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Cochrane Database of Systematic Reviews Protocol - Intervention

Internet‐based behavioral interventions for preventing HIV infection in men who have sex with men (MSM)

Authors' declarations of interest

Version published: 07 December 2011 Version history

https://doi.org/10.1002/14651858.CD009525

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Abstract

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:

1. To perform a systematic review of Internet‐based HIV prevention intervention studies in MSM compared with standard or other interventions or no interventions. All randomized clinical trials and rigorously designed quasi‐experimental studies will be included. The following key elements of the Internet‐based HIV interventions will be examined

  • Intervention strategies applied in the studies

  • Theoretical bases for these interventions

  • Measurements of intervention outcomes

  • Barriers and challenges in intervention delivery, informed consent provision, participation incentive distribution, data collection and subject retention, etc.

  • Cultural and geographical differences in the interventions

2. To perform a meta‐analysis, when possible and appropriate, of the efficacy of Internet‐based HIV interventions among MSM.

Background

Men who have sex with men (MSM) have been disproportionately affected by Human Immunodeficiency Virus (HIV) infection for nearly three decades since the first detection of  Acquired Immune Deficiency Syndrome (AIDS) (CDC 1981; UNAIDS 2010). In recent years, the HIV epidemic among MSM have been increasing in industrialized countries (Cowan 2008; German 2011; Xu 2010) while newly identified epidemics among this population have been continuously reported in Asia (van Griensven 2010), Africa (McIntyre 2010) and Latin America (Gondim 2009). Co‐infections with other sexually transmitted infections (STI) including syphilis (Guo 2009; Jakopanec 2010; Velicko 2008), Chlamydia, gonorrhea (Hoover 2010; van Veen 2010; Wei 2009; Wohlfeiler 2005), human papillomavirus (HPV) (Gao 2010; Goldstone 2011; Lu 2011), herpes simplex virus (HSV) (Lai 2003; Spielmann 2010; Xu 2010), hepatitis B (Lama 2010; McMillan 2006;Paat 2009; Ruan 2009), hepatitis C (Buxton 2010; Hao 2011; van de Laar 2010; van de Laar 2007) and lymphogranuloma venerum (Bremer 2006; Martin‐Iguacel 2010; Nieuwenhuis 2004; Sethi 2009; Singhrao 2011; Stark 2007; Vandenbruaene 2005) have also been rapidly emerging in their epidemiological trends among MSM, accompanying the high HIV prevalence.

 

Risky sexual behaviors largely determined the rapid increase in HIV transmission among the MSM population. Unprotected receptive anal intercourse (URAI) has the highest risk for HIV acquisition, followed by unprotected insertive anal intercourse (UIAI), receptive oral intercourse (UROI) and insertive oral intercourse (UIOI) (Dosekun 2010). Types of sex partners, concurrent sex partners, frequency of sex acts and condom use may all contribute to HIV transmission among MSM (Ha 2010; Jansen 2011; Sullivan 2009). Aside from daily oral pre‐exposure prophylaxis (PrEP) with a well‐tolerated combination of specific antiviral medications (CDC 2011; Grant 2010; Kelesidis 2011), which has shown to be somewhat effective in HIV prevention, most biomedical interventions have failed to demonstrate any efficacy in MSM (Bartholow 2005; Padian 2008). Thus, sustained behavior interventions are still required to successfully reduce HIV transmission among MSM (Hong 2009; Johnson 2008; Vergidis 2009).

 

