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Shattock AJ, Johnson HC, Sim SY, Carter A, Lambach P, Hutubessy RCW, Thompson KM, Badizadegan K, Lambert B, Ferrari MJ, Jit M, Fu H, Silal SP, Hounsell RA, White RG, Mosser JF, Gaythorpe KAM, Trotter CL, Lindstrand A, O'Brien KL, Bar-Zeev N. Contribution of vaccination to improved survival and health: modelling 50 years of the Expanded Programme on Immunization. Lancet 2024:S0140-6736(24)00850-X. [PMID: 38705159 DOI: 10.1016/s0140-6736(24)00850-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Revised: 04/18/2024] [Accepted: 04/22/2024] [Indexed: 05/07/2024]
Abstract
BACKGROUND WHO, as requested by its member states, launched the Expanded Programme on Immunization (EPI) in 1974 to make life-saving vaccines available to all globally. To mark the 50-year anniversary of EPI, we sought to quantify the public health impact of vaccination globally since the programme's inception. METHODS In this modelling study, we used a suite of mathematical and statistical models to estimate the global and regional public health impact of 50 years of vaccination against 14 pathogens in EPI. For the modelled pathogens, we considered coverage of all routine and supplementary vaccines delivered since 1974 and estimated the mortality and morbidity averted for each age cohort relative to a hypothetical scenario of no historical vaccination. We then used these modelled outcomes to estimate the contribution of vaccination to globally declining infant and child mortality rates over this period. FINDINGS Since 1974, vaccination has averted 154 million deaths, including 146 million among children younger than 5 years of whom 101 million were infants younger than 1 year. For every death averted, 66 years of full health were gained on average, translating to 10·2 billion years of full health gained. We estimate that vaccination has accounted for 40% of the observed decline in global infant mortality, 52% in the African region. In 2024, a child younger than 10 years is 40% more likely to survive to their next birthday relative to a hypothetical scenario of no historical vaccination. Increased survival probability is observed even well into late adulthood. INTERPRETATION Since 1974 substantial gains in childhood survival have occurred in every global region. We estimate that EPI has provided the single greatest contribution to improved infant survival over the past 50 years. In the context of strengthening primary health care, our results show that equitable universal access to immunisation remains crucial to sustain health gains and continue to save future lives from preventable infectious mortality. FUNDING WHO.
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Affiliation(s)
- Andrew J Shattock
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland; Telethon Kids Institute, Perth, Australia; University of Western Australia, Perth, Australia
| | - Helen C Johnson
- Safinea, London, UK; London School of Economics and Political Science, London, UK; London School of Hygiene & Tropical Medicine, London, UK; University of Cambridge, Cambridge, UK
| | - So Yoon Sim
- World Health Organization, Geneva, Switzerland
| | | | | | | | | | | | - Brian Lambert
- Pennsylvania State University, University Park, PA, USA
| | | | - Mark Jit
- London School of Hygiene & Tropical Medicine, London, UK
| | - Han Fu
- London School of Hygiene & Tropical Medicine, London, UK
| | - Sheetal P Silal
- Modelling and Simulation Hub, Africa, Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa; Centre for Global Health, Nuffield Department of Medicine, Oxford University, Oxford, UK
| | - Rachel A Hounsell
- Modelling and Simulation Hub, Africa, Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa; Centre for Global Health, Nuffield Department of Medicine, Oxford University, Oxford, UK
| | - Richard G White
- London School of Hygiene & Tropical Medicine, London, UK; TB Modelling Group, Infectious Disease Epidemiology Department, London School of Hygiene & Tropical Medicine, London, UK
| | | | | | - Caroline L Trotter
- University of Cambridge, Cambridge, UK; Imperial College London, London, UK
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White RG, Menzies NA, Portnoy A, Clark RA, Toscano CM, Weller C, Tufet Bayona M, Silal SP, Karron RA, Lee JS, Excler JL, Lauer JA, Giersing B, Lambach P, Hutubessy R, Jit M. The Full Value of Vaccine Assessments Concept-Current Opportunities and Recommendations. Vaccines (Basel) 2024; 12:435. [PMID: 38675817 PMCID: PMC11053419 DOI: 10.3390/vaccines12040435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/03/2024] [Accepted: 04/06/2024] [Indexed: 04/28/2024] Open
Abstract
For vaccine development and adoption decisions, the 'Full Value of Vaccine Assessment' (FVVA) framework has been proposed by the WHO to expand the range of evidence available to support the prioritization of candidate vaccines for investment and eventual uptake by low- and middle-income countries. Recent applications of the FVVA framework have already shown benefits. Building on the success of these applications, we see important new opportunities to maximize the future utility of FVVAs to country and global stakeholders and provide a proof-of-concept for analyses in other areas of disease control and prevention. These opportunities include the following: (1) FVVA producers should aim to create evidence that explicitly meets the needs of multiple key FVVA consumers, (2) the WHO and other key stakeholders should develop standardized methodologies for FVVAs, as well as guidance for how different stakeholders can explicitly reflect their values within the FVVA framework, and (3) the WHO should convene experts to further develop and prioritize the research agenda for outcomes and benefits relevant to the FVVA and elucidate methodological approaches and opportunities for standardization not only for less well-established benefits, but also for any relevant research gaps. We encourage FVVA stakeholders to engage with these opportunities.
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Affiliation(s)
- Richard G. White
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK; (R.A.C.); (M.J.)
| | - Nicolas A. Menzies
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA;
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA;
| | - Allison Portnoy
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA;
- Department of Global Health, Boston University School of Public Health, Boston, MA 02118, USA
| | - Rebecca A. Clark
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK; (R.A.C.); (M.J.)
| | - Cristiana M. Toscano
- Department of Collective Health, Institute for Tropical Medicine and Public Health, Federal University of Goiás (UFG), Goiânia 74690-900, Brazil;
| | | | | | - Sheetal Prakash Silal
- Modelling and Simulation Hub, Africa, Department of Statistical Sciences, University of Cape Town, Cape Town 7701, South Africa;
- Centre for Global Health, Nuffield Department of Medicine, Oxford University, Oxford OX3 7BN, UK
| | - Ruth A. Karron
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA;
| | - Jung-Seok Lee
- Policy and Economic Research Department, International Vaccine Institute, Seoul 08826, Republic of Korea;
| | | | - Jeremy A. Lauer
- Department of Management Science, Strathclyde Business School, Strathclyde University, Glasgow G1 1XQ, UK;
| | - Birgitte Giersing
- Immunization, Vaccines and Biologicals Department, WHO, 1211 Geneva, Switzerland; (B.G.); (P.L.); (R.H.)
| | - Philipp Lambach
- Immunization, Vaccines and Biologicals Department, WHO, 1211 Geneva, Switzerland; (B.G.); (P.L.); (R.H.)
| | - Raymond Hutubessy
- Immunization, Vaccines and Biologicals Department, WHO, 1211 Geneva, Switzerland; (B.G.); (P.L.); (R.H.)
| | - Mark Jit
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK; (R.A.C.); (M.J.)
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White RG, Rao JP. Towards improving the quality and usefulness of GBD tuberculosis estimates. Lancet Infect Dis 2024:S1473-3099(24)00079-3. [PMID: 38518790 DOI: 10.1016/s1473-3099(24)00079-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 02/01/2024] [Accepted: 02/01/2024] [Indexed: 03/24/2024]
Affiliation(s)
- Richard G White
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Jvr Prasada Rao
- Former Health Secretary, Government of India, Bangalore, India.
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White RG, Fiore-Gartland AJ, Hanekom WA, Vekemans J, Garcia-Basteiro AL, Churchyard G, Rangaka MX, Frick M, Behr MA, Hill PC, Mave V. What is next for BCG revaccination to prevent tuberculosis? Lancet Respir Med 2024; 12:e7-e8. [PMID: 38272048 DOI: 10.1016/s2213-2600(24)00009-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 01/16/2024] [Indexed: 01/27/2024]
Affiliation(s)
- Richard G White
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.
| | | | | | - Johan Vekemans
- International AIDS Vaccine Initiative, New York, NY, USA
| | - Alberto L Garcia-Basteiro
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique; ISGlobal, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Infecciosas, Barcelona, Spain
| | | | - Molebogeng X Rangaka
- Institute for Global Health, Medical Research Council Clinical Trials Unit, University College London, London, UK; CIDRI-AFRICA, Institute of Infectious Disease and Molecular Medicine and School of Public Health, University of Cape Town, Cape Town, South Africa
| | - Mike Frick
- Treatment Action Group, New York, NY, USA
| | - Marcel A Behr
- McGill International TB Centre, McGill University, Montreal, QC, Canada
| | | | - Vidya Mave
- Byramjee Jeejeebhoy Government Medical College, Johns Hopkins University Clinical Research Site, Pune, Maharashtra, India; Center for Infectious Diseases in India, Johns Hopkins India, Pune, India
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Sumner T, Clark RA, Mukandavire C, Portnoy A, Weerasuriya CK, Bakker R, Scarponi D, Hatherill M, Menzies NA, White RG. Modelling the health and economic impacts ofM72/AS01 E vaccination and BCG-revaccination: Estimates for South Africa. Vaccine 2024; 42:1311-1318. [PMID: 38307747 DOI: 10.1016/j.vaccine.2024.01.072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/11/2024] [Accepted: 01/22/2024] [Indexed: 02/04/2024]
Abstract
BACKGROUND Tuberculosis remains a major public health problem in South Africa, with an estimated 300,000 cases and 55,000 deaths in 2021. New tuberculosis vaccines could play an important role in reducing this burden. Phase IIb trials have suggested efficacy of the M72/AS01E vaccine candidate and BCG-revaccination. The potential population impact of these vaccines is unknown. METHODS We used an age-stratified transmission model of tuberculosis, calibrated to epidemiological data from South Africa, to estimate the potential health and economic impact of M72/AS01E vaccination and BCG-revaccination. We simulated M72/AS01E vaccination scenarios over the period 2030-2050 and BCG-revaccination scenarios over the period 2025-2050. We explored a range of product characteristics and delivery strategies. We calculated reductions in tuberculosis cases and deaths and costs and cost-effectiveness from health-system and societal perspectives. FINDINGS M72/AS01E vaccination may have a larger impact than BCG-revaccination, averting approximately 80% more cases and deaths by 2050. Both vaccines were found to be cost-effective or cost saving (compared to no new vaccine) across a range of vaccine characteristics and delivery strategies from both the health system and societal perspective. The impact of M72/AS01E is dependent on the assumed efficacy of the vaccine in uninfected individuals. Extending BCG-revaccination to HIV-infected individuals on ART increased health impact by approximately 15%, but increased health system costs by approximately 70%. INTERPRETATION Our results show that M72/AS01E vaccination or BCG-revaccination could be cost-effective in South Africa. However, there is considerable uncertainty in the estimated impact and costs due to uncertainty in vaccine characteristics and the choice of delivery strategy. FUNDING This work was funded by the Bill & Melinda Gates Foundation (INV-001754). This work used the Cirrus UK National Tier-2 HPC Service at EPCC (https://www.cirrus.ac.uk) funded by the University of Edinburgh and EPSRC (EP/P020267/1).
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Affiliation(s)
- Tom Sumner
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine, United Kingdom; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, United Kingdom; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, United Kingdom.
| | - Rebecca A Clark
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine, United Kingdom; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, United Kingdom; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, United Kingdom; Vaccine Centre, London School of Hygiene and Tropical Medicine, United Kingdom
| | - Christinah Mukandavire
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine, United Kingdom; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, United Kingdom; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, United Kingdom
| | - Allison Portnoy
- Department of Global Health, Boston University School of Public Health, Boston, USA; Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, USA
| | - Chathika K Weerasuriya
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine, United Kingdom; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, United Kingdom; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, United Kingdom
| | - Roel Bakker
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine, United Kingdom; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, United Kingdom; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, United Kingdom; KNCV Tuberculosis Foundation, USA
| | - Danny Scarponi
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine, United Kingdom; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, United Kingdom; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, United Kingdom
| | - Mark Hatherill
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine and Division of Immunology, Department of Pathology, University of Cape Town, South Africa
| | - Nicolas A Menzies
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, USA; Department of Global Health and Population, Harvard T.H. Chan School of Public Health, USA
| | - Richard G White
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine, United Kingdom; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, United Kingdom; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, United Kingdom
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Hill PC, Cobelens F, Martinez L, Garcia-Basteiro AL, Behr MA, Rangaka MX, Churchyard G, Evans T, Hanekom W, White RG. Reply to McShane. J Infect Dis 2024; 229:616. [PMID: 37897697 DOI: 10.1093/infdis/jiad466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 10/26/2023] [Indexed: 10/30/2023] Open
Affiliation(s)
- Philip C Hill
- Centre for International Health, University of Otago, Dunedin, New Zealand
| | - Frank Cobelens
- Department of Global Health, Amsterdam University Medical Centre, Amsterdam, The Netherlands
- Amsterdam Institute for Global Health and Development, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Leonardo Martinez
- Department of Epidemiology, School of Public Health, Boston University, Boston, Massachusetts, USA
| | - Alberto L Garcia-Basteiro
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique
- Instituto de Salud Global, Hospital Clínic-Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Infecciosas, Barcelona, Spain
| | - Marcel A Behr
- McGill International TB Centre, McGill University, Montreal, Quebec, Canada
| | - Molebogeng X Rangaka
- Institute for Global Health, University College London, London, United Kingdom
- MRC Clinical Trials Unit, University College London, London, United Kingdom
- Centre for Infectious Diseases Research Africa, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Gavin Churchyard
- The Aurum Institute, Parktown, South Africa
- Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA
- School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | | | - Willem Hanekom
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Richard G White
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
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Millington KA, White RG, Lipman M, McQuaid CF, Hauser J, Wooding V, Potter J, Abubakar I, Wingfield T. The 2023 UN high-level meeting on tuberculosis: renewing hope, momentum, and commitment to end tuberculosis. Lancet Respir Med 2024; 12:10-13. [PMID: 37972624 DOI: 10.1016/s2213-2600(23)00409-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023]
Affiliation(s)
- Kerry A Millington
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Richard G White
- TB Centre and Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Marc Lipman
- Respiratory Medicine, Division of Medicine, University College London, London, UK
| | - C Finn McQuaid
- TB Centre and Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | | | | | - Jessica Potter
- Respiratory Medicine, Division of Medicine, University College London, London, UK
| | - Ibrahim Abubakar
- Faculty of Population Health Sciences, School of Life and Medical Sciences, University College London, London, UK
| | - Tom Wingfield
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK; Department of International Public Health, Liverpool School of Tropical Medicine, Liverpool, UK; WHO Collaborating Centre on Tuberculosis and Social Medicine, Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden.
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Dodd PJ, McQuaid CF, Rao P, Abubakar I, Arinaminpathy N, Carnegie A, Cobelens F, Dowdy D, Fiekert K, Grant AD, Wu J, Nfii FN, Shaikh N, Houben RMGJ, White RG. Improving the quality of the Global Burden of Disease tuberculosis estimates from the Institute for Health Metrics and Evaluation. Int J Epidemiol 2023; 52:1681-1686. [PMID: 37759341 PMCID: PMC10749761 DOI: 10.1093/ije/dyad128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Affiliation(s)
- Peter J Dodd
- Sheffield Centre for Health and Related Research, University of Sheffield, Sheffield, UK
| | - Christopher Finn McQuaid
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Prasada Rao
- Former Health Secretary, Government of India, Bangalore, India
| | | | - Nimalan Arinaminpathy
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London, London, UK
| | - Anna Carnegie
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Frank Cobelens
- Department of Global Health and Amsterdam Institute for Global Health and Development, Amsterdam University Medical Centers location University of Amsterdam, Amsterdam, Netherlands
| | - David Dowdy
- Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
| | - Kathy Fiekert
- KNCV Tuberculosis Foundation, The Hague, Netherlands
| | - Alison D Grant
- TB Centre, London School of Hygiene & Tropical Medicine, London, UK
- Africa Health Research Institute, School of Laboratory Medicine & Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Jing Wu
- Center for Chronic Diseases Prevention and Control, China CDC, Beijing, China
| | - Faith Nekabari Nfii
- Africa Union-Africa Centres for Disease Control and Prevention, Addis Ababa, Ethiopia
| | - Nabila Shaikh
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Influenza & COVID-19 Franchise, Sanofi, Reading, UK
| | - Rein M G J Houben
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Richard G White
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
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Emery JC, Dodd PJ, Banu S, Frascella B, Garden FL, Horton KC, Hossain S, Law I, van Leth F, Marks GB, Nguyen HB, Nguyen HV, Onozaki I, Quelapio MID, Richards AS, Shaikh N, Tiemersma EW, White RG, Zaman K, Cobelens F, Houben RMGJ. Estimating the contribution of subclinical tuberculosis disease to transmission: An individual patient data analysis from prevalence surveys. eLife 2023; 12:e82469. [PMID: 38109277 PMCID: PMC10727500 DOI: 10.7554/elife.82469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 08/04/2023] [Indexed: 12/20/2023] Open
Abstract
Background Individuals with bacteriologically confirmed pulmonary tuberculosis (TB) disease who do not report symptoms (subclinical TB) represent around half of all prevalent cases of TB, yet their contribution to Mycobacterium tuberculosis (Mtb) transmission is unknown, especially compared to individuals who report symptoms at the time of diagnosis (clinical TB). Relative infectiousness can be approximated by cumulative infections in household contacts, but such data are rare. Methods We reviewed the literature to identify studies where surveys of Mtb infection were linked to population surveys of TB disease. We collated individual-level data on representative populations for analysis and used literature on the relative durations of subclinical and clinical TB to estimate relative infectiousness through a cumulative hazard model, accounting for sputum-smear status. Relative prevalence of subclinical and clinical disease in high-burden settings was used to estimate the contribution of subclinical TB to global Mtb transmission. Results We collated data on 414 index cases and 789 household contacts from three prevalence surveys (Bangladesh, the Philippines, and Viet Nam) and one case-finding trial in Viet Nam. The odds ratio for infection in a household with a clinical versus subclinical index case (irrespective of sputum smear status) was 1.2 (0.6-2.3, 95% confidence interval). Adjusting for duration of disease, we found a per-unit-time infectiousness of subclinical TB relative to clinical TB of 1.93 (0.62-6.18, 95% prediction interval [PrI]). Fourteen countries across Asia and Africa provided data on relative prevalence of subclinical and clinical TB, suggesting an estimated 68% (27-92%, 95% PrI) of global transmission is from subclinical TB. Conclusions Our results suggest that subclinical TB contributes substantially to transmission and needs to be diagnosed and treated for effective progress towards TB elimination. Funding JCE, KCH, ASR, NS, and RH have received funding from the European Research Council (ERC) under the Horizon 2020 research and innovation programme (ERC Starting Grant No. 757699) KCH is also supported by UK FCDO (Leaving no-one behind: transforming gendered pathways to health for TB). This research has been partially funded by UK aid from the UK government (to KCH); however, the views expressed do not necessarily reflect the UK government's official policies. PJD was supported by a fellowship from the UK Medical Research Council (MR/P022081/1); this UK-funded award is part of the EDCTP2 programme supported by the European Union. RGW is funded by the Wellcome Trust (218261/Z/19/Z), NIH (1R01AI147321-01), EDTCP (RIA208D-2505B), UK MRC (CCF17-7779 via SET Bloomsbury), ESRC (ES/P008011/1), BMGF (OPP1084276, OPP1135288 and INV-001754), and the WHO (2020/985800-0).
