American Journal of Epidemiology
© The Author 2013. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of
Public Health. All rights reserved. For permissions, please e-mail: [email protected].
Vol. 178, No. 2
DOI: 10.1093/aje/kws432
Advance Access publication:
June 25, 2013
Practice of Epidemiology
Beyond Surveillance: A Role for Respondent-driven Sampling in Implementation
Science
Sunil S. Solomon, Gregory M. Lucas, David D. Celentano, Frangiscos Sifakis, and
Shruti H. Mehta*
* Correspondence to Dr. Shruti H. Mehta, Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University,
615 N. Wolfe Street, Room E6541, Baltimore, MD 21205 (e-mail: [email protected]).
Initially submitted May 11, 2012; accepted for publication October 24, 2012.
We are now in the fourth decade of the human immunodeficiency virus (HIV) pandemic. Several novel prevention tools have been identified, and prevalence and incidence have declined in many settings. A remaining challenge is the delivery of preventive interventions to hard-to-reach populations, including men who have sex with
men and injection drug users. Leaders in the field of HIV have called for a new focus on implementation science,
which requires a shift in thinking from individual randomized controlled trials to cluster-randomized trials. Multiple
challenges need to be addressed in the conduct of cluster-randomized trials, including: 1) generalizability of the
study population to the target population, 2) potential contamination through overlap/exchange of members of control and intervention clusters, and 3) evaluation of effectiveness at multiple levels of influence. To address these
key challenges, we propose a novel application of respondent-driven sampling—a chain-referral strategy commonly used for surveillance—in the recruitment of participants for the evaluation of a cluster-randomized trial of a
community intervention. We illustrate this application with an empirical example of a cluster-randomized trial that is
currently under way to assess the effectiveness of men’s wellness centers in improving utilization of HIV counseling and testing among men who have sex with men in India.
HIV; implementation science; men who have sex with men; randomized controlled trial; respondent-driven
sampling
Abbreviations: AIDS, acquired immunodeficiency syndrome; CRT, cluster-randomized trial; HIV, human immunodeficiency virus;
MSM, men who have sex with men; RCT, randomized controlled trial; RDS, respondent-driven sampling.
Respondent-driven sampling (RDS) is a chain-referral strategy for recruiting hard-to-reach populations that has increasingly been used to assess the burden of disease, including
human immunodeficiency virus (HIV) infection. RDS has
been widely used in observational studies to reach populations for which there is no natural sampling frame, such as
men who have sex with men (MSM), injection drug users,
and female sex workers (1–3). We propose a novel application of RDS in the recruitment of participants to evaluate the
effectiveness of HIV prevention interventions in the context
of cluster-randomized trials (CRTs). To illustrate the practical
applicability of our concept, we have included an example of
an ongoing CRT.
EFFECTIVENESS STUDIES IN HIV RESEARCH
Rationale
Since the first cases of acquired immunodeficiency syndrome (AIDS) were reported in 1981 (4), tremendous advances
have been made in HIV prevention and treatment (5). The efficacy of prevention interventions, such as HIV counseling and
260
Am J Epidemiol. 2013;178(2):260–267
RDS in Implementation Science 261
testing (6), condom promotion, syringe exchange programs,
opiate substitution programs (7), and medical male circumcision (8, 9), has been demonstrated in randomized controlled
trials (RCTs). Most recently, the results of the HIV Prevention Trials Network 052 study reinforced the beneficial
effects of early initiation of antiretroviral therapy on both
clinical outcomes (10) and secondary transmission (11).
However, the population-level impact of these interventions, especially in marginalized populations such as injection
drug users and MSM in low- and middle-income countries,
appears to be minimal (12), resulting in continued transmission in these groups. Indeed, according to the most recent
Joint United Nations Programme on HIV/AIDS report, one
of the few regions where HIV prevalence has increased (>25%)
since 2000 is Eastern Europe—an epidemic driven by drug
use (12). There is an urgent need to develop and evaluate effective mechanisms to deliver efficacious interventions in the
“real world” to populations that need them the most. However, implementation research requires a shift from traditional epidemiologic methods used to evaluate efficacy at the
individual level to methods that evaluate effectiveness at the
population level.
