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Fabrizio Mazzonna and Franco Peracchi

Population aging, the combined effect of declining fertility and rising life expectancy, is one of the fundamental trends observed in developed counties and, increasingly, in developing countries as well. A key aspect of the aging process is the decline of cognitive ability. Cognitive aging is an important and complex phenomenon, and its risk factors and economic consequences are still not well understood. For instance, the relationship between cognitive aging and productivity matters for long-term economic growth. Cognitive functioning is also crucial for decision-making because it influences individuals’ ability to process information and to make the right choices, and older individuals are increasingly required to make complex financial, health, and long-term-care decisions that might affect their health, resources, and welfare. This article presents evidence from economics and other fields that have investigated this phenomenon from different perspectives. A common empirical finding is the hump-shaped profile of cognitive performance over the life cycle. Another is the large variability of observed age profiles, not only at the individual level but also across sociodemographic groups and countries. The age profiles of cognitive performance also vary depending on the cognitive task considered, reflecting the different combinations of cognitive skills that they require. The literature usually distinguishes between two main types of cognitive skills: fluid intelligence and crystallized intelligence. The first consists of the basic mechanisms of processing new information, while the second reflects acquired knowledge. Unlike fluid intelligence, which declines rapidly as people get older, crystallized intelligence tends to be maintained at older ages. Differences in the age profiles of cognitive performance across tasks partly reflect differences in the importance of these two types of intelligence. For instance, tasks where learning, problem-solving, and processing speed are essential tend to be associated with a faster decline, while tasks where experience matters more tend to be associated with a slower decline. Various life events and behaviors over the life cycle also contribute to the large heterogeneity in the observed age profiles of cognitive performance. This source of variation includes not only early-life events and investments (e.g., formal education), but also midlife and later-life events (e.g., health shocks) and individual choices (e.g., health behaviors or retirement). From an economic viewpoint, cognitive abilities may be regarded as one dimension of human capital, along with education, health, and noncognitive abilities. Economists have mainly focused their attention on human capital accumulation, and much less so on human capital deterioration. One explanation is that early-life investments appears to be more profitable than investments later in life. However, recent evidence from neuropsychology suggests that the human brain is malleable and open to enhancement even later in adulthood. Therefore, more economic research is needed to study how human capital depreciates over the life cycle and whether cognitive decline can be controlled.


Marisa Miraldo, Katharina Hauck, Antoine Vernet, and Ana Wheelock

Major medical innovations have greatly increased the efficacy of treatments, improved patient outcomes, and often reduced the cost of medical care. However, innovations do not diffuse uniformly across and within health systems. Due to the high complexity of medical treatment decisions, variations in clinical practice are inherent to healthcare delivery, regardless of technological advances, new ways of working, funding, and burden of disease. In this article we conduct a narrative literature review to identify and discuss peer-reviewed articles presenting a theoretical framework or empirical evidence of the factors associated with the adoption of innovation and clinical practice. We find that variation in innovation adoption and medical practice is associated with multiple factors. First, patients’ characteristics, including medical needs and genetic factors, can crucially affect clinical outcomes and the efficacy of treatments. Moreover, differences in patients’ preferences can be an important source of variation. Medical treatments may need to take such patient characteristics into account if they are to deliver optimal outcomes, and consequently, resulting practice variations should be considered warranted and in the best interests of patients. However, socioeconomic or demographic characteristics, such as ethnicity, income, or gender are often not considered legitimate grounds for differential treatment. Second, physician characteristics—such as socioeconomic profile, training, and work-related characteristics—are equally an influential component of practice variation. In particular, so-called “practice style” and physicians’ attitudes toward risk and innovation adoption are considered a major source of practice variation, but have proven difficult to investigate empirically. Lastly, features of healthcare systems—notably, public coverage of healthcare expenditure, cost-based reimbursement of providers, and service-delivery organization, are generally associated with higher utilization rates and adoption of innovation. Research shows some successful strategies aimed at reducing variation in medical decision-making, such as the use of decision aids, data feedback, benchmarking, clinical practice guidelines, blinded report cards, and pay for performance. But despite these advances, there is uneven diffusion of new technologies and procedures, with potentially severe adverse efficiency and equity implications.


Multi-criteria decision analysis (MCDA) is increasingly used to support healthcare decision-making. MCDA involves decision makers evaluating the alternatives under consideration based on the explicit weighting of criteria relevant to the overarching decision—in order to, depending on the application, rank (or prioritize) or choose between the alternatives. A prominent example of MCDA applied to healthcare decision-making that has received a lot of attention in recent years and is the main subject of this article is choosing which health “technologies” (i.e., drugs, devices, procedures, etc.) to fund—a process known as health technology assessment (HTA). Other applications include prioritizing patients for surgery, prioritizing diseases for R&D, and decision-making about licensing treatments. Most applications are based on weighted-sum models. Such models involve explicitly weighting the criteria and rating the alternatives on the criteria, with each alternative’s “performance” on the criteria aggregated using a linear (i.e., additive) equation to produce the alternative’s “total score,” by which the alternatives are ranked. The steps involved in a MCDA process are explained, including an overview of methods for scoring alternatives on the criteria and weighting the criteria. The steps are: structuring the decision problem being addressed, specifying criteria, measuring alternatives’ performance, scoring alternatives on the criteria and weighting the criteria, applying the scores and weights to rank the alternatives, and presenting the MCDA results, including sensitivity analysis, to decision makers to support their decision-making. Arguments recently advanced against using MCDA for HTA and counterarguments are also considered. Finally, five questions associated with how MCDA for HTA is operationalized are discussed: Whose preferences are relevant for MCDA? Should criteria and weights be decision-specific or identical for repeated applications? How should cost or cost-effectiveness be included in MCDA? How can the opportunity cost of decisions be captured in MCDA? How can uncertainty be incorporated into MCDA?