Browsing by Author "Dowdy D"

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Costs and cost-effectiveness of a comprehensive tuberculosis case finding strategy in Zambia.
(2021) Jo Y; Kagujje M; Johnson K; Dowdy D; Hangoma P; Chiliukutu L; Muyoyeta M; Sohn H; Centre For Infectious Disease Research in Zambia (CIDRZ), Lusaka, Zambia.; School of Public Health, University of Zambia, Lusaka, Zambia.; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.; University of North Carolina School of Global Public Health, Chapel Hill, North Carolina, United States of America.
INTRODUCTION: Active-case finding (ACF) programs have an important role in addressing case detection gaps and halting tuberculosis (TB) transmission. Evidence is limited on the cost-effectiveness of ACF interventions, particularly on how their value is impacted by different operational, epidemiological and patient care-seeking patterns. METHODS: We evaluated the costs and cost-effectiveness of a combined facility and community-based ACF intervention in Zambia that utilized mobile chest X-ray with computer-aided reading/interpretation software and laboratory-based Xpert MTB/RIF testing. Programmatic costs (in 2018 US dollars) were assessed from the health system perspective using prospectively collected cost and operational data. Cost-effectiveness of the ACF intervention was assessed as the incremental cost per TB death averted over a five-year time horizon using a multi-stage Markov state-transition model reflecting patient symptom-associated care-seeking and TB care under ACF compared to passive care. RESULTS: Over 18 months of field operations, the ACF intervention costed $435 to diagnose and initiate treatment for one person with TB. After accounting for patient symptom-associated care-seeking patterns in Zambia, we estimate that this one-time ACF intervention would incrementally diagnose 407 (7,207 versus 6,800) TB patients and avert 502 (611 versus 1,113) TB-associated deaths compared to the status quo (passive case finding), at an incremental cost of $2,284 per death averted over the next five-year period. HIV/TB mortality rate, patient symptom-associated care-seeking probabilities in the absence of ACF, and the costs of ACF patient screening were key drivers of cost-effectiveness. CONCLUSIONS: A one-time comprehensive ACF intervention simultaneously operating in public health clinics and corresponding catchment communities can have important medium-term impact on case-finding and be cost-effective in Zambia. The value of such interventions increases if targeted to populations with high HIV/TB mortality, substantial barriers (both behavioral and physical) to care-seeking exist, and when ACF interventions can optimize screening by achieving operational efficiency.
Differentiated Care Preferences of Stable Patients on Antiretroviral Therapy in Zambia: A Discrete Choice Experiment.
(2019-Aug-15) Eshun-Wilson I; Mukumbwa-Mwenechanya M; Kim HY; Zannolini A; Mwamba CP; Dowdy D; Kalunkumya E; Lumpa M; Beres LK; Roy M; Sharma A; Topp SM; Glidden DV; Padian N; Ehrenkranz P; Sikazwe I; Holmes CB; Bolton-Moore C; Geng EH; United Kingdom Department for International Development, Dar Es Salaam office, Tanzania.; University of California, San Francisco, San Francisco, CA.; University of California, Berkeley, Berkeley, CA.; Bill and Melinda Gates Foundation, Seattle, WA.; Georgetown University, Washington, DC.; Johns Hopkins University, Baltimore, MD.; James Cook University, Townsville, Australia.; Africa Health Research Institute, Durban, South Africa.; Centre for Infectious Disease Research in Zambia, Lusaka, Zambia.; University of Alabama at Birmingham, Birmingham, AL.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
BACKGROUND: Although differentiated service delivery (DSD) models for stable patients on antiretroviral therapy (ART) offer a range of health systems innovations, their comparative desirability to patients remains unknown. We conducted a discrete choice experiment to quantify service attributes most desired by patients to inform model prioritization. METHODS: Between July and December 2016, a sample of HIV-positive adults on ART at 12 clinics in Zambia were asked to choose between 2 hypothetical facilities that differed across 6 DSD attributes. We used mixed logit models to explore preferences, heterogeneity, and trade-offs. RESULTS: Of 486 respondents, 59% were female and 85% resided in urban locations. Patients strongly preferred infrequent clinic visits [3- vs. 1-month visits: β (ie, relative utility) = 2.84; P < 0.001]. Milder preferences were observed for waiting time for ART pick-up (1 vs. 6 hours.; β = -0.67; P < 0.001) or provider (1 vs. 3 hours.; β = -0.41; P = 0.002); "buddy" ART collection (β = 0.84; P < 0.001); and ART pick-up location (clinic vs. community: β = 0.35; P = 0.028). Urban patients demonstrated a preference for collecting ART at a clinic (β = 1.32, P < 0.001), and although most rural patients preferred community ART pick-up (β = -0.74, P = 0.049), 40% of rural patients still preferred facility ART collection. CONCLUSIONS: Stable patients on ART primarily want to attend clinic infrequently, supporting a focus in Zambia on optimizing multimonth prescribing over other DSD features-particularly in urban areas. Substantial preference heterogeneity highlights the need for DSD models to be flexible, and accommodate both setting features and patient choice in their design.
