Combination methods for HIV prevention in men who have
sex with men (MSM) (Protocol)
Verboom B, Melendez-Torres G, Bonell CP
This is a reprint of a Cochrane protocol, prepared and maintained by The Cochrane Collaboration and published in The Cochrane
Library 2014, Issue 1
http://www.thecochranelibrary.com
Combination methods for HIV prevention in men who have sex with men (MSM) (Protocol)
Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
TABLE OF CONTENTS
HEADER . . . . . . . . .
ABSTRACT . . . . . . . .
BACKGROUND . . . . . .
OBJECTIVES . . . . . . .
METHODS . . . . . . . .
REFERENCES . . . . . . .
DECLARATIONS OF INTEREST
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Combination methods for HIV prevention in men who have sex with men (MSM) (Protocol)
Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
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1
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12
i
[Intervention Protocol]
Combination methods for HIV prevention in men who have
sex with men (MSM)
Benjamin Verboom1 , Gerardo Melendez-Torres2 , Christopher P Bonell3
1 Department of Social Policy and Intervention, University of Oxford, Oxford, UK. 2 Centre for Evidence-Based Intervention, University
of Oxford, Oxford, UK. 3 Social Science Research Unit, Institute of Education, University of London, London, UK
Contact address: Gerardo Melendez-Torres, Centre for Evidence-Based Intervention, University of Oxford, Barnett House, 32 Wellington Square, Oxford, OX1 2ER, UK. [email protected].
Editorial group: Cochrane HIV/AIDS Group.
Publication status and date: New, published in Issue 1, 2014.
Citation: Verboom B, Melendez-Torres G, Bonell CP. Combination methods for HIV prevention in men who have sex with men
(MSM). Cochrane Database of Systematic Reviews 2014, Issue 1. Art. No.: CD010939. DOI: 10.1002/14651858.CD010939.
Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
ABSTRACT
This is the protocol for a review and there is no abstract. The objectives are as follows:
This review seeks to examine the effectiveness of prevention interventions for MSM that combine at least two of the three categories of
intervention modality (biomedical, behavioural and structural) compared either to other HIV prevention interventions or to minimal/
no HIV prevention.
Below, descriptions of the criteria that will be used to categorise intervention components are provided under the heading ‘Types of
interventions’ and the full list of outcomes of interest are provided in the section ‘Types of outcome measures’.
All planned comparisons are detailed in the section ‘Data synthesis’, followed by the planned variables of interest for subgroup and
meta-regression analyses.
BACKGROUND
Description of the condition
Addressing the global HIV/AIDS pandemic is a matter of extreme
urgency. At the end of 2011 an estimated 34 million people were
living with HIV globally, while 2.5 million became newly infected
with the virus and 1.7 million died of AIDS-related causes that
year (UNAIDS 2012). In nearly every country for which figures
are available, HIV prevalence among men who have sex with men
(MSM) exceeds that within the general population, with regional
prevalence estimates ranging from 3% in the Middle East and
North Africa to 25% in the Caribbean (Beyrer 2012). Myriad biological, social and institutional factors combine to explain the susceptibility of MSM populations to HIV infection. Unprotected
receptive anal intercourse (URAI) facilitates the transmission of
HIV more readily than unprotected vaginal intercourse; a recent
systematic review estimated that transmission probability during
URAI is approximately 18 times greater than during unprotected
vaginal intercourse (Baggaley 2010). Moreover, the stigma and discrimination faced by MSM in many societies, and the state-sanctioned homophobia of some countries, including the criminalisation of homosexuality, create environments in which MSM are
difficult to reach with prevention services, are disproportionately
Combination methods for HIV prevention in men who have sex with men (MSM) (Protocol)
Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
1
affected by HIV risk factors like untreated sexually transmitted
infections (STIs) and mental health problems, and cannot always
safely access health services, including HIV testing and treatment
(Altman 2012).
Description of the intervention, and how the
intervention might work
With no known cure for HIV/AIDS, prevention remains central
to the global response to the pandemic. Condom provision and
promotion, and behavioural interventions ranging from individual and group counselling to gay venue-based peer education, constitute the traditional approach to prevention.
