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McGuire F, Mohan S, Walker S, Nabyonga-Orem J, Ssengooba F, Kataika E, Revill P. Adapting Economic Evaluation Methods to Shifting Global Health Priorities: Assessing the Value of Health System Inputs. Value Health Reg Issues 2024; 39:31-39. [PMID: 37976775 DOI: 10.1016/j.vhri.2023.08.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 07/11/2023] [Accepted: 08/07/2023] [Indexed: 11/19/2023]
Abstract
OBJECTIVES We highlight the importance of undertaking value assessments for health system inputs if allocative efficiency is to be achieve with health sector resources, with a focus on low- and middle-income countries. However, methodological challenges complicated the application of current economic evaluation techniques to health system input investments. METHODS We undertake a review of the literature to examine how assessments of investments in health system inputs have been considered to date, highlighting several studies that have suggested ways to address the methodological issues. Additionally, we surveyed how empirical economic evaluations of health system inputs have approached these issues. Finally, we highlight the steps required to move toward a comprehensive standardized framework for undertaking economic evaluations to make value assessments for investments in health systems. RESULTS Although the methodological challenges have been illustrated, a comprehensive framework for value assessments of health system inputs, guiding the evidence required, does not exist. The applied literature of economic evaluations of health system inputs has largely ignored the issues, likely resulting in inaccurate assessments of cost-effectiveness. CONCLUSIONS A majority of health sector budgets are spent on health system inputs, facilitating the provision of healthcare interventions. Although economic evaluation methods are a key component in priority setting for healthcare interventions, such methods are less commonly applied to decision making for investments in health system inputs. Given the growing agenda for investments in health systems, a framework will be increasingly required to guide governments and development partners in prioritizing investments in scarce health sector budgets.
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Affiliation(s)
- Finn McGuire
- Centre for Health Economics, University of York, York, England, UK.
| | - Sakshi Mohan
- Centre for Health Economics, University of York, York, England, UK
| | - Simon Walker
- Centre for Health Economics, University of York, York, England, UK
| | - Juliet Nabyonga-Orem
- Inter-Country Support Team for Eastern and Southern Africa, UHC Life Course Cluster, World Health Organization, Brazzaville, Republic of Congo; Centre for Health Professions Education, Faculty of Health Sciences, North-West University, Potchefstroom, South Africa
| | - Freddie Ssengooba
- Department of Health Policy, Planning and Management, School of Public Health, Makerere University, Kampala, Uganda
| | - Edward Kataika
- East, Central and Southern Africa Health Community, Arusha, Tanzania
| | - Paul Revill
- Centre for Health Economics, University of York, York, England, UK
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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] [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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Kretzschmar ME, Ashby B, Fearon E, Overton CE, Panovska-Griffiths J, Pellis L, Quaife M, Rozhnova G, Scarabel F, Stage HB, Swallow B, Thompson RN, Tildesley MJ, Villela D. Challenges for modelling interventions for future pandemics. Epidemics 2022; 38:100546. [PMID: 35183834 PMCID: PMC8830929 DOI: 10.1016/j.epidem.2022.100546] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 02/04/2022] [Accepted: 02/09/2022] [Indexed: 12/16/2022] Open
Abstract
Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used to highlight the challenges for future pandemic control. We consider the availability and use of data, as well as the need for correct parameterisation and calibration for different model frameworks. We discuss challenges that arise in describing and distinguishing between different interventions, within different modelling structures, and allowing both within and between host dynamics. We also highlight challenges in modelling the health economic and political aspects of interventions. Given the diversity of these challenges, a broad variety of interdisciplinary expertise is needed to address them, combining mathematical knowledge with biological and social insights, and including health economics and communication skills. Addressing these challenges for the future requires strong cross-disciplinary collaboration together with close communication between scientists and policy makers.
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Affiliation(s)
- Mirjam E Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Ben Ashby
- Department of Mathematical Sciences, University of Bath, Bath BA2 7AY, UK
| | - Elizabeth Fearon
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, UK
| | - Christopher E Overton
- Department of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; Clinical Data Science Unit, Manchester University NHS Foundation Trust, UK
| | - Jasmina Panovska-Griffiths
- The Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK; The Queen's College, University of Oxford, Oxford, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; The Alan Turing Institute, London, UK
| | - Matthew Quaife
- TB Modelling Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, UK
| | - Ganna Rozhnova
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; BioISI-Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Francesca Scarabel
- Department of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; CDLab - Computational Dynamics Laboratory, Department of Mathematics, Computer Science and Physics, University of Udine, Italy
| | - Helena B Stage
- Department of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; University of Potsdam, Germany; Humboldt University of Berlin, Germany
| | - Ben Swallow
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK; Scottish Covid-19 Response Consortium, UK
| | - Robin N Thompson
- Joint UNIversities Pandemic and Epidemiological Research, UK; Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry CV4 7AL, UK
| | - Michael J Tildesley
- Joint UNIversities Pandemic and Epidemiological Research, UK; Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry CV4 7AL, UK
| | - Daniel Villela
- Program of Scientific Computing, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
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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] [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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Building resource constraints and feasibility considerations in mathematical models for infectious disease: A systematic literature review. Epidemics 2021; 35:100450. [PMID: 33761447 PMCID: PMC8207450 DOI: 10.1016/j.epidem.2021.100450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 11/20/2020] [Accepted: 03/10/2021] [Indexed: 02/01/2023] Open
Abstract
Mathematical model capabilities to explore complex systems now enable priority-setting to consider local resource constraints. Common objectives of model-based analyses incorporating constraints are to assess real-world feasibility or allocate resources efficiently. Constraints may be incorporated via (i) model-based estimation; (ii) linkage of mathematical and health system models; or (iii) optimisation. Models can then project constrained intervention effects and costs and resource requirement s for delivering interventions at full scale. 'Health system constraints' should be systematically defined for routine operationalisation in model-based priority-setting.
Priority setting for infectious disease control is increasingly concerned with physical input constraints and other real-world restrictions on implementation and on the decision process. These health system constraints determine the ‘feasibility’ of interventions and hence impact. However, considering them within mathematical models places additional demands on model structure and relies on data availability. This review aims to provide an overview of published methods for considering constraints in mathematical models of infectious disease. We systematically searched the literature to identify studies employing dynamic transmission models to assess interventions in any infectious disease and geographical area that included non-financial constraints to implementation. Information was extracted on the types of constraints considered and how these were identified and characterised, as well as on the model structures and techniques for incorporating the constraints. A total of 36 studies were retained for analysis. While most dynamic transmission models identified were deterministic compartmental models, stochastic models and agent-based simulations were also successfully used for assessing the effects of non-financial constraints on priority setting. Studies aimed to assess reductions in intervention coverage (and programme costs) as a result of constraints preventing successful roll-out and scale-up, and/or to calculate costs and resources needed to relax these constraints and achieve desired coverage levels. We identified three approaches for incorporating constraints within the analyses: (i) estimation within the disease transmission model; (ii) linking disease transmission and health system models; (iii) optimising under constraints (other than the budget). The review highlighted the viability of expanding model-based priority setting to consider health system constraints. We show strengths and limitations in current approaches to identify and quantify locally-relevant constraints, ranging from simple assumptions to structured elicitation and operational models. Overall, there is a clear need for transparency in the way feasibility is defined as a decision criteria for its systematic operationalisation within models.
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