Electronic Health (eHealth), an emerging concept in the late twentieth century, is defined as using information and communication technology (ICT)—such as computers, mobile phones, and satellite communications—for health services and information (ICT 2008; Vital Wave Counseling 2009). It incorporates public health, medical informatics, and business (ICT 2008). Internet‐based behavioral intervention is one of the strategies of eHealth targeted at both individual and population health (Glanz 2005). It bridges the innovative technology and the health behavior theories for disease prevention as compared with the traditional approach of giving tailored advice face‐to‐face (Glanz 2005). Several benefits enable the Internet as a tool for behavior interventions: its ability to reach geographically dispersed individuals; asynchronous communication; multiple modes and formats of communications (multimedia); interactivity; customization of contents; and flexibility (Glanz 2005; Noar 2009). By the end of 2010, the total number of Internet users had hit 2.04 billion worldwide, among which 0.85 billion were from developed countries and 1.20 billion from developing countries (International Telecommunication Union 2010). China topped other countries in the number of total Internet users (0.46 billion) by the end of 2010, followed by the United States (0.24 billion) (China Internet Watch 2011; Wikipedia 2011a). The Internet is the most common way to deliver interactive computer‐based programs for effective sexual health promotion such as sexual health knowledge and sexual behavior (Bailey 2010). Existing research has also shown strong evidence that the Internet provides a unique opportunity for MSM, young men in particular, to learn about their sexuality, to gain information on topics such as sexual health and relationships, to share stories with and get support from other men, and to seek dating partners in an anonymous, non‐judgmental fashion (Bull 2004; Horvath 2007; Hospers 2005; Mettey 2003; Mitchell 2007; Mitchell 2007a; Ybarra 2008; Young 2011). More and more online services such as chat rooms, websites, and mail lists have been created to cater to MSM (Adam 2011; Couch 2008; Grosskopf 2010; Hightow‐Weidman 2011; Hooper 2008; Margolis 2011; Sowell 2010). These services are especially popular among MSM because of their accessibility, affordability and, most importantly, their confidentiality and safety when compared with traditional public gatherings (Blas 2007; Hughes 2009; Levine 2005; Ross 2007). However, such an environment also permits those who frequent online MSM communities to find more sexual partners, which could have a huge impact on the sexual behaviors of MSM and thus spread disease (Bull 2000; McFarlane 2000). Existing studies implied that the Internet has the potential to be a unique place for physicians and public health professionals to provide various health promotions among MSM, such as partner notification for exposure of diseases (Ehlman 2010; Mimiaga 2008; Vest 2007), chat room outreach (Fields 2006; Rhodes 2007; Rhodes 2008), online HIV/STD testing (Levine 2005), health education (Beckjord 2007; Bolding 2004; Chiasson 2010; Sandstra 2008) and interactive interventions (Carpenter 2010; Lau 2008; Rosser 2010). Internet‐based prevention intervention is a highly cost‐effective approach that is able to reach a larger and more geographically diverse population. Since the early 21st Century, research on Internet‐based interventions has expanded (Ybarra 2007). A few general reviews of the Internet technology for HIV prevention interventions and barriers of its application (Chiasson 2006; Pequegnat 2007; Rietmeijer 2009; Swendeman 2010) have been published in the past 10 years. A meta‐analysis of the effects of a wide range of behavioral or social HIV prevention interventions to reduce the risk for sexual transmission of HIV among MSM population demonstrated that such interventions could reduce occasions of or partners for unprotected anal sex by 27% compared with minimal or no interventions and by 17% compared with standard or other interventions (Johnson 2008). Another one on the efficacy of computer technology‐based behavioral interventions documented the effectiveness of the interventions for increasing condom use, reducing frequency of sexual behavior, number of partners and incident STI among various at‐risk populations (Noar 2009). A most recent meta‐analysis showed some significant effects of interactive computer‐based interventions (ICBI) on sexual health promotions such as increasing sexual health knowledge, safer sex self‐efficacy and safer‐sex intentions compared with minimal interventions (Bailey 2010). However, there is no clear evidence of the effects of the interventions based on the Internet in the MSM population compared with standard or other interventions or no interventions. In recent years, randomized clinical trials and quasi‐experimental intervention studies have been emerging to reduce risky behaviors via the Internet among MSM, for example to increase HIV testing (Blas 2010; Rhodes 2011), HIV disclosure (Chiasson 2009), HIV‐related knowledge, self‐efficacy, and outcome expectancies (Bowen 2008; Lau 2008), and to reduce risky sexual behaviors (Carpenter 2010; Rosser 2010). We will perform a systematic literature review and meta‐analysis of Internet‐based HIV interventions in MSM population.