Efficacy versus effectiveness
Efficacy of interventions is measured through RCTs—the
“gold standard” for determining causality, wherein participants are individually randomized to an intervention or control condition. Traditional RCTs pose challenges with regard
to extrapolation of efficacy in a trial setting to effectiveness
at the population level. First, traditional RCTs do not provide
estimates of intervention uptake in the real world. An efficacious intervention with minimal population uptake might
have as much impact on an epidemic as an intervention with
modest efficacy but high utilization. Second, participants in
RCTs are often subject to stringent eligibility criteria before
inclusion. Injection drug users and other mobile populations
are commonly excluded from these studies to ensure optimal
retention, which is essential for internal validity. However,
exclusion of such populations limits external validity of the
findings. Third, traditional RCTs require rigorous follow-up
of participants under highly controlled conditions; such rates
of retention seldom occur outside of trial settings, especially
in the developing world. For example, at the Y.R. Gaitonde
Centre for AIDS Research and Education in Chennai, India,
a site of the HIV Prevention Trials Network 052 study, the
trial retention rate was greater than 95% (13). However, in an
analysis of patients in care at this same site, approximately
25% of patients dropped out of care within 1 year, with an
overall dropout rate of 38.1 per 100 person-years (14).
Effectiveness studies, particularly when they are evaluating a novel service delivery mechanism, generally require a
CRT wherein communities or clusters, rather than individuals, are randomized to an intervention or control condition.
Two methods can be used for evaluation in CRTs (15):
1) recruitment of a cohort in intervention and control communities and comparison of outcomes between these groups
after implementation of the intervention, or 2) serial crosssectional samples before and after implementation of the
intervention.
Am J Epidemiol. 2013;178(2):260–267
Challenges to implementation of CRTs
There are multiple challenges to implementation of a CRT.
First, because a key goal of a CRT is to assess effectiveness
of an intervention within a community, it is crucial that the
study population be representative of the target population.
This can be particularly challenging in MSM, injection drug
users, and female sex workers, for whom no natural or constructed sampling frame exists that can be used to derive a
random representative sample of the target population. In
many lower- and middle-income countries, these behaviors
are illegal and punishable by law, which drives these populations underground and further complicates sampling (16).
Although enrollment and follow-up of cohorts at each site
might be preferred to serial cross-sectional surveys, cohorts
tend to be more expensive because they require significant
resources to follow people over time. Furthermore, even with
intensive resources dedicated to tracking participants, a primary
challenge to fielding a cohort study remains differential losses
to follow-up, which can make the population less representative of the target population over time. Among those retained,
participants might modify their behavior purely by virtue of
being in a cohort (17, 18). With the serial cross-sectional sample option, the primary concern is potential key differences
between the baseline and evaluation samples/participants that
could alter estimates of effectiveness in unknown ways.
Second, an unbiased estimate of intervention effect in a
CRT requires that there be no overlap of members across intervention and control clusters; contamination can compromise
the ability to detect a difference (19–22). In prior HIV prevention trials, contamination has been cited as a potential
explanation for lack of efficacy. For example, in a large multisite HIV prevention CRT (23), exchange of participants in
the Chennai site between intervention and control clusters
(despite geographically discrete communities) was believed
to have significantly contributed to the lack of difference in
primary outcome (Aylur K. Srikrishnan, Y.R. Gaitonde Centre
for AIDS Research and Education, Chennai, India, personal
communication, 2012). Although contamination is not unique
to CRTs, the statistical power lost because of contamination
in a CRT is orders of magnitude higher than in individuallevel RCTs given the relatively small sample sizes of CRTs.
For example, it is uncommon to see CRTs with more than
50 clusters, whereas individual-level RCTs typically have
250–1,000 participants per study arm.
Third, although effectiveness studies offer an ideal setting
to evaluate multilevel interventions, it is challenging to evaluate the impact of the multiple facets (e.g., individual, network, community). The primary goal of a CRT is to evaluate
community-level effectiveness—that is, whether the prevalence or incidence of an outcome differs in communities that
did or did not receive the intervention. However, in an intervention that targets multiple levels, understanding which
component had the most impact is crucial to future implementation (24, 25).
We propose a novel approach to conducting CRTs that
uses RDS to address the 3 challenges detailed above. We
describe the design and the role of RDS below and illustrate
its application with an empirical example of a CRT that is
currently under way.
262 Solomon et al.
Respondent-driven sampling
RDS, a chain-referral strategy of recruitment (1, 2), has
gained popularity in recent years and is commonly used to
measure disease burden in hard-to-reach populations. RDS
is similar to snowball sampling (26), except that in RDS, the
number of referrals per participant is controlled by the investigators (approximately 2–5), and peer network information
(relationship between recruiter and recruit) is documented.