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Affiliation(s)
- Jon C Emery
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical MedicineLondonUnited Kingdom
| | - Peter J Dodd
- School of Health and Related Research, University of SheffieldSheffieldUnited Kingdom
| | - Sayera Banu
- International Centre for Diarrhoeal Disease ResearchDhakaBangladesh
| | | | - Frances L Garden
- South West Sydney Clinical Campuses, University of New South WalesSydneyAustralia
- Ingham Institute of Applied Medical ResearchSydneyAustralia
| | - Katherine C Horton
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical MedicineLondonUnited Kingdom
| | - Shahed Hossain
- James P. Grant School of Public Health, BRAC UniversityDhakaBangladesh
| | - Irwin Law
- Global Tuberculosis Programme, World Health OrganizationGenevaSwitzerland
| | - Frank van Leth
- Department of Health Sciences, VU UniversityAmsterdamNetherlands
- Amsterdam Public Health Research InstituteAmsterdamNetherlands
| | - Guy B Marks
- South West Sydney Clinical Campuses, University of New South WalesSydneyAustralia
- Woolcock Institute of Medical ResearchSydneyAustralia
| | - Hoa Binh Nguyen
- National Lung Hospital, National Tuberculosis Control ProgramHa NoiViet Nam
| | - Hai Viet Nguyen
- National Lung Hospital, National Tuberculosis Control ProgramHa NoiViet Nam
| | - Ikushi Onozaki
- Research Institute of Tuberculosis, Japan Anti-Tuberculosis AssociationTokyoJapan
| | | | - Alexandra S Richards
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical MedicineLondonUnited Kingdom
| | - Nabila Shaikh
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical MedicineLondonUnited Kingdom
- Sanofi PasteurReadingUnited Kingdom
| | | | - Richard G White
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical MedicineLondonUnited Kingdom
| | - Khalequ Zaman
- International Centre for Diarrhoeal Disease ResearchDhakaBangladesh
| | - Frank Cobelens
- Department of Global Health and Amsterdam Institute for Global Health and Development, Amsterdam University Medical Centers, University of AmsterdamAmsterdamNetherlands
| | - Rein MGJ Houben
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical MedicineLondonUnited Kingdom
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Hill PC, Cobelens F, Martinez L, Behr MA, Churchyard G, Evans T, Fiore-Gartland AJ, Garcia-Basteiro AL, Hanekom W, Rangaka MX, Vekemans J, White RG. An Aspiration to Radically Shorten Phase 3 Tuberculosis Vaccine Trials. J Infect Dis 2023; 228:1150-1153. [PMID: 37607272 DOI: 10.1093/infdis/jiad356] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 07/06/2023] [Accepted: 08/17/2023] [Indexed: 08/24/2023] Open
Abstract
A new tuberculosis vaccine is a high priority. However, the classical development pathway is a major deterrent. Most tuberculosis cases arise within 2 years after Mycobacterium tuberculosis exposure, suggesting a 3-year trial period should be possible if sample size is large to maximize the number of early exposures. Increased sample size could be facilitated by working alongside optimized routine services for case ascertainment, with strategies for enhanced case detection and safety monitoring. Shortening enrolment could be achieved by simplifying screening criteria and procedures and strengthening site capacity. Together, these measures could enable radically shortened phase 3 tuberculosis vaccine trials.
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Affiliation(s)
- Philip C Hill
- Centre for International Health, University of Otago, Dunedin, New Zealand
| | - Frank Cobelens
- Department of Global Health, Amsterdam University Medical Centre, Amsterdam, The Netherlands
- Amsterdam Institute for Global Health and Development, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Leonardo Martinez
- Department of Epidemiology, School of Public Health, Boston University, Boston, Massachusetts, USA
| | - Marcel A Behr
- McGill International TB Centre, McGill University, Montreal, Quebec, Canada
| | - Gavin Churchyard
- The Aurum Institute, Parktown, South Africa
- Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA
- School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | | | - Andrew J Fiore-Gartland
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Alberto L Garcia-Basteiro
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique
- Instituto de Salud Global, Hospital Clínic-Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Infecciosas, Barcelona, Spain
| | - Willem Hanekom
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Molebogeng X Rangaka
- MRC Clinical Trials Unit, University College London, London, United Kingdom
- Institute for Global Health, University College London, London, United Kingdom
- Centre for Infectious Diseases Research Africa, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | | | - Richard G White
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
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Clark RA, Portnoy A, Weerasuriya CK, Sumner T, Bakker R, Harris RC, Rade K, Mattoo SK, Tumu D, Menzies NA, White RG. The potential health and economic impacts of new tuberculosis vaccines under varying delivery strategies in Delhi and Gujarat, India: a modelling study. medRxiv 2023:2023.09.27.23296211. [PMID: 37808744 PMCID: PMC10557803 DOI: 10.1101/2023.09.27.23296211] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Background India has the largest tuberculosis burden globally, but this burden varies nationwide. All-age tuberculosis prevalence in 2021 ranged from 747/100,000 in Delhi to 137/100,000 in Gujarat. Previous modelling has demonstrated the benefits and costs of introducing novel tuberculosis vaccines in India overall. However, no studies have compared the potential impact of tuberculosis vaccines in regions within India with differing tuberculosis disease and infection prevalence. We used mathematical modelling to investigate how the health and economic impact of two potential tuberculosis vaccines, M72/AS01E and BCG-revaccination, could differ in Delhi and Gujarat under varying delivery strategies. Methods We applied a compartmental tuberculosis model separately for Delhi (higher disease and infection prevalence) and Gujarat (lower disease and infection prevalence), and projected epidemiological trends to 2050 assuming no new vaccine introduction. We simulated M72/AS01E and BCG-revaccination scenarios varying target ages and vaccine characteristics. We estimated cumulative cases, deaths, and disability-adjusted life years averted between 2025-2050 compared to the no-new-vaccine scenario and compared incremental cost-effectiveness ratios to three cost-effectiveness thresholds. Results M72/AS01E averted a higher proportion of tuberculosis cases than BCG-revaccination in both regions (Delhi: 16.0% vs 8.3%, Gujarat: 8.5% vs 5.1%) and had higher vaccination costs (Delhi: USD$118 million vs USD$27 million, Gujarat: US$366 million vs US$97 million). M72/AS01E in Delhi could be cost-effective, or even cost-saving, for all modelled vaccine characteristics. M72/AS01E could be cost-effective in Gujarat, unless efficacy was assumed only for those with current infection at vaccination. BCG-revaccination could be cost-effective, or cost-saving, in both regions for all modelled vaccine scenarios. Discussion M72/AS01E and BCG-revaccination could be impactful and cost-effective in Delhi and Gujarat. Differences in impact, costs, and cost-effectiveness between vaccines and regions, were determined partly by differences in disease and infection prevalence, and demography. Age-specific regional estimates of infection prevalence could help to inform delivery strategies for vaccines that may only be effective in people with a particular infection status. Evidence on the mechanism of effect of M72/AS01E and its effectiveness in uninfected individuals, which were important drivers of impact and cost-effectiveness, particularly in Gujarat, are also key to improve estimates of population-level impact.
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Affiliation(s)
- Rebecca A Clark
- TB Modelling Group and TB Centre, LSHTM
- Centre for the Mathematical Modelling of Infectious Diseases, LSHTM
- Department of Infectious Disease Epidemiology, LSHTM
- Vaccine Centre, LSHTM
| | - Allison Portnoy
- Department of Global Health, Boston University School of Public Health
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health
| | - Chathika K Weerasuriya
- TB Modelling Group and TB Centre, LSHTM
- Centre for the Mathematical Modelling of Infectious Diseases, LSHTM
- Department of Infectious Disease Epidemiology, LSHTM
| | - Tom Sumner
- TB Modelling Group and TB Centre, LSHTM
- Centre for the Mathematical Modelling of Infectious Diseases, LSHTM
- Department of Infectious Disease Epidemiology, LSHTM
| | - Roel Bakker
- TB Modelling Group and TB Centre, LSHTM
- Centre for the Mathematical Modelling of Infectious Diseases, LSHTM
- Department of Infectious Disease Epidemiology, LSHTM
- KNCV Tuberculosis Foundation
| | - Rebecca C Harris
- TB Modelling Group and TB Centre, LSHTM
- Centre for the Mathematical Modelling of Infectious Diseases, LSHTM
- Department of Infectious Disease Epidemiology, LSHTM
- Sanofi Pasteur, Singapore
| | | | - Sanjay Kumar Mattoo
- Central TB Division, National Tuberculosis Elimination Program, MoHFW Govt of India. New Delhi, India
| | - Dheeraj Tumu
- World Health Organization, India
- Central TB Division, National Tuberculosis Elimination Program, MoHFW Govt of India. New Delhi, India
| | - Nicolas A Menzies
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health
| | - Richard G White
- TB Modelling Group and TB Centre, LSHTM
- Centre for the Mathematical Modelling of Infectious Diseases, LSHTM
- Department of Infectious Disease Epidemiology, LSHTM
- Vaccine Centre, LSHTM
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12
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Clark RA, Weerasuriya CK, Portnoy A, Mukandavire C, Quaife M, Bakker R, Scarponi D, Harris RC, Rade K, Mattoo SK, Tumu D, Menzies NA, White RG. New tuberculosis vaccines in India: modelling the potential health and economic impacts of adolescent/adult vaccination with M72/AS01 E and BCG-revaccination. BMC Med 2023; 21:288. [PMID: 37542319 PMCID: PMC10403932 DOI: 10.1186/s12916-023-02992-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 07/20/2023] [Indexed: 08/06/2023] Open
Abstract
BACKGROUND India had an estimated 2.9 million tuberculosis cases and 506 thousand deaths in 2021. Novel vaccines effective in adolescents and adults could reduce this burden. M72/AS01E and BCG-revaccination have recently completed phase IIb trials and estimates of their population-level impact are needed. We estimated the potential health and economic impact of M72/AS01E and BCG-revaccination in India and investigated the impact of variation in vaccine characteristics and delivery strategies. METHODS We developed an age-stratified compartmental tuberculosis transmission model for India calibrated to country-specific epidemiology. We projected baseline epidemiology to 2050 assuming no-new-vaccine introduction, and M72/AS01E and BCG-revaccination scenarios over 2025-2050 exploring uncertainty in product characteristics (vaccine efficacy, mechanism of effect, infection status required for vaccine efficacy, duration of protection) and implementation (achieved vaccine coverage and ages targeted). We estimated reductions in tuberculosis cases and deaths by each scenario compared to the no-new-vaccine baseline, as well as costs and cost-effectiveness from health-system and societal perspectives. RESULTS M72/AS01E scenarios were predicted to avert 40% more tuberculosis cases and deaths by 2050 compared to BCG-revaccination scenarios. Cost-effectiveness ratios for M72/AS01E vaccines were around seven times higher than BCG-revaccination, but nearly all scenarios were cost-effective. The estimated average incremental cost was US$190 million for M72/AS01E and US$23 million for BCG-revaccination per year. Sources of uncertainty included whether M72/AS01E was efficacious in uninfected individuals at vaccination, and if BCG-revaccination could prevent disease. CONCLUSIONS M72/AS01E and BCG-revaccination could be impactful and cost-effective in India. However, there is great uncertainty in impact, especially given the unknowns surrounding the mechanism of effect and infection status required for vaccine efficacy. Greater investment in vaccine development and delivery is needed to resolve these unknowns in vaccine product characteristics.
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Affiliation(s)
- Rebecca A Clark
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
- Vaccine Centre, London School of Hygiene and Tropical Medicine, London, UK.
| | - Chathika K Weerasuriya
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Allison Portnoy
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, USA
- Department of Global Health, Boston University School of Public Health, Boston, USA
| | - Christinah Mukandavire
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Matthew Quaife
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Roel Bakker
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- KNCV Tuberculosis Foundation, The Hague, Netherlands
| | - Danny Scarponi
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Rebecca C Harris
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- Sanofi Pasteur, Singapore, Singapore
| | | | | | - Dheeraj Tumu
- World Health Organization, New Delhi, India
- Central TB Division, NTEP, MoHFW Govt of India, New Delhi, India
| | - Nicolas A Menzies
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, USA
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, USA
| | - Richard G White
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- Vaccine Centre, London School of Hygiene and Tropical Medicine, London, UK
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13
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Nelson KN, Churchyard G, Cobelens F, Hanekom WA, Hill PC, Lopman B, Mave V, Rangaka MX, Vekemans J, White RG, Wong EB, Martinez L, García-Basteiro AL. Measuring indirect transmission-reducing effects in tuberculosis vaccine efficacy trials: why and how? Lancet Microbe 2023; 4:e651-e656. [PMID: 37329893 PMCID: PMC10393779 DOI: 10.1016/s2666-5247(23)00112-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 02/24/2023] [Accepted: 03/27/2023] [Indexed: 06/19/2023]
Abstract
Tuberculosis is the leading bacterial cause of death globally. In 2021, 10·6 million people developed symptomatic tuberculosis and 1·6 million died. Seven promising vaccine candidates that aim to prevent tuberculosis disease in adolescents and adults are currently in late-stage clinical trials. Conventional phase 3 trials provide information on the direct protection conferred against infection or disease in vaccinated individuals, but they tell us little about possible indirect (ie, transmission-reducing) effects that afford protection to unvaccinated individuals. As a result, proposed phase 3 trial designs will not provide key information about the overall effect of introducing a vaccine programme. Information on the potential for indirect effects can be crucial for policy makers deciding whether and how to introduce tuberculosis vaccines into immunisation programmes. We describe the rationale for measuring indirect effects, in addition to direct effects, of tuberculosis vaccine candidates in pivotal trials and lay out several options for incorporating their measurement into phase 3 trial designs.
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Affiliation(s)
- Kristin N Nelson
- Department of Epidemiology, Rollins School of Public Health, Atlanta, GA, USA.
| | | | - Frank Cobelens
- Department of Global Health and Amsterdam Institute for Global Health and Development, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | | | - Philip C Hill
- Centre for International Health, University of Otago, Dunedin, New Zealand
| | - Benjamin Lopman
- Department of Epidemiology, Rollins School of Public Health, Atlanta, GA, USA
| | - Vidya Mave
- Johns Hopkins Center for Infectious Diseases in India, Pune, India
| | - Molebogeng X Rangaka
- Institute for Global Health and MRC Clinical Trials Unit, University College London, London, UK
| | | | - Richard G White
- Tuberculosis Modelling Group, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Emily B Wong
- Africa Health Research Institute, KwaZulu Natal, South Africa; Division of Infectious Diseases, Department of Medicine, Heersink School of Medicine, University of Alabama Birmingham, Birmingham, AL, USA
| | - Leonardo Martinez
- Department of Epidemiology, School of Public Health, Boston University, Boston, MA, USA
| | - Alberto L García-Basteiro
- Centro de Investigação em Saude de Manhiça (CISM), Maputo, Mozambique; ISGlobal, Hospital Clínic-Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC), Barcelona, Spain
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14
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Aalbers J, Akerib DS, Akerlof CW, Al Musalhi AK, Alder F, Alqahtani A, Alsum SK, Amarasinghe CS, Ames A, Anderson TJ, Angelides N, Araújo HM, Armstrong JE, Arthurs M, Azadi S, Bailey AJ, Baker A, Balajthy J, Balashov S, Bang J, Bargemann JW, Barry MJ, Barthel J, Bauer D, Baxter A, Beattie K, Belle J, Beltrame P, Bensinger J, Benson T, Bernard EP, Bhatti A, Biekert A, Biesiadzinski TP, Birch HJ, Birrittella B, Blockinger GM, Boast KE, Boxer B, Bramante R, Brew CAJ, Brás P, Buckley JH, Bugaev VV, Burdin S, Busenitz JK, Buuck M, Cabrita R, Carels C, Carlsmith DL, Carlson B, Carmona-Benitez MC, Cascella M, Chan C, Chawla A, Chen H, Cherwinka JJ, Chott NI, Cole A, Coleman J, Converse MV, Cottle A, Cox G, Craddock WW, Creaner O, Curran D, Currie A, Cutter JE, Dahl CE, David A, Davis J, Davison TJR, Delgaudio J, Dey S, de Viveiros L, Dobi A, Dobson JEY, Druszkiewicz E, Dushkin A, Edberg TK, Edwards WR, Elnimr MM, Emmet WT, Eriksen SR, Faham CH, Fan A, Fayer S, Fearon NM, Fiorucci S, Flaecher H, Ford P, Francis VB, Fraser ED, Fruth T, Gaitskell RJ, Gantos NJ, Garcia D, Geffre A, Gehman VM, Genovesi J, Ghag C, Gibbons R, Gibson E, Gilchriese MGD, Gokhale S, Gomber B, Green J, Greenall A, Greenwood S, van der Grinten MGD, Gwilliam CB, Hall CR, Hans S, Hanzel K, Harrison A, Hartigan-O'Connor E, Haselschwardt SJ, Hernandez MA, Hertel SA, Heuermann G, Hjemfelt C, Hoff MD, Holtom E, Hor JYK, Horn M, Huang DQ, Hunt D, Ignarra CM, Jacobsen RG, Jahangir O, James RS, Jeffery SN, Ji W, Johnson J, Kaboth AC, Kamaha AC, Kamdin K, Kasey V, Kazkaz K, Keefner J, Khaitan D, Khaleeq M, Khazov A, Khurana I, Kim YD, Kocher CD, Kodroff D, Korley L, Korolkova EV, Kras J, Kraus H, Kravitz S, Krebs HJ, Kreczko L, Krikler B, Kudryavtsev VA, Kyre S, Landerud B, Leason EA, Lee C, Lee J, Leonard DS, Leonard R, Lesko KT, Levy C, Li J, Liao FT, Liao J, Lin J, Lindote A, Linehan R, Lippincott WH, Liu R, Liu X, Liu Y, Loniewski C, Lopes MI, Lopez Asamar E, López Paredes B, Lorenzon W, Lucero D, Luitz S, Lyle JM, Majewski PA, Makkinje J, Malling DC, Manalaysay A, Manenti L, Mannino RL, Marangou N, Marzioni MF, Maupin C, McCarthy ME, McConnell CT, McKinsey DN, McLaughlin J, Meng Y, Migneault J, Miller EH, Mizrachi E, Mock JA, Monte A, Monzani ME, Morad JA, Morales Mendoza JD, Morrison E, Mount BJ, Murdy M, Murphy ASJ, Naim D, Naylor A, Nedlik C, Nehrkorn C, Neves F, Nguyen A, Nikoleyczik JA, Nilima A, O'Dell J, O'Neill FG, O'Sullivan K, Olcina I, Olevitch MA, Oliver-Mallory KC, Orpwood J, Pagenkopf D, Pal S, Palladino KJ, Palmer J, Pangilinan M, Parveen N, Patton SJ, Pease EK, Penning B, Pereira C, Pereira G, Perry E, Pershing T, Peterson IB, Piepke A, Podczerwinski J, Porzio D, Powell S, Preece RM, Pushkin K, Qie Y, Ratcliff BN, Reichenbacher J, Reichhart L, Rhyne CA, Richards A, Riffard Q, Rischbieter GRC, Rodrigues JP, Rodriguez A, Rose HJ, Rosero R, Rossiter P, Rushton T, Rutherford G, Rynders D, Saba JS, Santone D, Sazzad ABMR, Schnee RW, Scovell PR, Seymour D, Shaw S, Shutt T, Silk JJ, Silva C, Sinev G, Skarpaas K, Skulski W, Smith R, Solmaz M, Solovov VN, Sorensen P, Soria J, Stancu I, Stark MR, Stevens A, Stiegler TM, Stifter K, Studley R, Suerfu B, Sumner TJ, Sutcliffe P, Swanson N, Szydagis M, Tan M, Taylor DJ, Taylor R, Taylor WC, Temples DJ, Tennyson BP, Terman PA, Thomas KJ, Tiedt DR, Timalsina M, To WH, Tomás A, Tong Z, Tovey DR, Tranter J, Trask M, Tripathi M, Tronstad DR, Tull CE, Turner W, Tvrznikova L, Utku U, Va'vra J, Vacheret A, Vaitkus AC, Verbus JR, Voirin E, Waldron WL, Wang A, Wang B, Wang JJ, Wang W, Wang Y, Watson JR, Webb RC, White A, White DT, White JT, White RG, Whitis TJ, Williams M, Wisniewski WJ, Witherell MS, Wolfs FLH, Wolfs JD, Woodford S, Woodward D, Worm SD, Wright CJ, Xia Q, Xiang X, Xiao Q, Xu J, Yeh M, Yin J, Young I, Zarzhitsky P, Zuckerman A, Zweig EA. First Dark Matter Search Results from the LUX-ZEPLIN (LZ) Experiment. Phys Rev Lett 2023; 131:041002. [PMID: 37566836 DOI: 10.1103/physrevlett.131.041002] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 03/06/2023] [Accepted: 06/07/2023] [Indexed: 08/13/2023]
Abstract
The LUX-ZEPLIN experiment is a dark matter detector centered on a dual-phase xenon time projection chamber operating at the Sanford Underground Research Facility in Lead, South Dakota, USA. This Letter reports results from LUX-ZEPLIN's first search for weakly interacting massive particles (WIMPs) with an exposure of 60 live days using a fiducial mass of 5.5 t. A profile-likelihood ratio analysis shows the data to be consistent with a background-only hypothesis, setting new limits on spin-independent WIMP-nucleon, spin-dependent WIMP-neutron, and spin-dependent WIMP-proton cross sections for WIMP masses above 9 GeV/c^{2}. The most stringent limit is set for spin-independent scattering at 36 GeV/c^{2}, rejecting cross sections above 9.2×10^{-48} cm at the 90% confidence level.
