BACKGROUND: A number of African countries have planned campaigns against concurrency. It will not be possible to separate the effects of a reduction in concurrency from other behavior changes when evaluating these campaigns. This modeling study explores the potential impact of an intervention to reduce partnership concurrency on HIV incidence in contemporary rural Uganda, keeping incidence of sex acts and partnerships in the population constant. METHODS: Data on demography, sexual behavior, and HIV prevalence from Uganda were used to parameterize an individual-based HIV transmission model. Three baseline model scenarios were simulated, representing the best estimate of concurrency prevalence in this population, and low and high plausible bounds. Interventions that reduced concurrency by 20% and 50% between 2010 and 2020 were simulated, and the impact on HIV incidence in 2020 was calculated. RESULTS: Data showed 9.6% (7.9%-11.4%) of men and 0.2% (0.0%-0.4%) of women reported concurrency in 2008. Reducing concurrency had a nonlinear impact on HIV incidence. A 20% reduction in concurrency reduced HIV incidence by 4.1% (0.3%-5.7%) in men and 9.2% (2.1%-16.8%) in women; a 50% reduction in concurrency reduced HIV incidence by 6.0% (1.4%-10.8%) in men and 16.2% (6.3%-23.4%) in women. CONCLUSIONS: Interventions against concurrency have the potential to reduce HIV incidence and may have a higher impact in women than in men. In rural Uganda, overall impact was modest, and this study does not provide strong support for the prioritization of concurrency as a target for behavior change interventions. However, it may be more useful in higher concurrency settings and for reducing HIV incidence in women.
BACKGROUND: A number of African countries have planned campaigns against concurrency. It will not be possible to separate the effects of a reduction in concurrency from other behavior changes when evaluating these campaigns. This modeling study explores the potential impact of an intervention to reduce partnership concurrency on HIV incidence in contemporary rural Uganda, keeping incidence of sex acts and partnerships in the population constant. METHODS: Data on demography, sexual behavior, and HIV prevalence from Uganda were used to parameterize an individual-based HIV transmission model. Three baseline model scenarios were simulated, representing the best estimate of concurrency prevalence in this population, and low and high plausible bounds. Interventions that reduced concurrency by 20% and 50% between 2010 and 2020 were simulated, and the impact on HIV incidence in 2020 was calculated. RESULTS: Data showed 9.6% (7.9%-11.4%) of men and 0.2% (0.0%-0.4%) of women reported concurrency in 2008. Reducing concurrency had a nonlinear impact on HIV incidence. A 20% reduction in concurrency reduced HIV incidence by 4.1% (0.3%-5.7%) in men and 9.2% (2.1%-16.8%) in women; a 50% reduction in concurrency reduced HIV incidence by 6.0% (1.4%-10.8%) in men and 16.2% (6.3%-23.4%) in women. CONCLUSIONS: Interventions against concurrency have the potential to reduce HIV incidence and may have a higher impact in women than in men. In rural Uganda, overall impact was modest, and this study does not provide strong support for the prioritization of concurrency as a target for behavior change interventions. However, it may be more useful in higher concurrency settings and for reducing HIV incidence in women.
Authors: Pamela S Lilleston; Luciana E Hebert; Jacky M Jennings; David R Holtgrave; Jonathan M Ellen; Susan G Sherman Journal: AIDS Behav Date: 2015-12
Authors: Alethea W McCormick; Nadia N Abuelezam; Erin R Rhode; Taige Hou; Rochelle P Walensky; Pamela P Pei; Jessica E Becker; Madeline A DiLorenzo; Elena Losina; Kenneth A Freedberg; Marc Lipsitch; George R Seage Journal: PLoS One Date: 2014-05-27 Impact factor: 3.240
Authors: Ioannis Andrianakis; Ian R Vernon; Nicky McCreesh; Trevelyan J McKinley; Jeremy E Oakley; Rebecca N Nsubuga; Michael Goldstein; Richard G White Journal: PLoS Comput Biol Date: 2015-01-08 Impact factor: 4.475
Authors: I Andrianakis; I Vernon; N McCreesh; T J McKinley; J E Oakley; R N Nsubuga; M Goldstein; R G White Journal: J R Stat Soc Ser C Appl Stat Date: 2016-11-24 Impact factor: 1.864
Authors: Helen C Johnson; Erin I Lafferty; Rosalind M Eggo; Karly Louie; Kate Soldan; Jo Waller; W John Edmunds Journal: Lancet Public Health Date: 2017-12-19
Authors: Nicky McCreesh; Ioannis Andrianakis; Rebecca N Nsubuga; Mark Strong; Ian Vernon; Trevelyan J McKinley; Jeremy E Oakley; Michael Goldstein; Richard Hayes; Richard G White Journal: BMC Infect Dis Date: 2017-08-09 Impact factor: 3.090