The effect of HIV, behavioural change, and STD syndromic management on STD epidemiology in sub-Saharan Africa: simulations of Uganda.
ABSTRACT An assessment was made of how the HIV epidemic may have influenced sexually transmitted disease (STD) epidemiology in Uganda, and how HIV would affect the effectiveness of syndromic STD treatment programmes during different stages of the epidemic. The dynamic transmission model STDSIM was used to simulate the spread of HIV and four bacterial and one viral STD. Model parameters were quantified using demographic, behavioural, and epidemiological data from rural Rakai and other Ugandan populations. The findings suggest that severe HIV epidemics can markedly alter STD epidemiology, especially if accompanied by a behavioural response. Likely declines in bacterial causes of genital ulcers should be considered in defining policies on syndromic STD management in severe HIV epidemics.
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ABSTRACT: This thesis investigates the relationships between mobility, sexual risk behavior and HIV infection. We performed an ecological analysis, analyzed data from epidemiological cohort studies in Zimbabwe and Tanzania, and performed microsimulation modeling. The main conclusions are that: Urban immigration explains much of the differences between countries in the African HIV epidemic; Out-migrants do not constitute a high-risk group in rural Zimbabwe; Risky sexual behavior is seen in both mobile persons and in their partners staying behind; and non-participation of mobile groups can strongly reduce the impact of HIV interventions, and targeting them is a promising additional option in the control of HIV.
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ABSTRACT: Individual-based modeling is a growing technique in the HIV transmission and prevention literature, but insufficient attention has been paid to formally evaluate the quality of reporting in this field. We present reporting recommendations for individual-based models for HIV treatment and prevention, assess the quality of reporting in the existing literature, and comment on the contribution of this model type to HIV policy and prediction. We developed reporting recommendations for individual-based HIV transmission mathematical models, and through a systematic search, used them to evaluate the reporting in the existing literature. We identified papers that employed individual-based simulation models and were published in English prior to December 31, 2012. Articles were included if the models they employed simulated and tracked individuals, simulated HIV transmission between individuals in a particular population, and considered a particular treatment or prevention intervention. The papers were assessed with the reporting recommendations. Of 214 full text articles examined, 32 were included in the evaluation, representing 20 independent individual-based HIV treatment and prevention mathematical models. Manuscripts universally reported the objectives, context, and modeling conclusions in the context of the modeling assumptions and the model's predictive capabilities, but the reporting of individual-based modeling methods, parameterization and calibration was variable. Six papers discussed the time step used and one discussed efforts to maintain internal validity in coding. Individual-based models represent detailed HIV transmission processes with the potential to contribute to inference and policy making for many different regions and populations. The rigor in reporting of assumptions, methods, and calibration of individual-based models focused on HIV transmission and prevention varies greatly. Higher standards for reporting of statistically rigorous calibration and model assumption testing need to be implemented to increase confidence in existing and future modeling results.PLoS ONE 09/2013; 8(9):e75624. DOI:10.1371/journal.pone.0075624 · 3.53 Impact Factor
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ABSTRACT: Expanded access to antiretroviral therapy (ART) using universal test and treat (UTT) has been suggested as a strategy to eliminate HIV in South Africa within 7 y based on an influential mathematical modeling study. However, the underlying deterministic model was criticized widely, and other modeling studies did not always confirm the study's finding. The objective of our study is to better understand the implications of different model structures and assumptions, so as to arrive at the best possible predictions of the long-term impact of UTT and the possibility of elimination of HIV. We developed nine structurally different mathematical models of the South African HIV epidemic in a stepwise approach of increasing complexity and realism. The simplest model resembles the initial deterministic model, while the most comprehensive model is the stochastic microsimulation model STDSIM, which includes sexual networks and HIV stages with different degrees of infectiousness. We defined UTT as annual screening and immediate ART for all HIV-infected adults, starting at 13% in January 2012 and scaled up to 90% coverage by January 2019. All models predict elimination, yet those that capture more processes underlying the HIV transmission dynamics predict elimination at a later point in time, after 20 to 25 y. Importantly, the most comprehensive model predicts that the current strategy of ART at CD4 count ≤350 cells/µl will also lead to elimination, albeit 10 y later compared to UTT. Still, UTT remains cost-effective, as many additional life-years would be saved. The study's major limitations are that elimination was defined as incidence below 1/1,000 person-years rather than 0% prevalence, and drug resistance was not modeled. Our results confirm previous predictions that the HIV epidemic in South Africa can be eliminated through universal testing and immediate treatment at 90% coverage. However, more realistic models show that elimination is likely to occur at a much later point in time than the initial model suggested. Also, UTT is a cost-effective intervention, but less cost-effective than previously predicted because the current South African ART treatment policy alone could already drive HIV into elimination. Please see later in the article for the Editors' Summary.PLoS Medicine 10/2013; 10(10):e1001534. DOI:10.1371/journal.pmed.1001534 · 14.00 Impact Factor