In most countries, HIV surveillance systems do not generate good quality samples of most-at-risk populations. Respondent-driven sampling (RDS) is being widely used as an effective method to sample most-at-risk populations for HIV bio-behavioural surveys.

RDS is a chain-referral sampling used worldwide to recruit “hidden” populations that are less likely to be recruited from public venues. Since the first RDS study was conducted in 1994 in the United States, this method has been used in more than 150 studies in more than 50 countries to sample injecting drug users, sex workers, men who have sex with men, migrant/mobile populations, high risk heterosexual men and women and youth at risk. However, many of the countries have been unable to analyze their data due to their inability to use the specialized RDS analysis tool (RDSAT) and difficulties in understanding the complexities of social network theory and statistical adjustments inherent in using this programme.

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The course provides participants with knowledge and understanding of RDS data analysis, and the theoretical and statistical concepts upon which it is based. The course addresses all steps in RDS data management and analysis, including the best practices for bivariate and multivariate analysis. The training course will address new issues in RDS and RDSAT, and parameters that need to be taken into account when planning 2nd and any subsequent rounds of RDS. Importantly, the course addresses interpretation of RDS data, particularly the external validity.
Each participant will work on a computer with the following software:

• RDSAT 6.0.1
• Excel
• NETDRAW (freeware for recruitment chains)
• STATA or SPSS (for logistic regression)

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• Latest information about implementing RDS
• RDS software that is currently available, its capacity and limitations
• Theoretical parameters of RDS and the meaning of technical concepts such as homophily, social network sizes, differential recruitment and equilibrium
• Preparation of data files that can be used in RDS software and analysis of data using RDSAT
• Graphical presentation of recruitment chains using NetDraw and Gephi
• Analysis of trend data from RDS surveys
• Export of individualized weights to conduct regression analyses
• Regression analyses using either SPSS or STATA
• Data interpretation and analysis

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The course consists of lectures and individual work on computers so that participants can get hands-on experience in RDS data management and analysis. Participants are encouraged to bring their own RDS data sets to work on, particularly if they need to write up the results from RDS surveys for surveillance reports or scientific manuscripts. Those without actual data will be provided with datasets. Participants should be familiar in basic statistics and Excel software. Participants are expected to bring their own laptop computers at the course. They will be given statistical programmes at the course to work with.

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This training invites principal investigators, statisticians, data managers and other researchers who have experience using RDS.

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Aleksandar Stulhofer, PhD, Faculty of Arts and Humanities, University of Zagreb, Croatia

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Danijela Leso, WHO Collaborating Centre for Capacity Building in HIV Surveillance

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The course takes place in five days, from 13-17 February 2012 in Zagreb, Croatia.

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Web site of Respondent-Driven Sampling, includes downloads of RDSAT and the references
Cornell University
www.respondentdrivensampling.org

Heckathorn D. Respondent-Driven Sampling: A new approach to the study of hidden populations. Social Problems. 1997;44:174–199.

Heckathorn D, Semaan S, Broadhead RS, et al. Extensions of respondent-
driven sampling: a new approach to the study of injection drug users aged 18-25. AIDS
and Behavior 2002;6:55-67.

Townsend L, Johnston LG, Flisher AJ, Mathews C, Zembe Y. Effectiveness of respondent-driven sampling to recruit high risk heterosexual men who have multiple female sexual partners: differences in HIV prevalence and sexual risk behaviours measured at two time points. AIDS Behav. 2010;14(6):1330-9.

Bozicevic I, Rode OD, Lepej SZ, Johnston LG, Stulhofer A, Dominkovic Z, Bacak V, Lukas D, Begovac J.Prevalence of sexually transmitted infections among men who have sex with men in Zagreb, Croatia. AIDS Behav 2009;13:303-9.

Johnston LG, Malekinejad M, Kendall C, Iuppa IM, Rutherford GW.I mplementation challenges to using respondent-driven sampling methodology for HIV biological and behavioral surveillance: field experiences in international settings. AIDS Behav 2008;12(4 Suppl):S131-41 2008.

Malekinejad M, Johnston LG, Kendall C, Kerr LR, Rifkin MR, Rutherford GW.
Using respondent-driven sampling methodology for HIV biological and behavioral surveillance in international settings: a systematic review. AIDS Behav 2008;12(4 Suppl):S105-30.

Gile KJ, Handcock MS. Respondent-Driven Sampling: An Assessment of Current Methodology. Sociological Methods, 2010. 40, 1: 285-327.

Goel S, Salganik MJ: Respondent-driven sampling as Markov chain Monte Carlo, Statistics in Medicine, 2009, 28(17), 2202 – 2229.

Goel S, Salganik MJ. 2010. “Assessing respondent-driven sampling.” Proceedings of the National Academy of Sciences, 107:6743-6747.

Heckathorn DD. Extensions of respondent-driven sampling: analyzing continuous variables and controlling for differential recruitment. Sociol Methodol. 2007;37:151-207.

McPherson M, Smith-Lovin L, Cook JM. 2001. “Birds of a feather: homophily in social networks”. Annual Review of Sociology. 27: 415-444.