The efficacy of correct and consistent condom use for reducing
HIV transmission is well established (Weller 2002). A review of
five cohort studies of MSM and transgender people revealed an
overall 64% protective effect of consistent condom use (WHO
2011), while a large prospective cohort study of MSM found that
condom use reduced per-contact risk of HIV acquisition among
receptive partners by 78% compared to unprotected anal intercourse (UAI; Vitinghoff 1999). However, acceptability of and adherence to condoms is imperfect and varies across populations of
MSM (UNAIDS 2012), limiting their success in the production of
population-level prevention benefits (Sullivan 2012). Behavioural
interventions to combat these barriers to condom use, and to improve other sexual behaviour outcomes like reduced number of
partners, have proven only partially effective, and most evaluations
of these interventions have been unable to detect an effect on HIV
incidence (Herbst 2005; Herbst 2007; Johnson 2008; Lorimer
2013).
Increasing acknowledgement of the ‘structural’ drivers of HIV,
that is, the broader social, economic, political and environmental
factors that increase vulnerability to HIV infection, such as human rights violations, stigma, and poverty (Auerbach 2011), and
the consequent need for structural-level prevention interventions
(Blankenship 2000; Gupta 2008), combined with recent advances
in biomedical prevention strategies including oral pre-exposure
prophylaxis (Grant 2010), and the growing consensus that neither
biomedical nor behavioural interventions alone are sufficient to
curb the epidemic (Coates 2008; Padian 2008; Sullivan 2012) has
led to a shift in popular thinking on HIV prevention in recent
years. Combination HIV prevention, in which multilevel packages
of prevention components drawn from the three major classes of
intervention modality - biomedical, behavioural and structural are strategically assembled and tailored to the needs of target populations (UNAIDS 2010), is widely considered the best available
strategy for achieving population-level decreases in HIV incidence
(Chang 2013; Hankins 2010; Kurth 2011; Merson 2008; Piot
2008; Vermund 2013). If assembled appropriately, these tailored
intervention combinations are hypothesised to produce synergies
between single, partially effective prevention modalities, yielding
greater outcome effects than the sum of their constituent parts
(Merson 2008; Piot 2008; UNAIDS 2010).
For the purposes of this review, combination HIV prevention interventions will be defined as intervention packages that combine
components from at least two of the three classes of intervention
modality (biomedical, behavioural and structural). Drawing on
descriptions and representative examples outlined in the UNAIDS
Discussion Paper on combination HIV prevention (UNAIDS
2010), intervention component categories will be defined as follows. Biomedical intervention components reduce the risk of HIV
exposure, transmission, or acquisition either by providing a physical barrier between the virus and susceptible tissues (e.g., condoms) or by establishing physiological conditions that limit the
replication and/or survival of the virus in (e.g., oral antiretroviral-based prevention) or on (e.g., antiretroviral microbicides) the
body. Behavioural intervention components (e.g., risk reduction
counselling) aim to decrease the risk of HIV transmission among
MSM by promoting behaviour change within the MSM population through, for example, the enhancement of HIV-related
knowledge and attitudes, or the development of protective skills
and emotions (Herbst 2007). Finally, structural intervention components (e.g., population-wide homophobia reduction interventions) are designed to alter the broad contextual factors, operating
beyond the MSM network and gay community (Adimora 2010),
that work to increase the vulnerability of MSM to HIV infection
and/or hamper the effectiveness of other HIV prevention interventions (Auerbach 2011).
Mathematical modelling has provided conceptual support for the
hypothesis that a strategic combination of HIV prevention interventions, if adequately scaled up, could have considerable population-level effects on HIV incidence in both the general population
(Cremin 2013; Hallett 2008) and in MSM populations (Beyrer
2012a; Sullivan 2012; Wirtz 2013). Empirical evidence is required
to confirm these findings.
Why it is important to do this review
This review will aim to summarise the existing evidence on multicomponent HIV prevention interventions targeted at MSM populations that combine components from more than one category
of intervention modality. While no known trials of combination
prevention for MSM have been conducted with the explicit goal
of identifying synergies between individual components, it is informative to identify and summarise the results of existing intervention studies that have drawn components from multiple categories.
Our results will illuminate the effects on HIV and STI incidence
and risky sexual behaviour of the different intervention component combinations that have been tested to date, and the studylevel factors associated with favourable effects. This synthesis may
help to inform the design of future combination prevention trials for populations of MSM, both through the mapping of the
Combination methods for HIV prevention in men who have sex with men (MSM) (Protocol)
Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
2
component combinations that have hitherto produced positive,
negative, or null results, and the identification of combinations
yet to be evaluated in MSM samples.
Vermund 2013), it was determined that requiring included studies
to employ comparison groups was reasonable.
