Browsing by Author "Benade M"

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    Preferences for services in a patient's first six months on antiretroviral therapy for HIV in South Africa and Zambia (PREFER): research protocol for a prospective observational cohort study.
    (2023) Maskew M; Ntjikelane V; Juntunen A; Scott N; Benade M; Sande L; Hasweeka P; Haimbe P; Lumano-Mulenga P; Shakewelele H; Mukumbwa-Mwenechanya M; Rosen S; Center for Infectious Disease Research in Zambia, Lusaka, Lusaka Province, Zambia.; Department of Medical Microbiology, Amsterdam University Medical Center, Amstersdam, The Netherlands.; Global Health, Boston University, Boston, MA, 02118, USA.; CHAI-Zambia, Clinton Health Access Initiative, Lusaka, Zambia.; Health Economics and Epidemiology Research Office, Faculty of Health Sciences, University of the Witwatersrand Johannesburg, Johannesburg, Gauteng, South Africa.; MOH Zambia, Ministry of Health, Lusaka, Zambia.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
    BACKGROUND: For patients on HIV treatment in sub-Saharan Africa, the highest risk for loss from care remains the first six months after antiretroviral (ART) initiation, when patients are not yet eligible for differentiated service delivery (DSD) models that offer lower-burden, patient-centred care and thus improve treatment outcomes. To reduce early disengagement from care, the PREFER study will use a sequential mixed-methods approach to describe the characteristics, needs, concerns, and preferences of patients in South Africa and Zambia 0-6 months after ART initiation or re-initiation. PROTOCOL: PREFER is an observational, prospective cohort study of adults on ART for ≤6 months at 12 public healthcare facilities in Zambia and 18 in South Africa. Its objective is to describe and understand the needs and preferences of initiating and re-initiating ART clients to inform the design of DSD models for the early HIV treatment period, improve early treatment outcomes, and distinguish the barriers encountered by naïve patients from those facing re-initiators. It has four components: 1) survey of clients 0-6 months after ART initiation (identify characteristics and preferences of clients starting ART); 2) follow up through routinely collected medical records for <24 months after enrollment (describe resource utilization and patterns and predictors of engagement in care); 3) focus group discussions and discrete choice experiment (explore reported barriers to and facilitators of retention); and 4) in South Africa only, collection of blood samples (assess the prevalence of ARV metabolites indicating prior ART use). CONCLUSIONS: PREFER aims to understand why the early treatment period is so challenging and how service delivery can be amended to address the obstacles that lead to early disengagement from care. It will generate information about client characteristics and preferences to help respond to patients' needs and design better strategies for service delivery and improve resource allocation going forward.
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    The integration of tuberculosis and HIV testing on GeneXpert can substantially improve access and same-day diagnosis and benefit tuberculosis programmes: A diagnostic network optimization analysis in Zambia.
    (2023) Girdwood S; Pandey M; Machila T; Warrier R; Gautam J; Mukumbwa-Mwenechanya M; Benade M; Nichols K; Shibemba L; Mwewa J; Mzyece J; Lungu P; Albert H; Nichols B; Choonga P; Department of Internal Medicine, Health Economics and Epidemiology Research Office, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.; Department of Medical Microbiology, Amsterdam University Medical Center, Amsterdam, The Netherlands.; FIND, Geneva, Switzerland.; Ministry of Health, Lusaka, Zambia.; National TB and Leprosy Programme, Ministry of Health, Lusaka, Zambia.; Centre for Infectious Disease Research in Zambia, Lusaka, Zambia.; FIND, Cape Town, South Africa.; Department of Global Health, Boston University School of Public Health, Boston, MA, United States of America.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
    Diagnostic network optimization (DNO), a geospatial optimization technique, can improve access to diagnostics and reduce costs through informing policy-makers' decisions on diagnostic network changes. In Zambia, viral load (VL) testing and early infant diagnosis (EID) for HIV has been performed at centralized laboratories, whilst the TB-programme utilizes a decentralized network of GeneXpert platforms. Recently, the World Health Organization (WHO) has recommended point-of-care (POC) EID/VL to increase timely diagnosis. This analysis modelled the impact of integrating EID/VL testing for children and pregnant/breastfeeding-women (priority-HIV) with TB on GeneXpert in Zambia. Using OptiDx, we established the baseline diagnostic network using inputs for testing demand (October 2019-September 2020), referrals, testing sites, testing platforms, and costs for HIV/TB testing (transport, test, device) respectively in Zambia. Next, we integrated priority-HIV testing on GeneXpert platforms, historically only utilized by the TB-programme. 228,265 TB tests were conducted on GeneXpert devices and 167,458 (99%) of priority-HIV tests on centralized devices at baseline, of which 10% were tested onsite at the site of sample collection. With integration, the average distance travelled by priority-HIV tests decreased 10-fold (98km to 10km) and the proportion tested onsite increased (10% to 48%). 52% of EID tests are likely to be processed within the same-day from a baseline of zero. There were also benefits to the TB-programme: the average distance travelled/specimen decreased (11km to 7km), alongside potential savings in GeneXpert device-operating costs (30%) through cost-sharing with the HIV-programme. The total cost of the combined testing programmes reduced marginally by 1% through integration/optimization. DNO can be used to strategically leverage existing capacity to achieve the WHO's recommendation regarding POC VL/EID testing. Through DNO of the Zambian network, we have shown that TB/HIV testing integration can improve the performance of the diagnostic network and increase the proportion of specimens tested closer to the patient whilst not increasing costs.