Mental illnesses are highly prevalent and can have considerable, enduring consequences for individuals, families, communities, and economies. Despite these high prevalence rates, mental illnesses have not received as much public policy commitment or funding as might be expected. One result is that mental illness often goes unrecognized and untreated. The resultant costs are felt not only in healthcare systems, but across many other sectors, including housing, social care, criminal justice, welfare benefits, and employment. This article sets out the basic principles of economic evaluation, with illustrations in this mental health context. It also discusses the main practical challenges when conducting and interpreting evidence from such evaluations. Decisions about whether to spend resources on a treatment or prevention strategy are based on whether it is likely to be effective in avoiding, reducing, or curing symptoms, improving quality of life, or achieving other individual-level outcomes. The economic evaluation question is whether the outcomes achieved are sufficient to justify the cost that is incurred in delivering the intervention. An economic evaluation has five elements: clarification of the question to be addressed; specification of the intervention to be evaluated and with what alternative it is being compared; the outcomes to be measured; the costs to be measured (including the cost of implementing the intervention and any savings that might accrue); and finally, how outcome and cost findings are to be blended to make a recommendation to the decision-maker. Sometimes, if an evaluation finds that one intervention has better outcomes but higher costs, then the evaluation should also how one (the outcomes) might be trade-off for the other (the costs). The article illustrates how economic evaluations have been undertaken and employed to address a range of questions, from the very strategic issue to the more specific clinical question. The purpose of the study can, to some extent, determine the type of evaluation that is needed. Examples of evaluations are given in a number of areas: perinatal maternal mental illness; parenting programs for conduct disorder; anti-bullying programs in schools; early intervention services for psychosis; individual placement and support; collaborative care for physical health problems; and suicide prevention. The challenges of economic evaluation are discussed, specifically in the mental health field.
To guide climate change policymaking, we need to understand how technologies and behaviors should be transformed to avoid dangerous levels of global warming and what the implications of failing to bring forward such transformation might be. Integrated assessment models (IAMs) are computational tools developed by engineers, earth and natural scientists, and economists to provide projections of interconnected human and natural systems under various conditions. These models help researchers to understand possible implications of climate inaction. They evaluate the effects of national and international policies on global emissions and devise optimal emissions trajectories in line with long-term temperature targets and their implications for infrastructure, investment, and behavior. This research highlights the deep interconnection between climate policies and other sustainable development objectives. Evolving and focusing on one or more of these key policy questions, the large family of IAMs includes a wide array of tools that incorporate multiple dimensions and advances from a range of scientific fields.
Michael Drummond, Rosanna Tarricone, and Aleksandra Torbica
There are a number of challenges in the economic evaluation of medical devices (MDs). They are typically less regulated than pharmaceuticals, and the clinical evidence requirements for market authorization are generally lower. There are also specific characteristics of MDs, such as the device–user interaction (learning curve), the incremental nature of innovation, the dynamic nature of pricing, and the broader organizational impact. Therefore, a number of initiatives need to be taken in order to facilitate the economic evaluation of MDs. First, the regulatory processes for MDs need to be strengthened and more closely aligned to the needs of economic evaluation. Second, the methods of economic evaluation need to be enhanced by improving the analysis of the available clinical data, establishing high-quality clinical registries, and better recognizing MDs’ specific characteristics. Third, the market entry and diffusion of MDs need to be better managed by understanding the key influences on MD diffusion and linking diffusion with cost-effectiveness evidence through the use of performance-based risk-sharing arrangements.
Economic evaluation provides a framework to help inform decisions on which technologies represent the best use of healthcare resources (i.e., are cost-effective) by bringing together the available evidence about the benefits and costs of the alternative options. Critical to the economic evaluation framework is the need to accurately characterize the decision problem—this is the problem-structuring stage. Problem structuring encompasses the characterization of the target population; identification of the decision options to compare in the model (e.g., use of the technology in different ways, standard of care, etc.); and the development of the conceptual model, which maps out how the decision options relate to the costs and benefits in the target population. Problem structuring is central to the application of the economic evaluation framework and to development of the analysis, as it determines the specific questions that can be addressed and affects the relevance and credibility of the results. The methodological guidelines discuss problem structuring to some extent, although the practical implications warrant further consideration. With respect to the target population, questions emerge about how to define it, whether and which sources of heterogeneity to consider, and when and in whom to consider spillovers. Relating to the specification of decision options are questions about how to identify and select them, including restricting the comparison to standard of care, sequences of tests and/or treatments, and “do-nothing” approaches. There are also issues relating to the role and the process of development of the conceptual model. Based on a review of methodological guidelines and reflections on their implications, various recommendations for practice emerge. The process of developing the conceptual model and how to use it to inform choices and assumptions in the economic evaluation are two areas where further research is warranted.