Objectives

1. To perform a systematic review of Internet‐based HIV prevention intervention studies in MSM compared with standard or other interventions or no interventions. All randomized clinical trials and rigorously designed quasi‐experimental studies will be included. The following key elements of the Internet‐based HIV interventions will be examined

  • Intervention strategies applied in the studies

  • Theoretical bases for these interventions

  • Measurements of intervention outcomes

  • Barriers and challenges in intervention delivery, informed consent provision, participation incentive distribution, data collection and subject retention, etc.

  • Cultural and geographical differences in the interventions

2. To perform a meta‐analysis, when possible and appropriate, of the efficacy of Internet‐based HIV interventions among MSM.

Methods

Criteria for considering studies for this review

Types of studies

We will include quantitative studies of randomized clinical trial and quasi‐experimental studies with both intervention group and control group.

Types of participants

Men who have sex with men including homosexual or bisexual men, and male commercial sex workers who have sex with male clients without restriction on age, ethnicity, race, nationality, etc.

Types of interventions

Interventions of interests are those designed to reduce the risk of HIV or STIs and are delivered via the Internet, such as online graphics or video, chatting, email invitations, etc.

Comparison interventions, if used, should include inactive interventions (e.g., standard intervention or waiting list control) or active interventions (e.g., a different intervention approach).

Types of outcome measures

Biomedical, behavioral and cognitive measurements related to the effects of Internet‐based interventions will be included as outcomes.

Biomedical outcomes may include:

  • HIV incidence or prevalence

  • STI incidence or prevalence

Behavioral outcomes may include:

  • Proportion of participants reporting unprotected anal intercourse (UAI) in the past 3 or 6 months·     

  • Proportion of participants reporting unprotected oral intercourse (UOI) in the past 3 or 6 months·        

  • Frequency of UAI

  • Proportion of participants reporting condom use during UAI or UOI·

  • Participants reporting number of regular or casual male sex partners·

  • Proportion of participants reporting substance use, including alcohol, drugs, etc.·

  • Proportion of participants reporting PrEP use·  

  • Proportion of participants reporting having prior HIV testing ·    

  • Proportion of participants reporting HIV disclosure·   

  • Proportion of participants reporting HIV‐ or STI‐related knowledge

Cognitive outcomes may include:

  • Self‐efficacy

  • Outcome expectancies

  • Willingness

Search methods for identification of studies

We will first propose a search strategy according to the guidance of the Cochrane Handbook of Systematic Reviews of Interventions Version 5.1.0 (Higgins 2011).

We will then contact the Cochrane Review Group (CRG) on HIV/AIDS to seek further assistance for a more comprehensive search strategy to identify all relevant literatures regardless of language or publication status (published, unpublished, in press and in progress) (Higgins 2011).

Electronic searches

Global use of Internet initiated from the late 80’s and early 90’s (Wikipedia 2011). The internet users were merely content consumer during the Web 1.0 era (early 90’s‐2003) while they became content creator in the Web 2.0 era (2004‐present) (Rietmeijer 2009; Wikipedia 2011b; Wikipedia 2011c). The earliest connection between the Internet and STI were found in the MSM population in late 90’s (Bull 2000; Klausner 2000; McFarlane 2000). Thus, we will search the following electronic databases from 1985 to the present:

  • PubMed

  • PsycINFO

  • Web of Knowledge

  • CENTRAL (Cochrane Central Register of Controlled Trials)

  • EMBASE

  • ERIC (Education Resources Information Center)

  • Africa: African Index Medicus

  • CBM (Chinese Biomedical Literature Database)

  • CNKI (China National Knowledge Infrastructure)

  • Eastern Mediterranean: Index Medicus for the Eastern Mediterranean Region

  • LILACS (Latin America and the Caribbean)

  • IMSEAR (South‐East Asia: Index Medicus for the South‐East Asia Region)

  • WPRIM (Western Pacific: Western Pacific Region Index Medicus)

  • ProQuest Dissertations & Theses Database

 Appendix 1 details the proposed search strategy in PubMed, which will be modified in other electronic databases.

