Prophylaxis for COVID-19: a systematic review.

BACKGROUND: While the landscape of vaccine and treatment candidates against the novel coronavirus disease 2019 (COVID-19) has been reviewed systematically, prophylactic candidates remain unexplored. OBJECTIVES: To map pre- and postexposure prophylactic (PrEP and PEP) candidate for COVID-19. DATA SOURCES: PubMed/Medline, Embase, International Committee of Medical Journal Editors and International Clinical Trials Registry Platform clinical trial registries and medRxiv. STUDY ELIGIBILITY CRITERIA AND PARTICIPANTS: All studies in humans or animals and randomized controlled trials (RCTs) in humans reporting primary data on prophylactic candidates against COVID-19, excluding studies focused on key populations. INTERVENTIONS: PrEP and PEP candidate for COVID-19. METHODS: Systematic review and qualitative synthesis of COVID-19 PrEP and PEP studies and RCTs complemented by search of medRxiv and PubMed and Embase for studies reporting RCT outcomes since systematic review search completion. RESULTS: We identified 13 studies (from 2119 database records) and 117 RCTs (from 5565 RCTs listed in the registries) that met the inclusion criteria. Non-RCT studies reported on cross-sectional studies using hydroxychloroquine (HCQ) in humans (n = 2) or reported on animal studies (n = 7), most of which used antibodies. All five completed RCTs focused on the use of HCQ as either PrEP or PEP, and these and the cross-sectional studies reported no prophylactic effect. The majority of ongoing RCTs evaluated HCQ or other existing candidates including non-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines, anti(retro)virals or use of vitamins and supplements. CONCLUSIONS: The key message from completed studies and RCTs seems to be that HCQ does not work. There is little evidence regarding other compounds, with all RCTs using candidates other than HCQ still ongoing. It remains to be seen if the portfolio of existing molecules being evaluated in RCTs will identify successful prophylaxis against COVID-19 or if there is a need for the development of new candidates.

Recommendations for the use of mathematical modelling to support decision-making on integration of non-communicable diseases into HIV care.

INTRODUCTION: Integrating services for non-communicable diseases (NCDs) into existing primary care platforms such as HIV programmes has been recommended as a way of strengthening health systems, reducing redundancies and leveraging existing systems to rapidly scale-up underdeveloped programmes. Mathematical modelling provides a powerful tool to address questions around priorities, optimization and implementation of such programmes. In this study, we examine the case for NCD-HIV integration, use Kenya as a case-study to highlight how modelling has supported wider policy formulation and decision-making in healthcare and to collate stakeholders' recommendations on use of models for NCD-HIV integration decision-making. DISCUSSION: Across Africa, NCDs are increasingly posing challenges for health systems, which historically focused on the care of acute and infectious conditions. Pilot programmes using integrated care services have generated advantages for both provider and user, been cost-effective, practical and achieve rapid coverage scale-up. The shared chronic nature of NCDs and HIV means that many operational approaches and infrastructure developed for HIV programmes apply to NCDs, suggesting this to be a cost-effective and sustainable policy option for countries with large HIV programmes and small, un-resourced NCD programmes. However, the vertical nature of current disease programmes, policy financing and operations operate as barriers to NCD-HIV integration. Modelling has successfully been used to inform health decision-making across a number of disease areas and in a number of ways. Examples from Kenya include (i) estimating current and future disease burden to set priorities for public health interventions, (ii) forecasting the requisite investments by government, (iii) comparing the impact of different integration approaches, (iv) performing cost-benefit analysis for integration and (v) evaluating health system capacity needs. CONCLUSIONS: Modelling can and should play an integral part in the decision-making processes for health in general and NCD-HIV integration specifically. It is especially useful where little data is available. The successful use of modelling to inform decision-making will depend on several factors including policy makers' comfort with and understanding of models and their uncertainties, modellers understanding of national priorities, funding opportunities and building local modelling capacity to ensure sustainability.