Browsing by Author "Dowdy DW"

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A controlled study to assess the effects of a Fast Track (FT) service delivery model among stable HIV patients in Lusaka Zambia.
(2022) Bolton Moore C; Pry JM; Mukumbwa-Mwenechanya M; Eshun-Wilson I; Topp S; Mwamba C; Roy M; Sohn H; Dowdy DW; Padian N; Holmes CB; Geng EH; Sikazwe I; Georgetown University, School of Medicine, Washington, DC, United States of America.; University of California, School of Medicine, San Francisco, California, United States of America.; University of California, School of Public Health, Berkeley, California, United States of America.; Johns Hopkins University, School of Medicine, Baltimore, Maryland, United States of America.; University of Alabama, School of Medicine, Birmingham, Alabama, United States of America.; James Cook University, College of Public Health, Medical and Vet Sciences, Queensland, Australia.; Washington University, School of Medicine, St Louis, Missouri, United States of America.; Centre for Infectious Disease Research in Zambia (CIDRZ), Lusaka, Zambia.; University of California, School of Medicine, Davis, California, United States of America.
Fast Track models-in which patients coming to facility to pick up medications minimize waiting times through foregoing clinical review and collecting pre-packaged medications-present a potential strategy to reduce the burden of treatment. We examine effects of a Fast Track model (FT) in a real-world clinical HIV treatment program on retention to care comparing two clinics initiating FT care to five similar (in size and health care level), standard of care clinics in Zambia. Within each clinic, we selected a systematic sample of patients meeting FT eligibility to follow prospectively for retention using both electronic medical records as well as targeted chart review. We used a variety of methods including Kaplan Meier (KM) stratified by FT, to compare time to first late pick up, exploring late thresholds at >7, >14 and >28 days, Cox proportional hazards to describe associations between FT and late pick up, and linear mixed effects regression to assess the association of FT with medication possession ratio. A total of 905 participants were enrolled with a median age of 40 years (interquartile range [IQR]: 34-46 years), 67.1% were female, median CD4 count was 499 cells/mm3 (IQR: 354-691), and median time on ART was 5 years (IQR: 3-7). During the one-year follow-up period FT participants had a significantly reduced cumulative incidence of being >7 days late for ART pick-up (0.36, 95% confidence interval [CI]: 0.31-0.41) compared to control participants (0.66; 95% CI: 0.57-0.65). This trend held for >28 days late for ART pick-up appointments, at 23% (95% CI: 18%-28%) among intervention participants and 54% (95% CI: 47%-61%) among control participants. FT models significantly improved timely ART pick up among study participants. The apparent synergistic relationship between refill time and other elements of the FT suggest that FT may enhance the effects of extending visit spacing/multi-month scripting alone. ClinicalTrials.gov Identifier: NCT02776254 https://clinicaltrials.gov/ct2/show/NCT02776254.
Diagnostic yield as an important metric for the evaluation of novel tuberculosis tests: rationale and guidance for future research.