Types of participants
OBJECTIVES
This review seeks to examine the effectiveness of prevention interventions for MSM that combine at least two of the three categories
of intervention modality (biomedical, behavioural and structural)
compared either to other HIV prevention interventions or to minimal/no HIV prevention.
Below, descriptions of the criteria that will be used to categorise
intervention components are provided under the heading ‘Types of
interventions’ and the full list of outcomes of interest are provided
in the section ‘Types of outcome measures’.
All planned comparisons are detailed in the section ‘Data synthesis’, followed by the planned variables of interest for subgroup and
meta-regression analyses.
METHODS
Criteria for considering studies for this review
Studies specifically targeting MSM, irrespective of age, language,
geographic location, ethnicity, or any other sociodemographic indicator, will be included. For the purposes of this review, ‘MSM’
refers to a behavioural category inclusive of all biological males who
have sex with biological males, regardless of their sexual or gender
identity. MSM encompasses, for example, transgender women,
male sex workers, straight-identified MSM, gay and bisexual men,
and MSM who identify with a number of other culturally specific
sexual and gender identities.
It bears mentioning that the term ‘MSM’, which was coined in the
1990s with the intention of reducing stigma against gay, bisexual,
transgender and straight-identified men who have sex with other
men, oversimplifies the array of sexual behaviours in which men
engage, sacrificing specificity of behaviour in favour of the sensitive
inclusion of all biological males who have sex with biological males.
The term also fails to differentiate between the variety of sexual
and gender identities, and social and behavioural characteristics
thereof, represented in populations of ‘homosexually active’ men
(Young 2005). While the importance of understanding the social
and behavioural diversity of MSM is appreciated, the term ‘MSM’
continues to be widely used in the literature and, in the interest
of standardisation across studies, its use was deemed necessary in
this review.
If non-MSM participants are included in a study, we will obtain
outcome data for the subset of MSM included.
Types of studies
The review will include randomised controlled trials (RCTs), in
which participants were allocated to intervention conditions either
individually or by cluster, and non-randomised controlled studies
in which observations were made in all groups before and after
intervention implementation. Included studies will be limited to
those in which a combination HIV prevention intervention was
compared against a control group receiving a different HIV prevention intervention (including both single-component and other
combination interventions), standard care, or no intervention.
We recognise that the exclusion of less rigorous non-randomised
studies (e.g., single-group before-and-after studies) will probably
result in the de facto exclusion of a subset of intervention evaluations, particularly those containing structural components, in
which the use of control groups was considered not feasible, ethical and/or necessary by the study authors. Given the growing
consensus that, in principle, no defining characteristic of structural interventions precludes the use of comparison groups or random allocation in their evaluation (Bonell 2006; Hayes 2010), evidenced in part by the multiple RCTs of combination prevention
interventions containing structural components that have been
conducted (e.g., Pronyk 2006) or are presently underway (e.g.,
Types of interventions
Any HIV prevention intervention containing at least two of the
three major categories of intervention component (biomedical,
behavioural and structural) will be eligible for inclusion. For the
purposes of this review, components will be defined as below, based
largely on the descriptions and representative examples provided
in the UNAIDS Discussion Paper on combination HIV prevention (UNAIDS 2010). In order to be classified as biomedical, behavioural and/or structural, an intervention must explicitly aim to
reduce HIV transmission risk according to the means described in
the definitions below.
Biomedical interventions are those that reduce exposure to, transmission of, or infection with HIV, either by providing a physical
barrier between the virus and susceptible tissue or by creating a
physiological environment in or on the body that is hostile to viral
replication and/or survival. Examples include: condom provision,
medical male circumcision, oral pre- and post-exposure prophylaxis, rectal microbicides, antiretroviral ‘treatment as prevention’,
and biomedical STI treatment services.
Behavioural interventions are strategies that promote individual
behaviour change to reduce the risk of HIV transmission (though
Combination methods for HIV prevention in men who have sex with men (MSM) (Protocol)
Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
3
they need not be delivered at the individual level). While the categorisation of most behavioural intervention components is uncontroversial, some could arguably be classified as biomedical interventions. HIV testing, for example, is often justifiably considered a biomedical intervention because of its provision by medical practitioners and its effect as a gateway to engagement with
medical services for those who test positive. However, for the purposes of this review we will consider HIV testing a behavioural
intervention component because the well established preventive
effects associated with the receipt of a positive test result (Crepaz
2009; Denison 2008; Marks 2005) operate primarily through behaviour change resulting from knowledge of positive serostatus.