Searching other resources

We will search for conference abstracts or proceedings and other grey literature through the following electronic sources from the earliest available records to the present:

  • Aegis archive of HIV/AIDS conference abstracts

    • British HIV/AIDS Association (2001‐2011)

    • The European AIDS Clinical Society (2001‐2005)

    • Conference on Retroviruses and Opportunistic Infections (1993‐2008)

    • IAS HIV Pathogenesis and Treatment (2001‐2009)

    • National AIDS Prevention Conferences (1999‐2005)

    • National AIDS Update Conferences (2000‐2005)

    • International AIDS Conferences (1985‐2010)

  • IAS HIV Pathogenesis and Treatment 2011 website

  • Conference on Retroviruses and Opportunistic Infections websites (2009‐2011)

  • PsycEXTRA

  • BIOSIS

  • World Health Organization Library Information System (WHOLIS)

  • OpenSIGLE (System for Information on Grey Literature)

Data collection and analysis

Data collection and analysis will be conducted under the guidance of the Cochrane Handbook of Systematic Reviews of Interventions (Higgins 2011).

Selection of studies

Two authors (Lan Zhang and Lu Yin) will independently screen the titles and abstracts obtained from the comprehensive literature search. Disagreements between the reviewers will be resolved by consensus through discussion. We will retrieve full text of the potentially relevant reports and carefully examine them for compliance with the eligibility criteria. We will correspond with investigators, where appropriate, to clarify study eligibility and other information such as missing results of the literature. Final judgment for study inclusion will be made by senior authors (Sten Vermund and Han‐Zhu Qian, who also act as mentors) with expertise on epidemiology in HIV/AIDS.

Data extraction and management

Two authors (Lan Zhang and Lu Yin) will independently extract data from the selected studies using a standardized data extraction form. Meridith Blevins will perform the meta‐analysis where appropriate.

Assessment of risk of bias in included studies

Two authors (Lan Zhang and Lu Yin) will independently assess the risk of bias of each selected study using the domain‐based evaluation tool recommended in the Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 (Higgins 2011). Disagreement will be resolved by discussion between the reviewers on each criterion and by consultation with senior authors (Sten Vermund and Han‐Zhu Qian). We will use standardized table and figures to summarize the assessment of risk of bias.

We will evaluate seven domains for assessment of the selection bias, performance bias, detection bias, attrition bias, and reporting bias. We will assess each of the following domains as low risk of bias (low), high risk of bias (high), or uncertain risk of bias with correspondent judgment criteria:

  • Random sequence generation

    • Low: investigators described a random component in the sequence generation process, such as the use of random number table, computer random number generator, coin tossing, etc.

    • High: investigators described a non‐random component in the sequence generation process, such as the use of odd or even date of birth, pre‐specified rules based on the day or date of admission, hospital, or clinic record number, judgment of the clinician, etc.

    • Unclear: insufficient information to permit judgment of bias.

  • Allocation concealment

    • Low: participants and investigators enrolling participants cannot foresee assignment due to the use of central allocation, sequentially numbered, opaque, sealed envelopes, etc.

    • High: participants and investigators enrolling participants can foresee assignment due to the use of an open random allocation schedule, unsealed or non‐opaque assignment envelopes, date of birth, etc.

    • Unclear: insufficient information to permit judgment of bias or the method not described.

  • Blinding of participants and personnel

    • Low: no blinding or incomplete blinding that is not likely to introduce bias; blinding of participants and key study personnel that could have been unlikely to be broken.

    • High: no blinding or incomplete blinding that is likely to introduce bias; blinding of participants and key study personnel that could have been likely to be broken.

    • Unclear: insufficient information to permit judgment of bias or no description of this information.

  • Blinding of outcome assessment

    • Low: no blinding of outcome assessment but is unlikely to introduce bias; blinding of outcome assessment that could have been unlikely to be broken.

    • High: no blinding of outcome assessment that is likely to introduce bias; blinding of outcome assessment that could have been likely to be broken.

    • Unclear: insufficient information to permit judgment of bias or no description of this information

  • Incomplete outcome data

    • Low: no missing outcome data; reasons for missing outcome data unlikely to be related to true outcome; missing outcome data balanced in number across groups; the proportion of missing outcomes is not enough to have clinically relevant impact on the effect estimates compared with observed event risk for binary outcome data; plausible effect size among missing outcomes is not enough to have a clinically relevant impact on observed effect size for continuous outcome data; missing data is appropriately imputed.