(2024-Jul) Broger T; Marx FM; Theron G; Marais BJ; Nicol MP; Kerkhoff AD; Nathavitharana R; Huerga H; Gupta-Wright A; Kohli M; Nichols BE; Muyoyeta M; Meintjes G; Ruhwald M; Peeling RW; Pai NP; Pollock NR; Pai M; Cattamanchi A; Dowdy DW; Dewan P; Denkinger CM; Centre for Infectious Diseases Research in Zambia, Lusaka, Zambia.; Boston Children's Hospital, Boston, MA, USA.; The University of Sydney Infectious Diseases Institute, Sydney, NSW, Australia; Children's Hospital at Westmead, Sydney, NSW, Australia.; Bill & Melinda Gates Foundation, Seattle, WA, USA.; Department of Epidemiology, Epicentre, Paris, France.; McGill International TB Centre, McGill University, Montreal, QC, Canada.; Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA; Department of Medicine, Division of Pulmonary Diseases and Critical Care Medicine, University of California Irvine, Irvine, CA, USA.; Department of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, Heidelberg, Germany; DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Faculty of Science, Stellenbosch University, Stellenbosch, South Africa.; London School of Hygiene & Tropical Medicine, London, UK.; DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.; Department of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, Heidelberg, Germany; German Center for Infection Research, Heidelberg University Hospital, Heidelberg, Germany. Electronic address: claudia.denkinger@uni-heidelberg.de.; Department of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, Heidelberg, Germany.; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.; Department of Medicine, University of Cape Town, Cape Town, South Africa; Wellcome Centre for Infectious Diseases Research in Africa (CIDRI-Africa), Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa.; Division of HIV, Infectious Diseases, and Global Medicine, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA; Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA.; Division of Infection and Immunity, School of Biomedical Sciences, University of Western Australia, Perth, WA, Australia.; Department of Medicine, Centre for Outcomes Research & Evaluation, McGill University, Montreal, QC, Canada.; FIND, Geneva, Switzerland.; Division of Infectious Diseases, Beth Israel Deaconess Medical Center, Boston, MA, USA.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
Better access to tuberculosis testing is a key priority for fighting tuberculosis, the leading cause of infectious disease deaths in people. Despite the roll-out of molecular WHO-recommended rapid diagnostics to replace sputum smear microscopy over the past decade, a large diagnostic gap remains. Of the estimated 10·6 million people who developed tuberculosis globally in 2022, more than 3·1 million were not diagnosed. An exclusive focus on improving tuberculosis test accuracy alone will not be sufficient to close the diagnostic gap for tuberculosis. Diagnostic yield, which we define as the proportion of people in whom a diagnostic test identifies tuberculosis among all people we attempt to test for tuberculosis, is an important metric not adequately explored. Diagnostic yield is particularly relevant for subpopulations unable to produce sputum such as young children, people living with HIV, and people with subclinical tuberculosis. As more accessible non-sputum specimens (eg, urine, oral swabs, saliva, capillary blood, and breath) are being explored for point-of-care tuberculosis testing, the concept of yield will be of growing importance. Using the example of urine lipoarabinomannan testing, we illustrate how even tests with limited sensitivity can diagnose more people with tuberculosis if they enable increased diagnostic yield. Using tongue swab-based molecular tuberculosis testing as another example, we provide definitions and guidance for the design and conduct of pragmatic studies that assess diagnostic yield. Lastly, we show how diagnostic yield and other important test characteristics, such as cost and implementation feasibility, are essential for increased effective population coverage, which is required for optimal clinical care and transmission impact. We are calling for diagnostic yield to be incorporated into tuberculosis test evaluation processes, including the WHO Grading of Recommendations, Assessment, Development, and Evaluations process, providing a crucial real-life implementation metric that complements traditional accuracy measures.
Economic evaluation of implementation science outcomes in low- and middle-income countries: a scoping review.
