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This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Economics and Finance. Please check back later for the full article.
Outcomes from individuals often depend on their age, period, and cohort, where cohort + age = period. An example is consumption, where consumption patterns change with age, but the availability of product changes over time, the period, and this affects individuals of different birth years, the cohort, differently. Age-period-cohort models are linear models allowing different parameter values for each level of age, period, and cohort. Variations of the models are available for data aggregated over age, period, and cohort and for data stemming from repeated cross-sections, where the time effects can be combined with individual covariates. The models could potentially be extended to panel data. It is common to plot the estimated age, period, and cohort effects and analyze them as time series. Further, it is also common to conduct inference on the inclusion of the different time effects, and to use the models for forecasting, which involves extrapolation of the time effects.
The age, period, and cohort time effects are intertwined. Specifically, inclusion of an indicator variable for each level of age, period, and cohort will result in a collinarity, which is referred to as the age-period-cohort identification problem. A first approach to addressing the collinarity is to leave out a suitable number of indicator variables. This gives some difficulties in the interpretation, inference, and forecasting in relation to the time effects. A second approach is the canonical parametrization that is a freely varying parametrization, which is invariant to the identification problem and therefore more amenable to interpretation, inference, and forecasting.
Martin Karlsson, Tor Iversen, and Henning Øien
An open issue in the economics literature is whether health care expenditure (HCE) is so concentrated in the last years before death that the age profiles in spending will change when longevity increases. The seminal article “Ageing of Population and Health Care Expenditure: A Red Herring?” by Zweifel and colleagues argued that that age is a distraction in explaining growth in HCE. The argument was based on the observation that age did not predict HCE after controlling for time to death (TTD). The authors were soon criticized for the use of a Heckman selection model in this context. Most of the recent literature makes use of variants of a two-part model and seems to give some role to age as well in the explanation. Age seems to matter more for long-term care expenditures (LTCE) than for acute hospital care. When disability is accounted for, the effects of age and TTD diminish. Not many articles validate their approach by comparing properties of different estimation models. In order to evaluate popular models used in the literature and to gain an understanding of the divergent results of previous studies, an empirical analysis based on a claims data set from Germany is conducted. This analysis generates a number of useful insights. There is a significant age gradient in HCE, most for LTCE, and costs of dying are substantial. These “costs of dying” have, however, a limited impact on the age gradient in HCE. These findings are interpreted as evidence against the “red herring” hypothesis as initially stated. The results indicate that the choice of estimation method makes little difference and if they differ, ordinary least squares regression tends to perform better than the alternatives. When validating the methods out of sample and out of period, there is no evidence that including TTD leads to better predictions of aggregate future HCE. It appears that the literature might benefit from focusing on the predictive power of the estimators instead of their actual fit to the data within the sample.
“Antitrust” or “competition law,” a set of policies now existing in most market economies, largely consists of two or three specific rules applied in more or less the same way in most nations. It prohibits (1) multilateral agreements, (2) unilateral conduct, and (3) mergers or acquisitions, whenever any of them are judged to interfere unduly with the functioning of healthy markets. Most jurisdictions now apply or purport to apply these rules in the service of some notion of economic “efficiency,” more or less as defined in contemporary microeconomic theory.
The law has ancient roots, however, and over time it has varied a great deal in its details. Moreover, even as to its modern form, the policy and its goals remain controversial. In some sense most modern controversy arises from or is in reaction to the major intellectual reconceptualization of the law and its purposes that began in the 1960s. Specifically, academic critics in the United States urged revision of the law’s goals, such that it should serve only a narrowly defined microeconomic goal of allocational efficiency, whereas it had traditionally also sought to prevent accumulation of political power and to protect small firms, entrepreneurs, and individual liberty. While those critics enjoyed significant success in the United States, and to a somewhat lesser degree in Europe and elsewhere, the results remain contested. Specific disputes continue over the law’s general purpose, whether it poses net benefits, how a series of specific doctrines should be fashioned, how it should be enforced, and whether it really is appropriate for developing and small-market economies.
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.
Silvia Miranda-Agrippino and Giovanni Ricco
Bayesian vector autoregressions (BVARs) are standard multivariate autoregressive models routinely used in empirical macroeconomics and finance for structural analysis, forecasting, and scenario analysis in an ever-growing number of applications.
