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      Evaluation of the Tsima community mobilization intervention to improve engagement in HIV testing and care in South Africa: study protocol for a cluster randomized trial

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          Abstract

          Background

          HIV transmission can be decreased substantially by reducing the burden of undiagnosed HIV infection and expanding early and consistent use of antiretroviral therapy (ART). Treatment as prevention (TasP) has been proposed as key to ending the HIV epidemic. To activate TasP in high prevalence countries, like South Africa, communities must be motivated to know their status, engage in care, and remain in care. Community mobilization (CM) has the potential to significantly increase uptake testing, linkage to and retention in care by addressing the primary social barriers to engagement with HIV care—including poor understanding of HIV care; fear and stigma associated with infection, clinic attendance and disclosure; lack of social support; and gender norms that deter men from accessing care.

          Methods/design

          Using a cluster randomized trial design, we are implementing a 3-year-theory-based CM intervention and comparing gains in HIV testing, linkage, and retention in care among individuals residing in 8 intervention communities to that of individuals residing in 7 control communities. Eligible communities include 15 villages within a health and demographic surveillance site (HDSS) in rural Mpumalanga, South Africa, that were not exposed to previous CM efforts. CM activities conducted in the 8 intervention villages map onto six mobilization domains that comprise the key components for community mobilization around HIV prevention. To evaluate the intervention, we will link a clinic-based electronic clinical tracking system in all area clinics to the HDSS longitudinal census data, thus creating an open, population-based cohort with over 30,000 18–49-year-old residents. We will estimate the marginal effect of the intervention on individual outcomes using generalized estimating equations. In addition, we will evaluate CM processes by conducting baseline and endline surveys among a random sample of 1200 community residents at each time point to monitor intervention exposure and community level change using validated measures of CM.

          Discussion

          Given the known importance of community social factors with regard to uptake of testing and HIV care, and the lack of rigorously evaluated community-level interventions effective in improving testing uptake, linkage and retention, the proposed study will yield much needed data to understand the potential of CM to improve the prevention and care cascade. Further, our work in developing a CM framework and domain measures will permit validation of a CM conceptual framework and process, which should prove valuable for community programming in Africa.

          Trial Registration

          NCT02197793 Registered July 21, 2014.

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          Most cited references47

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          Akaike's information criterion in generalized estimating equations.

          W. Pan (2001)
          Correlated response data are common in biomedical studies. Regression analysis based on the generalized estimating equations (GEE) is an increasingly important method for such data. However, there seem to be few model-selection criteria available in GEE. The well-known Akaike Information Criterion (AIC) cannot be directly applied since AIC is based on maximum likelihood estimation while GEE is nonlikelihood based. We propose a modification to AIC, where the likelihood is replaced by the quasi-likelihood and a proper adjustment is made for the penalty term. Its performance is investigated through simulation studies. For illustration, the method is applied to a real data set.
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            A comparison of inclusive and restrictive strategies in modern missing data procedures.

            Two classes of modern missing data procedures, maximum likelihood (ML) and multiple imputation (MI), tend to yield similar results when implemented in comparable ways. In either approach, it is possible to include auxiliary variables solely for the purpose of improving the missing data procedure. A simulation was presented to assess the potential costs and benefits of a restrictive strategy, which makes minimal use of auxiliary variables, versus an inclusive strategy, which makes liberal use of such variables. The simulation showed that the inclusive strategy is to be greatly preferred. With an inclusive strategy not only is there a reduced chance of inadvertently omitting an important cause of missingness, there is also the possibility of noticeable gains in terms of increased efficiency and reduced bias, with only minor costs. As implemented in currently available software, the ML approach tends to encourage the use of a restrictive strategy, whereas the MI approach makes it relatively simple to use an inclusive strategy.
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              Masculinity and perceived normative health behaviors as predictors of men's health behaviors.

              This study examined the unique contributions of masculinity and men's perceptions of the normativeness of men's and women's health behaviors in predicting men's self-reported health behaviors. One hundred and forty men aged 18-78 were recruited from 27 unmoderated and moderated Internet listservs of potential interest to men. They completed measures on-line assessing masculinity, their perceptions of normative health behaviors for men and women, and 8 health behaviors (i.e., alcohol abuse, seatbelt use, tobacco use, physical fighting, use of social support, exercise, dietary habits, and receipt of annual medical check-ups). Findings suggest that masculinity and the perceived normativeness of other men's health behaviors significantly predicted participants' own health behaviors beyond that accounted for by socio-demographic variables (e.g., education, income). Perceptions of the normativeness of women's health behaviors were unrelated to participants' health behaviors. The findings support previous research which has found that traditional masculine gender socialization and social norms models encourage men to put their health at risk, and suggest directions for health promotion efforts when working with men.
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                Author and article information

                Contributors
                (415) 476-6319 , sheri.lippman@ucsf.edu
                Journal
                Implement Sci
                Implement Sci
                Implementation Science : IS
                BioMed Central (London )
                1748-5908
                17 January 2017
                17 January 2017
                2017
                : 12
                : 9
                Affiliations
                [1 ]Center for AIDS Prevention Studies, Department of Medicine, University of California San Francisco, 550 16th Street, 3rd Floor, San Francisco, 94158-2549 CA USA
                [2 ]MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), Faculty of Health Sciences, School of Public Health, University of the Witwatersrand, 27 St Andrews Road, Parktown 2193, Johannesburg, South Africa
                [3 ]Gillings School of Global Public Health, University of North Carolina, 135 Dauer Dr., Chapel Hill, 27599 NC USA
                [4 ]Sonke Gender Justice, 4th Floor Westminster House, 122 Longmarket Street, 8001, Cape Town, South Africa
                [5 ]Department of Population Health, London School of Hygiene and Tropical Medicine, Keppel Street London WC1E 7HT, London, UK
                [6 ]Clinical HIV Research Unit, Department of Medicine, Faculty of Health Sciences, University of the Witwatersrand, Helen Joseph Hospital, Perth Road, Westdene, 2092, Johannesburg, South Africa
                [7 ]Umeå Centre for Global Health Research, Division of Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umeå University, 90187, Umeå, Sweden
                [8 ]Division of Social and Behavioural Sciences, School of Public Health, University of Cape Town, Falmouth Rd, Observatory 7925, Cape Town, South Africa
                Article
                541
                10.1186/s13012-016-0541-0
                5240325
                28095904
                46a1c81e-219c-4100-a3bb-d680b6592251
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 27 November 2016
                : 27 December 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: R01MH103198
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100009054, U.S. President’s Emergency Plan for AIDS Relief;
                Award ID: 674-A-12-000020
                Award ID: OAA-14-00060
                Award Recipient :
                Categories
                Study Protocol
                Custom metadata
                © The Author(s) 2017

                Medicine
                cluster randomized trial,community mobilization,south africa,hiv testing,engagement in care,retention in care,treatment as prevention

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