes 5 

IA 
 
  

3 

10 
 
 
  

4 3 NR 471 NR 64 Yes 18 37.7 

                 

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Lafort et al. 2017 Kenya Yes 1 IA 16 16 NR NR 400 400 NR 12 Yes NR NR 

Lafort et al. 2017 Mozambique Yes 1 IA 13 13 NR NR 400 308 NR 12 Yes NR NR 

Lafort et al. 2017 South Africa Yes 1 IA 11 11 NR NR 400 458 NR 12 Yes NR NR 

Amogne et al. 2019 Ethiopia Yes 11 IA NR NR 5 2.5 4400 4900 NR 24 Yes 15 25.5 

Twahirwa 
Rwema et al. 2019 Senegal NR 3 IA 12 12 NR NR NR 758 14 12 Yes 18 48 

Parmley et al. 2019 South Africa Yes 2 IA; CAPI 30 30 9.5 4.5 400 410 NR 28 No 18 NR 

Lasater et al. 2019 Togo Yes 1 NR 5 5 NR NR 350 3545 NR 28 Yes 18 NR 

Chabata et al. 2019 Zimbabwe Yes 14 IA 6 8 NR NR 2800 2883 5 16 NR 18 39.2 

Cowan et al. 2019 Zimbabwe Yes 19 NR NR NR 5 2 NR 6096 7 6 years Yes 18 28.3 

Fearon et al. 2019 Zimbabwe No 7 NR 6 8 NR NR NR 1439 5 92 Yes 18 81 

Owen et al. 2020 eSwatini NR 1 IA 14 14 NR NR 325 325 7 12 Yes 16 66.5 

Jonas et al. 2020 Namibia Yes 4 ACASI 33 52 10 2 NR 1188 15 48 Yes 18 71.6 

Hakim et al. 2020 Sudan Yes 1 CASI 4 9 20 21 NR 838 17 20 Yes 15 19.8 

Chabata et al. 2020 Zimbabwe NR 6 IA 6 10 3 2 1810 1842 NR 24  18 19.6 

Boothe et al. 2021 Mozambique Yes 1 IA 13 13 NR NR 400 308 NR 12 Yes 15 NR 

Bitty-
Anderson et 
al. 

2021 Togo Yes 1 IA NR NR NR NR 348 1036 NR 8 No 18 45.8 

Yeo et al. 2022 South Africa Yes 2 IA; CASI 11 11 19 1.5 NR 664 NR 32 No 18 NR 

Hakim et al. 2022 Sudan Yes 2 IA 4 9 20 21 NR 838 NR 56 Yes 15 38 

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Bitty-
Anderson et 
al. 

2022 Togo Yes 8 IA NR NR NR NR 495 1036 NR 12 No 18 33.7 

Region of the Americas (AMR) 

Damacena et 
al. 2011 Brazil Yes 10 ACASI 2 4 2 1 2500 2523 6 48 Yes 18 NR 

Damacena et 
al. 2011 Brazil NR NR ACASI 5 5 NR 4 300 289 NR 52 NO NR NR 

Johnston et 
al.  2012 Honduras Yes 1 ACASI 5 5 2 3.5 200 182 11 8 Yes NR NR 

Dennis et al. 2013 El Salvador NR NR CASI, IA 10 10 NR NR NR 787 NR NR NO NR NR 

Lima et al. 2017 Brazil Yes 10 ACASI 5 10 NR NR 1000 2523 NR 48 Yes 18 NR 
Ferreira-
Janior et al. 2018 Brazil Yes 12 IA 5 10 NR NR 4200 4245 NR 20 Yes 18 NR 

Martins et al. 2018 Brazil Yes 2 CAPI 5 5 3.02 2.01 400 402 NR 16 NR 18 NR 
Szwarcwald 
et al. 2018 Brazil Yes 12 IA 5 10 5 2.5 4200 4328 NR 20 Yes 18 NR 