A preeminent field of application of BVARs is forecasting. BVARs with informative priors have often proved to be superior tools compared to standard frequentist/flat-prior VARs. In fact, VARs are highly parametrized autoregressive models, whose number of parameters grows with the square of the number of variables times the number of lags included. Prior information, in the form of prior distributions on the model parameters, helps in forming sharper posterior distributions of parameters, conditional on an observed sample. Hence, BVARs can be effective in reducing parameters uncertainty and improving forecast accuracy compared to standard frequentist/flat-prior VARs.
This feature in particular has favored the use of Bayesian techniques to address “big data” problems, in what is arguably one of the most active frontiers in the BVAR literature. Large-information BVARs have in fact proven to be valuable tools to handle empirical analysis in data-rich environments.
BVARs are also routinely employed to produce conditional forecasts and scenario analysis. Of particular interest for policy institutions, these applications permit evaluating “counterfactual” time evolution of the variables of interests conditional on a pre-determined path for some other variables, such as the path of interest rates over a certain horizon.
The “structural interpretation” of estimated VARs as the data generating process of the observed data requires the adoption of strict “identifying restrictions.” From a Bayesian perspective, such restrictions can be seen as dogmatic prior beliefs about some regions of the parameter space that determine the contemporaneous interactions among variables and for which the data are uninformative. More generally, Bayesian techniques offer a framework for structural analysis through priors that incorporate uncertainty about the identifying assumptions themselves.
Silvia Miranda-Agrippino and Giovanni Ricco
Vector autoregressions (VARs) are linear multivariate time-series models able to capture the joint dynamics of multiple time series. Bayesian inference treats the VAR parameters as random variables, and it provides a framework to estimate “posterior” probability distribution of the location of the model parameters by combining information provided by a sample of observed data and prior information derived from a variety of sources, such as other macro or micro datasets, theoretical models, other macroeconomic phenomena, or introspection.
In empirical work in economics and finance, informative prior probability distributions are often adopted. These are intended to summarize stylized representations of the data generating process. For example, “Minnesota” priors, one of the most commonly adopted macroeconomic priors for the VAR coefficients, express the belief that an independent random-walk model for each variable in the system is a reasonable “center” for the beliefs about their time-series behavior. Other commonly adopted priors, the “single-unit-root” and the “sum-of-coefficients” priors are used to enforce beliefs about relations among the VAR coefficients, such as for example the existence of co-integrating relationships among variables, or of independent unit-roots.
Priors for macroeconomic variables are often adopted as “conjugate prior distributions”—that is, distributions that yields a posterior distribution in the same family as the prior p.d.f.—in the form of Normal-Inverse-Wishart distributions that are conjugate prior for the likelihood of a VAR with normally distributed disturbances. Conjugate priors allow direct sampling from the posterior distribution and fast estimation. When this is not possible, numerical techniques such as Gibbs and Metropolis-Hastings sampling algorithms are adopted.
Bayesian techniques allow for the estimation of an ever-expanding class of sophisticated autoregressive models that includes conventional fixed-parameters VAR models; Large VARs incorporating hundreds of variables; Panel VARs, that permit analyzing the joint dynamics of multiple time series of heterogeneous and interacting units. And VAR models that relax the assumption of fixed coefficients, such as time-varying parameters, threshold, and Markov-switching VARs.
Matteo M. Galizzi and Daniel Wiesen
The state-of-the-art literature at the interface between experimental and behavioral economics and health economics is reviewed by identifying and discussing 10 areas of potential debate about behavioral experiments in health. By doing so, the different streams and areas of applications of the growing field of behavioral experiments in health are reviewed, by discussing which significant questions remain to be discussed, and by highlighting the rationale and the scope for the further development of behavioral experiments in health in the years to come.
Cristina Bellés-Obrero and Judit Vall Castello
The impact of macroeconomic fluctuations on health and mortality rates has been a highly studied topic in the field of economics. Many studies, using fixed-effects models, find that mortality is procyclical in many countries, such as the United States, Germany, Spain, France, Pacific-Asian nations, Mexico, and Canada. On the other hand, a small number of studies find that mortality decreases during economic expansion. Differences in the social insurance systems and labor market institutions across countries may explain some of the disparities found in the literature. Studies examining the effects of more recent recessions are less conclusive, finding mortality to be less procyclical, or even countercyclical. This new finding could be explained by changes over time in the mechanisms behind the association between business cycle conditions and mortality.
