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Article

The Economics of Informal Care  

Courtney Van Houtven, Fiona Carmichael, Josephine Jacobs, and Peter C. Coyte

Across the globe, the most common means of supporting older disabled adults in their homes is through “informal care.” An informal carer is a family member or friend, including children or adults, who help another person because of their illness, frailty, or disability. There is a rich economics literature on the direct benefits of caregiving, including allowing the care recipient to remain at home for longer than if there was no informal care provided. There is also a growing literature outlining the associated costs of care provision. Although informal care helps individuals with disabilities to remain at home and is rewarding to many carers, there are often negative effects such as depression and lost labor market earnings that may offset some of these rewards. Economists have taken several approaches to quantify the net societal benefit of informal care that consider the degree of choice in caregiving decisions and all direct and indirect benefits and costs of informal care.

Article

Pay-for-Performance and Long-Term Care  

Jun Li and Edward C. Norton

Pay-for-performance programs have become a prominent supply-side intervention to improve quality and decrease spending in health care, touching upon long-term care, acute care, and outpatient care. Pay-for-performance directly targets long-term care, with programs in nursing homes and home health. Indirectly, pay-for-performance programs targeting acute care settings affect clinical practice for long-term care providers through incentives for collaboration across settings. As a whole, pay-for-performance programs entail the identification of problems it seeks to solve, measurement of the dimensions it seeks to incentivize, methods to combine and translate performance to incentives, and application of the incentives to reward performance. For the long-term care population, pay-for-performance programs must also heed the unique challenges specific to the sector, such as patients with complex health needs and distinct health trajectories, and be structured to recognize the challenges of incentivizing performance improvement when there are multiple providers and payers involved in the care delivery. Although empirical results indicate modest effectiveness of pay-for-performance in long-term care on improving targeted measures, some research has provided more clarity on the role of pay-for-performance design on the output of the programs, highlighting room for future research. Further, because health care is interconnected, the indirect effects of pay-for-performance programs on long-term care is an underexplored topic. As the scope of pay-for-performance in long-term care expands, both within the United States and internationally, pay-for-performance offers ample opportunities for future research.

Article

Health Insurance and Labor Supply  

Gregory Colman, Dhaval Dave, and Otto Lenhart

Health insurance depends on labor market activity more in the U.S. than in any other high-income country. A majority of the population are insured through an employer (known as employer-sponsored insurance or ESI), benefiting from the risk pooling and economies of scale available to group insurance plans. Some workers may therefore be reluctant to leave a job for fear of losing such low-cost insurance, a tendency known as “job lock,” or may switch jobs or work more hours merely to obtain it, known as “job push.” Others obtain insurance through government programs for which eligibility depends on income. They too may adapt their work effort to remain eligible for insurance. Those without access to ESI or who are too young or earn too much to qualify for public coverage (Medicare and Medicaid) can buy insurance only in the individual or nongroup market, where prices are high and variable. Most studies using data from before the passage of the Patient Protection and Affordable Care Act (ACA) in 2010 support the prediction that ESI reduced job mobility, labor-force participation, retirement, and self-employment prior to the ACA, but find little effect on the labor supply of public insurance. The ACA profoundly changed the health insurance market in the U.S., removing restrictions on obtaining insurance from new employers or on the individual market and expanding Medicaid eligibility to previously ineligible adults. Research on the ACA, however, has not found substantial labor supply effects. These results may reflect that the reforms to the individual market mainly affected those who were previously uninsured rather than workers with ESI, that the theoretical labor market effects of expansions in public coverage are ambiguous, and that the effect would be found only among the relatively small number on the fringes of eligibility.

Article

The Economics of Early Interventions Aimed at Child Development  

Samuel Berlinski and Marcos Vera-Hernández

A set of policies is at the center of the agenda on early childhood development: parenting programs, childcare regulation and subsidies, cash and in-kind transfers, and parental leave policies. Incentives are embedded in these policies, and households react to them differently. They also have varying effects on child development, both in developed and developing countries. We have learned much about the impact of these policies in the past 20 years. We know that parenting programs can enhance child development, that centre based care might increase female labor force participation and child development, that parental leave policies beyond three months don’t cause improvement in children outcomes, and that the effects of transfers depend much on their design. In this review, we focus on the incentives embedded in these policies, and how they interact with the context and decision makers to understand the heterogeneity of effects and the mechanisms through which these policies work. We conclude by identifying areas of future research.