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Affiliation(s)
- J Aalbers
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - D S Akerib
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - C W Akerlof
- University of Michigan, Randall Laboratory of Physics, Ann Arbor, Michigan 48109-1040, USA
| | - A K Al Musalhi
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
| | - F Alder
- University College London (UCL), Department of Physics and Astronomy, London WC1E 6BT, United Kingdom
| | - A Alqahtani
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - S K Alsum
- University of Wisconsin-Madison, Department of Physics, Madison, Wisconsin 53706-1390, USA
| | - C S Amarasinghe
- University of Michigan, Randall Laboratory of Physics, Ann Arbor, Michigan 48109-1040, USA
| | - A Ames
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - T J Anderson
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - N Angelides
- University College London (UCL), Department of Physics and Astronomy, London WC1E 6BT, United Kingdom
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - H M Araújo
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - J E Armstrong
- University of Maryland, Department of Physics, College Park, Maryland 20742-4111, USA
| | - M Arthurs
- University of Michigan, Randall Laboratory of Physics, Ann Arbor, Michigan 48109-1040, USA
| | - S Azadi
- University of California, Santa Barbara, Department of Physics, Santa Barbara, California 93106-9530, USA
| | - A J Bailey
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - A Baker
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - J Balajthy
- University of California, Davis, Department of Physics, Davis, California 95616-5270, USA
| | - S Balashov
- STFC Rutherford Appleton Laboratory (RAL), Didcot, OX11 0QX, United Kingdom
| | - J Bang
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - J W Bargemann
- University of California, Santa Barbara, Department of Physics, Santa Barbara, California 93106-9530, USA
| | - M J Barry
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - J Barthel
- South Dakota Science and Technology Authority (SDSTA), Sanford Underground Research Facility, Lead, South Dakota 57754-1700, USA
| | - D Bauer
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - A Baxter
- University of Liverpool, Department of Physics, Liverpool L69 7ZE, United Kingdom
| | - K Beattie
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - J Belle
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510-5011, USA
| | - P Beltrame
- University College London (UCL), Department of Physics and Astronomy, London WC1E 6BT, United Kingdom
- University of Edinburgh, SUPA, School of Physics and Astronomy, Edinburgh EH9 3FD, United Kingdom
| | - J Bensinger
- Brandeis University, Department of Physics, Waltham, Massachusetts 02453, USA
| | - T Benson
- University of Wisconsin-Madison, Department of Physics, Madison, Wisconsin 53706-1390, USA
| | - E P Bernard
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - A Bhatti
- University of Maryland, Department of Physics, College Park, Maryland 20742-4111, USA
| | - A Biekert
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - T P Biesiadzinski
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - H J Birch
- University of Michigan, Randall Laboratory of Physics, Ann Arbor, Michigan 48109-1040, USA
- University of Liverpool, Department of Physics, Liverpool L69 7ZE, United Kingdom
| | - B Birrittella
- University of Wisconsin-Madison, Department of Physics, Madison, Wisconsin 53706-1390, USA
| | - G M Blockinger
- University at Albany (SUNY), Department of Physics, Albany, New York 12222-0100, USA
| | - K E Boast
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
| | - B Boxer
- University of California, Davis, Department of Physics, Davis, California 95616-5270, USA
- University of Liverpool, Department of Physics, Liverpool L69 7ZE, United Kingdom
| | - R Bramante
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - C A J Brew
- STFC Rutherford Appleton Laboratory (RAL), Didcot, OX11 0QX, United Kingdom
| | - P Brás
- Laboratório de Instrumentação e Física Experimental de Partículas (LIP), University of Coimbra, P-3004 516 Coimbra, Portugal
| | - J H Buckley
- Washington University in St. Louis, Department of Physics, St. Louis, Missouri 63130-4862, USA
| | - V V Bugaev
- Washington University in St. Louis, Department of Physics, St. Louis, Missouri 63130-4862, USA
| | - S Burdin
- University of Liverpool, Department of Physics, Liverpool L69 7ZE, United Kingdom
| | - J K Busenitz
- University of Alabama, Department of Physics and Astronomy, Tuscaloosa, Alabama 34587-0324, USA
| | - M Buuck
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - R Cabrita
- Laboratório de Instrumentação e Física Experimental de Partículas (LIP), University of Coimbra, P-3004 516 Coimbra, Portugal
| | - C Carels
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
| | - D L Carlsmith
- University of Wisconsin-Madison, Department of Physics, Madison, Wisconsin 53706-1390, USA
| | - B Carlson
- South Dakota Science and Technology Authority (SDSTA), Sanford Underground Research Facility, Lead, South Dakota 57754-1700, USA
| | - M C Carmona-Benitez
- Pennsylvania State University, Department of Physics, University Park, Pennsylvania 16802-6300, USA
| | - M Cascella
- University College London (UCL), Department of Physics and Astronomy, London WC1E 6BT, United Kingdom
| | - C Chan
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - A Chawla
- Royal Holloway, University of London, Department of Physics, Egham, TW20 0EX, United Kingdom
| | - H Chen
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - J J Cherwinka
- University of Wisconsin-Madison, Department of Physics, Madison, Wisconsin 53706-1390, USA
| | - N I Chott
- South Dakota School of Mines and Technology, Rapid City, South Dakota 57701-3901, USA
| | - A Cole
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - J Coleman
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - M V Converse
- University of Rochester, Department of Physics and Astronomy, Rochester, New York 14627-0171, USA
| | - A Cottle
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510-5011, USA
| | - G Cox
- South Dakota Science and Technology Authority (SDSTA), Sanford Underground Research Facility, Lead, South Dakota 57754-1700, USA
- Pennsylvania State University, Department of Physics, University Park, Pennsylvania 16802-6300, USA
| | - W W Craddock
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
| | - O Creaner
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - D Curran
- South Dakota Science and Technology Authority (SDSTA), Sanford Underground Research Facility, Lead, South Dakota 57754-1700, USA
| | - A Currie
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - J E Cutter
- University of California, Davis, Department of Physics, Davis, California 95616-5270, USA
| | - C E Dahl
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510-5011, USA
- Northwestern University, Department of Physics & Astronomy, Evanston, Illinois 60208-3112, USA
| | - A David
- University College London (UCL), Department of Physics and Astronomy, London WC1E 6BT, United Kingdom
| | - J Davis
- South Dakota Science and Technology Authority (SDSTA), Sanford Underground Research Facility, Lead, South Dakota 57754-1700, USA
| | - T J R Davison
- University of Edinburgh, SUPA, School of Physics and Astronomy, Edinburgh EH9 3FD, United Kingdom
| | - J Delgaudio
- South Dakota Science and Technology Authority (SDSTA), Sanford Underground Research Facility, Lead, South Dakota 57754-1700, USA
| | - S Dey
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
| | - L de Viveiros
- Pennsylvania State University, Department of Physics, University Park, Pennsylvania 16802-6300, USA
| | - A Dobi
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - J E Y Dobson
- University College London (UCL), Department of Physics and Astronomy, London WC1E 6BT, United Kingdom
| | - E Druszkiewicz
- University of Rochester, Department of Physics and Astronomy, Rochester, New York 14627-0171, USA
| | - A Dushkin
- Brandeis University, Department of Physics, Waltham, Massachusetts 02453, USA
| | - T K Edberg
- University of Maryland, Department of Physics, College Park, Maryland 20742-4111, USA
| | - W R Edwards
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - M M Elnimr
- University of Alabama, Department of Physics and Astronomy, Tuscaloosa, Alabama 34587-0324, USA
| | - W T Emmet
- Yale University, Department of Physics, New Haven, Connecticut 06511-8499, USA
| | - S R Eriksen
- University of Bristol, H.H. Wills Physics Laboratory, Bristol, BS8 1TL, United Kingdom
| | - C H Faham
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - A Fan
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - S Fayer
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - N M Fearon
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
| | - S Fiorucci
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - H Flaecher
- University of Bristol, H.H. Wills Physics Laboratory, Bristol, BS8 1TL, United Kingdom
| | - P Ford
- STFC Rutherford Appleton Laboratory (RAL), Didcot, OX11 0QX, United Kingdom
| | - V B Francis
- STFC Rutherford Appleton Laboratory (RAL), Didcot, OX11 0QX, United Kingdom
| | - E D Fraser
- University of Liverpool, Department of Physics, Liverpool L69 7ZE, United Kingdom
| | - T Fruth
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
- University College London (UCL), Department of Physics and Astronomy, London WC1E 6BT, United Kingdom
| | - R J Gaitskell
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - N J Gantos
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - D Garcia
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - A Geffre
- South Dakota Science and Technology Authority (SDSTA), Sanford Underground Research Facility, Lead, South Dakota 57754-1700, USA
| | - V M Gehman
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - J Genovesi
- South Dakota School of Mines and Technology, Rapid City, South Dakota 57701-3901, USA
| | - C Ghag
- University College London (UCL), Department of Physics and Astronomy, London WC1E 6BT, United Kingdom
| | - R Gibbons
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - E Gibson
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
| | - M G D Gilchriese
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - S Gokhale
- Brookhaven National Laboratory (BNL), Upton, New York 11973-5000, USA
| | - B Gomber
- University of Wisconsin-Madison, Department of Physics, Madison, Wisconsin 53706-1390, USA
| | - J Green
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
| | - A Greenall
- University of Liverpool, Department of Physics, Liverpool L69 7ZE, United Kingdom
| | - S Greenwood
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | | | - C B Gwilliam
- University of Liverpool, Department of Physics, Liverpool L69 7ZE, United Kingdom
| | - C R Hall
- University of Maryland, Department of Physics, College Park, Maryland 20742-4111, USA
| | - S Hans
- Brookhaven National Laboratory (BNL), Upton, New York 11973-5000, USA
| | - K Hanzel
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - A Harrison
- South Dakota School of Mines and Technology, Rapid City, South Dakota 57701-3901, USA
| | - E Hartigan-O'Connor
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - S J Haselschwardt
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - M A Hernandez
- University of Michigan, Randall Laboratory of Physics, Ann Arbor, Michigan 48109-1040, USA
| | - S A Hertel
- University of Massachusetts, Department of Physics, Amherst, Massachusetts 01003-9337, USA
| | - G Heuermann
- University of Michigan, Randall Laboratory of Physics, Ann Arbor, Michigan 48109-1040, USA
| | - C Hjemfelt
- South Dakota School of Mines and Technology, Rapid City, South Dakota 57701-3901, USA
| | - M D Hoff
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - E Holtom
- STFC Rutherford Appleton Laboratory (RAL), Didcot, OX11 0QX, United Kingdom
| | - J Y-K Hor
- University of Alabama, Department of Physics and Astronomy, Tuscaloosa, Alabama 34587-0324, USA
| | - M Horn
- South Dakota Science and Technology Authority (SDSTA), Sanford Underground Research Facility, Lead, South Dakota 57754-1700, USA
| | - D Q Huang
- University of Michigan, Randall Laboratory of Physics, Ann Arbor, Michigan 48109-1040, USA
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - D Hunt
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
| | - C M Ignarra
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - R G Jacobsen
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - O Jahangir
- University College London (UCL), Department of Physics and Astronomy, London WC1E 6BT, United Kingdom
| | - R S James
- University College London (UCL), Department of Physics and Astronomy, London WC1E 6BT, United Kingdom
| | - S N Jeffery
- STFC Rutherford Appleton Laboratory (RAL), Didcot, OX11 0QX, United Kingdom
| | - W Ji
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - J Johnson
- University of California, Davis, Department of Physics, Davis, California 95616-5270, USA
| | - A C Kaboth
- STFC Rutherford Appleton Laboratory (RAL), Didcot, OX11 0QX, United Kingdom
- Royal Holloway, University of London, Department of Physics, Egham, TW20 0EX, United Kingdom
| | - A C Kamaha
- University at Albany (SUNY), Department of Physics, Albany, New York 12222-0100, USA
- University of Califonia, Los Angeles, Department of Physics and Astronomy, Los Angeles, California 90095-1547
| | - K Kamdin
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - V Kasey
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - K Kazkaz
- Lawrence Livermore National Laboratory (LLNL), Livermore, California 94550-9698, USA
| | - J Keefner
- South Dakota Science and Technology Authority (SDSTA), Sanford Underground Research Facility, Lead, South Dakota 57754-1700, USA
| | - D Khaitan
- University of Rochester, Department of Physics and Astronomy, Rochester, New York 14627-0171, USA
| | - M Khaleeq
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - A Khazov
- STFC Rutherford Appleton Laboratory (RAL), Didcot, OX11 0QX, United Kingdom
| | - I Khurana
- University College London (UCL), Department of Physics and Astronomy, London WC1E 6BT, United Kingdom
| | - Y D Kim
- IBS Center for Underground Physics (CUP), Yuseong-gu, Daejeon, Korea
| | - C D Kocher
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - D Kodroff
- Pennsylvania State University, Department of Physics, University Park, Pennsylvania 16802-6300, USA
| | - L Korley
- University of Michigan, Randall Laboratory of Physics, Ann Arbor, Michigan 48109-1040, USA
- Brandeis University, Department of Physics, Waltham, Massachusetts 02453, USA
| | - E V Korolkova
- University of Sheffield, Department of Physics and Astronomy, Sheffield S3 7RH, United Kingdom
| | - J Kras
- University of Wisconsin-Madison, Department of Physics, Madison, Wisconsin 53706-1390, USA
| | - H Kraus
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
| | - S Kravitz
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - H J Krebs
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
| | - L Kreczko
- Brookhaven National Laboratory (BNL), Upton, New York 11973-5000, USA
| | - B Krikler
- Brookhaven National Laboratory (BNL), Upton, New York 11973-5000, USA
| | - V A Kudryavtsev
- University of Sheffield, Department of Physics and Astronomy, Sheffield S3 7RH, United Kingdom
| | - S Kyre
- University of California, Santa Barbara, Department of Physics, Santa Barbara, California 93106-9530, USA
| | - B Landerud
- University of Wisconsin-Madison, Department of Physics, Madison, Wisconsin 53706-1390, USA
| | - E A Leason
- University of Edinburgh, SUPA, School of Physics and Astronomy, Edinburgh EH9 3FD, United Kingdom
| | - C Lee
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - J Lee
- IBS Center for Underground Physics (CUP), Yuseong-gu, Daejeon, Korea
| | - D S Leonard
- IBS Center for Underground Physics (CUP), Yuseong-gu, Daejeon, Korea
| | - R Leonard
- South Dakota School of Mines and Technology, Rapid City, South Dakota 57701-3901, USA
| | - K T Lesko
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - C Levy
- University at Albany (SUNY), Department of Physics, Albany, New York 12222-0100, USA
| | - J Li
- IBS Center for Underground Physics (CUP), Yuseong-gu, Daejeon, Korea
| | - F-T Liao
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
| | - J Liao
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - J Lin
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - A Lindote
- Laboratório de Instrumentação e Física Experimental de Partículas (LIP), University of Coimbra, P-3004 516 Coimbra, Portugal
| | - R Linehan
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - W H Lippincott
- University of California, Santa Barbara, Department of Physics, Santa Barbara, California 93106-9530, USA
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510-5011, USA
| | - R Liu
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - X Liu
- University of Edinburgh, SUPA, School of Physics and Astronomy, Edinburgh EH9 3FD, United Kingdom
| | - Y Liu
- University of Wisconsin-Madison, Department of Physics, Madison, Wisconsin 53706-1390, USA
| | - C Loniewski
- University of Rochester, Department of Physics and Astronomy, Rochester, New York 14627-0171, USA
| | - M I Lopes
- Laboratório de Instrumentação e Física Experimental de Partículas (LIP), University of Coimbra, P-3004 516 Coimbra, Portugal
| | - E Lopez Asamar
- Laboratório de Instrumentação e Física Experimental de Partículas (LIP), University of Coimbra, P-3004 516 Coimbra, Portugal
| | - B López Paredes
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - W Lorenzon
- University of Michigan, Randall Laboratory of Physics, Ann Arbor, Michigan 48109-1040, USA
| | - D Lucero
- South Dakota Science and Technology Authority (SDSTA), Sanford Underground Research Facility, Lead, South Dakota 57754-1700, USA
| | - S Luitz
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
| | - J M Lyle
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - P A Majewski
- STFC Rutherford Appleton Laboratory (RAL), Didcot, OX11 0QX, United Kingdom
| | - J Makkinje
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - D C Malling
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - A Manalaysay
- University of California, Davis, Department of Physics, Davis, California 95616-5270, USA
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - L Manenti
- University College London (UCL), Department of Physics and Astronomy, London WC1E 6BT, United Kingdom
| | - R L Mannino
- University of Wisconsin-Madison, Department of Physics, Madison, Wisconsin 53706-1390, USA
| | - N Marangou
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - M F Marzioni
- University of Edinburgh, SUPA, School of Physics and Astronomy, Edinburgh EH9 3FD, United Kingdom
| | - C Maupin
- South Dakota Science and Technology Authority (SDSTA), Sanford Underground Research Facility, Lead, South Dakota 57754-1700, USA
| | - M E McCarthy
- University of Rochester, Department of Physics and Astronomy, Rochester, New York 14627-0171, USA
| | - C T McConnell
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - D N McKinsey
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - J McLaughlin
- Northwestern University, Department of Physics & Astronomy, Evanston, Illinois 60208-3112, USA
| | - Y Meng
- University of Alabama, Department of Physics and Astronomy, Tuscaloosa, Alabama 34587-0324, USA
| | - J Migneault
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - E H Miller
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
- South Dakota School of Mines and Technology, Rapid City, South Dakota 57701-3901, USA
| | - E Mizrachi
- University of Maryland, Department of Physics, College Park, Maryland 20742-4111, USA
- Lawrence Livermore National Laboratory (LLNL), Livermore, California 94550-9698, USA
| | - J A Mock
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University at Albany (SUNY), Department of Physics, Albany, New York 12222-0100, USA
| | - A Monte
- University of California, Santa Barbara, Department of Physics, Santa Barbara, California 93106-9530, USA
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510-5011, USA
| | - M E Monzani
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
- Vatican Observatory, Castel Gandolfo, V-00120, Vatican City State
| | - J A Morad
- University of California, Davis, Department of Physics, Davis, California 95616-5270, USA
| | - J D Morales Mendoza
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - E Morrison
- South Dakota School of Mines and Technology, Rapid City, South Dakota 57701-3901, USA
| | - B J Mount
- Black Hills State University, School of Natural Sciences, Spearfish, South Dakota 57799-0002, USA
| | - M Murdy
- University of Massachusetts, Department of Physics, Amherst, Massachusetts 01003-9337, USA
| | - A St J Murphy
- University of Edinburgh, SUPA, School of Physics and Astronomy, Edinburgh EH9 3FD, United Kingdom
| | - D Naim
- University of California, Davis, Department of Physics, Davis, California 95616-5270, USA
| | - A Naylor
- University of Sheffield, Department of Physics and Astronomy, Sheffield S3 7RH, United Kingdom
| | - C Nedlik
- University of Massachusetts, Department of Physics, Amherst, Massachusetts 01003-9337, USA
| | - C Nehrkorn
- University of California, Santa Barbara, Department of Physics, Santa Barbara, California 93106-9530, USA
| | - F Neves
- Laboratório de Instrumentação e Física Experimental de Partículas (LIP), University of Coimbra, P-3004 516 Coimbra, Portugal
| | - A Nguyen
- University of Edinburgh, SUPA, School of Physics and Astronomy, Edinburgh EH9 3FD, United Kingdom
| | - J A Nikoleyczik
- University of Wisconsin-Madison, Department of Physics, Madison, Wisconsin 53706-1390, USA
| | - A Nilima
- University of Edinburgh, SUPA, School of Physics and Astronomy, Edinburgh EH9 3FD, United Kingdom
| | - J O'Dell
- STFC Rutherford Appleton Laboratory (RAL), Didcot, OX11 0QX, United Kingdom
| | - F G O'Neill
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
| | - K O'Sullivan
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - I Olcina
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - M A Olevitch
- Washington University in St. Louis, Department of Physics, St. Louis, Missouri 63130-4862, USA
| | - K C Oliver-Mallory
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - J Orpwood
- University of Sheffield, Department of Physics and Astronomy, Sheffield S3 7RH, United Kingdom
| | - D Pagenkopf
- University of California, Santa Barbara, Department of Physics, Santa Barbara, California 93106-9530, USA
| | - S Pal
- Laboratório de Instrumentação e Física Experimental de Partículas (LIP), University of Coimbra, P-3004 516 Coimbra, Portugal
| | - K J Palladino
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
- University of Wisconsin-Madison, Department of Physics, Madison, Wisconsin 53706-1390, USA
| | - J Palmer
- Royal Holloway, University of London, Department of Physics, Egham, TW20 0EX, United Kingdom
| | - M Pangilinan
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - N Parveen
- University at Albany (SUNY), Department of Physics, Albany, New York 12222-0100, USA
| | - S J Patton
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - E K Pease
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - B Penning
- University of Michigan, Randall Laboratory of Physics, Ann Arbor, Michigan 48109-1040, USA
- Brandeis University, Department of Physics, Waltham, Massachusetts 02453, USA
| | - C Pereira
- Laboratório de Instrumentação e Física Experimental de Partículas (LIP), University of Coimbra, P-3004 516 Coimbra, Portugal
| | - G Pereira
- Laboratório de Instrumentação e Física Experimental de Partículas (LIP), University of Coimbra, P-3004 516 Coimbra, Portugal
| | - E Perry
- University College London (UCL), Department of Physics and Astronomy, London WC1E 6BT, United Kingdom
| | - T Pershing
- Lawrence Livermore National Laboratory (LLNL), Livermore, California 94550-9698, USA
| | - I B Peterson
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - A Piepke
- University of Alabama, Department of Physics and Astronomy, Tuscaloosa, Alabama 34587-0324, USA
| | - J Podczerwinski
- University of Wisconsin-Madison, Department of Physics, Madison, Wisconsin 53706-1390, USA
| | - D Porzio
- Laboratório de Instrumentação e Física Experimental de Partículas (LIP), University of Coimbra, P-3004 516 Coimbra, Portugal
| | - S Powell
- University of Liverpool, Department of Physics, Liverpool L69 7ZE, United Kingdom
| | - R M Preece
- STFC Rutherford Appleton Laboratory (RAL), Didcot, OX11 0QX, United Kingdom
| | - K Pushkin