Sustainable HIV treatment in Africa through viral-load-informed differentiated care.
(2015-Dec-03) Phillips A; Shroufi A; Vojnov L; Cohn J; Roberts T; Ellman T; Bonner K; Rousseau C; Garnett G; Cambiano V; Nakagawa F; Ford D; Bansi-Matharu L; Miners A; Lundgren JD; Eaton JW; Parkes-Ratanshi R; Katz Z; Maman D; Ford N; Vitoria M; Doherty M; Dowdy D; Nichols B; Murtagh M; Wareham M; Palamountain KM; Chakanyuka Musanhu C; Stevens W; Katzenstein D; Ciaranello A; Barnabas R; Braithwaite RS; Bendavid E; Nathoo KJ; van de Vijver D; Wilson DP; Holmes C; Bershteyn A; Walker S; Raizes E; Jani I; Nelson LJ; Peeling R; Terris-Prestholt F; Murungu J; Mutasa-Apollo T; Hallett TB; Revill P; Instituto Nacional de Saúde (INS), Ministry of Health, PO Box 264, Maputo, Mozambique.; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street E6531, Baltimore, Maryland 21205, USA.; Department of Population Health, New York University School of Medicine, 227 East 30th Street Office 615, New York, New York 10016, USA.; Infectious Diseases Institute (IDI), College of Health Sciences, Makerere University, PO Box 22418, Kampala, Uganda.; Kellogg School of Management, Northwestern University, 2001 Sheridan Road Evanston, Illinois 60208, USA.; Medicine, Global Health and Epidemiology, University of Washington (UW), 325 9th Avenue, Seattle, Washington 98104, USA.; Clinical Research Department, London School of Hygiene and Tropical Medicine, Keppel St, London WC1E 7HT, UK.; International Diagnostics Centre, London School of Hygiene &Tropical, Medicine, Keppel Street, London WC1E 7HT, UK.; HIV/AIDS and Global Hepatitis Programme, World Health Organization, 20 Ave Appia 1211, Geneva, Switzerland.; Division of Infectious Disease, Laboratory Grant Building S-146, Office Lane 154, Stanford University Medical Center, 300 Pasteur Drive, Stanford, California 94305-5107, USA.; CHIP, Department of infectious diseases, Rigshospitalet, University of Copenhagen, Blegdamsvej 92100 Copenhagen, Denmark.; Centre for Infectious Disease Research in Zambia, 5032 Great North Road, Lusaka, Zambia.; Health Services Research &Policy, London School of Hygiene and Tropical Medicine, Room 134, 15-17 Tavistock Place, London WC1H 9SY, UK.; 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.; Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK.; Massachusetts General Hospital Division of Infectious Diseases, 50 Staniford Street, 936 Boston, Massachusetts 02114, USA.; Institute for Disease Modeling, 3150 139th Avenue SE, Bellevue, Washington 98005, USA.; Department of Infection and Population Health, University College London, Rowland Hill Street, London NW3 2PF, UK.; WHO Country Office 86 Enterprise Road Cnr, Glenara PO Box CY 348, Causeway Harare, Zimbabwe.; 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.; 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.; Department of Global Health and Development, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London WC1H 9SH, UK.; Southern Africa Medical Unit (SAMU), Medecins sans Frontieres (MSF) SA, Waverley Business Park, Wyecroft Rd, Mowbray 7700, Cape Town, South Africa.; Médecins Sans Frontières, Access Campaign, rue du Lausanne 82, 1202 Geneva Switzerland.; Médecins Sans Frontières, 78 rue de Lausanne, Case Postale 116, 1211 Geneva 21, Switzerland.; Centre for Health Economics, University of York, Heslington, York YO10 5DD, UK.; Clinton Health Access Initiative, 383 Dorchester Avenue, Boston, Massachusetts 02127, USA.; Department of Viroscience, Erasmus Medical Center, PO Box 20403000CA Rotterdam, the Netherlands.; Division of General Medical Disciplines, Department of Medicine Stanford University, MSOB 1265 Welch Road x332 Stanford, California 94305, USA.; Ministry of Health and Child Care, P. O CY 1122, Causeway, Harare, Zimbabwe.; MRC Clinical Trials Unit at UCL, Institute of Clinical Trials &Methodology, Aviation House, 125 Kingsway, London WC2B 6NH, UK.; Department of Molecular Medicine and Haematology, University of the Witwatersrand, South Africa.; Bill and Melinda Gates Foundation, PO Box 23350, Seattle, Washington 98199, USA.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
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.