Article

Strategies to Counteract Risk Selection in Social Health Insurance Markets  

Richard C. van Kleef, Thomas G. McGuire, Frederik T. Schut, and Wynand P. M. M. van de Ven

Many countries rely on social health insurance supplied by competing insurers to enhance fairness and efficiency in healthcare financing. Premiums in these settings are typically community rated per health plan. Though community rating can help achieve fairness objectives, it also leads to a variety of problems due to risk selection, that is, actions by consumers and insurers to exploit “unpriced risk” heterogeneity. From the viewpoint of a consumer, unpriced risk refers to the gap between her expected spending under a health plan and the net premium for that plan. Heterogeneity in unpriced risk can lead to selection by consumers in and out of insurance and between high- and low-value plans. These forms of risk selection can result in upward premium spirals, inefficient take-up of basic coverage, and inefficient sorting of consumers between high- and low-value plans. From the viewpoint of an insurer, unpriced risk refers to the gap between his expected costs under a certain contract and the revenues he receives for that contract. Heterogeneity in unpriced risk incentivizes insurers to alter their plan offerings in order to attract profitable people, resulting in inefficient plan design and possibly in the unavailability of high-quality care. Moreover, insurers have incentives to target profitable people via marketing tools and customer service, which—from a societal perspective—can be considered a waste of resources. Common tools to counteract selection problems are risk equalization, risk sharing, and risk rating of premiums. All three strategies reduce unpriced risk heterogeneity faced by insurers and thus diminish selection actions by insurers such as the altering of plan offerings. Risk rating of premiums also reduces unpriced risk heterogeneity faced by consumers and thus mitigates selection in and out of insurance and between high- and low-value plans. All three strategies, however, come with trade-offs. A smart blend takes advantage of the strengths, while reducing the weaknesses of each strategy. The optimal payment system configuration will depend on how a regulator weighs fairness and efficiency and on how the healthcare system is organized.

Article

The Lifetime Dynamics of Health and Wealth  

Pascal St-Amour

Life-cycle choices and outcomes over financial (e.g., savings, portfolio, work) and health-related variables (e.g., medical spending, habits, sickness, and mortality) are complex and intertwined. Indeed, labor/leisure choices can both affect and be conditioned by health outcomes, precautionary savings is determined by exposure to sickness and longevity risks, where the latter can both be altered through preventive medical and leisure decisions. Moreover, inevitable aging induces changes in the incentives and in the constraints for investing in one’s own health and saving resources for old age. Understanding these pathways poses numerous challenges for economic models. The life-cycle data is indicative of continuous declines in health statuses and associated increases in exposure to morbidity, medical expenses, and mortality risks, with accelerating post-retirement dynamics. Theory suggests that risk-averse and forward-looking agents should rely on available instruments to insure against these risks. Indeed, market- and state-provided health insurance (e.g., Medicare) cover curative medical expenses. High end-of-life home and nursing-home expenses can be hedged through privately or publicly provided (e.g., Medicaid) long-term care insurance. The risk of outliving one’s financial resources can be hedged through annuities. The risk of not living long enough can be insured through life insurance. In practice, however, the recourse to these hedging instruments remains less than predicted by theory. Slow-observed wealth drawdown after retirement is unexplained by bequest motives and suggests precautionary motives against health-related expenses. The excessive reliance on public pension (e.g., Social Security) and the post-retirement drop in consumption not related to work or health are both indicative of insufficient financial preparedness and run counter to consumption smoothing objectives. Moreover, the capacity to self-insure through preventive care and healthy habits is limited when aging is factored in. In conclusion, the observed health and financial life-cycle dynamics remain challenging for economic theory.

Article

Valuation of Health Risks  

Henrik Andersson, Arne Risa Hole, and Mikael Svensson

Many public policies and individual actions have consequences for population health. To understand whether a (costly) policy undertaken to improve population health is a wise use of resources, analysts can use economic evaluation methods to assess the costs and benefits. To do this, it is necessary to evaluate the costs and benefits using the same metric, and for convenience, a monetary measure is commonly used. It is well established that money measures of a reduction in health risks can be theoretically derived using the willingness-to-pay concept. However, because a market price for health risks is not available, analysts have to rely on analytical techniques to estimate the willingness to pay using revealed- or stated-preference methods. Revealed-preference methods infer willingness to pay based on individuals’ actual behavior in markets related to health risks, and they include such approaches as hedonic pricing techniques. Stated-preference methods use a hypothetical market scenario in which respondents make trade-offs between wealth and health risks. Using, for example, a random utility framework, it is possible to directly estimate individuals’ willingness to pay by analyzing the trade-offs they make in the hypothetical scenario. Stated-preference methods are commonly applied using contingent valuation or discrete choice experiment techniques. Despite criticism and the shortcomings of both the revealed- and stated-preference methods, substantial progress has been made since the 1990s in using both approaches to estimate the willingness to pay for health-risk reductions.

