Avoiding Trouble Ahead: Lessons Learned and Suggestions for Economic Evaluations of COVID-19 Vaccines

COVID-19-related health utility values and changes in COVID-19 patients and the general population: a scoping review

PURPOSE

To summarise the diverse literature reporting the impact of COVID-19 on health utility in COVID-19 patients as well as in general populations being affected by COVID-19 control policies.

METHODS

A literature search up to April 2023 was conducted to identify papers reporting health utility in COVID-19 patients or in COVID-19-affected general populations. We present a narrative synthesis of the health utility values/losses of the retained studies to show the mean health utility values/losses with 95% confidence intervals. Mean utility values/losses for categories defined by medical attendance and data collection time were calculated using random-effects models.

RESULTS

In total, 98 studies-68 studies on COVID-19 patients and 30 studies on general populations-were retained for detailed review. Mean (95% CI) health utility values were 0.83 (0.81, 0.86), 0.78 (0.73, 0.83), 0.82 (0.78, 0.86) and 0.71 (0.65, 0.78) for general populations, non-hospitalised, hospitalised and ICU patients, respectively, irrespective of the data collection time. Mean utility losses in patients and general populations ranged from 0.03 to 0.34 and from 0.02 to 0.18, respectively.

CONCLUSIONS

This scoping review provides a summary of the health utility impact of COVID-19 and COVID-19 control policies. COVID-19-affected populations were reported to have poor health utility, while a high degree of heterogeneity was observed across studies. Population- and/or country-specific health utility is recommended for use in future economic evaluation on COVID-19-related interventions.

Assessing the cost-effectiveness of COVID-19 vaccines in a low incidence and low mortality setting: the case of Thailand at start of the pandemic

OBJECTIVE

This study aimed to assess the cost-effectiveness of COVID-19 vaccines, preferred COVID-19 vaccine profiles, and the preferred vaccination strategies in Thailand.

METHODS

An age-structured transmission dynamic model was developed based on key local data to evaluate economic consequences, including cost and health outcome in terms of life-years (LYs) saved. We considered COVID-19 vaccines with different profiles and different vaccination strategies such as vaccinating elderly age groups (over 65s) or high-incidence groups, i.e. adults between 20 and 39 years old who have contributed to more than 60% of total COVID-19 cases in the country thus far. Analyses employed a societal perspective in a 1-year time horizon using a cost-effectiveness threshold of 160,000 THB per LY saved. Deterministic and probabilistic sensitivity analyses were performed to identify and characterize uncertainty in the model.

RESULTS

COVID-19 vaccines that block infection combined with social distancing were cost-saving regardless of the target population compared to social distancing alone (with no vaccination). For vaccines that block infection, the preferred (cost-effective) strategy was to vaccinate the high incidence group. Meanwhile, COVID-19 vaccines that reduces severity (including hospitalization and mortality) were cost-effective when the elderly were vaccinated, while vaccinating the high-incidence group was not cost-effective with this vaccine type. Regardless of vaccine type, higher vaccination coverage, higher efficacy, and longer protection duration were always preferred. More so, vaccination with social distancing measures was always preferred to strategies without social distancing. Quarantine-related costs were a major cost component affecting the cost-effectiveness of COVID-19 vaccines.

CONCLUSION

COVID-19 vaccines are good value for money even in a relatively low-incidence and low-mortality setting such as Thailand, if the appropriate groups are vaccinated. The preferred vaccination strategies depend on the type of vaccine efficacy. Social distancing measures should accompany a vaccination strategy.

How can health technology assessment support our response to public health emergencies?

Public health emergencies (PHEs), such as the COVID-19 crisis, are threats to global health and public order. We recommend that countries bolster their PHE responses by investing in health technology assessment (HTA), defined as a systematic process of gathering pertinent information on and evaluating health technologies from a medical, economic, social and ethical standpoint. We present examples of how HTA organizations in low- and middle-income countries have adapted to supporting PHE-related decisions during COVID-19 and describe the ways HTA can help the response to a PHE. In turn, we advocate for HTA capacity to be further developed globally and for increased institutional acceptance of these methods as a building block for preparedness and response to future PHEs. Finally, the long-term potential of HTA in strengthening health systems and embedding confidence and transparency into scientific policy should be recognized.

Modelling the Future Clinical and Economic Burden of Antimicrobial Resistance: The Feasibility and Value of Models to Inform Policy

Due to the increasing threat to public health and the economy, governments internationally are interested in models to estimate the future clinical and economic burden of antimicrobial resistance (AMR) and to evaluate the cost-effectiveness of interventions to prevent or control resistance and to inform resource-allocation decision making. A widely cited UK report estimated that 10 million additional deaths will occur globally per annum due to AMR by 2050; however, the utility and accuracy of this prediction has been challenged. The precision of models predicting the future economic burden of AMR is dependent upon the accuracy of predicting future resistance rates. This paper reviews the feasibility and value of modelling to inform policy and resource allocation to manage and curb AMR. Here we describe methods used to estimate future resistance in published burden-of-disease models; the sources of uncertainty are highlighted, which could potentially mislead policy decision-making. While broad assumptions can be made regarding some predictable factors contributing to future resistance rates, the unexpected emergence, establishment and spread of new resistance genes introduces substantial uncertainty into estimates of future economic burden, and in models evaluating the effectiveness of interventions or policies to address AMR. Existing reporting standards for best practice in modelling should be adapted to guide the reporting of AMR economic models, to ensure model transparency and validation for interpretation by policymakers.

Recalibrating the notion of modelling for policymaking during pandemics

COVID-19 disease models have aided policymakers in low-and middle-income countries (LMICs) with many critical decisions. Many challenges remain surrounding their use, from inappropriate model selection and adoption, inadequate and untimely reporting of evidence, to the lack of iterative stakeholder engagement in policy formulation and deliberation. These issues can contribute to the misuse of models and hinder effective policy implementation. Without guidance on how to address such challenges, the true potential of such models may not be realised. The COVID-19 Multi-Model Comparison Collaboration (CMCC) was formed to address this gap. CMCC is a global collaboration between decision-makers from LMICs, modellers and researchers, and development partners. To understand the limitations of existing COVID-19 disease models (primarily from high income countries) and how they could be adequately support decision-making in LMICs, a desk review of modelling experience during the COVID-19 and past disease outbreaks, two online surveys, and regular online consultations were held among the collaborators. Three key recommendations from CMCC include: A ‘fitness-for-purpose’ flowchart, a tool that concurrently walks policymakers (or their advisors) and modellers through a model selection and development process. The flowchart is organised around the following: policy aims, modelling feasibility, model implementation, model reporting commitment. Holmdahl and Buckee (2020) A ‘reporting standards trajectory’, which includes three gradually increasing standard of reports, ‘minimum’, ‘acceptable’, and ‘ideal’, and seeks collaboration from funders, modellers, and decision-makers to enhance the quality of reports over time and accountability of researchers. Malla et al. (2018) A framework for “collaborative modelling for effective policy implementation and evaluation” which extends the definition of stakeholders to funders, ground-level implementers, public, and other researchers, and outlines how each can contribute to modelling. We advocate for standardisation of modelling processes and adoption of country-owned model through iterative stakeholder participation and discuss how they can enhance trust, accountability, and public ownership to decisions.

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COVID-19 models need appropriate adaptation to reflect contextual differences across settings.

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Upholding scientific standards is equally important as providing evidence for policymaking during pandemics.

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Wider stakeholder engagement with an iterative process for re-evaluating decisions is required for effective policy implementation.