Other examples of behavioural interventions include: individual
and group counselling, behaviour change communication (including gay venue-based risk reduction and condom promotion), educational interventions targeting MSM, peer education and persuasion, community development and/or mobilisation programs
aiming to alter predictors of HIV risk within the gay community
(and/or MSM network), and prevention commodity (e.g., condom) social marketing (e.g., via posters and billboards) targeting
MSM.
Structural interventions are defined, for the purposes of this review, as interventions that aim to alter environmental or societallevel factors beyond the gay community (and/or MSM network)
that may affect vulnerability to HIV infection or that may dilute
the effectiveness of other HIV prevention interventions. These factors include, inter alia, population-wide accessibility of prophylactic technologies (Blankenship 2000); HIV/AIDS-related stigma
among healthcare practitioners and policy-makers and in the general population (Valdiserri 2002); society-wide gay/MSM-related
stigma (i.e., homophobia), which acts as a barrier to health service and prophylactic access for MSM (Fay 2011; Santos 2013)
and, through the production of internalised homophobia among
individual MSM (Berg 2013), can increase HIV-related risk behaviours like substance abuse (Shoptaw 2009) and unprotected
anal sex (DeLonga 2011; Ross 2008; Ross 2013); and the presence
of discriminatory policies, including those that criminalise consensual homosexual sexual activity (Beyrer 2011; Itaborahy 2013)
and that outlaw or accord lesser status to same-sex domestic partnerships (Klausner 2006). While structural interventions may ultimately result in behaviour change, they are distinguished from
behavioural interventions by the targeting of the contextual factors beyond the gay community/MSM network, that nonetheless
affect individual or group behaviour, rather than the behaviours
themselves (Gupta 2008). For example, while a behavioural intervention may aim to directly change risky behaviour by providing
sexual health education to MSM and encouraging them to engage
with health services, seek testing, and disclose their HIV/STI status to partners, a structural intervention may aim to decrease homophobia and HIV-related stigma among healthcare providers,
creating ‘safe spaces’ the for testing and monitoring of STIs in the
MSM population, thus facilitating risk reduction behaviour. Still,
some intervention components could arguably be classified either
as structural or behavioural interventions. Community development and/or mobilisation programs, for example, may be considered structural interventions because they often work by addressing population- and society-wide determinants of HIV risk
like homophobia. The definition of structural interventions that
will be used in this review is largely consistent with this classification. However, many community development and/or mobilisation interventions aim specifically to decrease risky sexual behaviour among MSM by changing community norms within the
gay (or MSM) community, rather than the population at large. As
noted above, this subset of interventions will be categorised as behavioural interventions for the purposes of this review unless it is
clear that the community development/mobilisation is primarily
geared towards campaigns that are structural in orientation. Examples of structural interventions include: large-scale community development programs, community mobilisation and dialogue programs, and educational media interventions that address determinants of HIV risk operating beyond the MSM population; stigma
reduction programs and anti-homophobia campaigns targeted at
the general population, policy makers, healthcare providers, or
other practitioners or leaders; policy and legal reform; and broadbased programmes designed to improve access to health and HIV
prevention services, including prophylactic technologies, across
the general population.
Many condom-based prevention interventions, particularly those
that both provide and promote the use of condoms, are likely to
qualify both as biomedical and behavioural interventions. The distinction between these two types of component deserves elaboration. For an intervention to fulfil the classification criteria for any
component category, it must both explicitly aim to reduce HIV
transmission risk through the means described in the relevant definitions above and, if applicable, provide the material features of
the intervention modality thought to be required to achieve risk
reduction. The features involved in condom promotion interventions (behavioural) may include, for example, educational materials, posters, or counselling sessions, while those involved in condom provision (biomedical) are, at minimum, the condoms themselves. A condom promotion intervention that does not involve
condom provision would be classified as a single-component behavioural intervention, while a condom provision program implemented without a substantive behavioural condom promotion
component would be classified as a single-component biomedical intervention. A combination behavioural-biomedical condom
promotion and provision intervention would therefore need to explicitly include the behavioural and biomedical intervention features (i.e., respectively, condom promotion materials or activities,
and condoms) required to reduce HIV transmission risk in accordance with the above criteria. It is worth noting that large-scale
condom distribution interventions are often classified as structural
interventions (Charania 2011), particularly when their aims include the improvement of the accessibility, acceptability and/or
Combination methods for HIV prevention in men who have sex with men (MSM) (Protocol)
Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
4
availability of condoms (Blankenship 2000). While interventions
involving only the distribution of condoms to MSM will be defined as single-component biomedical interventions in this review,
those that combine widespread condom distribution with additional components addressing population-wide condom accessibility, acceptability, and/or availability (i.e., structural-level mediators of HIV vulnerability) will be categorised as combination
biomedical-structural interventions.