Article

Choice Inconsistencies in the Demand for Private Health Insurance  

Olena Stavrunova

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.

Article

Considering Health-Systems Constraints in Economic Evaluation in Low- and Middle-Income Settings  

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.

Article

Measuring Health Utility in Economics  

José Luis Pinto-Prades, Arthur Attema, and Fernando Ignacio Sánchez-Martínez

Quality-adjusted life years (QALYs) are one of the main health outcomes measures used to make health policy decisions. It is assumed that the objective of policymakers is to maximize QALYs. Since the QALY weighs life years according to their health-related quality of life, it is necessary to calculate those weights (also called utilities) in order to estimate the number of QALYs produced by a medical treatment. The methodology most commonly used to estimate utilities is to present standard gamble (SG) or time trade-off (TTO) questions to a representative sample of the general population. It is assumed that, in this way, utilities reflect public preferences. Two different assumptions should hold for utilities to be a valid representation of public preferences. One is that the standard (linear) QALY model has to be a good model of how subjects value health. The second is that subjects should have consistent preferences over health states. Based on the main assumptions of the popular linear QALY model, most of those assumptions do not hold. A modification of the linear model can be a tractable improvement. This suggests that utilities elicited under the assumption that the linear QALY model holds may be biased. In addition, the second assumption, namely that subjects have consistent preferences that are estimated by asking SG or TTO questions, does not seem to hold. Subjects are sensitive to features of the elicitation process (like the order of questions or the type of task) that should not matter in order to estimate utilities. The evidence suggests that questions (TTO, SG) that researchers ask members of the general population produce response patterns that do not agree with the assumption that subjects have well-defined preferences when researchers ask them to estimate the value of health states. Two approaches can deal with this problem. One is based on the assumption that subjects have true but biased preferences. True preferences can be recovered from biased ones. This approach is valid as long as the theory used to debias is correct. The second approach is based on the idea that preferences are imprecise. In practice, national bodies use utilities elicited using TTO or SG under the assumptions that the linear QALY model is a good enough representation of public preferences and that subjects’ responses to preference elicitation methods are coherent.

Article

The Economics of Infectious Diseases  

Katharina Hauck

Economics can make immensely valuable contributions to our understanding of infectious disease transmission and the design of effective policy responses. The one unique characteristic of infectious diseases makes it also particularly complicated to analyze: the fact that it is transmitted from person to person. It explains why individuals’ behavior and externalities are a central topic for the economics of infectious diseases. Many public health interventions are built on the assumption that individuals are altruistic and consider the benefits and costs of their actions to others. This would imply that even infected individuals demand prevention, which stands in conflict with the economic theory of rational behavior. Empirical evidence is conflicting for infected individuals. For healthy individuals, evidence suggests that the demand for prevention is affected by real or perceived risk of infection. However, studies are plagued by underreporting of preventive behavior and non-random selection into testing. Some empirical studies have shown that the impact of prevention interventions could be far greater than one case prevented, resulting in significant externalities. Therefore, economic evaluations need to build on dynamic transmission models in order to correctly estimate these externalities. Future research needs are significant. Economic research needs to improve our understanding of the role of human behavior in disease transmission; support the better integration of economic and epidemiological modeling, evaluation of large-scale public health interventions with quasi-experimental methods, design of optimal subsidies for tackling the global threat of antimicrobial resistance, refocusing the research agenda toward underresearched diseases; and most importantly to assure that progress translates into saved lives on the ground by advising on effective health system strengthening.