Braga et al. 2021 Brazil Yes 12 SA NR NR NR NR 4200 4328 NR 24 Yes 18 NR 

Kolling et al. 2021 Brazil Yes 12 SA NR NR NR NR 4200 4328 NR 24 Yes 18 NR 

Matteoni et al. 2021 Brazil Yes 12 IA 5 10 5 2.5 4200 4328 NR 20 Yes 18 NR 

South-East Asian Region (SEAR) 
Blankenship 
et al. 2010 India Yes 1 IA 5 5 NR NR NR 1485 NR 12 Yes 24 NR 

Gupta et al., 2011 India Yes 1 IA NR NR NR NR NR 812 NR 12 Yes NR NR 

Barua et al.  2012 India NR NR SA 10 10 NR NR 400 426 11 8 NO NR NR 

Erausquin et 
al. 2012 India No 1 IA 5 5 0 0 2335 2276 NR 40 Yes 18 38.8 

Medhi et al. 2012 India Yes 1 IA 10 9 NR NR 400 426 11 8 NO NR NR 

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Armstrong et 
al. 2013 India Yes 1 IA NR NR NR NR 400 426 NR NR Yes 18 55.8 

Lafort et al. 2016 India Yes 1 IA 8 8 NR NR 400 400 NR 12 Yes  NR 

Reed et al. 2016 India NR 3 IA NR NR 4.7 NR NR 2335 NR 12 NR 18 NR 
Manathunge 
et al. 2020 Sri Lanka Yes 3 IA 2 7 2 1 1100 1180 9 20 Yes 18 20 

European Region (EUR) 

Zohrabyan et 
al. 2013 Moldova NR NR IA 5 5 16 12 350 299 6 16 Yes NR NR 

Wirtz et al. 2015 Russia Yes 3 IA NR NR NR NR NR 754 NR 12 NR 18 14.6 
McLaughlin et 
al. 2019 Armenia Yes 3 IA NR NR 7 3 NR NR 9 24 NR 18 NR 

Jonsson et al. 2019 Sweden Yes 1 IA 57 34 11 NR 595 415 15 21 Yes 18 NR 

Eastern Mediterranean Region (EMR) 
Mahfoud et al. 2010 Lebanon NR NR NR NR NR 6.6 2 NR 81 NR NR Yes NR NR 
Kriitmaa, et 
al. 2010 Somalia NR 1 IA w/ 

CAPI 6 NR 4 3 146 237 NR 8 Yes NR NR 

Valadez et al. 2013 Libya Yes 1 IA NR 13 NR NR 314 69 10 20 Yes NR NR 

Ahmadi et al. 2012 Iran Yes 8 IA N N 0 0 NR 144 NR 48 NR 18 19 

Navadeh et 
al.  2012 Iran NR 1 IA 8 12 4 2 NR 177 NR 16 Yes NR NR 

Johnston et 
al. 2015 Morocco Yes 4 IA 4 10 NR NR NR 1447 10 8 Yes 18 NR 

Western Pacific Region (WPR) 

Li et al. 2010 China No 1 ACASI; 
IA 3 4 0 0 NR 320 16 20 Yes 16 82.6 

Li et al. 2012 China NR 1 NR NR 3 16 8 NR NR 20 16 Yes 15 NR 

Yamanis et al 2013 China Yes 1 IA 7 7 19.66 12.1 454 515 11 16 NR 18 0 

Liu et al.  2016 China NR 3 NR NR 3 16 8 NR 1245 20 16 Yes 15 NR 

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Guida et al. 2019 China Yes 3 CAI 4 5 10 3 1200 1245 11 12 No 35 37.2 

Weikum etal. 2019 Papua New 
Guinea Yes 3 IA 5 5 12 2 2100 2091 NR 28 No 12 NR 

Hakim et al. 2020 Papua New 
Guinea Yes 1 IA 5 9 14 4.5 700 674 NR 20 Yes 12 6 

Willie et al. 2021 Papua New 
Guinea Yes 2 NR NR NR NR NR NR 2091 NR 20 NR 12 NR 

 

                  