(2022-Nov-16) Malhotra A; Thompson RR; Kagoya F; Masiye F; Mbewe P; Mosepele M; Phiri J; Sambo J; Barker A; Cameron DB; Davila-Roman VG; Effah W; Hutchinson B; Laxy M; Newsome B; Watkins D; Sohn H; Dowdy DW; Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA.; Ezintsha, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa.; Washington University in Saint Louis, Saint Louis, MO, USA.; University of Washington, Seattle, WA, USA.; Infectious Diseases Research Collaboration, Kampala, Uganda.; Center for Global Noncommunicable Diseases, RTI International, Seattle, WA, USA.; University of Botswana, Gaborone, Botswana.; Fogarty International Center (FIC), National Institutes of Health (NIH), Bethesda, MD, USA.; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. ddowdy1@jhmi.edu.; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.; Cavendish University Zambia, Lusaka, Zambia.; Technical University of Munich, Munich, Germany.; University of Zambia, Lusaka, Zambia.; Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, South Korea.; Centre for Infectious Disease Research in Zambia, Lusaka, Zambia.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
BACKGROUND: Historically, the focus of cost-effectiveness analyses has been on the costs to operate and deliver interventions after their initial design and launch. The costs related to design and implementation of interventions have often been omitted. Ignoring these costs leads to an underestimation of the true price of interventions and biases economic analyses toward favoring new interventions. This is especially true in low- and middle-income countries (LMICs), where implementation may require substantial up-front investment. This scoping review was conducted to explore the topics, depth, and availability of scientific literature on integrating implementation science into economic evaluations of health interventions in LMICs. METHODS: We searched Web of Science and PubMed for papers published between January 1, 2010, and December 31, 2021, that included components of both implementation science and economic evaluation. Studies from LMICs were prioritized for review, but papers from high-income countries were included if their methodology/findings were relevant to LMIC settings. RESULTS: Six thousand nine hundred eighty-six studies were screened, of which 55 were included in full-text review and 23 selected for inclusion and data extraction. Most papers were theoretical, though some focused on a single disease or disease subset, including: mental health (n = 5), HIV (n = 3), tuberculosis (n = 3), and diabetes (n = 2). Manuscripts included a mix of methodology papers, empirical studies, and other (e.g., narrative) reviews. Authorship of the included literature was skewed toward high-income settings, with 22 of the 23 papers featuring first and senior authors from high-income countries. Of nine empirical studies included, no consistent implementation cost outcomes were measured, and only four could be mapped to an existing costing or implementation framework. There was also substantial heterogeneity across studies in how implementation costs were defined, and the methods used to collect them. CONCLUSION: A sparse but growing literature explores the intersection of implementation science and economic evaluation. Key needs include more research in LMICs, greater consensus on the definition of implementation costs, standardized methods to collect such costs, and identifying outcomes of greatest relevance. Addressing these gaps will result in stronger links between implementation science and economic evaluation and will create more robust and accurate estimates of intervention costs. TRIAL REGISTRATION: The protocol for this manuscript was published on the Open Science Framework. It is available at: https://osf.io/ms5fa/ (DOI: 10.17605/OSF.IO/32EPJ).
Operational characteristics of antiretroviral therapy clinics in Zambia: a time and motion analysis.
(2019-Apr-24) Tampi RP; Tembo T; Mukumba-Mwenechanya M; Sharma A; Dowdy DW; Holmes CB; Bolton-Moore C; Sikazwe I; Tucker A; Sohn H; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA. hsohn6@jhu.edu.; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA.; Centers for Infectious Disease Research (CIDRZ), Lusaka, Zambia.
BACKGROUND: The mass scale-up of antiretroviral therapy (ART) in Zambia has taken place in the context of limited infrastructure and human resources resulting in many operational side-effects. In this study, we aimed to empirically measure current workload of ART clinic staff and patient wait times and service utilization. METHODS: We conducted time and motion (TAM) studies from both the healthcare worker (HCW) and patient perspectives at 10 ART clinics throughout Zambia. Trained personnel recorded times for consecutive discrete activities based on direct observation of clinical and non-clinical activities performed by counselors, clinical officers, nurses, and pharmacy technicians. For patient TAM, we recruited consenting patients and recorded times of arrival and departure and major ART services utilized. Data from 10 clinics were pooled to evaluate median time per patient spent for each activity and patient duration of stay in the clinic. RESULTS: The percentage of observed clinical time for direct patient interaction (median time per patient encounter) was 43.1% for ART counselors (4 min, interquartile range [IQR] 2-7), 46.1% for nurses (3 min, IQR 2-4), 57.2% for pharmacy technicians (2 min, IQR 1-2), and 78.5% for clinical officers (3 min, IQR 2-5). Patient workloads for HCWs were heaviest between 8 AM and 12 PM with few clinical activities observed after 2 PM. The length of patient visits was inversely associated with arrival time - patients arriving prior to 8 AM spent 61% longer at the clinic than those arriving after 8 AM (277 vs. 171 min). Overall, patients spent 219 min on average for non-clinical visits, and 244 min for clinical visits, but this difference was not significant in rural clinics. In comparison, total time patients spent directly with clinic staff were 9 and 12 min on average for non-clinical and clinical visits. CONCLUSION: Current Zambian ART clinic operations include substantial inefficiencies for both patients and HCWs, with workloads heavily concentrated in the first few hours of clinic opening, limiting HCW and patient interaction time. Use of a differentiated care model may help to redistribute workloads during operational hours and prevent backlogs of patients waiting for hours before clinic opening, which may substantially improve ART delivery in the Zambian context.