- University of Michigan, Randall Laboratory of Physics, Ann Arbor, Michigan 48109-1040, USA
| | - Y Qie
- University of Rochester, Department of Physics and Astronomy, Rochester, New York 14627-0171, USA
| | - B N Ratcliff
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
| | - J Reichenbacher
- South Dakota School of Mines and Technology, Rapid City, South Dakota 57701-3901, USA
| | - L Reichhart
- University College London (UCL), Department of Physics and Astronomy, London WC1E 6BT, United Kingdom
| | - C A Rhyne
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - A Richards
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - Q Riffard
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - G R C Rischbieter
- University at Albany (SUNY), Department of Physics, Albany, New York 12222-0100, USA
| | - J P Rodrigues
- Laboratório de Instrumentação e Física Experimental de Partículas (LIP), University of Coimbra, P-3004 516 Coimbra, Portugal
| | - A Rodriguez
- Black Hills State University, School of Natural Sciences, Spearfish, South Dakota 57799-0002, USA
| | - H J Rose
- University of Liverpool, Department of Physics, Liverpool L69 7ZE, United Kingdom
| | - R Rosero
- Brookhaven National Laboratory (BNL), Upton, New York 11973-5000, USA
| | - P Rossiter
- University of Sheffield, Department of Physics and Astronomy, Sheffield S3 7RH, United Kingdom
| | - T Rushton
- University of Sheffield, Department of Physics and Astronomy, Sheffield S3 7RH, United Kingdom
| | - G Rutherford
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - D Rynders
- South Dakota Science and Technology Authority (SDSTA), Sanford Underground Research Facility, Lead, South Dakota 57754-1700, USA
| | - J S Saba
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - D Santone
- Royal Holloway, University of London, Department of Physics, Egham, TW20 0EX, United Kingdom
| | - A B M R Sazzad
- University of Alabama, Department of Physics and Astronomy, Tuscaloosa, Alabama 34587-0324, USA
| | - R W Schnee
- South Dakota School of Mines and Technology, Rapid City, South Dakota 57701-3901, USA
| | - P R Scovell
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
- STFC Rutherford Appleton Laboratory (RAL), Didcot, OX11 0QX, United Kingdom
| | - D Seymour
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - S Shaw
- University of California, Santa Barbara, Department of Physics, Santa Barbara, California 93106-9530, USA
| | - T Shutt
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - J J Silk
- University of Maryland, Department of Physics, College Park, Maryland 20742-4111, USA
| | - C Silva
- Laboratório de Instrumentação e Física Experimental de Partículas (LIP), University of Coimbra, P-3004 516 Coimbra, Portugal
| | - G Sinev
- South Dakota School of Mines and Technology, Rapid City, South Dakota 57701-3901, USA
| | - K Skarpaas
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
| | - W Skulski
- University of Rochester, Department of Physics and Astronomy, Rochester, New York 14627-0171, USA
| | - R Smith
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - M Solmaz
- University of California, Santa Barbara, Department of Physics, Santa Barbara, California 93106-9530, USA
| | - V N Solovov
- Laboratório de Instrumentação e Física Experimental de Partículas (LIP), University of Coimbra, P-3004 516 Coimbra, Portugal
| | - P Sorensen
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - J Soria
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - I Stancu
- University of Alabama, Department of Physics and Astronomy, Tuscaloosa, Alabama 34587-0324, USA
| | - M R Stark
- South Dakota School of Mines and Technology, Rapid City, South Dakota 57701-3901, USA
| | - A Stevens
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
- University College London (UCL), Department of Physics and Astronomy, London WC1E 6BT, United Kingdom
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - T M Stiegler
- Texas A&M University, Department of Physics and Astronomy, College Station, Texas 77843-4242, USA
| | - K Stifter
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510-5011, USA
| | - R Studley
- Brandeis University, Department of Physics, Waltham, Massachusetts 02453, USA
| | - B Suerfu
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - T J Sumner
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - P Sutcliffe
- University of Liverpool, Department of Physics, Liverpool L69 7ZE, United Kingdom
| | - N Swanson
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - M Szydagis
- University at Albany (SUNY), Department of Physics, Albany, New York 12222-0100, USA
| | - M Tan
- University of Oxford, Department of Physics, Oxford OX1 3RH, United Kingdom
| | - D J Taylor
- South Dakota Science and Technology Authority (SDSTA), Sanford Underground Research Facility, Lead, South Dakota 57754-1700, USA
| | - R Taylor
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - W C Taylor
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - D J Temples
- Northwestern University, Department of Physics & Astronomy, Evanston, Illinois 60208-3112, USA
| | - B P Tennyson
- Yale University, Department of Physics, New Haven, Connecticut 06511-8499, USA
| | - P A Terman
- Texas A&M University, Department of Physics and Astronomy, College Station, Texas 77843-4242, USA
| | - K J Thomas
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - D R Tiedt
- University of Maryland, Department of Physics, College Park, Maryland 20742-4111, USA
- South Dakota Science and Technology Authority (SDSTA), Sanford Underground Research Facility, Lead, South Dakota 57754-1700, USA
- South Dakota School of Mines and Technology, Rapid City, South Dakota 57701-3901, USA
| | - M Timalsina
- South Dakota School of Mines and Technology, Rapid City, South Dakota 57701-3901, USA
| | - W H To
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - A Tomás
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - Z Tong
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - D R Tovey
- University of Sheffield, Department of Physics and Astronomy, Sheffield S3 7RH, United Kingdom
| | - J Tranter
- University of Sheffield, Department of Physics and Astronomy, Sheffield S3 7RH, United Kingdom
| | - M Trask
- University of California, Santa Barbara, Department of Physics, Santa Barbara, California 93106-9530, USA
| | - M Tripathi
- University of California, Davis, Department of Physics, Davis, California 95616-5270, USA
| | - D R Tronstad
- South Dakota School of Mines and Technology, Rapid City, South Dakota 57701-3901, USA
| | - C E Tull
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - W Turner
- University of Liverpool, Department of Physics, Liverpool L69 7ZE, United Kingdom
| | - L Tvrznikova
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
- Yale University, Department of Physics, New Haven, Connecticut 06511-8499, USA
- Lawrence Livermore National Laboratory (LLNL), Livermore, California 94550-9698, USA
| | - U Utku
- University College London (UCL), Department of Physics and Astronomy, London WC1E 6BT, United Kingdom
| | - J Va'vra
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
| | - A Vacheret
- Imperial College London, Physics Department, Blackett Laboratory, London SW7 2AZ, United Kingdom
| | - A C Vaitkus
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - J R Verbus
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - E Voirin
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510-5011, USA
| | - W L Waldron
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - A Wang
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - B Wang
- University of Alabama, Department of Physics and Astronomy, Tuscaloosa, Alabama 34587-0324, USA
| | - J J Wang
- University of Alabama, Department of Physics and Astronomy, Tuscaloosa, Alabama 34587-0324, USA
| | - W Wang
- University of Wisconsin-Madison, Department of Physics, Madison, Wisconsin 53706-1390, USA
- University of Massachusetts, Department of Physics, Amherst, Massachusetts 01003-9337, USA
| | - Y Wang
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - J R Watson
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - R C Webb
- Texas A&M University, Department of Physics and Astronomy, College Station, Texas 77843-4242, USA
| | - A White
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - D T White
- University of California, Santa Barbara, Department of Physics, Santa Barbara, California 93106-9530, USA
| | - J T White
- Texas A&M University, Department of Physics and Astronomy, College Station, Texas 77843-4242, USA
| | - R G White
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California 94305-4085 USA
| | - T J Whitis
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
- University of California, Santa Barbara, Department of Physics, Santa Barbara, California 93106-9530, USA
| | - M Williams
- University of Michigan, Randall Laboratory of Physics, Ann Arbor, Michigan 48109-1040, USA
- Brandeis University, Department of Physics, Waltham, Massachusetts 02453, USA
| | - W J Wisniewski
- SLAC National Accelerator Laboratory, Menlo Park, California 94025-7015, USA
| | - M S Witherell
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
- University of California, Berkeley, Department of Physics, Berkeley, California 94720-7300, USA
| | - F L H Wolfs
- University of Rochester, Department of Physics and Astronomy, Rochester, New York 14627-0171, USA
| | - J D Wolfs
- University of Rochester, Department of Physics and Astronomy, Rochester, New York 14627-0171, USA
| | - S Woodford
- University of Liverpool, Department of Physics, Liverpool L69 7ZE, United Kingdom
| | - D Woodward
- Pennsylvania State University, Department of Physics, University Park, Pennsylvania 16802-6300, USA
| | - S D Worm
- STFC Rutherford Appleton Laboratory (RAL), Didcot, OX11 0QX, United Kingdom
| | - C J Wright
- Brookhaven National Laboratory (BNL), Upton, New York 11973-5000, USA
| | - Q Xia
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, California 94720-8099, USA
| | - X Xiang
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
- Brookhaven National Laboratory (BNL), Upton, New York 11973-5000, USA
| | - Q Xiao
- University of Wisconsin-Madison, Department of Physics, Madison, Wisconsin 53706-1390, USA
| | - J Xu
- Lawrence Livermore National Laboratory (LLNL), Livermore, California 94550-9698, USA
| | - M Yeh
- Brookhaven National Laboratory (BNL), Upton, New York 11973-5000, USA
| | - J Yin
- University of Rochester, Department of Physics and Astronomy, Rochester, New York 14627-0171, USA
| | - I Young
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510-5011, USA
| | - P Zarzhitsky
- University of Alabama, Department of Physics and Astronomy, Tuscaloosa, Alabama 34587-0324, USA
| | - A Zuckerman
- Brown University, Department of Physics, Providence, Rhode Island 02912-9037, USA
| | - E A Zweig
- University of Califonia, Los Angeles, Department of Physics and Astronomy, Los Angeles, California 90095-1547
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15
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Clark RA, Weerasuriya CK, Portnoy A, Mukandavire C, Quaife M, Bakker R, Scarponi D, Harris RC, Rade K, Mattoo SK, Tumu D, Menzies NA, White RG. New tuberculosis vaccines in India: Modelling the potential health and economic impacts of adolescent/adult vaccination with M72/AS01 E and BCG-revaccination. medRxiv 2023:2023.02.24.23286406. [PMID: 36865172 PMCID: PMC9980245 DOI: 10.1101/2023.02.24.23286406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Background India had an estimated 2.9 million tuberculosis cases and 506 thousand deaths in 2021. Novel vaccines effective in adolescents and adults could reduce this burden. M72/AS01E and BCG-revaccination have recently completed Phase IIb trials and estimates of their population-level impact are needed. We estimated the potential health and economic impact of M72/AS01E and BCG-revaccination in India and investigated the impact of variation in vaccine characteristics and delivery strategies. Methods We developed an age-stratified compartmental tuberculosis transmission model for India calibrated to country-specific epidemiology. We projected baseline epidemiology to 2050 assuming no-new-vaccine introduction, and M72/AS01E and BCG-revaccination scenarios over 2025-2050 exploring uncertainty in product characteristics (vaccine efficacy, mechanism of effect, infection status required for vaccine efficacy, duration of protection) and implementation (achieved vaccine coverage and ages targeted). We estimated reductions in tuberculosis cases and deaths by each scenario compared to no-new-vaccine introduction, as well as costs and cost-effectiveness from health-system and societal perspectives. Results M72/AS01E scenarios were predicted to avert 40% more tuberculosis cases and deaths by 2050 compared to BCG-revaccination scenarios. Cost-effectiveness ratios for M72/AS01E vaccines were around seven times higher than BCG-revaccination, but nearly all scenarios were cost-effective. The estimated average incremental cost was US$190 million for M72/AS01E and US$23 million for BCG-revaccination per year. Sources of uncertainty included whether M72/AS01E was efficacious in uninfected individuals at vaccination, and if BCG-revaccination could prevent disease. Conclusions M72/AS01E and BCG-revaccination could be impactful and cost-effective in India. However, there is great uncertainty in impact, especially given unknowns surrounding mechanism of effect and infection status required for vaccine efficacy. Greater investment in vaccine development and delivery is needed to resolve these unknowns in vaccine product characteristics.
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Affiliation(s)
- Rebecca A Clark
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine
- Vaccine Centre, London School of Hygiene and Tropical Medicine
| | - Chathika K Weerasuriya
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine
| | - Allison Portnoy
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, USA
| | - Christinah Mukandavire
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine
| | - Matthew Quaife
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine
| | - Roel Bakker
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine
- KNCV Tuberculosis Foundation
| | - Danny Scarponi
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine
| | - Rebecca C Harris
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine
- Sanofi Pasteur, Singapore
| | | | | | - Dheeraj Tumu
- World Health Organization, India
- Central TB Division, NTEP, MoHFW Govt of India. New Delhi, India
| | - Nicolas A Menzies
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, USA
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health
| | - Richard G White
- TB Modelling Group and TB Centre, London School of Hygiene and Tropical Medicine
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine
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16
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Portnoy A, Arcand JL, Clark RA, Weerasuriya CK, Mukandavire C, Bakker R, Patouillard E, Gebreselassie N, Zignol M, Jit M, White RG, Menzies NA. The potential impact of novel tuberculosis vaccine introduction on economic growth in low- and middle-income countries: A modeling study. PLoS Med 2023; 20:e1004252. [PMID: 37432972 PMCID: PMC10335702 DOI: 10.1371/journal.pmed.1004252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 05/30/2023] [Indexed: 07/13/2023] Open
Abstract
BACKGROUND Most individuals developing tuberculosis (TB) are working age adults living in low- and middle-income countries (LMICs). The resulting disability and death impact economic productivity and burden health systems. New TB vaccine products may reduce this burden. In this study, we estimated the impact of introducing novel TB vaccines on gross domestic product (GDP) growth in 105 LMICs. METHODS AND FINDINGS We adapted an existing macroeconomic model to simulate country-level GDP trends between 2020 and 2080, comparing scenarios for introduction of hypothetical infant and adolescent/adult vaccines to a no-new-vaccine counterfactual. We parameterized each scenario using estimates of TB-related mortality, morbidity, and healthcare spending from linked epidemiological and costing models. We assumed vaccines would be introduced between 2028 and 2047 and estimated incremental changes in GDP within each country from introduction to 2080, in 2020 US dollars. We tested the robustness of results to alternative analytic specifications. Both vaccine scenarios produced greater cumulative GDP in the modeled countries over the study period, equivalent to $1.6 (95% uncertainty interval: $0.8, 3.0) trillion for the adolescent/adult vaccine and $0.2 ($0.1, 0.4) trillion for the infant vaccine. These GDP gains were substantially lagged relative to the time of vaccine introduction, particularly for the infant vaccine. GDP gains resulting from vaccine introduction were concentrated in countries with higher current TB incidence and earlier vaccine introduction. Results were sensitive to secular trends in GDP growth but relatively robust to other analytic assumptions. Uncertain projections of GDP could alter these projections and affect the conclusions drawn by this analysis. CONCLUSIONS Under a range of assumptions, introducing novel TB vaccines would increase economic growth in LMICs.
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Affiliation(s)
- Allison Portnoy
- Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, United States of America
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Jean-Louis Arcand
- Department of International Economics, The Graduate Institute of International and Development Studies, Geneva, Switzerland
- Fondation pour les études et recherches sur le développement international (FERDI), Clermont-Ferrand, France
- Global Development Network, New Delhi, India
- Université Mohammed VI Polytechnique, Rabat, Morocco
| | - Rebecca A. Clark
- TB Modelling Group, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Chathika K. Weerasuriya
- TB Modelling Group, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | - Roel Bakker
- TB Modelling Group, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- KNCV Tuberculosis Foundation, The Hague, the Netherlands
| | - Edith Patouillard
- Department of Health Systems Governance and Financing, World Health Organization, Geneva, Switzerland
| | | | - Matteo Zignol
- Global TB Programme, World Health Organization, Geneva, Switzerland
| | - Mark Jit
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- School of Public Health, University of Hong Kong, Hong Kong SAR, China
| | - Richard G. White
- TB Modelling Group, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Nicolas A. Menzies
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
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17
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Portnoy A, Clark RA, Weerasuriya CK, Mukandavire C, Quaife M, Bakker R, Garcia Baena I, Gebreselassie N, Zignol M, Jit M, White RG, Menzies NA. The potential impact of novel tuberculosis vaccines on health equity and financial protection in low-income and middle-income countries. BMJ Glob Health 2023; 8:e012466. [PMID: 37438049 PMCID: PMC10347450 DOI: 10.1136/bmjgh-2023-012466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 06/10/2023] [Indexed: 07/14/2023] Open
Abstract
INTRODUCTION One in two patients developing tuberculosis (TB) in low-income and middle-income countries (LMICs) faces catastrophic household costs. We assessed the potential financial risk protection from introducing novel TB vaccines, and how health and economic benefits would be distributed across income quintiles. METHODS We modelled the impact of introducing TB vaccines meeting the World Health Organization preferred product characteristics in 105 LMICs. For each country, we assessed the distribution of health gains, patient costs and household financial vulnerability following introduction of an infant vaccine and separately for an adolescent/adult vaccine, compared with a 'no-new-vaccine' counterfactual. Patient-incurred direct and indirect costs of TB disease exceeding 20% of annual household income were defined as catastrophic. RESULTS Over 2028-2050, the health gains resulting from vaccine introduction were greatest in lower income quintiles, with the poorest 2 quintiles in each country accounting for 56% of total LMIC TB cases averted. Over this period, the infant vaccine was estimated to avert US$5.9 (95% uncertainty interval: US$5.3-6.5) billion in patient-incurred total costs, and the adolescent/adult vaccine was estimated to avert US$38.9 (US$36.6-41.5) billion. Additionally, 3.7 (3.3-4.1) million fewer households were projected to face catastrophic costs with the infant vaccine and 22.9 (21.4-24.5) million with the adolescent/adult vaccine, with 66% of gains accruing in the poorest 2 income quintiles. CONCLUSION Under a range of assumptions, introducing novel TB vaccines would reduce income-based inequalities in the health and household economic outcomes of TB in LMICs.
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Affiliation(s)
- Allison Portnoy
- Department of Global Health, Boston University School of Public Health, Boston, MA, USA
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Rebecca A Clark
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
- TB Modelling Group, London School of Hygiene & Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropica Medicine, London, UK
| | - Chathika K Weerasuriya
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
- TB Modelling Group, London School of Hygiene & Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropica Medicine, London, UK
| | | | - Matthew Quaife
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
- TB Modelling Group, London School of Hygiene & Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropica Medicine, London, UK
| | - Roel Bakker
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
- TB Modelling Group, London School of Hygiene & Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropica Medicine, London, UK
- KNCV Tuberculosis Foundation, Den Haag, The Netherlands
| | - Inés Garcia Baena
- Global Tuberculosis Programme, World Health Organization, Geneva, Switzerland
| | | | - Matteo Zignol
- Global Tuberculosis Programme, World Health Organization, Geneva, Switzerland
| | - Mark Jit
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropica Medicine, London, UK
- School of Public Health, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Richard G White
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
- TB Modelling Group, London School of Hygiene & Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropica Medicine, London, UK
| | - Nicolas A Menzies
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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18
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Scarponi D, Iskauskas A, Clark RA, Vernon I, McKinley TJ, Goldstein M, Mukandavire C, Deol A, Weerasuriya C, Bakker R, White RG, McCreesh N. Demonstrating multi-country calibration of a tuberculosis model using new history matching and emulation package - hmer. Epidemics 2023; 43:100678. [PMID: 36913805 DOI: 10.1016/j.epidem.2023.100678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 02/23/2023] [Accepted: 03/06/2023] [Indexed: 03/09/2023] Open
Abstract
Infectious disease models are widely used by epidemiologists to improve the understanding of transmission dynamics and disease natural history, and to predict the possible effects of interventions. As the complexity of such models increases, however, it becomes increasingly challenging to robustly calibrate them to empirical data. History matching with emulation is a calibration method that has been successfully applied to such models, but has not been widely used in epidemiology partly due to the lack of available software. To address this issue, we developed a new, user-friendly R package hmer to simply and efficiently perform history matching with emulation. In this paper, we demonstrate the first use of hmer for calibrating a complex deterministic model for the country-level implementation of tuberculosis vaccines to 115 low- and middle-income countries. The model was fit to 9-13 target measures, by varying 19-22 input parameters. Overall, 105 countries were successfully calibrated. Among the remaining countries, hmer visualisation tools, combined with derivative emulation methods, provided strong evidence that the models were misspecified and could not be calibrated to the target ranges. This work shows that hmer can be used to simply and rapidly calibrate a complex model to data from over 100 countries, making it a useful addition to the epidemiologist's calibration tool-kit.