NR: not reported  
IA: interviewer administered 
SA: self-administered 
CASI: computer assisted structured interview  
ACASI: computer assisted structured interview  
CAPI: computer-assisted personal interviewing 

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ISSN 2656-6052 (online) | 2656-1107 (print) 

 

      10.26555/eshr.v6i2.9845   
 

 
 

  

Here, we like clear that key informants are individuals who have significant and specialized 
knowledge about the community. This knowledge may stem from their professional roles, 
personal experiences, or long-term engagement with the community. They are often consulted 
in qualitative research and needs assessments to provide in-depth information, perspectives, 
and contextual understanding that might not be accessible through other data collection 
methods. The number of seeds should be guided by population size and the type of networking 
within it. Utmost care should be taken not to miss selected geographical pockets while 
distributing the seeds. Considering the stigmatization and social isolation associated with this 
occupation, sensitization of administrative and legal officials is an essential prerequisite before 
initiating the data collection.88 

 

Figure 2. Published articles of RDS surveys were conducted in the WHO demarcated 
regions 

DISCUSSION  

It is essential to explain the methodological and analytical aspects of RDS in any study through 
the survey strategy, execution, and examination, which are necessary to evaluate the RDS 
surveys and their findings.84 The majority of surveys, accumulated here reported the most vital 
information as the place/country of study, study year, population sampled, methods of 
interview, and final sample size. RDS adjustment consists of applying variable-specific 
weights to account for the potential biases arising due to homophily and the variation in 
network sizes of different individuals.89 Recent advance in software-based weighted analysis 
has made the opportunity to obtain a much more robust estimate that approximates random 
sampling if it fulfils certain assumptions.90 

It is evident that providing compensation to the respondents increases the rate of recruitment. 
Moreover, secondary compensation increases the rate of recruitment in comparison with the 
single compensation system.79 However, several published articles, also mentioned that their 

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field team got physically assaulted to denied some respondents, who were willing to participate 
to avail the incentive without being eligible as a participant, and argued for providing more 
money as secondary incentive.91  This tendency of avail extra money, encourages respondent 
to give multiple responses at different RDS venues. Sometime a single responder can get 
coupon from two different typologies. Suppose, an FSW person who inject drugs can get 
coupon from FSW RDS site as well as from FIDU RDS site. FIDU refers here female who 
inject drugs. These undesirable events could be prevented by strong inter-sectoral 
communication and convergence before initiating the field activity, and by judiciously using 
screening tools to prevent duplication 84,92. Different groups of workers are also cited that 
documentation of refusal rate is difficult in RDS,26 which may be solved by using the different 
indicators for assessing the speed of recruitment process82,83. Survey conductors must monitor 
the coupon return rate, in such a way that they can modify the number of seeds accordingly 
to rate the data collection to complete the survey within a stipulated time.27 

Difficulties in accessing FSW with higher social status is a general limitation any sampling 
designs used (time location cluster sampling, conventional cluster sampling, snowball 
sampling etc), till date.83 For public health perspective, they are expected to be having 
sufficient knowledge on regular health and hygiene, more empowered to convince condom 
use, and less likely to disclose their status and come under the umbrella of government/NGO-
run free health services, so they are unlikely to get included in this kind of activities.83 The 
advantage of RDS over other methods for sampling similar populations, is that it can reach 
hidden population networks that the other methods fail to reach.21 Despite non-random 
selection of initial seeds, this sampling can produce weighted estimates which can 
approximate random sampling if certain assumptions are fulfilled.81 The use of RDS in 
recruiting different population has been increasing multi fold in recent years. This sampling 
technique largely depends on social networks and it can be effective to recruit those 
populations that are expected to be linked by their social network, like injecting Drug Users 
(IDUs) or Men who have sex with Men (MSMs). Literature review revealed that there has been 
an increasing number of research activities in the current year where the present sampling 
technique was used for recruiting Female Sex Workers.81-83 Sex workers are a highly 
vulnerable occupational group, involved in selling sex for money or kind. Globally in most of 
the countries, sex work is not a socially acceptable occupation, and sex workers face 
considerable stigma and discrimination.  They are likely to have a social network because of 
their need to be united to fight against occupational vulnerability. A counter argument can also 
be raised. FSWs are distinct occupational groups, each of them is a potential competitor for 
other group members. Networking within FSW is not essential for continuing their occupation, 
rather they need networking with pimps and clients for their sustainability. So, a considerable 
variation in their social connectivity is expected among this population group. As RDS largely 
relies on social networks, it will be interesting to know whether RDS is useful to recruit FSWs, 
and what type of challenges that could be faced by the researchers if they want to recruit 
FSWs with RDS.85 

