ALBERT

Beschreibung: There are inefficiencies in current approaches to monitoring patients on antiretroviral therapy in sub-Saharan Africa. Patients typically attend clinics every 1 to 3 months for clinical assessment. The clinic costs are comparable with the costs of the drugs themselves and CD4 counts are measured every 6 months, but patients are rarely switched to second-line therapies. To ensure sustainability of treatment programmes, a transition to more cost-effective delivery of antiretroviral therapy is needed. In contrast to the CD4 count, measurement of the level of HIV RNA in plasma (the viral load) provides a direct measure of the current treatment effect. Viral-load-informed differentiated care is a means of tailoring care so that those with suppressed viral load visit the clinic less frequently and attention is focussed on those with unsuppressed viral load to promote adherence and timely switching to a second-line regimen. The most feasible approach to measuring viral load in many countries is to collect dried blood spot samples for testing in regional laboratories; however, there have been concerns over the sensitivity and specificity of this approach to define treatment failure and the delay in returning results to the clinic. We use modelling to synthesize evidence and evaluate the cost-effectiveness of viral-load-informed differentiated care, accounting for limitations of dried blood sample testing. We find that viral-load-informed differentiated care using dried blood sample testing is cost-effective and is a recommended strategy for patient monitoring, although further empirical evidence as the approach is rolled out would be of value. We also explore the potential benefits of point-of-care viral load tests that may become available in the future.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Working Group on Modelling of Antiretroviral Therapy Monitoring Strategies in Sub-Saharan Africa -- Phillips, Andrew -- Shroufi, Amir -- Vojnov, Lara -- Cohn, Jennifer -- Roberts, Teri -- Ellman, Tom -- Bonner, Kimberly -- Rousseau, Christine -- Garnett, Geoff -- Cambiano, Valentina -- Nakagawa, Fumiyo -- Ford, Deborah -- Bansi-Matharu, Loveleen -- Miners, Alec -- Lundgren, Jens D -- Eaton, Jeffrey W -- Parkes-Ratanshi, Rosalind -- Katz, Zachary -- Maman, David -- Ford, Nathan -- Vitoria, Marco -- Doherty, Meg -- Dowdy, David -- Nichols, Brooke -- Murtagh, Maurine -- Wareham, Meghan -- Palamountain, Kara M -- Chakanyuka Musanhu, Christine -- Stevens, Wendy -- Katzenstein, David -- Ciaranello, Andrea -- Barnabas, Ruanne -- Braithwaite, R Scott -- Bendavid, Eran -- Nathoo, Kusum J -- van de Vijver, David -- Wilson, David P -- Holmes, Charles -- Bershteyn, Anna -- Walker, Simon -- Raizes, Elliot -- Jani, Ilesh -- Nelson, Lisa J -- Peeling, Rosanna -- Terris-Prestholt, Fern -- Murungu, Joseph -- Mutasa-Apollo, Tsitsi -- Hallett, Timothy B -- Revill, Paul -- England -- Nature. 2015 Dec 3;528(7580):S68-76. doi: 10.1038/nature16046.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Infection and Population Health, University College London, Rowland Hill Street, London NW3 2PF, UK. ; Southern Africa Medical Unit (SAMU), Medecins sans Frontieres (MSF) SA, Waverley Business Park, Wyecroft Rd, Mowbray 7700, Cape Town, South Africa. ; Clinton Health Access Initiative, 383 Dorchester Avenue, Boston, Massachusetts 02127, USA. ; Medecins Sans Frontieres, Access Campaign, rue du Lausanne 82, 1202 Geneva Switzerland. ; Medecins Sans Frontieres, 78 rue de Lausanne, Case Postale 116, 1211 Geneva 21, Switzerland. ; Bill and Melinda Gates Foundation, PO Box 23350, Seattle, Washington 98199, USA. ; MRC Clinical Trials Unit at UCL, Institute of Clinical Trials &Methodology, Aviation House, 125 Kingsway, London WC2B 6NH, UK. ; Health Services Research &Policy, London School of Hygiene and Tropical Medicine, Room 134, 15-17 Tavistock Place, London WC1H 9SY, UK. ; CHIP, Department of infectious diseases, Rigshospitalet, University of Copenhagen, Blegdamsvej 92100 Copenhagen, Denmark. ; Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK. ; Infectious Diseases Institute (IDI), College of Health Sciences, Makerere University, PO Box 22418, Kampala, Uganda. ; HIV/AIDS and Global Hepatitis Programme, World Health Organization, 20 Ave Appia 1211, Geneva, Switzerland. ; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street E6531, Baltimore, Maryland 21205, USA. ; Department of Viroscience, Erasmus Medical Center, PO Box 20403000CA Rotterdam, the Netherlands. ; International Diagnostics Centre, London School of Hygiene &Tropical, Medicine, Keppel Street, London WC1E 7HT, UK. ; Kellogg School of Management, Northwestern University, 2001 Sheridan Road Evanston, Illinois 60208, USA. ; WHO Country Office 86 Enterprise Road Cnr, Glenara PO Box CY 348, Causeway Harare, Zimbabwe. ; Department of Molecular Medicine and Haematology, University of the Witwatersrand, South Africa. ; Division of Infectious Disease, Laboratory Grant Building S-146, Office Lane 154, Stanford University Medical Center, 300 Pasteur Drive, Stanford, California 94305-5107, USA. ; Massachusetts General Hospital Division of Infectious Diseases, 50 Staniford Street, 936 Boston, Massachusetts 02114, USA. ; Medicine, Global Health and Epidemiology, University of Washington (UW), 325 9th Avenue, Seattle, Washington 98104, USA. ; Department of Population Health, New York University School of Medicine, 227 East 30th Street Office 615, New York, New York 10016, USA. ; Division of General Medical Disciplines, Department of Medicine Stanford University, MSOB 1265 Welch Road x332 Stanford, California 94305, USA. ; University of Zimbabwe, College of Health Sciences, Department of Paediatrics and Child Health, PO Box A178, Avondale, Harare, Zimbabwe. ; University of New South Wales, Level 6, Wallace Wurth Building, UNSW Campus, Sydney, New South Wales 2052, Australia. ; Centre for Infectious Disease Research in Zambia, 5032 Great North Road, Lusaka, Zambia. ; Institute for Disease Modeling, 3150 139th Avenue SE, Bellevue, Washington 98005, USA. ; Centre for Health Economics, University of York, Heslington, York YO10 5DD, UK. ; Care and Treatment Branch Center for Global Health, Division of Global HIV/AIDS (GAP), CDC, MS-E04, 1600 Clifton Road NE, Atlanta, Georgia 30333, USA. ; Instituto Nacional de Saude (INS), Ministry of Health, PO Box 264, Maputo, Mozambique. ; The Office of the US Global AIDS Coordinator and Health Diplomacy (S/GAC), U.S. Department of State, SA-22, Suite 10300, 2201 C Street, Washington DC 20520, USA. ; Clinical Research Department, London School of Hygiene and Tropical Medicine, Keppel St, London WC1E 7HT, UK. ; Department of Global Health and Development, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London WC1H 9SH, UK. ; Ministry of Health and Child Care, P. O CY 1122, Causeway, Harare, Zimbabwe.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26633768" target="_blank"〉PubMed〈/a〉