Because of potential disagreement about the definitions of intervention categories, the description of intervention components by
study authors as biomedical, behavioural or structural will not be
used to categorise interventions for the purposes of this review.
Rather, we will examine the content of interventions as described
in study reports (or via communication with authors, as needed)
to classify intervention components and categorise interventions
according to the above criteria.
We anticipate considerable heterogeneity between studies on the
basis of the different combinations of intervention components
that are evaluated. As discussed below in the ‘Data synthesis’ section we will analyse pooled study results in subgroups on the basis
of the presence of different types of intervention component and
combinations thereof.
Types of outcome measures
To be eligible for inclusion in the review a study must have assessed at least one of the outcomes listed below. Biological outcomes (especially HIV incidence) are the most accurate indicators
of the success of HIV prevention interventions. However, given
that only one behavioural intervention trial (Koblin 2004) and
no known evaluations of structural interventions for MSM have
included HIV incidence as an endpoint, and that many interventions are designed to reduce HIV transmission risk among MSM
who are living with HIV (for whom HIV incidence is an irrelevant outcome), it is useful and necessary to record several relevant
sexual behaviour outcomes. It may also be informative to record
any structural-level pathway variables targeted by interventions,
though ex ante determination of these variables is problematic because their relevance as predictors of HIV vulnerability tends to
vary depending on the context and the presence of other factors,
and evidential support for hypothesised pathways between putative structural factors and HIV risk is limited (Auerbach 2011).
Nonetheless, in addition to biological and behavioural outcomes,
we will collect data on the broad categories of structural factors
thought to be associated with HIV risk that are listed below.
When outcome data are reported at multiple follow-up points,
data from the latest endpoint will be recorded in order to capture
sustained intervention effects.
Primary outcomes
Biological outcomes:
• Incidence of HIV
• Incidence of other STIs
Behavioural outcomes:
• Number of occasions of unprotected anal intercourse (UAI)
during recall period
• Proportion of participants reporting at least one occasion of
UAI during recall period
• Number of partners for UAI during recall period
Secondary outcomes
Behavioural outcomes:
• Number of occasions of unprotected receptive anal
intercourse (URAI) during recall period
• Proportion of participants reporting at least one occasion of
URAI during recall period
• Number of partners for URAI during recall period
• Number of occasions of unprotected insertive anal
intercourse (UIAI) during recall period
• Proportion of participants reporting at least one occasion of
UIAI during recall period
• Number of partners for UIAI during recall period
• Number of occasions of UAI with serodiscordant partner(s)
and/or partner(s) of unknown serostatus during recall period
• Proportion of participants reporting at least one occasion of
UAI with serodiscordant partner(s) and/or partner(s) of
unknown serostatus during recall period
• Number of serodiscordant partners and/or partners of
unknown serostatus for UAI during recall period
Structural outcomes:
• Indicators of societal homophobia and/or anti-MSM
stigma, including tolerance of and attitudes towards
homosexuality reported by decision-makers, healthcare providers,
and members of the general population; frequency of reported
anti-gay physical and verbal abuse and human rights violations;
and introduction of punitive anti-gay laws and policies
• Indicators of societal HIV/AIDS-related stigma, including
tolerance of and attitudes toward people living with HIV
(PLHIV) reported by decision-makers, healthcare providers, and
members of the general population; frequency of reported
physical and verbal abuse against PLHIV and HIV-related
human rights violations; and introduction of coercive or
discriminatory legislation against PLHIV
• Indicators of the successful enforcement of laws and
implementation of policies protecting MSM or PLHIV rights or
preventing MSM- or HIV/AIDS-related discrimination
• Potential society-wide economic mediators of HIV risk,
including income per capita, economic inequality metrics (e.g.,
Gini coefficient), and affordability of housing and other basic
needs
• Indicators of the society-wide accessibility and uptake of
health services including HIV prevention programming, HIV
Combination methods for HIV prevention in men who have sex with men (MSM) (Protocol)
Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
5
counselling and testing, STI prevention and treatment services,
and HIV prophylactic technologies
• Educational indicators, including societal-wide educational
attainment, and levels of HIV/AIDS- and sexual health-related
knowledge
Search methods for identification of studies
It is possible that reporting of relevant studies may not be uniform and that pertinent reports may be published in grey literature, local publications, bulletins of international, regional and national organisations, and conference proceedings. Moreover, relevant reports may be published in languages other than English.