RDS is based on the theory of “6 degrees of separation,” and
it is believed that if the approach is implemented appropriately, the study sample should be representative of the
underlying target population. In addition to providing a representative sample, RDS provides network data, which can
be used to examine linkages between samples from geographically distinct clusters, particularly in populations that
are highly mobile.
Utility of RDS in CRTs
Our proposed design involves 4 steps (Figure 1): 1) baseline assessment and identification of discrete study sites
through RDS-derived samples, 2) randomization, 3) implementation of the intervention in communities randomized to
the intervention, and 4) evaluation assessment (another sample
recruited by RDS). We posit that RDS can assist with the 3
previously described key challenges for a CRT.
Of these 3 challenges, selection of a representative sample
is the most established feature of RDS. Furthermore, RDS
has been shown to be effective in recruiting hard-to-reach
injection drug users (27, 28), MSM (29), and female sex
workers (3) in multiple settings; these high-risk groups have
been identified as populations in which the HIV epidemic has
not yet stabilized or declined in lower- and middle-income
countries (12). Although traditional chain-referral methods
might also reach these populations, RDS uses systematically
collected data about the relationships between recruiters and
recruits to adjust for recruitment bias in the analysis. Furthermore, to produce estimates that are generalizable to the target
population, the personal network size of each respondent is
collected to allow weighted analysis through “poststratification” to compensate for potential oversampling of respondents with larger networks (1, 2, 26). RDS is initiated by
selecting 4–6 “seeds,” or persons believed to be the most
connected among the target population. Seed participants
are given referral coupons (2–5) to hand out at random to
members of their (sexual or drug-using) network, thereby
initiating the chain-referral process. As recruitment continues
from wave to wave, equilibrium eventually will be attained,
such that the final sample will be independent of the characteristics of the subjects enrolled when recruitment began. In
Figure 1. Proposed study design. CRT, cluster-randomized trial; RDS, respondent-driven sampling.
Am J Epidemiol. 2013;178(2):260–267
RDS in Implementation Science 263
other words, regardless of who the seed subjects are, the final
sample accrued will be similar.
What is not yet established is the reliability of RDS samples over time. A key assumption of RDS is that the target
population in a particular city or region belongs to 1 large
network. This assumption, though crucial in assessing the
reliability of RDS samples, is a difficult assumption to test
in practice. If this assumption holds within a city, then
regardless of who the seeds are (in terms of geography and
other characteristics), samples should be comparable as long
as RDS process measures hold (e.g., equilibrium (when
sample composition becomes independent of seeds) and network homophily (measure of subgroup recruitment homogeneity between different networks)). However, if the target
population within a city or region does not belong to the
same underlying network but rather to more than 1 nonoverlapping network, different seeds could lead to markedly
different samples. Indeed, 3 studies (30–32) compared serial
RDS samples over time, and in all, characteristics of participants were different at the 2 time points. Explanations for
the differences have included true changes over time and
changing geography. Because the hypothesis of 1 underlying network cannot truly be tested without a large sample, it
is important that populations recruited at different time
points target the same underlying population. This can be
done by incorporating ethnography to understand in depth
the network structure of the target population at each site. As
an additional measure, seed participants at the evaluation
assessment (step 4) should be selected to be as similar as
possible to the seeds at baseline, especially in relation to
region. With these additional measures, RDS samples at 2
time points should be representative of the underlying population at those time points, facilitating comparisons.
Although the theory of RDS applies to peer-driven interventions (33–35), it has not been used extensively in the context of community-level interventions to date. Thus, the role
of RDS in evaluation of contamination and interventions at
multiple levels (community, network, and individual) has
not been extensively explored. Nevertheless, an understanding of the relationships of individuals within networks and
the degrees of these connections has a clear role in the conduct of CRTs. Contamination is a major concern in CRTs
because it can dilute the difference in the outcome between
intervention and control arms (15). Although randomization
at the cluster (vs. individual) level is thought to be less subject
to contamination, contamination can still occur when extensive contact occurs between individuals across control and
intervention clusters. When combined with questions about
mobility in the baseline survey, RDS can provide information
on potential for contamination (overlap between clusters).
Although investigators might have a priori hypotheses about
such linkages, RDS allows identification of network linkages
in real time that might not have been expected. Knowledge of
these linkages could allow randomization of truly discrete clusters, thus minimizing the possibility of contamination.
Finally, RDS provides a means of evaluating the intervention across multiple levels. It has been increasingly recognized
that interventions that target multiple levels of influence
achieve greater impact than those that target just 1 level (24).