Eline Aas, Emily Burger, and Kine Pedersen
The objective of medical screening is to prevent future disease (secondary prevention) or to improve prognosis by detecting the disease at an earlier stage (early detection). This involves examination of individuals with no symptoms of disease. Introducing a screening program is resource demanding, therefore stakeholders emphasize the need for comprehensive evaluation, where costs and health outcomes are reasonably balanced, prior to population-based implementation. Economic evaluation of population-based screening programs involves quantifying health benefits (e.g., life-years gained) and monetary costs of all relevant screening strategies. The alternative strategies can vary by starting- and stopping-age, frequency of the screening and follow-up regimens after a positive test result. Following evaluation of all strategies, the efficiency frontier displays the efficient strategies and the country-specific cost-effectiveness threshold is used to determine the optimal, i.e., most cost-effective, screening strategy. Similar to other preventive interventions, the costs of screening are immediate, while the health benefits accumulate after several years. Hence, the effect of discounting can be substantial when estimating the net present value (NPV) of each strategy. Reporting both discounting and undiscounted results is recommended. In addition, intermediate outcome measures, such as number of positive tests, cases detected, and events prevented, can be valuable supplemental outcomes to report. Estimating the cost-effectiveness of alternative screening strategies is often based on decision-analytic models, synthesizing evidence from clinical trials, literature, guidelines, and registries. Decision-analytic modeling can include evidence from trials with intermediate or surrogate endpoints and extrapolate to long-term endpoints, such as incidence and mortality, by means of sophisticated calibration methods. Furthermore, decision-analytic models are unique, as a large number of screening alternatives can be evaluated simultaneously, which is not feasible in a randomized controlled trial (RCT). Still, evaluation of screening based on RCT data are valuable as both costs and health benefits are measured for the same individual, enabling more advanced analysis of the interaction of costs and health benefits. Evaluation of screening involves multiple stakeholders and other considerations besides cost-effectiveness, such as distributional concerns, severity of the disease, and capacity influence decision-making. Analysis of harm-benefit trade-offs is a useful tool to supplement cost-effectiveness analyses. Decision-analytic models are often based on 100% participation, which is rarely the case in practice. If those participating are different from those not choosing to participate, with regard to, for instance, risk of the disease or condition, this would result in selection bias, and the result in practice could deviate from the results based on 100% participation. The development of new diagnostics or preventive interventions requires re-evaluation of the cost-effectiveness of screening. For example, if treatment of a disease becomes more efficient, screening becomes less cost-effective. Similarly, the introduction of vaccines (e.g., HPV-vaccination for cervical cancer) may influence the cost-effectiveness of screening. With access to individual level data from registries, there is an opportunity to better represent heterogeneity and long-term consequences of screening on health behavior in the analysis.
Mónica Hernández Alava
The assessment of health-related quality of life is crucially important in the evaluation of healthcare technologies and services. In many countries, economic evaluation plays a prominent role in informing decision making often requiring preference-based measures (PBMs) to assess quality of life. These measures comprise two aspects: a descriptive system where patients can indicate the impact of ill health, and a value set based on the preferences of individuals for each of the health states that can be described. These values are required for the calculation of quality adjusted life years (QALYs), the measure for health benefit used in the vast majority of economic evaluations. The National Institute for Health and Care Excellence (NICE) has used cost per QALY as its preferred framework for economic evaluation of healthcare technologies since its inception in 1999. However, there is often an evidence gap between the clinical measures that are available from clinical studies on the effect of a specific health technology and the PBMs needed to construct QALY measures. Instruments such as the EQ-5D have preference-based scoring systems and are favored by organizations such as NICE but are frequently absent from clinical studies of treatment effect. Even where a PBM is included this may still be insufficient for the needs of the economic evaluation. Trials may have insufficient follow-up, be underpowered to detect relevant events, or include the wrong PBM for the decision- making body. Often this gap is bridged by “mapping”—estimating a relationship between observed clinical outcomes and PBMs, using data from a reference dataset containing both types of information. The estimated statistical model can then be used to predict what the PBM would have been in the clinical study given the available information. There are two approaches to mapping linked to the structure of a PBM. The indirect approach (or response mapping) models the responses to the descriptive system using discrete data models. The expected health utility is calculated as a subsequent step using the estimated probability distribution of health states. The second approach (the direct approach) models the health state utility values directly. Statistical models routinely used in the past for mapping are unable to consider the idiosyncrasies of health utility data. Often they do not work well in practice and can give seriously biased estimates of the value of treatments. Although the bias could, in principle, go in any direction, in practice it tends to result in underestimation of cost effectiveness and consequently distorted funding decisions. This has real effects on patients, clinicians, industry, and the general public. These problems have led some analysts to mistakenly conclude that mapping always induces biases and should be avoided. However, the development and use of more appropriate models has refuted this claim. The need to improve the quality of mapping studies led to the formation of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Mapping to Estimate Health State Utility values from Non-Preference-Based Outcome Measures Task Force to develop good practice guidance in mapping.