Several sources of published and unpublished data (listed below)
will therefore be searched without restriction on the basis of publication date, language of report, location in which studies were
conducted, or their publication status.
Issues of major HIV-related journals from the past five years, including AIDS, AIDS Care, AIDS & Behavior, Journal of Acquired
Immune Deficiency Syndromes, HIV Clinical Trials, AIDS Prevention and Mental Health, AIDS Education and Prevention, and
AIDS Patient Care & STDs, will be hand searched in order to
identify reports not published in the electronic databases listed
above
Reference lists of included study reports will be scanned for mention of other, previously unidentified studies.
Study authors will be contacted by email, as needed, in order
to access unpublished data of trials located through the above
methods and to identify any other studies not identified through
the above methods.
Data collection and analysis
Selection of studies
Electronic searches
Comprehensive search strategies, developed with the guidance of
the Cochrane Review Group on HIV/AIDS, will be tailored to
search a broad range of relevant databases, including the Cochrane
Central Register of Controlled Trials (CENTRAL), MedLine,
EMBASE, PsycInfo, Global Health, Web of Science, ERIC, the
Social Sciences Citation Index, CINAHL Plus, Sociological Abstracts, TRIP, Social Policy and Practice, LILACS, and the WHO
Global Health Library. The search strategy will combine the
Cochrane HIV/AIDS Review Group HIV string, the Cochrane
Collaboration highly sensitive search string for RCTs (altered to
capture non-randomised studies), and a population filter for identifying studies involving MSM.
The AIDS Education Global Information System (AEGIS; http:
//www.aegis.org/en/), the U.S. National Library of Medicine
(NLM) Gateway (http://gateway.nlm.nih.gov), and the websites
of the International Society for Sexually Transmitted Disease
Research (ISSTDR; http://www.isstdr.org/index.php) and international AIDS Society (IAS; http://www.iasociety.org/) will be
searched for reports included in various HIV/AIDS conference
proceedings
Websites of relevant organisations and agencies, including UNAIDS, WHO, other UN agencies, the World Bank, the U.S. Centres for Disease Control and Prevention (CDC), and the U.K.
National Health Service (NHS) Centre for Reviews and Dissemination (CRD) at University of York, will be searched for relevant
reports. The WHO International Clinical Trials Registry Platform
(ICTRP) and ClinicalTrials.gov will be searched for ongoing or
prospective trials.
Searching other resources
Two authors (BV & GJMT) will independently screen titles
and abstracts for potential relevance. Full text versions of articles
deemed potentially relevant by at least one author will be obtained
for closer review by both authors, who will independently identify studies meeting the inclusion criteria. Disagreements will be
resolved by deferring to the third author (CB). Reasons for exclusion of any full-text articles will be provided in a table of excluded
studies.
Data extraction and management
Two authors (BV & GJMT) will independently conduct data
extraction using a specialised form designed with the assistance
of the Cochrane HIV/AIDS Review Group. Data will be extracted (or derived) on: study characteristics, including design and
methodological quality, allocation procedure, blinding and sampling methods; description of the intervention, including its theoretical basis (if applicable), processes involved, delivery method,
length of intervention and/or number of sessions, and categorization of intervention components (i.e., biomedical, behavioural
and/or structural); study location and context; duration of followup; sample size, number of participants in each group, and attrition rates; information about participants (age, sexual orientation,
gender identity, and other demographic characteristics); baseline
health information; baseline sexual behaviour practice; and all sexual behaviour (e.g., UAI) and HIV/STI incidence outcome data
provided. Conflicting assessments will be discussed and resolved
between the two authors. The third author (CB) will resolve any
persistent disagreements. Missing data will be sought from study
authors, as needed.
Data will be entered into, managed and analysed using Review
Manager (RevMan) 5.2 software. One author (BV) will enter data
Combination methods for HIV prevention in men who have sex with men (MSM) (Protocol)
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The methodological quality of included studies will be evaluated
using the Cochrane Collaboration’s tool for assessing risk of bias,
as described in the Cochrane Handbook for Systematic Reviews
of Interventions (Higgins 2011). Two authors (BV & GJMT) will
independently classify studies as presenting either a ‘low risk’ of
bias, ‘high risk’ of bias, or ‘unclear risk’ of bias in the following domains: random sequence generation, allocation sequence concealment, blinding of outcome assessment, incomplete outcome data,
selective outcome reporting, and other potential biases or threats
to validity. Criteria described in Chapter 8 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011) will
be used to appraise studies.