Redefining and revisiting cost estimates of routine ART care in Zambia: an analysis of ten clinics.
(2020-Feb) Tucker A; Tembo T; Tampi RP; Mutale J; Mukumba-Mwenechanya M; Sharma A; Dowdy DW; Moore CB; Geng E; Holmes CB; Sikazwe I; Sohn H; Center for Dissemination and Implementation, Institute for Public Health at Washington University in St. Louis, St. Louis, MO, USA.; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.; Department of Internal Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.; Johns Hopkins University School of Medicine, Baltimore, MD, USA.; University of Alabama, Birmingham, AL, USA.; Georgetown University School of Medicine, Washington, DC, USA.; Center for Infectious Disease Research (CIDRZ), Lusaka, Zambia.
INTRODUCTION: Accurate costing is key for programme planning and policy implementation. Since 2011, there have been major changes in eligibility criteria and treatment regimens with price reductions in ART drugs, programmatic changes resulting in clinical task-shifting and decentralization of ART delivery to peripheral health centres making existing evidence on ART care costs in Zambia out-of-date. As decision makers consider further changes in ART service delivery, it is important to understand the current drivers of costs for ART care. This study provides updates on costs of ART services for HIV-positive patients in Zambia. METHODS: We evaluated costs, assessed from the health systems perspective and expressed in 2016 USD, based on an activity-based costing framework using both top-down and bottom-up methods with an assessment of process and capacity. We collected primary site-level costs and resource utilization data from government documents, patient chart reviews and time-and-motion studies conducted in 10 purposively selected ART clinics. RESULTS: The cost of providing ART varied considerably among the ten clinics. The average per-patient annual cost of ART service was $116.69 (range: $59.38 to $145.62) using a bottom-up method and $130.32 (range: $94.02 to $162.64) using a top-down method. ART drug costs were the main cost driver (67% to 7% of all costs) and are highly sensitive to the types of patient included in the analysis (long-term vs. all ART patients, including those recently initiated) and the data sources used (facility vs. patient level). Missing capacity costs made up 57% of the total difference between the top-down and bottom-up estimates. Variability in cost across the ten clinics was associated with operational characteristics. CONCLUSIONS: Real-world costs of current routine ART services in Zambia are considerably lower than previously reported estimates and sensitive to operational factors and methods used. We recommend collection and monitoring of resource use and capacity data to periodically update cost estimates.
Validating novel diagnostic assays for tuberculosis in the context of existing tools.
(2021-Sep) Kerkhoff AD; Cattamanchi A; Muyoyeta M; Denkinger CM; Dowdy DW; Center for Tuberculosis and Division of Pulmonary and Critical Care Medicine, Zuckerberg San Francisco General Hospital and Trauma Center, University of California San Francisco, San Francisco, CA 94110, USA; Uganda Tuberculosis Implementation Research Consortium, Makerere University, Kampala, Uganda.; Division of HIV, Infectious Diseases and Global Medicine, Zuckerberg San Francisco General Hospital and Trauma Center, University of California San Francisco, San Francisco, CA 94110, USA. Electronic address: andrew.kerkhoff@ucsf.edu.; Division of Tropical Medicine, Center of Infectious Diseases, University of Heidelberg, Heidelberg, Germany.; TB Department, Centre for Infectious Disease Research in Zambia, Lusaka, Zambia.; Uganda Tuberculosis Implementation Research Consortium, Makerere University, Kampala, Uganda; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)