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Affiliation(s)
- Danny Scarponi
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK.
| | | | - Rebecca A Clark
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - Ian Vernon
- Department of Mathematical Sciences, Durham University, UK
| | | | | | - Christinah Mukandavire
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - Arminder Deol
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - Chathika Weerasuriya
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - Roel Bakker
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - Richard G White
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - Nicky McCreesh
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
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19
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Scarponi D, Clark RA, Weerasuriya C, Emery JC, Houben RM, White RG, McCreesh N. Is neglect of self-clearance biassing TB vaccine impact estimates? medRxiv 2023:2023.04.11.23288400. [PMID: 37090535 PMCID: PMC10120796 DOI: 10.1101/2023.04.11.23288400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Background Mathematical modelling has been used extensively to estimate the potential impact of new tuberculosis vaccines, with the majority of existing models assuming that individuals with Mycobacterium tuberculosis (Mtb) infection remain at lifelong risk of tuberculosis disease. Recent research provides evidence that self-clearance of Mtb infection may be common, which may affect the potential impact of new vaccines that only take in infected or uninfected individuals. We explored how the inclusion of self-clearance in models of tuberculosis affects the estimates of vaccine impact in China and India. Methods For both countries, we calibrated a tuberculosis model to a scenario without self-clearance and to various scenarios with self-clearance. To account for the current uncertainty in self-clearance properties, we varied the rate of self-clearance, and the level of protection against reinfection in self-cleared individuals. We introduced potential new vaccines in 2025, exploring vaccines that work in uninfected or infected individuals only, or that are effective regardless of infection status, and modelling scenarios with different levels of vaccine efficacy in self-cleared individuals. We then estimated the relative incidence reduction in 2050 for each vaccine compared to the no vaccination scenario. Findings The inclusion of self-clearance increased the estimated relative reductions in incidence in 2050 for vaccines effective only in uninfected individuals, by a maximum of 12% in China and 8% in India. The inclusion of self-clearance increased the estimated impact of vaccines only effective in infected individuals in some scenarios and decreased it in others, by a maximum of 14% in China and 15% in India. As would be expected, the inclusion of self-clearance had minimal impact on estimated reductions in incidence for vaccines that work regardless of infection status. Interpretations Our work suggests that the neglect of self-clearance in mathematical models of tuberculosis vaccines does not result in substantially biased estimates of tuberculosis vaccine impact. It may, however, mean that we are slightly underestimating the relative advantages of vaccines that work in uninfected individuals only compared to those that work in infected individuals.
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Affiliation(s)
- Danny Scarponi
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine
| | - Rebecca A Clark
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine
| | - Chathika Weerasuriya
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine
| | - Jon C Emery
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine
| | - Rein Mgj Houben
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine
| | - Richard G White
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine
| | - Nicky McCreesh
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine
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20
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Clark RA, Mukandavire C, Portnoy A, Weerasuriya CK, Deol A, Scarponi D, Iskauskas A, Bakker R, Quaife M, Malhotra S, Gebreselassie N, Zignol M, Hutubessy RCW, Giersing B, Jit M, Harris RC, Menzies NA, White RG. The impact of alternative delivery strategies for novel tuberculosis vaccines in low-income and middle-income countries: a modelling study. Lancet Glob Health 2023; 11:e546-e555. [PMID: 36925175 PMCID: PMC10030455 DOI: 10.1016/s2214-109x(23)00045-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 11/03/2022] [Accepted: 01/13/2023] [Indexed: 03/15/2023]
Abstract
BACKGROUND Tuberculosis is a leading infectious cause of death worldwide. Novel vaccines will be required to reach global targets and reverse setbacks resulting from the COVID-19 pandemic. We estimated the impact of novel tuberculosis vaccines in low-income and middle-income countries (LMICs) in several delivery scenarios. METHODS We calibrated a tuberculosis model to 105 LMICs (accounting for 93% of global incidence). Vaccine scenarios were implemented as the base-case (routine vaccination of those aged 9 years and one-off vaccination for those aged 10 years and older, with country-specific introduction between 2028 and 2047, and 5-year scale-up to target coverage); accelerated scale-up similar to the base-case, but with all countries introducing vaccines in 2025, with instant scale-up; and routine-only (similar to the base-case, but including routine vaccination only). Vaccines were assumed to protect against disease for 10 years, with 50% efficacy. FINDINGS The base-case scenario would prevent 44·0 million (95% uncertainty range 37·2-51·6) tuberculosis cases and 5·0 million (4·6-5·4) tuberculosis deaths before 2050, compared with equivalent estimates of cases and deaths that would be predicted to occur before 2050 with no new vaccine introduction (the baseline scenario). The accelerated scale-up scenario would prevent 65·5 million (55·6-76·0) cases and 7·9 million (7·3-8·5) deaths before 2050, relative to baseline. The routine-only scenario would prevent 8·8 million (95% uncertainty range 7·6-10·1) cases and 1·1 million (0·9-1·2) deaths before 2050, relative to baseline. INTERPRETATION Our results suggest novel tuberculosis vaccines could have substantial impact, which will vary depending on delivery strategy. Including a one-off vaccination campaign will be crucial for rapid impact. Accelerated introduction-at a pace similar to that seen for COVID-19 vaccines-would increase the number of lives saved before 2050 by around 60%. Investment is required to support vaccine development, manufacturing, prompt introduction, and scale-up. FUNDING WHO (2020/985800-0). TRANSLATIONS For the French, Spanish, Italian and Dutch translations of the abstract see Supplementary Materials section.
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Affiliation(s)
- Rebecca A Clark
- TB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; Vaccine Centre, London School of Hygiene & Tropical Medicine, London, UK.
| | - Christinah Mukandavire
- TB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Allison Portnoy
- Center for Health Decision Science, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Chathika K Weerasuriya
- TB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Arminder Deol
- TB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Danny Scarponi
- TB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Andrew Iskauskas
- Department of Mathematical Sciences, Durham University, Durham, UK
| | - Roel Bakker
- TB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; KNCV Tuberculosis Foundation, The Hague, Netherlands
| | - Matthew Quaife
- TB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | | | | | - Matteo Zignol
- Global TB Programme, World Health Organization, Geneva, Switzerland
| | - Raymond C W Hutubessy
- Department of Immunization, Vaccines, and Biologicals, World Health Organization, Geneva, Switzerland
| | - Birgitte Giersing
- The Initiative for Vaccine Research, World Health Organization, Geneva, Switzerland
| | - Mark Jit
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Rebecca C Harris
- TB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; Global Medical Evidence Generation for Influenza Vaccines, Sanofi Pasteur, Singapore
| | - Nicolas A Menzies
- Center for Health Decision Science, Harvard TH Chan School of Public Health, Boston, MA, USA; Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Richard G White
- TB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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21
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Rangaka MX, Frick M, Churchyard G, García-Basteiro AL, Hatherill M, Hanekom W, Hill PC, Hamada Y, Quaife M, Vekemans J, White RG, Cobelens F. Clinical trials of tuberculosis vaccines in the era of increased access to preventive antibiotic treatment. Lancet Respir Med 2023; 11:380-390. [PMID: 36966794 DOI: 10.1016/s2213-2600(23)00084-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/06/2023] [Accepted: 02/28/2023] [Indexed: 03/30/2023]
Abstract
Approximately 10·6 million people worldwide develop tuberculosis each year, representing a failure in epidemic control that is accentuated by the absence of effective vaccines to prevent infection or disease in adolescents and adults. Without effective vaccines, tuberculosis prevention has relied on testing for Mycobacterium tuberculosis infection and treating with antibiotics to prevent progression to tuberculosis disease, known as tuberculosis preventive treatment (TPT). Novel tuberculosis vaccines are in development and phase 3 efficacy trials are imminent. The development of effective, shorter, and safer TPT regimens has broadened the groups eligible for TPT beyond people with HIV and child contacts of people with tuberculosis; future vaccine trials will be undertaken in an era of increased TPT access. Changes in the prevention standard will have implications for tuberculosis vaccine trials of disease prevention, for which safety and sufficient accrual of cases are crucial. In this paper, we examine the urgent need for trials that allow the evaluation of new vaccines and fulfil the ethical duty of researchers to provide TPT. We observe how HIV vaccine trials have incorporated preventive treatment in the form of pre-exposure prophylaxis, propose trial designs that integrate TPT, and summarise considerations for each design in terms of trial validity, efficiency, participant safety, and ethics.
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Affiliation(s)
- Molebogeng X Rangaka
- Institute for Global Health and MRC Clinical Trials Unit, University College London, London, UK; Wellcome Centre for Infectious Diseases Research in Africa (CIDRI-Africa), Institute of Infectious Disease and Molecular Medicine, and School of Public Health, University of Cape Town, Cape Town, South Africa; Aurum Institute, Parktown, South Africa.
| | - Mike Frick
- Treatment Action Group, New York, NY, USA
| | - Gavin Churchyard
- Aurum Institute, Parktown, South Africa; School of Public Health, University of Witwatersrand, Johannesburg, South Africa; Department of Medicine, Vanderbilt University, Nashville, TN, USA
| | - Alberto L García-Basteiro
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique; ISGlobal, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Infecciosas, Barcelona, Spain
| | - Mark Hatherill
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, and Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Willem Hanekom
- Africa Health Research Institute, Durban, South Africa; Division of Infection and Immunity, University College London, London, UK
| | - Philip C Hill
- Centre for International Health, University of Otago Medical School, Dunedin, New Zealand
| | - Yohhei Hamada
- Institute for Global Health and MRC Clinical Trials Unit, University College London, London, UK
| | - Matthew Quaife
- TB Centre, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Richard G White
- TB Centre, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Frank Cobelens
- Department of Global Health and Amsterdam Institute for Global Health and Development, Amsterdam University Medical Centres Location University of Amsterdam, Netherlands
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22
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Bozzani FM, McCreesh N, Diaconu K, Govender I, White RG, Kielmann K, Grant AD, Vassall A. Cost-effectiveness of tuberculosis infection prevention and control interventions in South African clinics: a model-based economic evaluation informed by complexity science methods. BMJ Glob Health 2023; 8:e010306. [PMID: 36792227 PMCID: PMC9933667 DOI: 10.1136/bmjgh-2022-010306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 12/16/2022] [Indexed: 02/17/2023] Open
Abstract
INTRODUCTION Nosocomial Mycobacterium tuberculosis (Mtb) transmission substantially impacts health workers, patients and communities. Guidelines for tuberculosis infection prevention and control (TB IPC) exist but implementation in many settings remains suboptimal. Evidence is needed on cost-effective investments to prevent Mtb transmission that are feasible in routine clinic environments. METHODS A set of TB IPC interventions was codesigned with local stakeholders using system dynamics modelling techniques that addressed both core activities and enabling actions to support implementation. An economic evaluation of these interventions was conducted at two clinics in KwaZulu-Natal, employing agent-based models of Mtb transmission within the clinics and in their catchment populations. Intervention costs included the costs of the enablers (eg, strengthened supervision, community sensitisation) identified by stakeholders to ensure uptake and adherence. RESULTS All intervention scenarios modelled, inclusive of the relevant enablers, cost less than US$200 per disability-adjusted life-year (DALY) averted and were very cost-effective in comparison to South Africa's opportunity cost-based threshold (US$3200 per DALY averted). Two interventions, building modifications to improve ventilation and maximising use of the existing Central Chronic Medicines Dispensing and Distribution system to reduce the number of clinic attendees, were found to be cost saving over the 10-year model time horizon. Incremental cost-effectiveness ratios were sensitive to assumptions on baseline clinic ventilation rates, the prevalence of infectious TB in clinic attendees and future HIV incidence but remained highly cost-effective under all uncertainty analysis scenarios. CONCLUSION TB IPC interventions in clinics, including the enabling actions to ensure their feasibility, afford very good value for money and should be prioritised for implementation within the South African health system.
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Affiliation(s)
- Fiammetta Maria Bozzani
- Department of Global Health and Development, London School of Hygiene & Tropical Medicine, London, UK
| | - Nicky McCreesh
- TB Centre, London School of Hygiene & Tropical Medicine, London, UK
| | - Karin Diaconu
- Institute of Global Health and Development, Queen Margaret University Edinburgh, Musselburgh, UK
| | - Indira Govender
- TB Centre, London School of Hygiene & Tropical Medicine, London, UK
- Africa Health Research Institute, School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, Kwa-Zulu Natal, South Africa
| | - Richard G White
- TB Centre, London School of Hygiene & Tropical Medicine, London, UK
| | - Karina Kielmann
- Institute of Global Health and Development, Queen Margaret University Edinburgh, Musselburgh, UK
- Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium
| | - Alison D Grant
- TB Centre, London School of Hygiene & Tropical Medicine, London, UK
- Africa Health Research Institute, School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, Kwa-Zulu Natal, South Africa
| | - Anna Vassall
- Department of Global Health and Development, London School of Hygiene & Tropical Medicine, London, UK
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23
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Quaife M, Medley GF, Jit M, Drake T, Asaria M, van Baal P, Baltussen R, Bollinger L, Bozzani F, Brady O, Broekhuizen H, Chalkidou K, Chi YL, Dowdy DW, Griffin S, Haghparast-Bidgoli H, Hallett T, Hauck K, Hollingsworth TD, McQuaid CF, Menzies NA, Merritt MW, Mirelman A, Morton A, Ruiz FJ, Siapka M, Skordis J, Tediosi F, Walker P, White RG, Winskill P, Vassall A, Gomez GB. Considering equity in priority setting using transmission models: Recommendations and data needs. Epidemics 2022; 41:100648. [PMID: 36343495 PMCID: PMC9623400 DOI: 10.1016/j.epidem.2022.100648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/20/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVES Disease transmission models are used in impact assessment and economic evaluations of infectious disease prevention and treatment strategies, prominently so in the COVID-19 response. These models rarely consider dimensions of equity relating to the differential health burden between individuals and groups. We describe concepts and approaches which are useful when considering equity in the priority setting process, and outline the technical choices concerning model structure, outputs, and data requirements needed to use transmission models in analyses of health equity. METHODS We reviewed the literature on equity concepts and approaches to their application in economic evaluation and undertook a technical consultation on how equity can be incorporated in priority setting for infectious disease control. The technical consultation brought together health economists with an interest in equity-informative economic evaluation, ethicists specialising in public health, mathematical modellers from various disease backgrounds, and representatives of global health funding and technical assistance organisations, to formulate key areas of consensus and recommendations. RESULTS We provide a series of recommendations for applying the Reference Case for Economic Evaluation in Global Health to infectious disease interventions, comprising guidance on 1) the specification of equity concepts; 2) choice of evaluation framework; 3) model structure; and 4) data needs. We present available conceptual and analytical choices, for example how correlation between different equity- and disease-relevant strata should be considered dependent on available data, and outline how assumptions and data limitations can be reported transparently by noting key factors for consideration. CONCLUSIONS Current developments in economic evaluations in global health provide a wide range of methodologies to incorporate equity into economic evaluations. Those employing infectious disease models need to use these frameworks more in priority setting to accurately represent health inequities. We provide guidance on the technical approaches to support this goal and ultimately, to achieve more equitable health policies.
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Affiliation(s)
- M. Quaife
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK,Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - GF Medley
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK
| | - M. Jit
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - T. Drake
- Center for Global Development in Europe (CGD Europe), UK
| | - M. Asaria
- LSE Health, London School of Economics, UK
| | - P. van Baal
- Erasmus School of Health Policy & Management, Erasmus University Rotterdam, the Netherlands
| | - R. Baltussen
- Nijmegen International Center for Health Systems Research and Education, Radboudmc, the Netherlands
| | | | - F. Bozzani
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK
| | - O. Brady
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - H. Broekhuizen
- Centre for Space, Place, and Society, Wageningen University and Research, Netherlands
| | - K. Chalkidou
- International Decision Support Initiative, Imperial College London, UK
| | - Y.-L. Chi
- International Decision Support Initiative, Imperial College London, UK
| | - DW Dowdy
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, USA
| | - S. Griffin
- Centre for Health Economics, University of York, UK
| | - H. Haghparast-Bidgoli
- Institute for Global Health, Centre for Global Health Economics, University College London, UK
| | - T. Hallett
- Department of Infectious Disease Epidemiology, Imperial College London, UK
| | - K. Hauck
- Department of Infectious Disease Epidemiology, Imperial College London, UK
| | - TD Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
| | - CF McQuaid
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - NA Menzies
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, USA
| | - MW Merritt
- Johns Hopkins Berman Institute of Bioethics and Department of International Health, Johns Hopkins Bloomberg School of Public Health, United States
| | - A. Mirelman
- Centre for Health Economics, University of York, UK
| | - A. Morton
- Department of Management Science, University of Strathclyde, UK
| | - FJ Ruiz
- International Decision Support Initiative, Imperial College London, UK
| | - M. Siapka
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK,Impact Elipsis, Greece
| | - J. Skordis
- Institute for Global Health, Centre for Global Health Economics, University College London, UK
| | - F. Tediosi
- Swiss Tropical and Public Health Institute and Universität Basel, Switzerland
| | - P. Walker
- Department of Infectious Disease Epidemiology, Imperial College London, UK
| | - RG White
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - P. Winskill
- Department of Infectious Disease Epidemiology, Imperial College London, UK
| | - A. Vassall
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK,Correspondence to: London School of Hygiene and Tropical Medicine, 15 – 17 Tavistock Place, London WC1H 9SH, UK
| | - GB Gomez
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK
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24
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Jayawardana S, Weerasuriya CK, Pelzer PT, Seeley J, Harris RC, Tameris M, Tait D, White RG, Asaria M. Feasibility of novel adult tuberculosis vaccination in South Africa: a cost-effectiveness and budget impact analysis. NPJ Vaccines 2022; 7:138. [DOI: 10.1038/s41541-022-00554-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/10/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractEarly trials of novel vaccines against tuberculosis (TB) in adults have suggested substantial protection against TB. However, little is known about the feasibility and affordability of rolling out such vaccines in practice. We conducted expert interviews to identify plausible vaccination implementation strategies for the novel M72/AS01E vaccine candidate. The strategies were defined in terms of target population, coverage, vaccination schedule and delivery mode. We modelled these strategies to estimate long-term resource requirements and health benefits arising from vaccination over 2025–2050. We presented these to experts who excluded strategies that were deemed infeasible, and estimated cost-effectiveness and budget impact for each remaining strategy. The four strategies modelled combined target populations: either everyone aged 18–50, or all adults living with HIV, with delivery strategies: either a mass campaign followed by routine vaccination of 18-year olds, or two mass campaigns 10 years apart. Delivering two mass campaigns to all 18–50-year olds was found to be the most cost-effective strategy conferring the greatest net health benefit of 1.2 million DALYs averted having a probability of being cost-effective of 65–70%. This strategy required 38 million vaccine courses to be delivered at a cost of USD 507 million, reducing TB-related costs by USD 184 million while increasing ART costs by USD 79 million. A suitably designed adult TB vaccination programme built around novel TB vaccines is likely to be cost-effective and affordable given the resource and budget constraints in South Africa.
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25
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Miner MD, Hatherill M, Mave V, Gray GE, Nachman S, Read SW, White RG, Hesseling A, Cobelens F, Patel S, Frick M, Bailey T, Seder R, Flynn J, Rengarajan J, Kaushal D, Hanekom W, Schmidt AC, Scriba TJ, Nemes E, Andersen-Nissen E, Landay A, Dorman SE, Aldrovandi G, Cranmer LM, Day CL, Garcia-Basteiro AL, Fiore-Gartland A, Mogg R, Kublin JG, Gupta A, Churchyard G. Developing tuberculosis vaccines for people with HIV: consensus statements from an international expert panel. Lancet HIV 2022; 9:e791-e800. [PMID: 36240834 PMCID: PMC9667733 DOI: 10.1016/s2352-3018(22)00255-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 08/16/2022] [Accepted: 08/30/2022] [Indexed: 11/06/2022]
Abstract
New tuberculosis vaccine candidates that are in the development pipeline need to be studied in people with HIV, who are at high risk of acquiring Mycobacterium tuberculosis infection and tuberculosis disease and tend to develop less robust vaccine-induced immune responses. To address the gaps in developing tuberculosis vaccines for people with HIV, a series of symposia was held that posed six framing questions to a panel of international experts: What is the use case or rationale for developing tuberculosis vaccines? What is the landscape of tuberculosis vaccines? Which vaccine candidates should be prioritised? What are the tuberculosis vaccine trial design considerations? What is the role of immunological correlates of protection? What are the gaps in preclinical models for studying tuberculosis vaccines? The international expert panel formulated consensus statements to each of the framing questions, with the intention of informing tuberculosis vaccine development and the prioritisation of clinical trials for inclusion of people with HIV.