Discordant assessments will be resolved by discussion between the
two assessing authors. Unresolved disagreements will be settled
through consultation with the third author (CB).
to confirm this. If unadjusted data are presented, we will use methods described in Section 16.3 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011) to adjust data by
deriving a ‘design effect’ adjustment factor for each relevant trial
using an intra-cluster correlation coefficient (ICC) imputed based
on values reported in other included studies. If no included studies
report an ICC value, we will use an estimate of 0.005, the value
reported in a community-level trial of HIV prevention for MSM
(Kelly 1997) and employed in a Cochrane review of behavioural
HIV prevention interventions for MSM (Johnson 2008). Where
imputation is necessary, sensitivity analysis (discussed below) will
be performed to assess the robustness of our results to the imputation of a range of different ICC values.
If studies are included in which multiple active intervention groups
in which we are interested are compared to a single control group,
we will divide the control participants by the number of intervention groups, as described in Section 16.5 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011), to
create separate pair-wise comparisons such that no single participant’s data are analysed more than once.
Measures of treatment effect
Dealing with missing data
For ease of comparison between interventions and for consistency
of analysis it is desirable to express all intervention effect estimates
using a consistent metric. All outcomes will therefore be expressed
as odds ratios (OR) with 95% confidence intervals (CI).
For studies reporting dichotomous outcomes, such as incidence of
UAI, we will calculate ORs and variances using dichotomous event
tables of treatment successes and failures and standard OR formulae described in the Cochrane Handbook for Systematic Reviews
of Interventions (Higgins 2011). For studies reporting means and
standard deviations of continuous or interval-level outcomes, such
as number of partners for UAI or number of occasions of UAI
during the recall period, we will calculate standardized mean differences (and associated variances), and convert these to ORs (and
variances) using methods described by Hasselblad 1995 (cited in
Johnson 2002).
We will calculate all ORs such that values below one represent a
favourable effect of the intervention and values greater than one
represent a favourable effect of the comparison group. Values of
approximately one represent null effects.
All OR and variance estimates will be converted to the logarithmic
scale for meta-analysis. Resultant summary effect and variance
estimates in the log format will be converted back to ORs for
presentation and reported with 95% CIs.
Any missing data (e.g., outcomes) or statistics (e.g., standard deviations) will be requested from study authors.
Attrition rates for all studies will be noted and we will assess
whether analyses have been performed on an intention-to-treat
(ITT) basis by study authors. If not, we will perform ITT analyses
under the assumption that all missing outcome data were treatment failures in the intervention group and successes in the comparison group. Sensitivity analyses (described below) will be performed to assess the robustness of the review’s results to changes
to this ‘worst-case scenario’ assumption.
The likely impact of missing data will be discussed in the interpretation of the review’s results and loss to follow-up will be assessed
as a potential source of bias.
into RevMan and another (GJMT) will check all entries for fidelity
with the data recorded on the final data extraction forms.
Assessment of risk of bias in included studies
Unit of analysis issues
For all included cluster RCTs, in which groups or clusters are the
unit of randomisation rather than individuals, we will check that
reported outcome data have been adjusted by authors to account
for intra-cluster correlation. If necessary, authors will be contacted
Assessment of heterogeneity
Statistical heterogeneity will be assessed visually using the forest
plot, and quantitatively using the Chi-squared and I2 statistics
(Higgins 2003). Heterogeneity statistics will not, however, be used
to determine the appropriateness of a particular model for metaanalysis.
Assessment of reporting biases
The potential risk for publication bias will be explored by generating and examining funnel plots of individual study effect size
estimates versus standard errors, and evaluating any asymmetry
thereof (Egger 1997). Funnel plots are not informative when few
studies are included and/or when included studies have similar
sample sizes (Higgins 2011). We will therefore not use funnel plots
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7
for outcomes for which ten or fewer studies are included, or for
which studies are of similar size.
Data synthesis
If included studies, and/or subgroups of included studies (e.g.,
studies with similar combinations of the three categories of intervention component), are deemed sufficiently similar in terms of
participants, outcomes and intervention components to allow for
meaningful quantitative synthesis, standard meta-analytic methods will be employed, as per the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). Random-effects
models, based on the method of moments (DerSimonian 1986),
will be used under the assumption that the true effect sizes estimated by studies are drawn from a distribution of true effects
rather than a single true value.