    • High: reason for missing outcome data is likely to be related to true outcome, with either imbalance in number across groups or reasons for missing data; the proportion of missing outcomes is enough to have clinically relevant impact on the effect estimates compared with observed event risk for binary outcome data; plausible effect size among missing outcomes is enough to have a clinically relevant impact on observed effect size for continuous outcome data; missing data is inappropriately imputed.

    • Unclear: insufficient reporting of attrition or exclusions to permit judgment of bias; no description of the missing data.

  • Selective reporting

    • Low: a protocol is available which clearly states all the outcomes as the same as in the final trial report; a protocol is unavailable but the published reports including all expected outcomes are clear

    • High: the primary outcome differs between the protocol and final trial report (e.g., incomplete report of pre‐specified outcomes or report of outcomes that are not pre‐specified) Unclear: insufficient information to permit judgment of this bias.

  • Other bias

    • Low: no evidence of other sources of bias.

    • High: potential bias related to a specific study design or being fraudulent or other problems.

    • Unclear: insufficient information to permit judgment of from other sources or insufficient rationale for identified problems that will introduce bias.

Measures of treatment effect

We will use risk ratio (RR) for dichotomous outcomes and risk difference (RD) for continuous outcomes. We will present a 95% confidence interval (CI) for either measure.

Unit of analysis issues

The unit of analysis will be the included RCT or quasi‐experimental studies.

Dealing with missing data

We will evaluate the dropout rate for each included study. We will also assess whether an intention‐to‐treat analysis has been conducted or could be conducted using existing information of each study. We will contact the original authors, where appropriate, to inquiry the original dataset for intention‐to‐treat analysis and how missing data occurred. For data missing at random, analyze only the complete data. For data missing not at random, we will use the following two options:

  • Impute the missing data with replacement values, and treat these as if they were observed if enough information could be obtained from the original dataset and the project;

  • Impute the missing data and account for the fact that these were imputed with uncertainty if little information could be obtained from the original dataset and the project (this will be done in consultation with Meridith Blevins).

We will perform sensitivity analyses, if appropriate, to assess how sensitive results are to reasonable changes in the assumptions made and will discuss the potential impact of missing data on the results.

Assessment of heterogeneity

We will use the I2 (describing the percentage of total variation across trials that is due to heterogeneity rather than sampling error) statistic to quantify heterogeneity. If I2>50%, we will recognize as significant statistical heterogeneity (Higgins 2011).

 We will not perform a meta‐analysis of the selected studies only if there is strong evidence of inconsistent results across studies (I2>80%).

Assessment of reporting biases

We will assess publication bias by using a funnel plot to illustrate variability among studies. If asymmetry is detected, causes other than publication bias will be explored in reference to the Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 (Higgins 2011). The funnel plot will be conducted if 10 or more randomized controlled trials (RCTs) are included.

Data synthesis

If the eligible studies are statistically homogeneous (I2<50%), we will perform the meta‐analysis using a fixed‐effect model. Otherwise, we will use the random‐effect model. We will also evaluate the causes of heterogeneity to supplement choice of model using the Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 (Higgins 2011).

Subgroup analysis and investigation of heterogeneity

We anticipate a heterogeneous effect in the interventions between interactive and non‐interactive Internet‐based interventions. We also anticipate heterogeneity by intervention type and geographical locations. We will compare the intervention effect between interactive and non‐interactive Internet‐based interventions. We will also conduct subgroup analysis by type of intervention (chat‐room, online video, bulletin board, virtual community, email etc.) and by country (high‐income countries vs. low‐and middle‐income countries).

Sensitivity analysis

We will perform a sensitivity analysis if specific issues are identified during the review process from any individual peculiarities of the studies under scrutiny.

 Summary of findings table

We will evaluate the quality of evidence associated with outcomes specified in “types of outcome measures” according to the criteria of the GRADE system (Guyatt 2008) and construct a Summary of Findings table using the GRADEpro software Version 3.6 (http://ims.cochrane.org/revman/gradepro). The GRADE approach appraises the quality of a body of evidence based on the extent to which one can be confident that an estimate of effect or association is correct. The quality of a body of evidence is classified as high, moderate, low, or very low according to factors including the study methodology, consistency and precision of the results, and directness of the evidence.