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Affiliation(s)
| | - Mark Hatherill
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine and Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Vidya Mave
- Johns Hopkins India, Byramjee-Jeejeebhoy Government Medical College Clinical Research Site, Pune, India
| | - Glenda E Gray
- South African Medical Research Council, Cape Town, South Africa
| | - Sharon Nachman
- Department of Pediatrics, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Sarah W Read
- Division of AIDS, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Richard G White
- Department of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Anneke Hesseling
- Desmond Tutu Tuberculosis Centre, Stellenbosch University, Stellenbosch, South Africa
| | - Frank Cobelens
- Department of Global Health, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Sheral Patel
- US Food and Drug Administration, Silver Spring, MD, USA
| | - Mike Frick
- Treatment Action Group, New York, NY, USA
| | | | - Robert Seder
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Joanne Flynn
- Microbiology and Molecular Genetics, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | | | - Deepak Kaushal
- Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Willem Hanekom
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
| | | | - Thomas J Scriba
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine and Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Elisa Nemes
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine and Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Erica Andersen-Nissen
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Cape Town HIV Vaccine Trials Network (HVTN) Immunology Laboratory, Cape Town, South Africa
| | | | - Susan E Dorman
- Department of Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - Grace Aldrovandi
- Department of Pediatrics, University of California, Los Angeles, CA, USA
| | - Lisa M Cranmer
- Emory School of Medicine, Emory University, Atlanta, GA, USA
| | - Cheryl L Day
- Emory School of Medicine, Emory University, Atlanta, GA, USA
| | - Alberto L Garcia-Basteiro
- ISGlobal, Hospital Clínic Universitat de Barcelona, Barcelona, Spain; Centro de investigação de Saúde de Manhiça, Maputo, Mozambique
| | | | - Robin Mogg
- Takeda Pharmaceutical Company, Cambridge, MA, USA
| | - James G Kublin
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Amita Gupta
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Gavin Churchyard
- The Aurum Institute, Johannesburg, South Africa; School of Public Health, University of Witwatersrand, Johannesburg, South Africa; Department of Medicine, Vanderbilt University, Nashville, TN, USA.
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26
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Sumner T, White RG. Variance-based sensitivity analysis of tuberculosis transmission models. J R Soc Interface 2022; 19:20220413. [PMID: 36415976 PMCID: PMC9682306 DOI: 10.1098/rsif.2022.0413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Mathematical models are widely used to provide evidence to inform policies for tuberculosis (TB) control. These models contain many sources of input uncertainty including the choice of model structure, parameter values and input data. Quantifying the role of these different sources of input uncertainty on the model outputs is important for understanding model dynamics and improving evidence for policy making. In this paper, we applied the Sobol sensitivity analysis method to a TB transmission model used to simulate the effects of a hypothetical population-wide screening strategy. We demonstrated how the method can be used to quantify the importance of both model parameters and model structure and how the analysis can be conducted on groups of inputs. Uncertainty in the model outputs was dominated by uncertainty in the intervention parameters. The important inputs were context dependent, depending on the setting, time horizon and outcome measure considered. In particular, the choice of model structure had an increasing effect on output uncertainty in high TB incidence settings. Grouping inputs identified the same influential inputs. Wider use of the Sobol method could inform ongoing development of infectious disease models and improve the use of modelling evidence in decision making.
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Affiliation(s)
- Tom Sumner
- TB Modelling Group, TB Centre, Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Richard G. White
- TB Modelling Group, TB Centre, Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
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27
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Benest J, Rhodes S, Evans TG, White RG. The Correlated Beta Dose Optimisation Approach: Optimal Vaccine Dosing Using Mathematical Modelling and Adaptive Trial Design. Vaccines (Basel) 2022; 10:vaccines10111838. [PMID: 36366347 PMCID: PMC9693615 DOI: 10.3390/vaccines10111838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/16/2022] [Accepted: 10/28/2022] [Indexed: 12/02/2022] Open
Abstract
Mathematical modelling methods and adaptive trial design are likely to be effective for optimising vaccine dose but are not yet commonly used. This may be due to uncertainty with regard to the correct choice of parametric model for dose-efficacy or dose-toxicity. Non-parametric models have previously been suggested to be potentially useful in this situation. We propose a novel approach for locating optimal vaccine dose based on the non-parametric Continuous Correlated Beta Process model and adaptive trial design. We call this the ‘Correlated Beta’ or ‘CoBe’ dose optimisation approach. We evaluated the CoBe dose optimisation approach compared to other vaccine dose optimisation approaches using a simulation study. Despite using simpler assumptions than other modelling-based methods, we found that the CoBe dose optimisation approach was able to effectively locate the maximum efficacy dose for both single and prime/boost administration vaccines. The CoBe dose optimisation approach was also effective in finding a dose that maximises vaccine efficacy and minimises vaccine-related toxicity. Further, we found that these modelling methods can benefit from the inclusion of expert knowledge, which has been difficult for previous parametric modelling methods. This work further shows that using mathematical modelling and adaptive trial design is likely to be beneficial to locating optimal vaccine dose, ensuring maximum vaccine benefit and disease burden reduction, ultimately saving lives
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Affiliation(s)
- John Benest
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
- Correspondence:
| | - Sophie Rhodes
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Thomas G. Evans
- Vaccitech Ltd., The Schrodinger Building, Heatley Road, The Oxford Science Park, Oxford OX4 4GE, UK
| | - Richard G. White
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
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28
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McCreesh N, Mohlamonyane M, Edwards A, Olivier S, Dikgale K, Dayi N, Gareta D, Wood R, Grant AD, White RG, Middelkoop K. Improving Estimates of Social Contact Patterns for Airborne Transmission of Respiratory Pathogens. Emerg Infect Dis 2022; 28:2016-2026. [PMID: 36048756 PMCID: PMC9514345 DOI: 10.3201/eid2810.212567] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Data on social contact patterns are widely used to parameterize age-mixing matrices in mathematical models of infectious diseases. Most studies focus on close contacts only (i.e., persons spoken with face-to-face). This focus may be appropriate for studies of droplet and short-range aerosol transmission but neglects casual or shared air contacts, who may be at risk from airborne transmission. Using data from 2 provinces in South Africa, we estimated age mixing patterns relevant for droplet transmission, nonsaturating airborne transmission, and Mycobacterium tuberculosis transmission, an airborne infection where saturation of household contacts occurs. Estimated contact patterns by age did not vary greatly between the infection types, indicating that widespread use of close contact data may not be resulting in major inaccuracies. However, contact in persons >50 years of age was lower when we considered casual contacts, and therefore the contribution of older age groups to airborne transmission may be overestimated.
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29
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Horton KC, White RG, Hoa NB, Nguyen HV, Bakker R, Sumner T, Corbett EL, Houben RMGJ. Population benefits of addressing programmatic and social determinants of gender disparities in tuberculosis in Viet Nam: A modelling study. PLOS Glob Public Health 2022; 2:e0000784. [PMID: 36962475 PMCID: PMC10021793 DOI: 10.1371/journal.pgph.0000784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 06/23/2022] [Indexed: 11/18/2022]
Abstract
High prevalence of infectious tuberculosis among men suggests potential population-wide benefits from addressing programmatic and social determinants of gender disparities. Utilising a sex-stratified compartmental transmission model calibrated to tuberculosis burden estimates for Viet Nam, we modelled interventions to increase active case finding, to reduce tobacco smoking, and to reduce alcohol consumption by 2025 in line with national and global targets. For each intervention, we examined scenarios differentially targeting men and women and evaluated impact on tuberculosis morbidity and mortality in men, women, and children in 2035. Active case finding interventions targeting men projected greater reductions in tuberculosis incidence in men, women, and children (16.2%, uncertainty interval, UI, 11.4-23.0%, 11.8%, UI 8.0-18.6%, and 21.5%, UI 16.9-28.5%, respectively) than those targeting women (5.2%, UI 3.8-7.1%, 5.4%, UI 3.9-7.3%, and 8.6%, UI 6.9-10.7%, respectively). Projected reductions in tuberculosis incidence for interventions to reduce male tobacco smoking and alcohol consumption were greatest for men (17.4%, UI 11.8-24.7%, and 11.0%, UI 5.4-19.4%, respectively), but still substantial for women (6.9%, UI 3.8-12.5%, and 4.4%, UI 1.9-10.6%, respectively) and children (12.7%, UI 8.4-19.0%, and 8.0%, UI 3.9-15.0%, respectively). Comparable interventions targeting women projected limited impact, with declines of 0.3% (UI 0.2%-0.3%) and 0.1% (UI 0.0%-0.1%), respectively. Addressing programmatic and social determinants of men's tuberculosis burden has population-wide benefits. Future interventions to increase active case finding, to reduce tobacco smoking, and to reduce harmful alcohol consumption, whilst not ignoring women, should focus on men to most effectively reduce tuberculosis morbidity and mortality in men, women, and children.
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Affiliation(s)
- Katherine C. Horton
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- TB Modelling Group, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Richard G. White
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- TB Modelling Group, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | - Hai Viet Nguyen
- National Tuberculosis Control Programme, Hanoi, Viet Nam
- Department of Global Health and Amsterdam Institute of Global Health and Development, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Roel Bakker
- Skardahl IT Solutions, Delft, The Netherlands
| | - Tom Sumner
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- TB Modelling Group, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Elizabeth L. Corbett
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Rein M. G. J. Houben
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- TB Modelling Group, London School of Hygiene and Tropical Medicine, London, United Kingdom
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30
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Garcia-Basteiro AL, White RG, Tait D, Schmidt AC, Rangaka MX, Quaife M, Nemes E, Mogg R, Hill PC, Harris RC, Hanekom WA, Frick M, Fiore-Gartland A, Evans T, Dagnew AF, Churchyard G, Cobelens F, Behr MA, Hatherill M. End-point definition and trial design to advance tuberculosis vaccine development. Eur Respir Rev 2022; 31:220044. [PMID: 35675923 PMCID: PMC9488660 DOI: 10.1183/16000617.0044-2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 04/04/2022] [Indexed: 11/05/2022] Open
Abstract
Tuberculosis (TB) remains a leading infectious cause of death worldwide and the coronavirus disease 2019 pandemic has negatively impacted the global TB burden of disease indicators. If the targets of TB mortality and incidence reduction set by the international community are to be met, new more effective adult and adolescent TB vaccines are urgently needed. There are several new vaccine candidates at different stages of clinical development. Given the limited funding for vaccine development, it is crucial that trial designs are as efficient as possible. Prevention of infection (POI) approaches offer an attractive opportunity to accelerate new candidate vaccines to advance into large and expensive prevention of disease (POD) efficacy trials. However, POI approaches are limited by imperfect current tools to measure Mycobacterium tuberculosis infection end-points. POD trials need to carefully consider the type and number of microbiological tests that define TB disease and, if efficacy against subclinical (asymptomatic) TB disease is to be tested, POD trials need to explore how best to define and measure this form of TB. Prevention of recurrence trials are an alternative approach to generate proof of concept for efficacy, but optimal timing of vaccination relative to treatment must still be explored. Novel and efficient approaches to efficacy trial design, in addition to an increasing number of candidates entering phase 2-3 trials, would accelerate the long-standing quest for a new TB vaccine.
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Affiliation(s)
- Alberto L Garcia-Basteiro
- Centro de Investigação em Sade de Manhiça (CISM), Maputo, Mozambique
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFECT), Barcelona, Spain
| | | | - Dereck Tait
- International AIDS Vaccine Initiative (IAVI) NPC, Cape Town, South Africa
| | | | - Molebogeng X Rangaka
- Institute for Global Health and MRC Clinical Trials Unit at University College London, London, UK
- CIDRI-AFRICA, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Matthew Quaife
- London School of Hygiene and Tropical Medicine, London, UK
| | - Elisa Nemes
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine and Division of Immunology, Dept of Pathology, University of Cape Town, Cape Town, South Africa
| | - Robin Mogg
- Takeda Pharmaceutical Company Limited, Cambridge, MA, USA
| | - Philip C Hill
- Centre for International Health, University of Otago, Dunedin, New Zealand
| | - Rebecca C Harris
- London School of Hygiene and Tropical Medicine, London, UK
- Sanofi Pasteur, Singapore
| | - Willem A Hanekom
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- Division of Infection and Immunity, University College London, London, UK
| | - Mike Frick
- Treatment Action Group, New York, NY, USA
| | - Andrew Fiore-Gartland
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Alemnew F Dagnew
- Bill and Melinda Gates Medical Research Institute, Cambridge, MA, USA
| | - Gavin Churchyard
- The Aurum Institute, Parktown, South Africa
- Vanderbilt University, Nashville, TN, USA
- University of the Witwatersrand, Johannesburg, South Africa
| | - Frank Cobelens
- Dept of Global Health and Amsterdam Institute for Global health and development, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Marcel A Behr
- Dept of Medicine, McGill University; McGill International TB Centre, Montreal, QC, Canada
| | - Mark Hatherill
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine and Division of Immunology, Dept of Pathology, University of Cape Town, Cape Town, South Africa
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31
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Benest J, Rhodes S, Evans TG, White RG. Mathematical Modelling for Optimal Vaccine Dose Finding: Maximising Efficacy and Minimising Toxicity. Vaccines (Basel) 2022; 10:vaccines10050756. [PMID: 35632511 PMCID: PMC9144167 DOI: 10.3390/vaccines10050756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/03/2022] [Accepted: 05/06/2022] [Indexed: 02/06/2023] Open
Abstract
Vaccination is a key tool to reduce global disease burden. Vaccine dose can affect vaccine efficacy and toxicity. Given the expense of developing vaccines, optimising vaccine dose is essential. Mathematical modelling has been suggested as an approach for optimising vaccine dose by quantitatively establishing the relationships between dose and efficacy/toxicity. In this work, we performed simulation studies to assess the performance of modelling approaches in determining optimal dose. We found that the ability of modelling approaches to determine optimal dose improved with trial size, particularly for studies with at least 30 trial participants, and that, generally, using a peaking or a weighted model-averaging-based dose–efficacy relationship was most effective in finding optimal dose. Most methods of trial dose selection were similarly effective for the purpose of determining optimal dose; however, including modelling to adapt doses during a trial may lead to more trial participants receiving a more optimal dose. Clinical trial dosing around the predicted optimal dose, rather than only at the predicted optimal dose, may improve final dose selection. This work suggests modelling can be used effectively for vaccine dose finding, prompting potential practical applications of these methods in accelerating effective vaccine development and saving lives.
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Affiliation(s)
- John Benest
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; (S.R.); (R.G.W.)
- Correspondence:
| | - Sophie Rhodes
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; (S.R.); (R.G.W.)
| | - Thomas G. Evans
- Vaccitech Ltd., The Schrodinger Building, Heatley Road, The Oxford Science Park, Oxford OX4 4GE, UK;
| | - Richard G. White
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; (S.R.); (R.G.W.)
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32
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Pelzer PT, Seeley J, Sun FY, Tameris M, Tao L, Yanlin Z, Moosan H, Weerasuriya C, Asaria M, Jayawardana S, White RG, Harris RC. Potential implementation strategies, acceptability, and feasibility of new and repurposed TB vaccines. PLOS Glob Public Health 2022; 2:e0000076. [PMID: 36962104 PMCID: PMC10021736 DOI: 10.1371/journal.pgph.0000076] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/15/2021] [Indexed: 12/27/2022]
Abstract
Recently, two Phase 2B tuberculosis vaccine trials reported positive efficacy results in adolescents and adults. However, experience in vaccinating these age groups is limited. We identified potential implementation strategies for the M72/AS01E vaccination and BCG-revaccination-like candidates and explored their acceptability and feasibility. We conducted in-depth semi-structured interviews among key decision makers to identify implementation strategies and target groups in South Africa, India, and China. Thematic and deductive analysis using a coding framework were used to identify themes across and within settings. In all three countries there was interest in novel TB vaccines, with school-attending adolescents named as a likely target group. In China and India, older people were also identified as a target group. Routine vaccination was preferred in all countries due to stigma and logistical issues with targeted mass campaigns. Perceived benefits for implementation of M72/AS01E were the likely efficacy in individuals with Mycobacterium tuberculosis (Mtb) infection and efficacy for people living with HIV. Perceived challenges for M72/AS01E included the infrastructure and the two-dose regimen required. Stakeholders valued the familiarity of BCG but were concerned about the adverse effects in people living with HIV, a particular concern in South Africa. Implementation challenges and opportunities were identified in all three countries. Our study provides crucial information for implementing novel TB vaccines in specific target groups and on country specific acceptability and feasibility. Key groups for vaccine implementation in these settings were identified, and should be included in clinical trials and implementation planning.
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Affiliation(s)
- Puck T. Pelzer
- KNCV Tuberculosis Foundation, Amsterdam, Netherlands
- London School of Hygiene & Tropical Medicine (LSHTM), London, United Kingdom
| | - Janet Seeley
- London School of Hygiene & Tropical Medicine (LSHTM), London, United Kingdom
| | - Fiona Yueqian Sun
- London School of Hygiene & Tropical Medicine (LSHTM), London, United Kingdom
| | - Michele Tameris
- South African Tuberculosis Vaccine Initiative (SATVI), Department of Pathology, Institute of Infectious Disease and Molecular Medicine, University of Cape Town (UCT), Cape Town, South Africa
| | - Li Tao
- Chinese Centre for Disease Control and Prevention, Beijing, China
| | - Zhao Yanlin
- Chinese Centre for Disease Control and Prevention, Beijing, China
| | - Hisham Moosan
- Health Action by People, Thriuvananthapuram, Kerala, India
| | | | - Miqdad Asaria
- London School of Economics (LSE), London, United Kingdom
| | | | - Richard G. White
- London School of Hygiene & Tropical Medicine (LSHTM), London, United Kingdom
| | - Rebecca C. Harris
- London School of Hygiene & Tropical Medicine (LSHTM), London, United Kingdom
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33
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Weerasuriya CK, Harris RC, McQuaid CF, Gomez GB, White RG. Updating age-specific contact structures to match evolving demography in a dynamic mathematical model of tuberculosis vaccination. PLoS Comput Biol 2022; 18:e1010002. [PMID: 35452459 PMCID: PMC9067655 DOI: 10.1371/journal.pcbi.1010002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 05/04/2022] [Accepted: 03/08/2022] [Indexed: 11/18/2022] Open
Abstract
We investigated the effects of updating age-specific social contact matrices to match evolving demography on vaccine impact estimates. We used a dynamic transmission model of tuberculosis in India as a case study. We modelled four incremental methods to update contact matrices over time, where each method incorporated its predecessor: fixed contact matrix (M0), preserved contact reciprocity (M1), preserved contact assortativity (M2), and preserved average contacts per individual (M3). We updated the contact matrices of a deterministic compartmental model of tuberculosis transmission, calibrated to epidemiologic data between 2000 and 2019 derived from India. We additionally calibrated the M0, M2, and M3 models to the 2050 TB incidence rate projected by the calibrated M1 model. We stratified age into three groups, children (<15y), adults (≥15y, <65y), and the elderly (≥65y), using World Population Prospects demographic data, between which we applied POLYMOD-derived social contact matrices. We simulated an M72-AS01E-like tuberculosis vaccine delivered from 2027 and estimated the per cent TB incidence rate reduction (IRR) in 2050 under each update method. We found that vaccine impact estimates in all age groups remained relatively stable between the M0–M3 models, irrespective of vaccine-targeting by age group. The maximum difference in impact, observed following adult-targeted vaccination, was 7% in the elderly, in whom we observed IRRs of 19% (uncertainty range 13–32), 20% (UR 13–31), 22% (UR 14–37), and 26% (UR 18–38) following M0, M1, M2 and M3 updates, respectively. We found that model-based TB vaccine impact estimates were relatively insensitive to demography-matched contact matrix updates in an India-like demographic and epidemiologic scenario. Current model-based TB vaccine impact estimates may be reasonably robust to the lack of contact matrix updates, but further research is needed to confirm and generalise this finding. Mathematical models are increasingly used to predict the impact of new and existing tools, e.g., vaccines, that aim to control the transmission of infectious diseases. Within these models, investigators often assume that individuals contact each other according to specific patterns, particularly between and within different age groups. These patterns are typically derived from surveys of social contact or other models and reflect the particular age composition of their source population. However, when models are set over long time scales, e.g., decades, population age composition is likely to change. Despite this reality, few models update their contact patterns to match changing age composition. Furthermore, none have assessed whether their final estimates of disease-control intervention impact are affected by updating contact patterns. We measured whether different techniques to update social contact patterns to match evolving demography produce different vaccine impact estimates, using a mathematical model of tuberculosis set in an India-like scenario between 2025–2050. We found that vaccine impact was stable across a range of different update methods. Thus, existing model-based vaccine impact estimates may be stable to a lack of these updates, but further work is required to confirm these findings.