Beschreibung: Tuberculosis is a major source of global mortality caused by infection, partly because of a tremendous ongoing burden of undiagnosed disease. Improved diagnostic technology may play an increasingly crucial part in global efforts to end tuberculosis, but the ability of diagnostic tests to curb tuberculosis transmission is dependent on multiple factors, including the time taken by a patient to seek health care, the patient's symptoms, and the patterns of transmission before diagnosis. Novel diagnostic assays for tuberculosis have conventionally been evaluated on the basis of characteristics such as sensitivity and specificity, using assumptions that probably overestimate the impact of diagnostic tests on transmission. We argue for a shift in focus to the evaluation of such tests' incremental value, defining outcomes that reflect each test's purpose (for example, transmissions averted) and comparing systems with the test against those without, in terms of those outcomes. Incremental value can also be measured in units of outcome per incremental unit of resource (for example, money or human capacity). Using a novel, simplified model of tuberculosis transmission that addresses some of the limitations of earlier tuberculosis diagnostic models, we demonstrate that the incremental value of any novel test depends not just on its accuracy, but also on elements such as patient behaviour, tuberculosis natural history and health systems. By integrating these factors into a single unified framework, we advance an approach to the evaluation of new diagnostic tests for tuberculosis that considers the incremental value at the population level and demonstrates how additional data could inform more-effective implementation of tuberculosis diagnostic tests under various conditions.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Arinaminpathy, Nimalan -- Dowdy, David -- England -- Nature. 2015 Dec 3;528(7580):S60-7. doi: 10.1038/nature16045.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College London, Norfolk Place, London W2 1PG, UK. ; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26633767" target="_blank"〉PubMed〈/a〉

Beschreibung: The importance of high-incidence “hotspots” to population-level tuberculosis (TB) incidence remains poorly understood. TB incidence varies widely across countries, but within smaller geographic areas (e.g., cities), TB transmission may be more homogeneous than other infectious diseases. We constructed a steady-state compartmental model of TB in Rio de Janeiro, replicating nine epidemiological variables (e.g., TB incidence) within 1% of their observed values. We estimated the proportion of TB transmission originating from a high-incidence hotspot (6.0% of the city’s population, 16.5% of TB incidence) and the relative impact of TB control measures targeting the hotspot vs. the general community. If each case of active TB in the hotspot caused 0.5 secondary transmissions in the general community for each within-hotspot transmission, the 6.0% of people living in the hotspot accounted for 35.3% of city-wide TB transmission. Reducing the TB transmission rate (i.e., number of secondary infections per infectious case) in the hotspot to that in the general community reduced city-wide TB incidence by 9.8% in year 5, and 29.7% in year 50—an effect similar to halving time to diagnosis for the remaining 94% of the community. The importance of the hotspot to city-wide TB control depended strongly on the extent of TB transmission from the hotspot to the general community. High-incidence hotspots may play an important role in propagating TB epidemics. Achieving TB control targets in a hotspot containing 6% of a city’s population can have similar impact on city-wide TB incidence as achieving the same targets throughout the remaining community.