A related strand of the literature has focused on understanding the effect of economic fluctuations on infant health at birth and/or child mortality. While infant mortality is found to be procyclical in countries like the United States and Spain, the opposite is found in developing countries.
Even though the association between business cycle conditions and mortality has been extensively documented, a much stronger effort is needed to understand the mechanisms behind the relationship between business cycle conditions and health. Many studies have examined the association between macroeconomic fluctuations and smoking, drinking, weight disorders, eating habits, and physical activity, although results are rather mixed. The only well-established finding is that mental health deteriorates during economic slowdowns.
An important challenge is the fact that the comparison of the main results across studies proves to be complicated due to the variety of empirical methods and time spans used. Furthermore, estimates have been found to be sensitive to the use of different levels of geographic aggregation, model specifications, and proxies of macroeconomic fluctuations.
Diane McIntyre, Amarech G. Obse, Edwine W. Barasa, and John E. Ataguba
Within the context of the Sustainable Development Goals, it is important to critically review research on healthcare financing in sub-Saharan Africa (SSA) from the perspective of the universal health coverage (UHC) goals of financial protection and access to quality health services for all. There is a concerning reliance on direct out-of-pocket payments in many SSA countries, accounting for an average of 36% of current health expenditure compared to only 22% in the rest of the world. Contributions to health insurance schemes, whether voluntary or mandatory, contribute a small share of current health expenditure. While domestic mandatory prepayment mechanisms (tax and mandatory insurance) is the next largest category of healthcare financing in SSA (35%), a relatively large share of funding in SSA (14% compared to <1% in the rest of the world) is attributable to, sometimes unstable, external funding sources. There is a growing recognition of the need to reduce out-of-pocket payments and increase domestic mandatory prepayment financing to move towards UHC. Many SSA countries have declared a preference for achieving this through contributory health insurance schemes, particularly for formal sector workers, with service entitlements tied to contributions. Policy debates about whether a contributory approach is the most efficient, equitable and sustainable means of financing progress to UHC are emotive and infused with “conventional wisdom.” A range of research questions must be addressed to provide a more comprehensive empirical evidence base for these debates and to support progress to UHC.
In many countries of the world, consumers choose their health insurance coverage from a large menu of often complex options supplied by private insurance companies. Economic benefits of the wide choice of health insurance options depend on the extent to which the consumers are active, well informed, and sophisticated decision makers capable of choosing plans that are well-suited to their individual circumstances.
There are many possible ways how consumers’ actual decision making in the health insurance domain can depart from the standard model of health insurance demand of a rational risk-averse consumer. For example, consumers can have inaccurate subjective beliefs about characteristics of alternative plans in their choice set or about the distribution of health expenditure risk because of cognitive or informational constraints; or they can prefer to rely on heuristics when the plan choice problem features a large number of options with complex cost-sharing design.
The second decade of the 21st century has seen a burgeoning number of studies assessing the quality of consumer choices of health insurance, both in the lab and in the field, and financial and welfare consequences of poor choices in this context. These studies demonstrate that consumers often find it difficult to make efficient choices of private health insurance due to reasons such as inertia, misinformation, and the lack of basic insurance literacy. These findings challenge the conventional rationality assumptions of the standard economic model of insurance choice and call for policies that can enhance the quality of consumer choices in the health insurance domain.
In the wake of the 2008 financial collapse, clearinghouses have emerged as critical players in the implementation of the post-crisis regulatory reform agenda. Recognizing serious shortcomings in the design of the over-the-counter derivatives market for swaps, regulators are now relying on clearinghouses to cure these deficiencies by taking on a central role in mitigating the risks of these instruments. Rather than leave trading firms to manage the risks of transacting in swaps privately, as was largely the case prior to 2008, post-crisis regulation requires that clearinghouses assume responsibility for ensuring that trades are properly settled, reported to authorities, and supported by strong cushions of protective collateral. With clearinghouses effectively guaranteeing that the terms of a trade will be honored—even if one of the trading parties cannot perform—the market can operate with reduced levels of counterparty risk, opacity, and the threat of systemic collapse brought on by recklessness and over-complexity.