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Affiliation(s)
- Chathika Krishan Weerasuriya
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
- * E-mail:
| | - Rebecca Claire Harris
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Christopher Finn McQuaid
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Gabriela B. Gomez
- Department of Global Health & Development, Faculty of Public Health & Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Richard G. White
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
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34
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Weerasuriya CK, Harris RC, McQuaid CF, Bozzani F, Ruan Y, Li R, Li T, Rade K, Rao R, Ginsberg AM, Gomez GB, White RG. Correction to: The epidemiologic impact and cost-effectiveness of new tuberculosis vaccines on multidrug-resistant tuberculosis in India and China. BMC Med 2022; 20:99. [PMID: 35227254 PMCID: PMC8887015 DOI: 10.1186/s12916-022-02306-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Chathika K Weerasuriya
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, UK.
| | - Rebecca C Harris
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, UK.,Currently employed at Sanofi Pasteur, Singapore, Singapore
| | - C Finn McQuaid
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Fiammetta Bozzani
- Department of Global Health and Development, Faculty of Public Health & Policy, London School of Hygiene and Tropical Medicine, London, UK
| | - Yunzhou Ruan
- Chinese Centre for Disease Control and Prevention, Beijing, China
| | - Renzhong Li
- Chinese Centre for Disease Control and Prevention, Beijing, China
| | - Tao Li
- Chinese Centre for Disease Control and Prevention, Beijing, China
| | | | - Raghuram Rao
- National Tuberculosis Elimination Programme, New Delhi, India
| | - Ann M Ginsberg
- International AIDS Vaccine Initiative, New York, USA.,Current Affiliation: Bill and Melinda Gates Foundation, Washington, DC, USA
| | - Gabriela B Gomez
- Department of Global Health and Development, Faculty of Public Health & Policy, London School of Hygiene and Tropical Medicine, London, UK.,Currently employed at Sanofi Pasteur, Lyon, France
| | - Richard G White
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, UK
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35
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Harris RC, Quaife M, Weerasuriya C, Gomez GB, Sumner T, Bozzani F, White RG. Cost-effectiveness of routine adolescent vaccination with an M72/AS01 E-like tuberculosis vaccine in South Africa and India. Nat Commun 2022; 13:602. [PMID: 35105879 PMCID: PMC8807591 DOI: 10.1038/s41467-022-28234-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 01/07/2022] [Indexed: 12/26/2022] Open
Abstract
The M72/AS01E tuberculosis vaccine showed 50% (95%CI: 2-74%) efficacy in a phase 2B trial in preventing active pulmonary tuberculosis disease, but potential cost-effectiveness of adolescent immunisation is unknown. We estimated the impact and cost-effectiveness of six scenarios of routine adolescent M72/AS01E-like vaccination in South Africa and India. All scenarios suggested an M72/AS01E-like vaccine would be highly (94-100%) cost-effective in South Africa compared to a cost-effectiveness threshold of $2480/disability-adjusted life-year (DALY) averted. For India, a prevention of disease vaccine, effective irrespective of recipient's M. tuberculosis infection status at time of administration, was also highly likely (92-100%) cost-effective at a threshold of $264/DALY averted; however, a prevention of disease vaccine, effective only if the recipient was already infected, had 0-6% probability of cost-effectiveness. In both settings, vaccinating 50% of 18 year-olds was similarly cost-effective to vaccinating 80% of 15 year-olds, and more cost-effective than vaccinating 80% of 10 year-olds. Vaccine trials should include adolescents to ensure vaccines can be delivered to this efficient-to-target population.
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Affiliation(s)
- Rebecca C Harris
- TB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK. .,Sanofi Pasteur, Singapore, Singapore.
| | - Matthew Quaife
- TB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Chathika Weerasuriya
- TB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Gabriela B Gomez
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK.,Sanofi Pasteur, Lyon, France
| | - Tom Sumner
- TB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Fiammetta Bozzani
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
| | - Richard G White
- TB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
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Murray EJ, Dodd PJ, Marais B, Ayles H, Shanaube K, Schaap A, White RG, Bond V. Sociological variety and the transmission efficiency of Mycobacterium tuberculosis: a secondary analysis of qualitative and quantitative data from 15 communities in Zambia. BMJ Open 2021; 11:e047136. [PMID: 34907038 PMCID: PMC8671921 DOI: 10.1136/bmjopen-2020-047136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVES Selected Zambian communities formed part of a cluster randomised trial: the Zambia and South Africa TB and AIDS Reduction study (ZAMSTAR). There was wide variability in the prevalence of Mycobacterium tuberculosis infection and tuberculosis (TB) disease across these communities. We sought to clarify whether specific communities could have been more/less vulnerable to M. tuberculosis transmission as a result of sociological variety relevant to transmission efficiency. DESIGN We conducted a mixed methods secondary analysis using existing data sets. First, we analysed qualitative data to categorise and synthesise patterns of socio-spatial engagement across communities. Second, we compared emergent sociological variables with a measure of transmission efficiency: the ratio of the annual risk of infection to TB prevalence. SETTING ZAMSTAR communities in urban and peri-urban Zambia, spanning five provinces. PARTICIPANTS Fifteen communities, each served by a health facility offering TB treatment to a population of at least 25 000. TB notification rates were at least 400 per 100 000 per annum and HIV seroprevalence was estimated to be high. RESULTS Crowding, movement, livelihoods and participation in recreational activity differed across communities. Based on 12 socio-spatial indicators, communities were qualitatively classified as more/less spatially crowded and as more/less socially 'open' to contact with others, with implications for the presumptive risk of M. tuberculosis transmission. For example, watching video shows in poorly ventilated structures posed a presumptive risk in more socially open communities, while outdoor farming and/or fishing were particularly widespread in communities with lower transmission measures. CONCLUSIONS A dual dynamic of 'social permeability' and crowding appeared relevant to disparities in M. tuberculosis transmission efficiency. To reduce transmission, certain socio-spatial aspects could be adjusted (eg, increasing ventilation on transport), while more structural aspects are less malleable (eg, reliance on public transport). We recommend integrating community level typologies with genome sequencing techniques to further explore the significance of 'social permeability'. TRIAL REGISTRATION NUMBER ISRCTN36729271.
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Affiliation(s)
| | - Peter J Dodd
- School of Health and Related Research, The University of Sheffield, Sheffield, UK
| | - Ben Marais
- Children's Hospital Westmead Clinical School, The University of Sydney, Westmead, New South Wales, Australia
| | - Helen Ayles
- Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Zambart, School of Public Health, University of Zambia, Lusaka, Zambia
| | - Kwame Shanaube
- Zambart, School of Public Health, University of Zambia, Lusaka, Zambia
| | - Albertus Schaap
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Richard G White
- TB Modelling Group, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Virginia Bond
- Zambart, School of Public Health, University of Zambia, Lusaka, Zambia
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK
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Sumner T, Fiore-Gartland A, Hatherill M, Houben RMGJ, Scriba TJ, White RG. The effect of new Mycobacterium tuberculosis infection on the sensitivity of prognostic TB signatures. Int J Tuberc Lung Dis 2021; 25:1001-1005. [PMID: 34886930 DOI: 10.5588/ijtld.21.0323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND: Tests that identify individuals at greatest risk of TB will allow more efficient targeting of preventive therapy. The WHO target product profile for such tests defines optimal sensitivity of 90% and minimum sensitivity of 75% for predicting incident TB. The CORTIS (Correlate of Risk Targeted Intervention Study) evaluated a blood transcriptomic signature (RISK11) for predicting incident TB in a high transmission setting. RISK11 is able to predict TB disease progression but optimal prognostic performance was limited to a 6-month horizon.METHODS: Using a mathematical model, we estimated how subsequent Mycobacterium tuberculosis (MTB) infection may have contributed to the decline in sensitivity of RISK11. We calculated the effect at different RISK11 thresholds (60% and 26%) and for different assumptions about the risk of MTB infection.RESULTS: Modelled sensitivity over 15 months, excluding new infection, was 28.7% (95% CI 12.3-74.1) compared to 25.0% (95% CI 12.7-45.9) observed in the trial. Modelled sensitivity exceeded the minimum criteria (>75%) over a 9-month horizon at the 60% threshold and over 12 months at the 26% threshold.CONCLUSIONS: The effect of new infection on prognostic signature performance is likely to be small. Signatures such as RISK11 may be most useful in individuals, such as household contacts, where probable time of infection is known.
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Affiliation(s)
- T Sumner
- TB Modelling Group, TB Centre, Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - A Fiore-Gartland
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - M Hatherill
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Observatory, South Africa
| | - R M G J Houben
- TB Modelling Group, TB Centre, Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - T J Scriba
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Observatory, South Africa
| | - R G White
- TB Modelling Group, TB Centre, Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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Sumner T, Mendelsohn SC, Scriba TJ, Hatherill M, White RG. The impact of blood transcriptomic biomarker targeted tuberculosis preventive therapy in people living with HIV: a mathematical modelling study. BMC Med 2021; 19:252. [PMID: 34711213 PMCID: PMC8555196 DOI: 10.1186/s12916-021-02127-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 09/14/2021] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Tuberculosis (TB) preventive therapy is recommended for all people living with HIV (PLHIV). Despite the elevated risk of TB amongst PLHIV, most of those eligible for preventive therapy would never develop TB. Tests which can identify individuals at greatest risk of disease would allow more efficient targeting of preventive therapy. METHODS We used mathematical modelling to estimate the potential impact of using a blood transcriptomic biomarker (RISK11) to target preventive therapy amongst PLHIV. We compared universal treatment to RISK11 targeted treatment and explored the effect of repeat screening of the population with RISK11. RESULTS Annual RISK11 screening, with preventive therapy provided to those testing positive, could avert 26% (95% CI 13-34) more cases over 10 years compared to one round of universal treatment. For the cost per case averted to be lower than universal treatment, the maximum cost of the RISK11 test was approximately 10% of the cost of preventive therapy. The benefit of RISK11 screening may be greatest amongst PLHIV on ART (compared to ART naïve individuals) due to the increased specificity of the test in this group. CONCLUSIONS Biomarker targeted preventive therapy may be more effective than universal treatment amongst PLHIV in high incidence settings but would require repeat screening.
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Affiliation(s)
- Tom Sumner
- TB Modelling Group, TB Centre, Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
| | - Simon C Mendelsohn
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Thomas J Scriba
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Mark Hatherill
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Richard G White
- TB Modelling Group, TB Centre, Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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Shaikh N, Pelzer PT, Thysen SM, Roy P, Harris RC, White RG. Impact of COVID-19 Disruptions on Global BCG Coverage and Paediatric TB Mortality: A Modelling Study. Vaccines (Basel) 2021; 9:1228. [PMID: 34835161 PMCID: PMC8624525 DOI: 10.3390/vaccines9111228] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 10/15/2021] [Accepted: 10/18/2021] [Indexed: 11/17/2022] Open
Abstract
The impact of COVID-19 disruptions on global Bacillus Calmette-Guérin (BCG) coverage and paediatric tuberculosis (TB) mortality is still unknown. To fill this evidence-gap and guide mitigation measures, we estimated the impact of COVID-19 disruptions on global BCG coverage and paediatric TB mortality. First, we used data from multiple sources to estimate COVID-19-disrupted BCG vaccination coverage. Second, using a static mathematical model, we estimated the number of additional paediatric TB deaths in the first 15 years of life due to delayed/missed vaccinations in 14 scenarios-varying in duration of disruption, and magnitude and timing of catch-up. We estimated a 25% reduction in global BCG coverage within the disruption period. The best-case scenario (3-month disruption, 100% catch-up within 3 months) resulted in an additional 886 (0.5%) paediatric TB deaths, and the worst-case scenario (6-month disruption with no catch-up) resulted in an additional 33,074 (17%) deaths. The magnitude of catch-up was found to be the most influential variable in minimising excess paediatric TB mortality. Our results show that ensuring catch-up vaccination of missed children is a critical priority, and delivery of BCG alongside other routine vaccines may be a feasible way to achieve catch-up. Urgent action is required to support countries with recovering vaccination coverages to minimise paediatric deaths.
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Affiliation(s)
- Nabila Shaikh
- TB Modelling Group, TB Centre, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK; (R.C.H.); (R.G.W.)
| | - Puck T. Pelzer
- Technical Division, KNCV Tuberculosis, Maanweg 174, 2516 AB The Hague, The Netherlands;
| | - Sanne M. Thysen
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, 2004 Frederiksberg, Denmark;
- Bandim Health Project, Apartado 861, Bissau 1004, Guinea-Bissau
| | - Partho Roy
- Immunisation and Countermeasures, National Infection Service, Public Health England, London NW9 5EQ, UK;
| | - Rebecca C. Harris
- TB Modelling Group, TB Centre, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK; (R.C.H.); (R.G.W.)
- Sanofi Pasteur, South Beach Tower 18-11, Singapore 189767, Singapore
| | - Richard G. White
- TB Modelling Group, TB Centre, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK; (R.C.H.); (R.G.W.)
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McCreesh N, Karat AS, Baisley K, Diaconu K, Bozzani F, Govender I, Beckwith P, Yates TA, Deol AK, Houben RMGJ, Kielmann K, White RG, Grant AD. Modelling the effect of infection prevention and control measures on rate of Mycobacterium tuberculosis transmission to clinic attendees in primary health clinics in South Africa. BMJ Glob Health 2021; 6:e007124. [PMID: 34697087 PMCID: PMC8547367 DOI: 10.1136/bmjgh-2021-007124] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 10/08/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Elevated rates of tuberculosis in healthcare workers demonstrate the high rate of Mycobacterium tuberculosis (Mtb) transmission in health facilities in high-burden settings. In the context of a project taking a whole systems approach to tuberculosis infection prevention and control (IPC), we aimed to evaluate the potential impact of conventional and novel IPC measures on Mtb transmission to patients and other clinic attendees. METHODS An individual-based model of patient movements through clinics, ventilation in waiting areas, and Mtb transmission was developed, and parameterised using empirical data from eight clinics in two provinces in South Africa. Seven interventions-codeveloped with health professionals and policy-makers-were simulated: (1) queue management systems with outdoor waiting areas, (2) ultraviolet germicidal irradiation (UVGI) systems, (3) appointment systems, (4) opening windows and doors, (5) surgical mask wearing by clinic attendees, (6) simple clinic retrofits and (7) increased coverage of long antiretroviral therapy prescriptions and community medicine collection points through the Central Chronic Medicine Dispensing and Distribution (CCMDD) service. RESULTS In the model, (1) outdoor waiting areas reduced the transmission to clinic attendees by 83% (IQR 76%-88%), (2) UVGI by 77% (IQR 64%-85%), (3) appointment systems by 62% (IQR 45%-75%), (4) opening windows and doors by 55% (IQR 25%-72%), (5) masks by 47% (IQR 42%-50%), (6) clinic retrofits by 45% (IQR 16%-64%) and (7) increasing the coverage of CCMDD by 22% (IQR 12%-32%). CONCLUSIONS The majority of the interventions achieved median reductions in the rate of transmission to clinic attendees of at least 45%, meaning that a range of highly effective intervention options are available, that can be tailored to the local context. Measures that are not traditionally considered to be IPC interventions, such as appointment systems, may be as effective as more traditional IPC measures, such as mask wearing.
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Affiliation(s)
- Nicky McCreesh
- TB Centre, London School of Hygiene and Tropical Medicine, London, UK
| | - Aaron S Karat
- TB Centre, London School of Hygiene and Tropical Medicine, London, UK
- Institute for Global Health & Development, Queen Margaret University Edinburgh, Musselburgh, UK
| | - Kathy Baisley
- TB Centre, London School of Hygiene and Tropical Medicine, London, UK
| | - Karin Diaconu
- Institute for Global Health & Development, Queen Margaret University Edinburgh, Musselburgh, UK
| | - Fiammetta Bozzani
- TB Centre, London School of Hygiene and Tropical Medicine, London, UK
| | - Indira Govender
- TB Centre, London School of Hygiene and Tropical Medicine, London, UK
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
| | - Peter Beckwith
- Department of Medicine, University of Cape Town, Rondebosch, South Africa
| | - Tom A Yates
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, UK
| | - Arminder K Deol
- TB Centre, London School of Hygiene and Tropical Medicine, London, UK
| | - Rein M G J Houben
- TB Centre, London School of Hygiene and Tropical Medicine, London, UK
| | - Karina Kielmann
- Institute for Global Health & Development, Queen Margaret University Edinburgh, Musselburgh, UK
| | - Richard G White
- TB Centre, London School of Hygiene and Tropical Medicine, London, UK
| | - Alison D Grant
- TB Centre, London School of Hygiene and Tropical Medicine, London, UK
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
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McCreesh N, Dlamini V, Edwards A, Olivier S, Dayi N, Dikgale K, Nxumalo S, Dreyer J, Baisley K, Siedner MJ, White RG, Herbst K, Grant AD, Harling G. Impact of the Covid-19 epidemic and related social distancing regulations on social contact and SARS-CoV-2 transmission potential in rural South Africa: analysis of repeated cross-sectional surveys. BMC Infect Dis 2021; 21:928. [PMID: 34496771 PMCID: PMC8424154 DOI: 10.1186/s12879-021-06604-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 08/23/2021] [Indexed: 12/11/2022] Open
Abstract
Background South Africa implemented rapid and strict physical distancing regulations to minimize SARS-CoV-2 epidemic spread. Evidence on the impact of such measures on interpersonal contact in rural and lower-income settings is limited. Methods We compared population-representative social contact surveys conducted in the same rural KwaZulu-Natal location once in 2019 and twice in mid-2020. Respondents reported characteristics of physical and conversational (‘close interaction’) contacts over 24 hours. We built age-mixing matrices and estimated the proportional change in the SARS-CoV-2 reproduction number (R0). Respondents also reported counts of others present at locations visited and transport used, from which we evaluated change in potential exposure to airborne infection due to shared indoor space (‘shared air’). Results Respondents in March–December 2019 (n = 1704) reported a mean of 7.4 close interaction contacts and 196 shared air person-hours beyond their homes. Respondents in June-July 2020 (n = 216), as the epidemic peaked locally, reported 4.1 close interaction contacts and 21 shared air person-hours outside their home, with significant declines in others’ homes and public spaces. Adults aged over 50 had fewer close contacts with others over 50, but little change in contact with 15–29 year olds, reflecting ongoing contact within multigenerational households. We estimate potential R0 fell by 42% (95% plausible range 14–59%) between 2019 and June-July 2020. Conclusions Extra-household social contact fell substantially following imposition of Covid-19 distancing regulations in rural South Africa. Ongoing contact within intergenerational households highlighted a potential limitation of social distancing measures in protecting older adults. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-021-06604-8.
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Affiliation(s)
- Nicky McCreesh
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Vuyiswa Dlamini
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Anita Edwards
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Stephen Olivier
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Njabulo Dayi
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Keabetswe Dikgale
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Siyabonga Nxumalo
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Jaco Dreyer
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Kathy Baisley
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.,Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Mark J Siedner
- Harvard Medical School and the Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, MA, USA
| | - Richard G White
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Kobus Herbst
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa.,DSI-MRC South African Population Research Infrastructure Network, Durban, South Africa
| | - Alison D Grant
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa.,TB Centre, London School of Hygiene and Tropical Medicine, London, UK.,School of Laboratory and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, KwaZulu-Natal, Durban, South Africa.,School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | - Guy Harling
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa. .,Institute for Global Health, University College London, London, UK. .,Department of Epidemiology & Harvard Center for Population and Development Studies, Harvard T.H. Chan School of Public Health, Boston, MA, USA. .,MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, University of the Witwatersrand, Johannesburg, South Africa.