Article

The Economics of Malaria Prevention  

Bénédicte Apouey, Gabriel Picone, and Joshua Wilde

Malaria is a potentially life-threatening disease transmitted through the bites of female anopheline mosquitos infected with protozoan parasites. Malaria remains one of the major causes of mortality by infectious disease: in 2015, there were an estimated 212 million cases and 429,000 deaths globally, according to the 2016 World Malaria Report. Children under 5 years in sub-Saharan Africa bear the greatest burden of the disease worldwide. However, most of these cases could be prevented or treated. Several methods are highly effective in preventing malaria: in particular, sleeping under an insecticide-treated mosquito net (ITN), indoor residual spraying (IRS), and taking intermittent preventive treatment for pregnant women (IPTp). Regarding treatment, artesiminin-based combination therapy (ACT) is recommended as first-line treatment in many countries. Compared with other actions, malaria prevention behaviors have some specific features. In particular, they produce public health externalities. For example, bed net usage creates positive externalities since bed nets not only directly protect the user, but also reduce transmission probabilities through reduction in the number of disease hosts, and in the case of ITNs, reduction of the vector itself. In contrast, ACT uptake creates both positive externalities when individuals with malaria are treated, and negative externalities in the case of overtreatment that speeds up the spread of long-run parasite resistance. Moreover, ITNs, IPTp, and ACTs are experience goods (meaning individuals only ascertain their benefits upon usage), which implies that current preventive actions are linked to past preventive behaviors. Malaria prevention and eradication produce unambiguous benefits across various domains: economic conditions, educational outcomes, survival, fertility, and health. However, despite the high private returns to prevention, the adoption of antimalarial products and behaviors remains relatively low in malaria-affected areas. A variety of explanations have been proposed for low adoption rates, including financial constraints, high prices, and absence of information. While recent studies highlight that all of these factors play a role, the main barrier to adoption is probably financial constraints. This finding has implications regarding the appropriate pricing policy for these health products. In addition, there is a shortage of causally identified research on the effect of cultural and psychological barriers to the adoption of preventive behaviors. The literature which does exist is from a few randomized control trials of few individuals in very specific geographic and cultural contexts, and may not be generalizable. As a result, there are still ample opportunities for research on applying the insights of behavioral economics to malaria-preventive behavior in particular. Moreover, little research has been done on the supply side, such as whether free or heavily subsidized distribution of prevention technologies is fiscally sustainable; finding effective methods to solve logistical problems which lead to shortages and ineffective alternative treatments to fill the gap; or training sufficient healthcare workers to ensure smooth and effective delivery. Given these gaps in the literature, there are still multiple fruitful avenues for research which may have a first-order effect on reducing the prevalence of malaria in the developing world.

Article

Machine Learning in Policy Evaluation: New Tools for Causal Inference  

Noémi Kreif and Karla DiazOrdaz

While machine learning (ML) methods have received a lot of attention in recent years, these methods are primarily for prediction. Empirical researchers conducting policy evaluations are, on the other hand, preoccupied with causal problems, trying to answer counterfactual questions: what would have happened in the absence of a policy? Because these counterfactuals can never be directly observed (described as the “fundamental problem of causal inference”) prediction tools from the ML literature cannot be readily used for causal inference. In the last decade, major innovations have taken place incorporating supervised ML tools into estimators for causal parameters such as the average treatment effect (ATE). This holds the promise of attenuating model misspecification issues, and increasing of transparency in model selection. One particularly mature strand of the literature include approaches that incorporate supervised ML approaches in the estimation of the ATE of a binary treatment, under the unconfoundedness and positivity assumptions (also known as exchangeability and overlap assumptions). This article begins by reviewing popular supervised machine learning algorithms, including trees-based methods and the lasso, as well as ensembles, with a focus on the Super Learner. Then, some specific uses of machine learning for treatment effect estimation are introduced and illustrated, namely (1) to create balance among treated and control groups, (2) to estimate so-called nuisance models (e.g., the propensity score, or conditional expectations of the outcome) in semi-parametric estimators that target causal parameters (e.g., targeted maximum likelihood estimation or the double ML estimator), and (3) the use of machine learning for variable selection in situations with a high number of covariates. Since there is no universal best estimator, whether parametric or data-adaptive, it is best practice to incorporate a semi-automated approach than can select the models best supported by the observed data, thus attenuating the reliance on subjective choices.

Article

Health Insurance and the Demand for Healthcare  

Michael Gerfin

Health insurance increases the demand for healthcare. Since the RAND Health Insurance Experiment in the 1970s this has been demonstrated in many contexts and many countries. From an economic point of view this fact raises the concern that individuals demand too much healthcare if insured, which generates a welfare loss to society. This so-called moral hazard effect arises because individuals demand healthcare that has less value to them than it costs to provide it. For that reason, modern health insurance plans include demand side cost-sharing instruments like deductibles and copayments. There is a large and growing literature analyzing the effects of these cost-sharing instruments on healthcare demand. Three issues have recently received increasing attention. First, cost-sharing instruments such as yearly deductibles combined with stop losses create nonlinear price schedules and dynamic incentives. This generates the question of whether patients understand the incentives and what price individuals use to determine their healthcare demand. Second, it appears implausible that patients know the benefits of healthcare (which is crucial for the moral hazard argument). If patients systematically underestimated these benefits they would demand too little healthcare without health insurance. Providing health insurance and increasing healthcare demand in this case may increase social welfare. Finally, what is the role of healthcare providers? They have been completely absent in the majority of the literature analyzing the demand for healthcare, but there is striking evidence that the physicians often determine large parts of healthcare spending.

