A packaged intervention to improve viral load monitoring within a deeply rural health district of South Africa.

BACKGROUND: The KwaZulu-Natal (KZN) province of South Africa has the highest prevalence of HIV infection in the world. Viral load (VL) testing is a crucial tool for clinical and programmatic monitoring. Within uMkhanyakude district, VL suppression rates were 91% among patients with VL data; however, VL performance rates averaged only 38·7%. The objective of this study was to determine if enhanced clinic processes and community outreach could improve VL monitoring within this district. METHODS: A packaged intervention was implemented at three rural clinics in the setting of the KZN HIV AIDS Drug Resistance Surveillance Study. This included file hygiene, outreach, a VL register and documentation revisions. Chart audits were used to assess fidelity. Outcome measures included percentage VL performed and suppressed. Each rural clinic was matched with a peri-urban clinic for comparison before and after the start of each phase of the intervention. Monthly sample proportions were modelled using quasi-likelihood regression methods for over-dispersed binomial data. RESULTS: Mkuze and Jozini clinics increased VL performance overall from 33·9% and 35·3% to 75·8% and 72·4%, respectively which was significantly greater than the increases in the comparison clinics (RR 1·86 and 1·68, p < 0·01). VL suppression rates similarly increased overall by 39·3% and 36·2% (RR 1·84 and 1·70, p < 0·01). The Chart Intervention phase showed significant increases in fidelity 16 months after implementation. CONCLUSIONS: The packaged intervention improved VL performance and suppression rates overall but was significant in Mkuze and Jozini. Larger sustained efforts will be needed to have a similar impact throughout the province.

Comparative analyses of published cost effectiveness models highlight critical considerations which are useful to inform development of new models.

Background: Comparative analyses of published cost effectiveness models provide useful insights into critical issues to inform the development of new cost effectiveness models in the same disease area.Objective: The purpose of this study was to describe a comparative analysis of cost-effectiveness models and highlight the importance of such work in informing development of new models. This research uses genotypic antiretroviral resistance testing after first line treatment failure for Human Immunodeficiency Virus (HIV) as an example.Method: A literature search was performed, and published cost effectiveness models were selected according to predetermined eligibility criteria. A comprehensive comparative analysis was undertaken for all aspects of the models.Results: Five published models were compared, and several critical issues were identified for consideration when developing a new model. These include the comparator, time horizon and scope of the model. In addition, the composite effect of drug resistance prevalence, antiretroviral therapy efficacy, test performance and the proportion of patients switching to second-line ART potentially have a measurable effect on model results. When considering CD4 count and viral load, dichotomizing patients according to higher cost and lower quality of life (AIDS) versus lower cost and higher quality of life (non-AIDS) status will potentially capture differences between resistance testing and other strategies, which could be confirmed by cross-validation/convergent validation. A quality adjusted life year is an essential outcome which should be explicitly explored in probabilistic sensitivity analysis, where possible.Conclusions: Using an example of GART for HIV, this study demonstrates comparative analysis of previously published cost effectiveness models yields critical information which can be used to inform the structure and specifications of new models.