But despite their obvious benefit for regulators, clearinghouses also pose risks of their own. First, given their deepening significance for market stability, ensuring that clearinghouses themselves operate safely represents a matter of the highest policy priority. Yet overseeing clearinghouses is far from easy and understanding what works best to undergird their safe operation can be a contentious and uncertain matter. U.S. and EU authorities, for example, have diverged in important ways on what rules should apply to the workings of international clearinghouses. Secondly, clearinghouse oversight is critical because these institutions now warehouse enormous levels of counterparty risk. By promising counterparties across the market that their trades will settle as agreed, even if one or the other firm goes bust, clearinghouses assume almost inconceivably large and complicated risks within their institutions. For swaps in particular—whose obligations can last for months, or even years—the scale of these risks can be far more extensive than that entailed in a one-off sale or a stock or bond. In this way, commentators note that by becoming the go-to bulwark against risk-taking and its spread in the financial system, clearinghouses have themselves become the too-big-to-fail institution par excellence.
The cointegrated VAR approach combines differences of variables with cointegration among them and by doing so allows the user to study both long-run and short-run effects in the same model. The CVAR describes an economic system where variables have been pushed away from long-run equilibria by exogenous shocks (the pushing forces) and where short-run adjustments forces pull them back toward long-run equilibria (the pulling forces). In this model framework, basic assumptions underlying a theory model can be translated into testable hypotheses on the order of integration and cointegration of key variables and their relationships. The set of hypotheses describes the empirical regularities we would expect to see in the data if the long-run properties of a theory model are empirically relevant.
Peter Sivey and Yijuan Chen
Quality competition between alternative providers is an increasingly important topic in the health economics literature. This literature includes theoretical and empirical studies that have been developed in parallel to 21st-century policies to increase competition between doctors or hospitals. Theoretical studies have clarified how competitive markets can give healthcare providers the incentive to improve quality. Broadly speaking, if providers have an incentive to attract more patients and patients value quality, providers will raise quality until the costs of raising quality are equal to the additional revenue from patients attracted by the rise in quality. The theoretical literature has also investigated how institutional and policy parameters determine quality levels in equilibrium. Important parameters in models of quality competition include the degree of horizontal differentiation, the level of information about provider quality, the costs of switching between providers, and the time-horizon of quality investment decisions.
Empirical studies have focused on the prerequisites of quality competition (e.g., do patients choose higher quality providers?) and the impact of pro-competition policies on quality levels. The most influential studies have used modern econometric approaches, including difference-in differences and instrumental variables, to identify plausibly causal effects. The evidence suggests that in most contexts, quality is a determinant of patient choice of provider, especially after greater patient choice is made available or information is published about provider quality.
The evidence that increases in competition improve quality in healthcare is less clear cut. Perhaps reflecting the economic theory of quality competition, showing that different parameter combinations or assumptions can produce different outcomes, empirical results are also mixed. While a series of high-quality studies in the United Kingdom appear to show strong improvements in quality in more competitive areas following pro-competition reforms introducing more choice and competition, other studies showed that these quality improvements do not extend to all types of healthcare or alternative measures of quality.
The most promising areas for future research include investigating the “black box” of quality improvement under competition, and behavioral studies investigating financial and nonfinancial motivations for quality improvements in competitive markets.
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.
A patent is a legal right to exclude granted by the state to the inventor of a novel and useful invention. Much legal ink has been spilled on the meaning of these terms. “Novel” means that the invention has not been anticipated in the art prior to its creation by the inventor. “Useful” means that the invention has a practical application. The words “inventor” and “invention” are also legal terms of art. An invention is a work that advances a particular field, moving practitioners forward not simply through accretions of knowledge but through concrete implementations. An inventor is someone who contributes to an invention either as an individual or as part of a team. The exclusive right, finally, is not granted gratuitously. The inventor must apply and go through a review process for the invention. Furthermore, a price for the patent being granted is full, clear disclosure by the inventor of how to practice the invention. The public can use this disclosure once the patent expires or through a license during the duration of the patent.
These institutional details are common features of all patent systems. What is interesting is the economic justification for patents. As a property right, a patent resolves certain externality problems that arise in markets for knowledge. The establishment of property rights allows for trade in the invention and the dissemination of knowledge. However, the economic case for property rights is made complex because of the institutional need to apply for a patent. While in theory, patent grants could be automatic, inventions must meet certain standards for the grant to be justified. These procedural hurdles create possibilities for gamesmanship in how property rights are allocated.
