Strategies for isoniazid preventive therapy in HIV-positive patients who consume alcohol.

WHO guidance to defer isoniazid preventive therapy (IPT) among those with regular alcohol use because of hepatotoxicity concerns may exclude many people living with HIV (PLWH) at high TB risk in these settings.To evaluate hepatotoxicity during TB preventive therapy (TPT) in PLWH who report alcohol use in Uganda over 10 years.We developed a Markov model of latent TB infection, isoniazid preventive therapy (IPT - a type of TPT), and TB disease using data from the Alcohol Drinkers' Exposure to Preventive Therapy for TB (ADEPTT) study. We modeled several treatment scenarios, including no IPT, IPT with liver enzyme monitoring (AST/ALT) during treatment, and IPT with pre-screening using the tuberculin skin test (TST).The no IPT scenario had 230 TB deaths/100,000 population over 10 years, which is more than that seen in any IPT scenario. IPT, even with no monitoring, was preferred over no IPT when population TB disease incidence was >50 in 100,000.For PLWH who report alcohol use in high TB burden settings, IPT should be offered, ideally with regular AST/ALT monitoring. However, even if regular monitoring is not possible, IPT is still preferable to no IPT in almost every modeled scenario..

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.