Am J Epidemiol. 2013;178(2):260–267
Inherent in most community-level interventions are 2 levels:
the community and the individual. A third component that
drives impact, but often goes unmeasured, is the network/peer
level. Furthermore, although many interventions target multiple levels, it is often challenging to tease apart which level
has the greatest impact on the outcome. Population-averaged
estimates give an overall picture of community-level effectiveness, and comparisons among individuals in intervention
clusters who were or were not exposed to the intervention
can provide evidence of individual-level impact. These 2
levels can be evaluated in most CRTs, provided the information is captured in surveys. RDS-accrued samples provide
information on a third level: the network. Evaluation of
effectiveness at the network level can inform the role that
peers play in the adoption of practices ( precautionary and
risk) among network members. For example, peer leaders
with large networks could have more impact on intervention
uptake than persons with small networks (33–35). The availability of network-level information in RDS samples will
allow for comparisons of intervention uptake across networks
of varying size and across recruitment chains and key factors
within networks that drive intervention utilization.
EXAMPLE
CRT to test the effectiveness of men’s wellness centers
in improving HIV testing outcomes
To illustrate the utility of RDS in the context of CRTs and
practical challenges that might be faced, we describe an
ongoing study designed to test the following hypothesis:
Establishment of gay-friendly men’s wellness centers will
improve adoption of routine HIV counseling and testing
among MSM in India. The hypothesis will be tested through
a CRT that will be evaluated through serial cross-sectional
samples recruited via RDS at 12 candidate sites in India
across 6 states: Andhra Pradesh, Tamil Nadu, New Delhi,
Uttar Pradesh, Madhya Pradesh, and Karnataka. Our goal is
to select 10 discrete study sites (5 pairs) for the CRT. The
primary outcome of interest is improved utilization of HIV
counseling and testing in the previous 12 months. Below,
we describe the design of this trial in the context of the 3 previously described challenges.
Representativeness
MSM in India remain a hidden population with no existing sampling frame from which a random sample can be
selected. Estimates of the number of MSM in India vary.
The most recent estimate from the National AIDS Control
Organization suggested approximately 2.35 million highrisk MSM living in India (36). By contrast, estimates of
same-sex behavior among men in India range from 7.6
million to 45.8 million (37, 38). This discrepancy highlights
that although male-to-male sexual contact is not necessarily
unacceptable in India, the open practice of a homosexual
lifestyle is uncommon (39). The primary reasons for this are:
1) the recently overturned Section 377 of the Indian Penal Code,
which criminalized anal sex and has driven many MSM to
264 Solomon et al.
live secret lives (16) (the repeal of Section 377 is still under
debate in the Indian Supreme Court), and 2) the prevailing
norm of opposite-sex marriage. Taken together, these barriers result in many MSM marrying to satisfy social pressures
or to prove their masculinity to themselves. These legal and
societal norms place MSM at risk for stigma, discrimination,
and violence and pose a major barrier to recruiting MSM
into research studies (40, 41).
For the proposed CRT, our interest is in reaching all MSM,
regardless of how they self-identify—gay, heterosexual, or
bisexual. There have been few epidemiologic assessments of
HIV burden among MSM in India (42, 43). In response, we
used RDS to recruit a sample of 721 MSM in South India
over 2 months (44). That study demonstrated the feasibility
of RDS in sampling MSM in this region. Consequently, for
the present CRT, we propose the use of RDS to obtain representative samples of MSM from multiple cities across India.
We will begin with 2–6 seeds that represent the diversity of
MSM behavior at each of the sites and offer 2–5 referral
coupons to each of these seeds and each participant subsequently recruited. We propose a sample size of 1,000 participants at each of the 12 candidate sites to ensure that the RDS
will run at least 6 waves deep, at which point convergence of
the sample on the basis of demographic and risk behavior
has been shown to occur (1, 2). This is also sufficient to
allow for an RDS design effect of 2 (i.e., RDS sample has to
be 2 times as large as a simple random sample) (45).
Contamination
A key requirement for this trial is the identification of
truly distinct (nonoverlapping) MSM clusters for randomization. Common convention would be to select samples of
MSM from geographically distinct sites. However, we posit
that geography alone does not ensure discrete samples, particularly for mobile populations. For example, it is conceivable that MSM in one city could travel to another in search
of partners, services, or employment. We propose to capture
information on mobility in the ethnographic phase and to
use the network information that is collected in RDS to
further evaluate contamination and ensure that clusters are
truly discrete.