Ciaran N. Kohli-Lynch and Andrew H. Briggs
Cost-effectiveness analysis is conducted with the aim of maximizing population-level health outcomes given an exogenously determined budget constraint. Considerable health economic benefits can be achieved by reflecting heterogeneity in cost-effectiveness studies and implementing interventions based on this analysis. The following article describes forms of subgroup and heterogeneity in patient populations. It further discusses traditional decision rules employed in cost-effectiveness analysis and shows how these can be adapted to account for heterogeneity. This article discusses the theoretical basis for reflecting heterogeneity in cost-effectiveness analysis and methodology that can be employed to conduct such analysis. Reflecting heterogeneity in cost-effectiveness analysis allows decision-makers to define limited use criteria for treatments with a fixed price. This ensures that only those patients who are cost-effective to treat receive an intervention. Moreover, when price is not fixed, reflecting heterogeneity in cost-effectiveness analysis allows decision-makers to signal demand for healthcare interventions and ensure that payers achieve welfare gains when investing in health.
Anna Vassall, Fiammetta Bozzani, and Kara Hanson
In order to secure effective service access, coverage, and impact, it is increasingly recognized that the introduction of novel health technologies such as diagnostics, drugs, and vaccines may require additional investment to address the constraints under which many health systems operate. Health-system constraints include a shortage of health workers, ineffective supply chains, or inadequate information systems, or organizational constraints such as weak incentives and poor service integration. Decision makers may be faced with the question of whether to invest in a new technology, including the specific health system strengthening needed to ensure effective implementation; or they may be seeking to optimize resource allocation across a range of interventions including investment in broad health system functions or platforms. Investment in measures to address health-system constraints therefore increasingly need to undergo economic evaluation, but this poses several methodological challenges for health economists, particularly in the context of low- and middle-income countries. Designing the appropriate analysis to inform investment decisions concerning new technologies incorporating health systems investment can be broken down into several steps. First, the analysis needs to comprehensively outline the interface between the new intervention and the system through which it is to be delivered, in order to identify the relevant constraints and the measures needed to relax them. Second, the analysis needs to be rooted in a theoretical approach to appropriately characterize constraints and consider joint investment in the health system and technology. Third, the analysis needs to consider how the overarching priority- setting process influences the scope and output of the analysis informing the way in which complex evidence is used to support the decision, including how to represent and manage system wide trade-offs. Finally, there are several ways in which decision analytical models can be structured, and parameterized, in a context of data scarcity around constraints. This article draws together current approaches to health system thinking with the emerging literature on analytical approaches to integrating health-system constraints into economic evaluation to guide economists through these four issues. It aims to contribute to a more health-system-informed approach to both appraising the cost-effectiveness of new technologies and setting priorities across a range of program activities.
Andrea Gabrio, Gianluca Baio, and Andrea Manca
The evidence produced by healthcare economic evaluation studies is a key component of any Health Technology Assessment (HTA) process designed to inform resource allocation decisions in a budget-limited context. To improve the quality (and harmonize the generation process) of such evidence, many HTA agencies have established methodological guidelines describing the normative framework inspiring their decision-making process. The information requirements that economic evaluation analyses for HTA must satisfy typically involve the use of complex quantitative syntheses of multiple available datasets, handling mixtures of aggregate and patient-level information, and the use of sophisticated statistical models for the analysis of non-Normal data (e.g., time-to-event, quality of life and costs). Much of the recent methodological research in economic evaluation for healthcare has developed in response to these needs, in terms of sound statistical decision-theoretic foundations, and is increasingly being formulated within a Bayesian paradigm. The rationale for this preference lies in the fact that by taking a probabilistic approach, based on decision rules and available information, a Bayesian economic evaluation study can explicitly account for relevant sources of uncertainty in the decision process and produce information to identify an “optimal” course of actions. Moreover, the Bayesian approach naturally allows the incorporation of an element of judgment or evidence from different sources (e.g., expert opinion or multiple studies) into the analysis. This is particularly important when, as often occurs in economic evaluation for HTA, the evidence base is sparse and requires some inevitable mathematical modeling to bridge the gaps in the available data. The availability of free and open source software in the last two decades has greatly reduced the computational costs and facilitated the application of Bayesian methods and has the potential to improve the work of modelers and regulators alike, thus advancing the fields of economic evaluation of healthcare interventions. This chapter provides an overview of the areas where Bayesian methods have contributed to the address the methodological needs that stem from the normative framework adopted by a number of HTA agencies.