One of the most important unanswered questions for any society is how best to invest in children’s mental health. Childhood is a sensitive and opportune period in which to invest in programs and services that can mitigate a range of downstream risks for health and mental health conditions. Investing in such programs and services will require a shift from focusing not only on reducing deficits but also enhancing the child’s skills and other assets. Economic evaluation is crucial for determining which programs and services represent optimal investments. Several registries curate lists of programs with high evidence of effectiveness; many of these programs also have evidence of positive benefit-cost differentials, although the economic evidence is typically limited and uncertain. Even the programs with the strongest evidence are currently reaching only a small fraction of young people who would potentially benefit. Thus, it is important to understand and address factors that impede or facilitate the implementation of best practices. One example of a program that represents a promising investment is home visiting, in which health workers visit the homes of new parents to advise on parenting skills, child needs, and the home environment. Another example is social emotional learning programs delivered in schools, where children are taught to regulate emotions, manage behaviors, and enhance relationships with peers. Investing in these and other programs with a strong evidence base, and assuring their faithful implementation in practice settings, can produce improvements on a range of mental health, academic, and social outcomes for children, extending into their lives as adults.
Daniel Eisenberg and Ramesh Raghavan
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.
Ya-Chen Tina Shih
The goal of cancer prevention and control is to reduce cancer risk, morbidity, and mortality through transdisciplinary collaborations across biomedical, behavioral, and social sciences. Risk reduction, early detection, and timely treatment are the rationales behind policy efforts to promote cancer prevention. Economics makes three important contributions to cancer prevention and control research. Firstly, research built upon the human capital model by Grossman and the insurance model by Ehrlich and Becker offers solid theoretical foundations to study human behaviors related to preventive care. Secondly, economic evaluation provides useful analytical tools to assess the “cancer premium” (through the stated preference research approach) and to identify the optimal screening strategy (through cost-effectiveness analysis). Lastly, the rich set of quantitative methods in applied economics contributes to the estimation of the relative contribution of prevention versus treatment in the reduction of cancer mortality and the evaluation of the impact of guidelines to regulate screening practices or policy initiatives to promote cancer screening.
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.
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.
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.