Salganik MJ, Heckathorn D. 2003. “Sampling and estimation in hidden populations using respondent driven sampling.” Presented at the Sunbelt Social Networks Conference, Cancun, Mexico.

Salganik MJ: Variance estimation, design effects and sample size calculations for respondent driven sampling. Journal of Urban Health, 2006, 83(Suppl 7), 98–112.

Wejnert C: An Empirical Test of Respondent-Driven Sampling: Point Estimates, Variance, Degree Measures, and Out-of-Equilibrium Data. Sociological Methodology, 2009, 39, 73-116

Volz E, Heckathorn DD: Probability-Based Estimation Theory for Respondent-Driven Sampling. Journal of Official Statistics, 2008, 24 (Suppl 1), 79-97.

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The aim of this workshop is to provide participants with theoretical knowledge and practical skills in implementation of HIV surveillance methods in high-risk groups, as well as methods used to carry out studies that aim to evaluate HIV prevention interventions in high-risk groups.

The first part of the course gives an overview of the components of a comprehensive HIV surveillance in high-risk groups, such as pre-surveillance assessment, mapping, and surveys that are implemented as part of the community-based surveillance. Study designs that will be described include cluster-based stratified sampling, time-location sampling and respondent-driven sampling. Advanced methods in HIV surveillance will be also outlined, such as HIV incidence-based surveillance and data triangulation as a programme evaluation tool.

In the second part of the course participants are provided with practical guidance on how to use different study designs to evaluate HIV interventions that are implemented in their countries among key populations at higher risk.

The most important part of the course is the protocol development exercise. Participants can choose to develop two types of protocols: (I) a protocol for an HIV surveillance survey in high-risk groups; or (II) a protocol for a study that evaluate HIV intervention(s) in high-risk groups using simple (cross-sectional surveys, programmatic data) or more complex designs (cohort studies, randomised control trials, data triangulation). Participants are encouraged to work on protocols that might be later on used in their own countries.

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• Components of HIV surveillance in high-risk groups
• Non-probabilistic and probabilistic sample designs in community-based surveys in high-risk groups
• Overview of HIV prevention interventions in high-risk groups
• Methods for evaluation of preventions using simple and more complex designs

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The course consists of lectures, presentations of case studies of surveillance surveys and evaluation studies, exercises and group work.

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Epidemiologists, public health professionals, social scientists

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The increases in funding for HIV prevention and surveillance provided by the Global Fund for AIDS, Tuberculosis and Malaria and the (US) President’s Emergency Plan for AIDS Relief (PEPFAR), increased also the imperative and the opportunities for evaluation of the scaled-up HIV interventions among key populations at higher risk of HIV.(1) HIV transmission can be decreased if prevention programmes include a mix of behavioural, biomedical and structural interventions appropriate for the epidemic context. (2) (3) Evaluation of HIV programmes enables to document how improvements in availability, access, utilization, and coverage with HIV interventions result in decreased rates of HIV infection.(4)

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The aim of this course is to provide participants with practical guidance on how to set up an evaluation plan for HIV interventions implemented among key populations at higher risk (sex workers, injecting drug users, men who have sex with men) and other vulnerable groups.

The course starts with the outline of the current evidence of effectiveness of biological, behavioural and structural HIV prevention strategies, obtained mainly from systematic reviews and meta-analysis. The lectures on the second day will outline the key sources of evidence on effectiveness of interventions, such as Cochrane Collaboration and latest WHO and CDC recommendations and guidelines. (5) (6) (7) Participants will be given an opportunity to share their own country-specific experiences of evaluation of HIV programmes among populations at higher risk of HIV on the first and the second day.

The next part of the course will present the simple approaches to designing HIV evaluation protocols, which rely on programmatic and surveillance data, and more complex approaches which use prospectively collected data, step-wedged and randomized trial designs. The issues to be addressed include the selection of the study population, sampling, statistical inference and measuring outcomes and impact.  The key part of the course is a group work which consists of the development of an evaluation protocol that participants will present at the end of the course. Participants can choose the topic of the evaluation protocol which they can later on use in their countries. An evaluation protocol describes every step of the evaluation, with special focus on the design, methodology, and analytical procedures of the evaluation.

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• Current evidence of efficacy and effectiveness of biological, behavioural and structural HIV prevention interventions
• Choosing a prevention intervention –Cochrane Reviews and CDC and WHO guidelines and recommendations
• Implementing interventions: challenges in scaling up
• Choosing and monitoring indicators
• Designing a programme evaluation

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The course consists of lectures, presentation of case studies, exercises and group work. The course is designed to provide participants with practical skills and knowledge on how to choose, monitor and evaluate HIV interventions in their countries.

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Epidemiologists, public health professionals, social scientists

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The course takes place in five days, from 20-24 June 2011 in Cavtat (close to Dubrovnik), Croatia. The course will take place at the Hotel Croatia in Cavtat ( )

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(1) Hallett TB, White PJ, Garnett GP. Appropriate evaluation of HIV prevention interventions: from experiment to full-scale implementation. cialis 5mg best price australia2007;83:i55-i60cialis 5mg best price australia