Furthermore, even if granted correctly, property rights can become murky because of the problems of enforcement through litigation. Courts must determine when an invention has been used, made, or sold without permission by a third party in violation of the rights of the patent owner. This legal process can lead to gamesmanship as patent owners try to force settlements from alleged infringers. Meanwhile, third parties may act opportunistically to take advantage of the uncertain boundaries of patent rights and engage in undetectable infringement. Exacerbating these tendencies are the difficulties in determining damages and the possibility of injunctive relief.
Some caution against these criticisms through the observation that most patents are not enforced. In fact, most granted patents turn out to be worthless, when gauged in commercial value. But worthless patents still have potential litigation value. While a patent owner might view a worthless patent as a sunk cost, there is incentive to recoup investment through the sale of worthless patents to parties willing to assume the risk of litigation. Hence the phenomenon of “trolling,” or the rise of non-practicing entities, troubles the patent landscape. This phenomenon gives rise to concerns with the anticompetitive uses of patents, demonstrating the need for some limitations on patent enforcement.
With all the policy concerns arising from patents, it is no surprise that patent law has been ripe for reform. Economic analysis can inform these reform efforts by identifying ways in which patents fail to create a vibrant market for inventions. Appreciation of the political economy of patents invites a rich academic and policy debate over the direction of patent law.
Helen Hayes and Matt Sutton
Contracts and working conditions are important influences on the medical workforce that must be carefully constructed and considered by policymakers. Contracts involve an enforceable agreement of the rights and responsibilities of both employer and employee. The principal-agent relationship and presence of asymmetric information in healthcare means that contracts must be incentive compatible and create sufficient incentive for doctors to act in the payer’s best interests. Within medicine, there are special characteristics that are believed to be particularly pertinent to doctors, who act as agents to both the patient and the payer. These include intrinsic motivation, professionalism, altruism, and multitasking, and they influence the success of these contracts. The three most popular methods of payment are fee-for-service, capitation, and salaries. In most contexts a blend of each of these three payment methods is used; however, guidance on the most appropriate blend is unclear and the evidence on the special nature of doctors is insubstantial. The role of skill mix and teamwork in a healthcare setting is an important consideration as it impacts the success of incentives and payment systems and the efficiency of workers. Additionally, with increasing demand for healthcare, changing skill mix is one response to problems with recruitment and retention in health services. Health systems in many settings depend on a large proportion of foreign-born workers and so migration is a key consideration in retention and recruitment of health workers. Finally, forms of external regulation such as accreditation, inspection, and revalidation are widely used in healthcare systems; however, robust evidence of their effectiveness is lacking.
Michael P. Clements and Ana Beatriz Galvão
At a given point in time, a forecaster will have access to data on macroeconomic variables that have been subject to different numbers of rounds of revisions, leading to varying degrees of data maturity. Observations referring to the very recent past will be first-release data, or data which has as yet been revised only a few times. Observations referring to a decade ago will typically have been subject to many rounds of revisions. How should the forecaster use the data to generate forecasts of the future? The conventional approach would be to estimate the forecasting model using the latest vintage of data available at that time, implicitly ignoring the differences in data maturity across observations.
The conventional approach for real-time forecasting treats the data as given, that is, it ignores the fact that it will be revised. In some cases, the costs of this approach are point predictions and assessments of forecasting uncertainty that are less accurate than approaches to forecasting that explicitly allow for data revisions. There are several ways to “allow for data revisions,” including modeling the data revisions explicitly, an agnostic or reduced-form approach, and using only largely unrevised data. The choice of method partly depends on whether the aim is to forecast an earlier release or the fully revised values.
Denzil G. Fiebig and Hong Il Yoo
Stated preference methods are used to collect individual level data on what respondents say they would do when faced with a hypothetical but realistic situation. The hypothetical nature of the data has long been a source of concern among researchers as such data stand in contrast to revealed preference data, which record the choices made by individuals in actual market situations. But there is considerable support for stated preference methods as they are a cost-effective means of generating data that can be specifically tailored to a research question and, in some cases, such as gauging preferences for a new product or non-market good, there may be no practical alternative source of data. While stated preference data come in many forms, the primary focus in this article will be data generated by discrete choice experiments, and thus the econometric methods will be those associated with modeling binary and multinomial choices with panel data.
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.
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.