Beyond cost-effectiveness: Using systems analysis for infectious disease preparedness

Phelps, Charles E.; Madhavan, Guruprasad; Rappuoli, Rino; Colwell, Rita R.; Fineberg, Harvey V.

doi:10.1016/j.vaccine.2016.08.090

Cited by 13 publications

(16 citation statements)

References 5 publications

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“…So the selection of a group of comparable interventions is an incomplete and defective approach. Rankings of investment priorities can readily shift importantly when other criteria—such as public fear during a disease outbreak—enter the analysis beyond cost-effectiveness [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

“…Both cost–benefit and cost-effectiveness approaches share a common defect: they are narrow, and cannot include practical critical factors such as the distribution of benefits and costs, broader impact of societal programs, public values and perceptions, and related matters [ 10 ]. The importance of these distributional issues have been considered [ 8 , 11 – 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Resource allocation in decision support frameworks

Phelps

Madhavan

2018

Cost Eff Resour Alloc

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BackgroundCost–benefit and cost-effectiveness analysis place limits on the dimensions of value that the models can incorporate. Cost–benefit analysis requires monetization of all measures of value (including life), a task sometimes deemed either difficult to accomplish or even repugnant. Cost-effectiveness analyses include health care gains in natural units (e.g., quality-adjusted life years or QALYs) rather than purely monetizing them (e.g., in dollars) and offers an efficiency perspective based on the ratio of cost per QALYs or similar health measures. These two methods use different rules for investment. Cost–benefit analysis says to invest whenever benefits exceed costs. Cost-effectiveness analysis says to invest if the intervention has a cost per QALY that meets—or is below—a designated cutoff value.MethodsMulti-criteria frameworks expand decision analyses by considering value tradeoffs from decision makers, and then producing a synthetic measure that summarizes the performance of investment options. This evaluation is done across all chosen dimensions of value, based on the weights provided by the decision makers, but this flexibility comes at a cost. To date, no approach is widely accepted to suggest how much to invest (how to determine a budget constraint) using multi-attribute models. Moreover, there is no agreed-upon method to measure willingness to pay for incremental multi-attribute value improvements. Our paper proposes a way forward.ResultsBased on existing dollar estimates of willingness to pay for QALYs, our concept creates a comparable cutoff for multi-criteria value measures. Our proposed method expands the acceptable cost per QALYs in proportion to how much of the total measure is accounted for by the QALY component. Agreed-upon values for cost per QALY are thus extrapolated to account for extra value created by non-QALY attributes of each intervention.ConclusionUsing our proposed methods, the cost per QALY cutoff can serve as a benchmark toward creating a resource allocation cutoff in multi-criteria frameworks.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resource allocation in decision support frameworks

Phelps

Madhavan

2018

Cost Eff Resour Alloc

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“…The sudden and major Ebola outbreak in 2014 and the more recent outbreak of Zika in Brazil highlight the benefits of this approach-data can readily be added to evaluate an Ebola or Zika vaccine against other candidates. The Ebola outbreak also provided a clear example of how the ability to formally incorporate the values of more than one attribute of a vaccine (e.g., costeffectiveness) can lead to markedly different rankings than single metric ranks provide [25]. Static processes cannot achieve this flexibility.…”

Section: Beyond Priority-settingmentioning

confidence: 99%

Planning and priority setting for vaccine development and immunization

Phelps

Madhavan

Gellin³

2017

Vaccine

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We review a sequence of strategic planning efforts over time in the United States, all involving processes to prioritize new vaccine candidates. The Institute of Medicine of the National Academies of Sciences, Engineering, and Medicine has been involved in three priority setting processes, each using different metrics and methodologies: infant mortality equivalents (1985-1986), cost-effectiveness (2000), and more recently, the implementation of a software system based on a broader multi-criteria systems approach that can include either of the earlier metrics among other various considerations (2015). The systems approach offers users the flexibility to select, combine, rank, weigh and evaluate different attributes representing their perspectives, assumptions, and particular needs. This approach also overcomes concerns relating to the previous single-metric ranking approaches that yielded lists that, once published, were static, and could not readily accommodate new information about emerging pathogens, new scientific advances, or changes in the costs and performance features of interventions. We discuss the rationale and reasoning behind the design of this multi-criteria decision support approach, stakeholder feedback about the tool, and highlight the potential advantages from using this expanded approach to better inform and support vaccine policies.

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“…Mathematical epidemiological models have a long history of examining the impact of both prophylaxis and treatment on the dynamics of disease within populations [1][2][3][4][5]. These examinations have incorporated certain aspects of financial concerns, usually when considering cost-benefit analyses of population-level investment into particular interventions, such as vaccination [6][7][8]. Many economic epidemiology models have been built to explore both targeted and non-target specific vaccination to determine maximal effectiveness at population-wide reduction of both incidence and/or disease-related mortality [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

How resource limitations and household economics may compromise efforts to safeguard children during outbreaks

2020

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Background: Epidemiological models have been employed with great success to explore the efficacy of alternative strategies at combating disease outbreaks. These models have often incorporated an understanding of age-based susceptibility and severity of outcome, considering how to limit the adverse outcomes or disease burden relative to an age structure. Such models frequently recommend the preferential treatment/vaccination of children or the elderly, demonstrating how prevention of serious disease within these etiological subgroups can provide both protection within the subgroup itself and indirect protection to the broader population. However, it is most frequently the case that these target populations are consumers, rather than providers, of household resources. In areas of the globe where continued health of household members relies on continued provision of resources, these models may fail to provide the most effective overall strategies for health outcomes in both target populations and overall. This is particularly important for tropical diseases impacting rural and low-income areas in which the disease may be endemic or newly emergent, particularly in the wake of natural disasters. Methods: We propose a modified epidemiological model with targeted treatment in resource-limited populations. We evaluate the model over a broad parameter space. Results: This model demonstrates how economic limitations may shift the optimal strategy. It may be advantageous to treat populations at lesser direct risk if they are responsible for providing secondary protection to higher-risk population(s) by producing household resources. Evaluation of this model over the parameter space reveals that, in some cases, targeting treatment towards consumers may result in greater numbers of consumer infections. Conclusions: Our results demonstrate how household resource limitation can drastically affect the impact of targeted treatment strategies for limiting epidemics. Depending on the economic circumstances, it is possible that focusing treatment on consumers such as children can produce a counter-intuitive outcome in which more children contract the disease.

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Beyond cost-effectiveness: Using systems analysis for infectious disease preparedness

Cited by 13 publications

References 5 publications

Resource allocation in decision support frameworks

Resource allocation in decision support frameworks

Planning and priority setting for vaccine development and immunization

How resource limitations and household economics may compromise efforts to safeguard children during outbreaks

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