Article

The Economics of Long-Term Care  

Norman Bannenberg, Martin Karlsson, and Hendrik Schmitz

Long-term care (LTC) is arguably the sector of the economy that is most sensitive to population aging: its recipients are typically older than 80 years whereas most care providers are of working age. Thus, a number of ongoing societal trends interact in the determination of market outcomes in the LTC sector: trends in longevity and healthy life expectancy interact with changing family structures and norms in shaping the need for services. The supply side is additionally affected by changes in employment patterns, in particular regarding the transition into retirement, as well as by cross-regional imbalances in demographic and economic conditions. The economic literature on long-term care considers many of these issues, aims at understanding this steadily growing sector, and at guiding policy. Key economic studies on long-term care address determinants of the demand for long-term care, like disability and socio-economic status; the two most important providers: informal family caregivers and nursing homes; and the financing and funding of LTC.

Article

Price Regulation and Pharmaceuticals  

A. McGuire

Pharmaceutical expenditure accounts for approximately 20% of healthcare expenditure across the Organisation for Economic Cooperation and Development (OECD) countries. Pharmaceutical products are regulated in all major global markets primarily to ensure product quality but also to regulate the reimbursed prices of insurance companies and central purchasing authorities that dominate this sector. Price regulation is justified as patent protection, which acts as an incentive to invest in R&D given the difficulties in appropriating the returns to such activity and creates monopoly rights to suppliers. Price regulation does itself reduce the ability of producers’ to recapture the substantial R&D investment costs incurred. Traditional price regulation through Ramsey pricing and yardstick competition is not efficient given the distortionary impact of insurance holdings, which are extensive in this sector and the inherent uncertainties that characterize Research and Development (R&D) activity. A range of other pricing regulations aimed at establishing pharmaceutical reimbursement that covers both dynamic efficiency (tied to R&D incentives) and static efficiency (tied to reducing monopoly rents) have been suggested. These range from cost-plus pricing, to internal and external reference pricing, rate-of-return pricing and, most recently value-based (essential health benefit maximization) pricing. Reimbursed prices reflecting value based pricing are, in some countries, associated with clinical treatment guidelines and cost-effectiveness analysis. Some countries are also requiring or allowing post-launch price regulation thorough a range of patient access agreements based on predefined population health targets and/or financial incentives. There is no simple, single solution to the determination of dynamic and static efficiency in this sector given the uncertainty associated with innovation, the large monopoly interests in the area, the distortionary impact of health insurance and the informational asymmetries that exist across providers and purchasers.

Article

Frameworks for Priority Setting in Health and Social Care  

Marissa Collins, Neil McHugh, Rachel Baker, Alec Morton, Lucy Frith, Keith Syrett, and Cam Donaldson

Health and social care organizations work within the context of limited resources. Different techniques to aid resource allocation and decision-making exist and are important as scarcity of resources in health and social care is inescapable. Healthcare systems, regardless of how they are organized, must decide what services to provide given the resources available. This is particularly clear in systems funded by taxation, which have limited budgets and other limited resources (staff, skills, facilities, etc.) and in which the claims on these resources outstrip supply. Healthcare spending in many countries is not expected to increase over the short or medium term. Therefore, frameworks to set priorities are increasingly required. Four disciplines provide perspectives on priority setting: economics, decision analysis, ethics, and law. Although there is overlap amongst these perspectives, they are underpinned by different principles and processes for priority setting. As the values and viewpoints of those involved in priority setting in health and social care will differ, it is important to consider how these could be included to inform a priority setting process. It is proposed that these perspectives and the consideration of values and viewpoints could be brought together in a combined priority setting framework for use within local health and social care organizations.

Article

Heterogeneity in Cost-Effectiveness Analysis  

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.

Article

Evaluation of Mental Health Interventions  

Martin Knapp

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.

Article

The Economics of Dementia  

Sheelah Connolly

In the coming years, it is predicted that there will be a significant increase in the number of people living with dementia and consequently, the demand for health and social care services. Given the budget constraints facing health systems, it is anticipated that economic analysis will play an increasingly important role in informing decisions regarding the provision of services for people with dementia. However, compared with other conditions and diseases, research in dementia has been relatively limited. While in the past this may have been related to an assumption that dementia was a natural part of aging, there are features of dementia that make applying research methods particularly challenging. A number of economic methods have been applied to dementia, including cost-of-illness analysis and economic evaluation; however, methodological issues in this area persist. These include reaching a consensus on how best to measure and value informal care, how to capture the many impacts and costs of the condition as the disease progresses, and how to measure health outcomes. Addressing these existing methodological issues will help realize the potential of economic analysis in answering difficult questions around care for people with dementia.