We propose to conduct baseline assessments in 12 candidate sites. The reason for the oversampling is to be able to
discard or combine sites when contamination is identified.
Ideally, RDS would be conducted concurrently at all sites;
however, because of logistical challenges, we will run 3 consecutive 4-month RDS recruitment periods, each at 4 concurrently open study sites. We divided our 12 candidate sites
into 3 groups according to potential for contamination. On the
basis of formative work, we deemed that sites in the same
state would have the highest probability of contamination
because of common language. Therefore, we will run sites
within each state concurrently, rather than grouping according to actual geographic distance.
This strategy will allow for multiple assessments of contamination. First, duplication can be monitored through a
continually updated central database, which will hold all
recruitment data for all sites. We can also monitor in real
time whether participants visit a study site other than the site
to which they were referred. To identify duplicates, we will
use software that scans an individual’s fingerprint and transforms it into a 128-bit alphanumeric code that is unique to
each fingerprint. The actual image of the fingerprint will not
be stored. The central database with this information will be
updated in real time and will be available across all sites,
allowing identification of duplicates through a simple fingerprint scan.
Once all sites have accrued samples, data can be examined
for contamination (overlapping enrollment). If contamination
is observed, we have 2 options: 1) The 2 or 3 sites where
overlap was observed can be combined into 1 site, or 2) only
1 of the 2 overlapping sites is used and the other is dropped.
Our oversampling of sites at baseline allows for 2 instances of
contamination to occur without affecting the overall objective of 10 discrete sites. Using RDS in this capacity to select
our clusters for randomization ensures that the definition of
“distinct” communities is driven by the network information
rather than by geographic boundaries alone. In other words,
clusters are defined by communities rather than simply by
geography.
Evaluation of intervention effectiveness at multiple
levels
The final step is evaluation of the intervention, which in
this context will be done through serial cross-sectional samples accrued by using RDS at baseline (before intervention
implementation) and 2 years after implementation. The baseline RDS will provide important information on key confounders. For example, of particular importance is the baseline
prevalence of HIV counseling and testing in each cluster,
which will be used to pair-match clusters or conduct stratified randomization.
After the intervention (in this case, “gay-friendly men’s
wellness centers”) has been implemented in intervention clusters and allowed to run for an appropriate length of time (in
this case, 24 months), we will conduct a second (evaluation)
RDS in the intervention and control clusters. The purpose of
this second RDS is to evaluate the impact of these health centers
on HIV counseling and testing. We propose to use procedures
similar to those used for the baseline RDS. It is important that,
to obtain a sample comparable to the baseline RDS, we use
seeds as similar as possible to the seeds used to recruit the
baseline sample, for the reasons described earlier. We will at a
minimum ensure that the characteristics of the seeds are similar
to those of the baseline RDS seeds in terms of HIV status,
marital status, sexual orientation, and community.
Community-level outcomes can be compared as the change
in outcome of interest from baseline to evaluation among
intervention versus control clusters (Table 1). Because samples at both points in time have been accrued via RDS with
similar seeds, they should be representative of the underlying target population, facilitating comparison of the outcome
of interest across the cross-sectional samples. Multivariate
models can be used to adjust for differences across samples.
Beyond assessment of community-level effectiveness,
RDS will allow an assessment of individual- and networklevel influences. Estimation of individual-level impact likely
can be achieved with other designs (e.g., cohorts or random
Am J Epidemiol. 2013;178(2):260–267
RDS in Implementation Science 265
Table 1. Evaluation of Intervention Effectiveness Across Multiple Levels of Influence
Intervention Component
Target
Outcome
Measurement of Outcome
Community
• Establish gay-friendly
men’s wellness
centers
• Availability of gay-friendly health
services
• Provider attitudes toward MSM
• Stigma/discrimination
• Increased frequency of HIV
testing
• Lower levels of substance
use and depressive
symptoms
• Decreased percentage
reporting unprotected anal
intercourse
• Lower HIV/STI prevalence
• Comparison of change in
outcome prevalence in
intervention vs. control
clusters
• Peer service utilization
• HIV testing status of peers
• Risk behavior of peers
• Percentage using men’s
wellness centers
• Percentage using specific
services within men’s
wellness centers
• Correlation of withinnetwork utilization of
centers/services vs.