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Clarkson MC, McQuaid CF, Houben RM, Kranzer K, White RG. Better data for country-level TB resource allocation are urgently required. Int J Tuberc Lung Dis 2021; 25:662-664. [PMID: 34330352 DOI: 10.5588/ijtld.20.0912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- M C Clarkson
- TB Modelling Group, TB Centre, Centre for Mathematical Modelling of Infectious Diseases, Department of Epidemiology and Population Health, London, UK
| | - C F McQuaid
- TB Modelling Group, TB Centre, Centre for Mathematical Modelling of Infectious Diseases, Department of Epidemiology and Population Health, London, UK
| | - R M Houben
- TB Modelling Group, TB Centre, Centre for Mathematical Modelling of Infectious Diseases, Department of Epidemiology and Population Health, London, UK
| | - K Kranzer
- Department of Clinical Research, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK, Biomedical Research and Training Institute, Harare, Zimbabwe, Division of Infectious and Tropical Medicine, Medical Centre of the University of Munich, Munich, Germany
| | - R G White
- TB Modelling Group, TB Centre, Centre for Mathematical Modelling of Infectious Diseases, Department of Epidemiology and Population Health, London, UK
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McQuaid CF, Clarkson MC, Bellerose M, Floyd K, White RG, Menzies NA. An approach for improving the quality of country-level TB modelling. Int J Tuberc Lung Dis 2021; 25:614-619. [PMID: 34330345 PMCID: PMC8327628 DOI: 10.5588/ijtld.21.0127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Mathematical modelling is increasingly used to inform budgeting and strategic decision-making by national TB programmes. Despite the importance of these decisions, there is currently no mechanism to review and confirm the appropriateness of modelling analyses. We have developed a benchmarking, reporting, and review (BRR) approach and accompanying tools to allow constructive review of country-level TB modelling applications. This approach has been piloted in five modelling applications and the results of this study have been used to revise and finalise the approach. The BRR approach consists of 1) quantitative benchmarks against which model assumptions and results can be compared, 2) standardised reporting templates and review criteria, and 3) a multi-stage review process providing feedback to modellers during the application, as well as a summary evaluation after completion. During the pilot, use of the tools prompted important changes in the approaches taken to modelling. The pilot also identified issues beyond the scope of a review mechanism, such as a lack of empirical evidence and capacity constraints. This approach provides independent evaluation of the appropriateness of modelling decisions during the course of an application, allowing meaningful changes to be made before results are used to inform decision-making. The use of these tools can improve the quality and transparency of country-level TB modelling applications.
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Affiliation(s)
- C F McQuaid
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - M C Clarkson
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - M Bellerose
- Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - K Floyd
- Global TB Programme, World Health Organization, Geneva, Switzerland
| | - R G White
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - N A Menzies
- Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA, Center for Health Decision Science, Harvard TH Chan School of Public Health, Boston, MA, USA
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Deol AK, Scarponi D, Beckwith P, Yates TA, Karat AS, Yan AWC, Baisley KS, Grant AD, White RG, McCreesh N. Estimating ventilation rates in rooms with varying occupancy levels: Relevance for reducing transmission risk of airborne pathogens. PLoS One 2021; 16:e0253096. [PMID: 34166388 PMCID: PMC8224849 DOI: 10.1371/journal.pone.0253096] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/27/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND In light of the role that airborne transmission plays in the spread of SARS-CoV-2, as well as the ongoing high global mortality from well-known airborne diseases such as tuberculosis and measles, there is an urgent need for practical ways of identifying congregate spaces where low ventilation levels contribute to high transmission risk. Poorly ventilated clinic spaces in particular may be high risk, due to the presence of both infectious and susceptible people. While relatively simple approaches to estimating ventilation rates exist, the approaches most frequently used in epidemiology cannot be used where occupancy varies, and so cannot be reliably applied in many of the types of spaces where they are most needed. METHODS The aim of this study was to demonstrate the use of a non-steady state method to estimate the absolute ventilation rate, which can be applied in rooms where occupancy levels vary. We used data from a room in a primary healthcare clinic in a high TB and HIV prevalence setting, comprising indoor and outdoor carbon dioxide measurements and head counts (by age), taken over time. Two approaches were compared: approach 1 using a simple linear regression model and approach 2 using an ordinary differential equation model. RESULTS The absolute ventilation rate, Q, using approach 1 was 2407 l/s [95% CI: 1632-3181] and Q from approach 2 was 2743 l/s [95% CI: 2139-4429]. CONCLUSIONS We demonstrate two methods that can be used to estimate ventilation rate in busy congregate settings, such as clinic waiting rooms. Both approaches produced comparable results, however the simple linear regression method has the advantage of not requiring room volume measurements. These methods can be used to identify poorly-ventilated spaces, allowing measures to be taken to reduce the airborne transmission of pathogens such as Mycobacterium tuberculosis, measles, and SARS-CoV-2.
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Affiliation(s)
- Arminder K. Deol
- Department of Infectious Disease Epidemiology, TB Centre, The London School of Hygiene & Tropical Medicine, London, United Kingdom
- * E-mail:
| | - Danny Scarponi
- Department of Infectious Disease Epidemiology, TB Centre, The London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Peter Beckwith
- Department of Medicine, University of Cape Town, Cape Town, South Africa
- The Institute for Global Health and Development, Queen Margaret University, Edinburgh, United Kingdom
| | - Tom A. Yates
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Aaron S. Karat
- Department of Infectious Disease Epidemiology, TB Centre, The London School of Hygiene & Tropical Medicine, London, United Kingdom
- The Institute for Global Health and Development, Queen Margaret University, Edinburgh, United Kingdom
| | - Ada W. C. Yan
- Section of Immunology of Infection, Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Kathy S. Baisley
- Department of Infectious Disease Epidemiology, The London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Alison D. Grant
- Department of Infectious Disease Epidemiology, TB Centre, The London School of Hygiene & Tropical Medicine, London, United Kingdom
- Africa Health Research Institute, School of Laboratory Medicine & Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
- School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | - Richard G. White
- Department of Infectious Disease Epidemiology, TB Centre, The London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Nicky McCreesh
- Department of Infectious Disease Epidemiology, TB Centre, The London School of Hygiene & Tropical Medicine, London, United Kingdom
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Horton KC, Hoey AL, Béraud G, Corbett EL, White RG. Systematic Review and Meta-Analysis of Sex Differences in Social Contact Patterns and Implications for Tuberculosis Transmission and Control. Emerg Infect Dis 2021; 26:910-919. [PMID: 32310063 PMCID: PMC7181919 DOI: 10.3201/eid2605.190574] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Social contact patterns might contribute to excess burden of tuberculosis in men. We conducted a study of social contact surveys to evaluate contact patterns relevant to tuberculosis transmission. Available data describe 21 surveys in 17 countries and show profound differences in sex-based and age-based patterns of contact. Adults reported more adult contacts than children. Children preferentially mixed with women in all surveys (median sex assortativity 58%, interquartile range [IQR] 57%–59% for boys, 61% [IQR 60%–63%] for girls). Men and women reported sex-assortative mixing in 80% and 95% of surveys (median sex assortativity 56% [IQR 54%–58%] for men, 59% [IQR 57%–63%] for women). Sex-specific patterns of contact with adults were similar at home and outside the home for children; adults reported greater sex assortativity outside the home in most surveys. Sex assortativity in adult contacts likely contributes to sex disparities in adult tuberculosis burden by amplifying incidence among men.
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Abstract
Early in the COVID-19 pandemic, models predicted hundreds of thousands of additional TB deaths as a result of health service disruption. To date, empirical evidence on the effects of COVID-19 on TB outcomes has been limited. Here we summarise the evidence available at a country level, identifying broad mechanisms by which COVID-19 may modify TB burden and mitigation efforts. From the data, it is clear that there have been substantial disruptions to TB health services and an increase in vulnerability to TB. Evidence for changes in Mycobacterium tuberculosis transmission is limited, and it remains unclear how the resources required and available for the TB response have changed. To advocate for additional funding to mitigate the impact of COVID-19 on the global TB burden, and to efficiently allocate resources for the TB response, requires a significant improvement in the TB data available.
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Affiliation(s)
- C F McQuaid
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine (LSHTM), London, UK
| | - A Vassall
- Department of Global Health Development, Faculty of Public Health and Policy, LSHTM, London, UK
| | - T Cohen
- Yale School of Public Health, Laboratory of Epidemiology and Public Health, New Haven, CT, USA
| | - K Fiekert
- KNCV Tuberculosefonds, The Hague, the Netherlands
| | - R G White
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine (LSHTM), London, UK
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McCreesh N, Grant AD, Yates TA, Karat AS, White RG. Tuberculosis from transmission in clinics in high HIV settings may be far higher than contact data suggest. Int J Tuberc Lung Dis 2021; 24:403-408. [PMID: 32317064 DOI: 10.5588/ijtld.19.0410] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND: In South Africa, it is generally estimated that only 0.5-0.6% of people's contacts occur in clinics. Both people with infectious tuberculosis and people with increased susceptibility to disease progression may spend more time in clinics, however, increasing the importance of clinic-based transmission to overall disease incidence.METHODS: We developed an illustrative mathematical model of Mycobacterium tuberculosis transmission in clinics and other settings. We assumed that 1% of contact time occurs in clinics. We varied the ratio of clinic contact time of human immunodeficiency virus (HIV) positive people compared to HIV-negative people, and of people with infectious TB compared to people without TB, while keeping the overall proportion of contact time occurring in clinics, and each person's total contact time, constant.RESULTS: With clinic contact rates respectively 10 and 5 times higher in HIV-positive people and people with TB, 10.7% (plausible range 8.5-13.4%) of TB resulted from transmission in clinics. With contact rates in HIV-positive people and people with TB respectively 5 and 2 times higher, 5.3% (plausible range 4.3-6.3%) of all TB was due to transmission in clinics.CONCLUSION: The small amount of contact time that generally occurs in clinics may greatly underestimate their contribution to TB disease in high TB-HIV burden settings.
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Affiliation(s)
- N McCreesh
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London
| | - A D Grant
- TB Centre, London School of Hygiene & Tropical Medicine, London, UK, Africa Health Research Institute, School of Nursing and Public Health, University of KwaZulu-Natal, School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | - T A Yates
- Section of Infectious Diseases and Immunity, Imperial College London, London, Institute for Global Health, University College London, London, UK
| | - A S Karat
- TB Centre, London School of Hygiene & Tropical Medicine, London, UK
| | - R G White
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London
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Mendelsohn SC, Fiore-Gartland A, Penn-Nicholson A, Mulenga H, Mbandi SK, Borate B, Hadley K, Hikuam C, Musvosvi M, Bilek N, Erasmus M, Jaxa L, Raphela R, Nombida O, Kaskar M, Sumner T, White RG, Innes C, Brumskine W, Hiemstra A, Malherbe ST, Hassan-Moosa R, Tameris M, Walzl G, Naidoo K, Churchyard G, Scriba TJ, Hatherill M. Validation of a host blood transcriptomic biomarker for pulmonary tuberculosis in people living with HIV: a prospective diagnostic and prognostic accuracy study. Lancet Glob Health 2021; 9:e841-e853. [PMID: 33862012 PMCID: PMC8131200 DOI: 10.1016/s2214-109x(21)00045-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/18/2021] [Accepted: 01/27/2021] [Indexed: 12/21/2022]
Abstract
Background A rapid, blood-based triage test that allows targeted investigation for tuberculosis at the point of care could shorten the time to tuberculosis treatment and reduce mortality. We aimed to test the performance of a host blood transcriptomic signature (RISK11) in diagnosing tuberculosis and predicting progression to active pulmonary disease (prognosis) in people with HIV in a community setting. Methods In this prospective diagnostic and prognostic accuracy study, adults (aged 18–59 years) with HIV were recruited from five communities in South Africa. Individuals with a history of tuberculosis or household exposure to multidrug-resistant tuberculosis within the past 3 years, comorbid risk factors for tuberculosis, or any condition that would interfere with the study were excluded. RISK11 status was assessed at baseline by real-time PCR; participants and study staff were masked to the result. Participants underwent active surveillance for microbiologically confirmed tuberculosis by providing spontaneously expectorated sputum samples at baseline, if symptomatic during 15 months of follow-up, and at 15 months (the end of the study). The coprimary outcomes were the prevalence and cumulative incidence of tuberculosis disease confirmed by a positive Xpert MTB/RIF, Xpert Ultra, or Mycobacteria Growth Indicator Tube culture, or a combination of such, on at least two separate sputum samples collected within any 30-day period. Findings Between March 22, 2017, and May 15, 2018, 963 participants were assessed for eligibility and 861 were enrolled. Among 820 participants with valid RISK11 results, eight (1%) had prevalent tuberculosis at baseline: seven (2·5%; 95% CI 1·2–5·0) of 285 RISK11-positive participants and one (0·2%; 0·0–1·1) of 535 RISK11-negative participants. The relative risk (RR) of prevalent tuberculosis was 13·1 times (95% CI 2·1–81·6) greater in RISK11-positive participants than in RISK11-negative participants. RISK11 had a diagnostic area under the receiver operating characteristic curve (AUC) of 88·2% (95% CI 77·6–96·7), and a sensitivity of 87·5% (58·3–100·0) and specificity of 65·8% (62·5–69·0) at a predefined score threshold (60%). Of those with RISK11 results, eight had primary endpoint incident tuberculosis during 15 months of follow-up. Tuberculosis incidence was 2·5 per 100 person-years (95% CI 0·7–4·4) in the RISK11-positive group and 0·2 per 100 person-years (0·0–0·5) in the RISK11-negative group. The probability of primary endpoint incident tuberculosis was greater in the RISK11-positive group than in the RISK11-negative group (cumulative incidence ratio 16·0 [95% CI 2·0–129·5]). RISK11 had a prognostic AUC of 80·0% (95% CI 70·6–86·9), and a sensitivity of 88·6% (43·5–98·7) and a specificity of 68·9% (65·3–72·3) for incident tuberculosis at the 60% threshold. Interpretation RISK11 identified prevalent tuberculosis and predicted risk of progression to incident tuberculosis within 15 months in ambulant people living with HIV. RISK11's performance approached, but did not meet, WHO's target product profile benchmarks for screening and prognostic tests for tuberculosis. Funding Bill & Melinda Gates Foundation and the South African Medical Research Council.
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Affiliation(s)
- Simon C Mendelsohn
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Andrew Fiore-Gartland
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Adam Penn-Nicholson
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Humphrey Mulenga
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Stanley Kimbung Mbandi
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Bhavesh Borate
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Katie Hadley
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Chris Hikuam
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Munyaradzi Musvosvi
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Nicole Bilek
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Mzwandile Erasmus
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Lungisa Jaxa
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Rodney Raphela
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Onke Nombida
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Masooda Kaskar
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Tom Sumner
- TB Modelling Group, TB Centre, Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Richard G White
- TB Modelling Group, TB Centre, Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Craig Innes
- The Aurum Institute, Johannesburg, South Africa
| | | | - Andriëtte Hiemstra
- DST/NRF Centre of Excellence for Biomedical TB Research, Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa; SAMRC Centre for TB Research, Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
| | - Stephanus T Malherbe
- DST/NRF Centre of Excellence for Biomedical TB Research, Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa; SAMRC Centre for TB Research, Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
| | - Razia Hassan-Moosa
- Centre for the AIDS Programme of Research in South Africa, Durban, South Africa; MRC-CAPRISA HIV-TB Pathogenesis and Treatment Research Unit, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban, South Africa
| | - Michèle Tameris
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Gerhard Walzl
- DST/NRF Centre of Excellence for Biomedical TB Research, Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa; SAMRC Centre for TB Research, Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
| | - Kogieleum Naidoo
- Centre for the AIDS Programme of Research in South Africa, Durban, South Africa; MRC-CAPRISA HIV-TB Pathogenesis and Treatment Research Unit, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban, South Africa
| | - Gavin Churchyard
- The Aurum Institute, Johannesburg, South Africa; School of Public Health, University of Witwatersrand, Johannesburg, South Africa
| | - Thomas J Scriba
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Mark Hatherill
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa.
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Weerasuriya CK, Harris RC, McQuaid CF, Bozzani F, Ruan Y, Li R, Li T, Rade K, Rao R, Ginsberg AM, Gomez GB, White RG. The epidemiologic impact and cost-effectiveness of new tuberculosis vaccines on multidrug-resistant tuberculosis in India and China. BMC Med 2021; 19:60. [PMID: 33632218 PMCID: PMC7908776 DOI: 10.1186/s12916-021-01932-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 01/29/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Despite recent advances through the development pipeline, how novel tuberculosis (TB) vaccines might affect rifampicin-resistant and multidrug-resistant tuberculosis (RR/MDR-TB) is unknown. We investigated the epidemiologic impact, cost-effectiveness, and budget impact of hypothetical novel prophylactic prevention of disease TB vaccines on RR/MDR-TB in China and India. METHODS We constructed a deterministic, compartmental, age-, drug-resistance- and treatment history-stratified dynamic transmission model of tuberculosis. We introduced novel vaccines from 2027, with post- (PSI) or both pre- and post-infection (P&PI) efficacy, conferring 10 years of protection, with 50% efficacy. We measured vaccine cost-effectiveness over 2027-2050 as USD/DALY averted-against 1-times GDP/capita, and two healthcare opportunity cost-based (HCOC), thresholds. We carried out scenario analyses. RESULTS By 2050, the P&PI vaccine reduced RR/MDR-TB incidence rate by 71% (UI: 69-72) and 72% (UI: 70-74), and the PSI vaccine by 31% (UI: 30-32) and 44% (UI: 42-47) in China and India, respectively. In India, we found both USD 10 P&PI and PSI vaccines cost-effective at the 1-times GDP and upper HCOC thresholds and P&PI vaccines cost-effective at the lower HCOC threshold. In China, both vaccines were cost-effective at the 1-times GDP threshold. P&PI vaccine remained cost-effective at the lower HCOC threshold with 49% probability and PSI vaccines at the upper HCOC threshold with 21% probability. The P&PI vaccine was predicted to avert 0.9 million (UI: 0.8-1.1) and 1.1 million (UI: 0.9-1.4) second-line therapy regimens in China and India between 2027 and 2050, respectively. CONCLUSIONS Novel TB vaccination is likely to substantially reduce the future burden of RR/MDR-TB, while averting the need for second-line therapy. Vaccination may be cost-effective depending on vaccine characteristics and setting.
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Affiliation(s)
- Chathika K Weerasuriya
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, UK.
| | - Rebecca C Harris
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, UK.,Currently employed at Sanofi Pasteur, Singapore, Singapore
| | - C Finn McQuaid
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Fiammetta Bozzani
- Department of Global Health and Development, Faculty of Public Health & Policy, London School of Hygiene and Tropical Medicine, London, UK
| | - Yunzhou Ruan
- Chinese Centre for Disease Control and Prevention, Beijing, China
| | - Renzhong Li
- Chinese Centre for Disease Control and Prevention, Beijing, China
| | - Tao Li
- Chinese Centre for Disease Control and Prevention, Beijing, China
| | | | - Raghuram Rao
- National Tuberculosis Elimination Programme, New Delhi, India
| | - Ann M Ginsberg
- International AIDS Vaccine Initiative, New York, USA.,Current Affiliation: Bill and Melinda Gates Foundation, Washington DC, USA
| | - Gabriela B Gomez
- Department of Global Health and Development, Faculty of Public Health & Policy, London School of Hygiene and Tropical Medicine, London, UK.,Currently employed at Sanofi Pasteur, Lyon, France
| | - Richard G White
- TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, UK
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50
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McQuaid CF, Cohen T, Dean AS, Houben RMGJ, Knight GM, Zignol M, White RG. Ongoing challenges to understanding multidrug- and rifampicin-resistant tuberculosis in children versus adults. Eur Respir J 2021; 57:2002504. [PMID: 32855219 DOI: 10.1183/13993003.02504-2020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 08/06/2020] [Indexed: 11/05/2022]
Abstract
Previous analyses suggest that children with tuberculosis (TB) are no more or no less likely to have multidrug (MDR)- or rifampicin-resistant (RR)-TB than adults. However, the availability of new data, particularly for high MDR/RR-TB burden countries, suggest updates of country-specific estimates are warranted.We used data from population-representative surveys and surveillance collected between 2000 and 2018 to compare the odds ratio of MDR/RR-TB among children (aged <15 years) with TB, compared to the odds of MDR/RR-TB among adults (aged ≥15 years) with TB.In most settings (45 out of 55 countries), and globally as a whole, there is no evidence that age is associated with odds of MDR/RR-TB. However, in some settings, such as former Soviet Union countries in general, and Georgia, Kazakhstan, Lithuania, Tajikistan and Uzbekistan in particular, as well as Peru, MDR/RR-TB is positively associated with age ≥15 years. Meanwhile, in Western Europe in general, and the United Kingdom, Poland, Finland and Luxembourg in particular, MDR/RR-TB is positively associated with age <15 years. 16 countries had sufficient data to compare over time between 2000-2011 and 2012-2018, with evidence for decreases in the odds ratio in children compared to adults in Germany, Kazakhstan and the United States of America.Our results support findings that in most settings a child with TB is as likely as an adult with TB to have MDR/RR-TB. However, setting-specific heterogeneity requires further investigation. Furthermore, the odds ratio for MDR/RR-TB in children compared to adults is generally either stable or decreasing. There are important gaps in detection, recording and reporting of drug resistance among paediatric TB cases, limiting our understanding of transmission risks and measures needed to combat the global TB epidemic.
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Affiliation(s)
- C Finn McQuaid
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Dept of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Ted Cohen
- Yale School of Public Health, Laboratory of Epidemiology and Public Health, New Haven, CT, USA
| | - Anna S Dean
- Global Tuberculosis Programme, World Health Organization, Geneva, Switzerland
| | - Rein M G J Houben
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Dept of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Gwenan M Knight
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Dept of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Matteo Zignol
- Global Tuberculosis Programme, World Health Organization, Geneva, Switzerland
| | - Richard G White
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Dept of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
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