We anticipate considerable heterogeneity between interventions
based on the presence of different categories of intervention modality (biomedical, behavioural and structural). To assess this, we will
stratify interventions based on the four possible intervention component combinations included in the review (i.e., biomedical +
behavioural; behavioural + structural; biomedical + structural; and
biomedical + behavioural + structural).
Comparisons specified a priori are:
• All combination interventions against minimal/no HIV
prevention,
• All combination interventions against other HIV
prevention,
• Biomedical and behavioural against minimal/no HIV
prevention,
• Behavioural and structural against minimal/no HIV
prevention,
• Biomedical and structural against minimal/no HIV
prevention,
• Biomedical, behavioural, and structural against minimal/no
HIV prevention,
• Biomedical and behavioural against other HIV prevention,
• Behavioural and structural against other HIV prevention,
• Biomedical and structural against other HIV prevention,
and
• Biomedical, behavioural and structural against other HIV
prevention
If possible, we will also conduct a multiple interventions metaanalysis in the Bayesian framework, grouping interventions in
the evidence network based on the combination of components.
Put otherwise, each node in the network of evidence represents a
specific combination of prevention components (biomedical, behavioural, or structural), with nodes for both no/minimal HIV
prevention and for single-component HIV prevention interventions against which combination interventions have been tested.
This analysis will be implemented in WinBUGS using code developed by the Centre for Research Synthesis and Decision Analysis
at the University of Bristol. Analyses will be run on each of the
primary outcomes on two independent chains for at least 100,000
iterations, with the first 50,000 discarded. Convergence will be assessed using Brooks-Gelman-Rubin plots. Information presented
will include pairwise ORs between each node in the network, the
probability of rank for each node, and the surface under the cumulative ranking line (SUCRA) for each node. Before analysis, the
network will be evaluated for transitivity; if applicable, the effect
modifiers listed below will be used for network meta-regression.
Subgroup analysis and investigation of heterogeneity
To assess the impact of potential study-level moderators of effect size subgroup analyses will be performed. Previous reviews of
behavioural HIV prevention interventions for MSM have found
differential effects on the basis of various participant and study
characteristics (Herbst 2005; Johnson 2008). Moreover, we recognise the inequitable geographic distribution of the HIV/AIDS
intervention trials performed previously, which have historically
been disproportionately conducted in high-income countries (especially the United States), neglecting the low and middle income
countries in which the burden of HIV/AIDS tends to be greatest (Ahmad 2011). It is useful, therefore, to map the geographic
distribution of included studies, both to identify cultural and geographic populations for whom, and contexts in which, further
research is required, and to explore whether previous trials have
tended to produce differential effects by geographic location.
We will perform subgroup analyses based on the following variables:
Study characteristics
• Date of study commencement
• Geographic location in which the study was conducted
• Study design (i.e., randomised versus non-randomised)
• Duration of follow-up
Participant characteristics
• Mean age
• HIV serostatus
• Proportion who are gay-identified
• Education level
If possible, the effect of potential study-level covariates will be
assessed by conducting multivariable random-effects meta-regression analyses (Thompson 2002) using the ‘metareg’ macro for
STATA 12.0. We will include the following potential effect modifiers in our analyses
Intervention characteristics
• Types and combinations of intervention components
present
• Duration of intervention
Participant characteristics
• Mean age
• Proportion who are gay-identified
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8
• Proportion who are living with HIV
Study characteristics
• Income stratum of country in which study is conducted, as
defined by the World Bank
Sensitivity analysis
If possible we will perform sensitivity analyses to test the robustness
of our meta-analyses by calculating summary effects for all studies
and then progressively excluding studies of lowest methodological
quality and assessing the impact on the summary effect size.
We will also assess differences in the summary effect estimates on
the basis of changes in assumptions related to missing outcome
data. Results of an ‘available cases’ analysis will be compared to
those of imputed case analyses under a ‘best case’ scenario (i.e.,
intervention participants lost to follow-up are assumed to be treatment successes and comparison participants lost to follow-up are
assumed to be treatment failures) and a ‘worst case’ scenario (i.e.,
intervention participants lost to follow-up are assumed to be treatment failures and comparison participants lost to follow-up are
assumed to be treatment successes).
For data from cluster RCTs in which imputation of an ICC estimate is necessary we will we assess the robustness of our findings
to changes in the assumed ICC value.
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∗
Indicates the major publication for the study
DECLARATIONS OF INTEREST
We declare that we have no conflicts of interest.
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