Like any systematic review, the present study is also restricted by the comprehensiveness of 
the published articles and whether workers published their study in open accessed and peer-
reviewed journals. Moreover, included articles having surveys that accumulate biological data 
leaving an approach for further assessment of those surveys that described RDS on FSW 
population and give any information on biological data.86 It was found, that the number of 
articles regarding RDS surveys on FSW is much lower than the number of surveys were 

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conducted. One of the important findings of the present study is that published articles are 
devoid of essential data regarding RDS which supports the need to homogeneously report 
results from RDS surveys that limited the scope of analyses and acquaint with uncertainty in 
some of the other outcomes.89 The articles are excluded, undoubtedly not fulfilling the 
necessary features of the RDS method. However, two to three articles were included that did 
not incorporate all the features of RDS, showing the incompleteness of reporting.90 On those 
occasions, surveys were categorized as using RDS up to some extent. It was found that 
seventy-five articles claimed to use RDS, but did not, reported using a ‘modified’ or ‘mixed 
methods’ of RDS.91 These studies are not providing any definite indication of the assemblage 
and use of the personal network, size data, recruitment treaties, coupon management along 
with numerous recruitment waves. These articles are not included in the final analysis. A few 
extracted publications were found with significant limitations, including less competent staff, 
several ineligible persons were trying to participate in the survey and compelled to close or 
move survey sites at the period of data collection. These articles are used to make a note on 
the challenges of implementing RDS among FSW and recommendations was suggested from 
same or from other similar published articles on RDS among FSW.92 

The majority of published surveys were from African Region (AFR) and the south-east Asian 
Region (SEAR); it may be useful to find more publications on RDS survey on FSWs from other 
regions. The socio-economic position of FSW from these regions may have an impact on the 
survey procedure using RDS, regarding the networking among subjects and incentives may 
have a role here.82  

CONCLUSION 

It was found through the present review that most of the published articles were largely 
successful to recruit FSWs through RDS, with the varying response rate in almost all the study 
settings globally.  Despite ample pieces of evidence of challenges in implementing RDS 
among FSW and threats of selection bias during the sampling process, this technique is 
regarded as the most suitable technique available till date for sampling those populations 
where the sampling frame is non-existing. The key advantage is that RDS offers a systematic 
and theoretically grounded approach to recruiting participants from “hidden” or “hard to reach” 
populations. This peer-to-peer recruitment is driven by anonymous coupons, making it 
successful among populations that are stigmatized or practice those behaviours considered 
illegal in the existing social structure. As sex work also carries similar occupational 
vulnerability, this approach is expected to be appropriate and the best suited for this high-risk 
population. Present findings will help researchers, policymakers and service providers to 
improve RDS methods for surveillance of any key population by providing a minimum set of 
parameters of specific methodological, analytical, and testing procedures including RDS 
methods to evaluate the overall quality of any RDS survey. 

Authors' contribution 

MB, SiB and SuB conceived and designed the study. MB, SD and DC established the search 
strategy. PG, AM, MB and SS prepare the master table. MB and PG extracted the data. MB 
and SiB perform the analysis. MB, AM, PG, SiB, SuB and SD wrote the article. All the authors 
read the manuscript before they have given the final approval for publication.  

 
99

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Funding 

This research has not received external funding for research.  

Conflict of interest 

There is no conflict of interest in this research.  

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