between-network
utilization
• Network membership as
a predictor of service
utilization
•
•
•
•
•
• Increased frequency of HIV
testing
• Lower levels of substance
use and depressive
symptoms
• Decreased percentage
reporting unprotected anal
intercourse
• Lower HIV/STI prevalence
• Comparison of outcome
prevalence in individuals
who did and did not use
men’s wellness centers
within intervention
clusters
Network
• Peer/network-based
referrals to men’s
wellness centers
Individual
• Tailor services in
men’s wellness
centers to the
individual needs
of clients
HIV status
STIs
Substance use
Depressive symptoms
Perceived need for services
Abbreviations: HIV, human immunodeficiency virus; MSM, men who have sex with men; STI, sexually transmitted infection.
cross-sectional samples) as well and will be assessed by comparing outcomes among individuals within intervention clusters who did and did not use the intervention by the addition
of a question in the evaluation RDS that queries intervention
utilization. However, an added advantage of RDS is that we
can evaluate the impact of networks (33–35). Because we will
have data on linkages among individuals, we can evaluate
whether actual utilization within the clinics tracked across
baseline network linkages as well as whether self-reported utilization in the evaluation RDS tracked across network linkages in the evaluation RDS.
Challenges
Important limitations must be considered. First, estimates
from RDS tend to be less precise than those from simple
random samples (46, 47). The increased variance due to RDS
(design effect) is a complex function of the network structure in a population. Although common convention is that a
design effect of 2 is appropriate, it has been suggested that
in some cases, design effects of 5–10 are more appropriate
(46). Even with oversampling, costs of serial RDS samples
are likely to be lower than those associated with retaining a
cohort. However, some outcomes, such as death, cannot be
measured with the RDS design but could be measured if a
cohort were followed.
Am J Epidemiol. 2013;178(2):260–267
Second, although estimates derived from RDS are expected
to approximate population prevalence, a sample derived
through RDS might not achieve equilibrium or might produce prevalence estimates that are different from those of the
population at large (1, 2). In this case, there are methods for
weighting prevalence estimates (48, 49); however, in the case
of CRTs, this need for weighting adds additional complexity
to the analysis, which could already be cumbersome.
Third, to date, few studies have characterized the consistency of RDS samples over time (30–32). Thus, differences
observed over time in serial RDS samples might be due to
intervention effects but also might reflect inherent variability
that arises because of RDS methodology. It is also possible
that RDS could reach only a subset of the target population
(10, 15). This further reinforces the importance of choosing
a well-defined geographic region from which to sample, selecting seeds that share characteristics in the baseline and evaluation RDS samples, and monitoring environmental changes.
Fourth, RDS alone might not ensure discrete samples in
the context of highly mobile populations. Although RDS will
assist in identifying contamination, the absence of linkages
between clusters does not ensure discrete clusters. Additional
steps can be taken to ensure selection of discrete study sites:
1) Conduct preliminary ethnography to collect information
on mobility among members of the community, and use
these data in the selection of potential sites for the CRT.
266 Solomon et al.
2) Ensure that the minimum distance between the clusters is
such that they are not easily connected by public transport
and that persons would require sufficient resources and time
(e.g., ≥6 hours) to travel between clusters. 3) Select sites
from different states within a country (where language can
be different and distance significant) or from different countries. 4) Oversample the number of sites required for the trial
to allow randomization even if 1 or more sites need to be
dropped.
CONCLUSIONS
Dr. Anthony Fauci, Director of the National Institute of
Allergy and Infectious Diseases of the US National Institutes of Health, referred to the lack of implementation studies
in HIV research as a “responsibility gap” (50). We propose a
novel design for evaluating a prevention intervention among
MSM in India that will facilitate implementation. This design
could be applicable to the evaluation of other HIV prevention
and treatment interventions across a wide range of populations
and even could be extendable to CRTs not focused exclusively on HIV.
ACKNOWLEDGMENTS
Author affiliations: Department of Medicine, School of
Medicine, Johns Hopkins University, Baltimore, Maryland
(Sunil S. Solomon, Gregory M. Lucas); Department of
Epidemiology, Bloomberg School of Public Health, Johns
Hopkins University, Baltimore, Maryland (Sunil S. Solomon,
David D. Celentano, Frangiscos Sifakis, Shruti H. Mehta);
MedImmune, Gaithersburg, Maryland (Frangiscos Sifakis);
and Y.R. Gaitonde Centre for AIDS Research and Education,
Chennai, India (Sunil S. Solomon).
This work was supported by grants from the National Institute
of Mental Health (R01MH089266) and the National Institute
of Drug Abuse (R01DA032059), US National Institutes of
Health.
We thank Eva Noble for assistance in the preparation of
the final manuscript.
Conflict of interest: none declared.
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