While definitional and measurement problems pose a challenge, there is no doubt that disability affects a noticeable share of the population, the vast majority of whom live in low- and middle-income countries (LMICs). The still comparatively scarce empirical data and evidence suggests that disability is closely associated with poverty and other indicators of economic deprivation at both the country and—if with slightly greater nuance—at the individual/household level. There is also a growing body of evidence documenting the sizeable additional costs incurred by persons with disabilities (PwDs) as a direct or indirect consequence of their disability, underlining the increased risk of PwDs (and the households they are part of) falling under the absolute poverty line in any given LMIC. Looking ahead, there remains considerable scope for more evidence on the causal nature of the link between disability and poverty, as well as on the (cost-)effectiveness of interventions and policies attempting to improve the well-being of PwDs.
Florence Jusot and Sandy Tubeuf
Recent developments in the analysis of inequality in health and healthcare have turned their interest into an explicit normative understanding of the sources of inequalities that calls upon the concept of equality of opportunity. According to this concept, some sources of inequality are more objectionable than others and could represent priorities for policies aiming to reduce inequality in healthcare use, access, or health status. Equality of opportunity draws a distinction between “legitimate” and “illegitimate” sources of inequality. While legitimate sources of differences can be attributed to the consequences of individual effort (i.e. determinants within the individual’s control), illegitimate sources of differences are related to circumstances (i.e. determinants beyond the individual’s responsibility). The study of inequality of opportunity is rooted in social justice research, and the last decade has seen a rapid growth in empirical work using this literature at the core of its approach in both developed and developing countries. Empirical research on inequality of opportunity in health and healthcare is mainly driven by data availability. Most studies in adult populations are based on data from European countries, especially from the UK, while studies analyzing inequalities of opportunity among children are usually based on data from low- or middle-income countries and focus on children under five years old. Regarding the choice of circumstances, most studies have considered social background to be an illegitimate source of inequality in health and healthcare. Geographical dimensions have also been taken into account, but to a lesser extent, and more frequently in studies focusing on children or those based on data from countries outside Europe. Regarding effort variables or legitimate sources of health inequality, there is wide use of smoking-related variables. Regardless of the population, health outcome, and circumstances considered, scholars have provided evidence of illegitimate inequality in health and healthcare. Studies on inequality of opportunity in healthcare are mainly found in children population; this emphasizes the need to tackle inequality as early as possible.
Gabriella Conti, Giacomo Mason, and Stavros Poupakis
Building on early animal studies, 20th-century researchers increasingly explored the fact that early events—ranging from conception to childhood—affect a child’s health trajectory in the long-term. By the 21st century, a wide body of research had emerged, incorporating the original fetal origins hypothesis into the developmental origins of health and disease. Evidence from Organization for Economic Cooperation and Development (OECD) countries suggests that health inequalities are strongly correlated with many dimensions of socioeconomic status, such as educational attainment, and that they tend to increase with age and carry stark intergenerational implications. Different economic theories have been developed to rationalize this evidence, with an overarching comprehensive framework still lacking. Existing models widely rely on human capital theory, which has given rise to separate dynamic models of adult and child health capital within a production function framework. A large body of empirical evidence has also found support for the developmental origins of inequalities in health. On the one hand, studies exploiting quasi-random exposure to adverse events have shown long-term physical and mental health impacts of exposure to early shocks, including pandemics or maternal illness, famine, malnutrition, stress, vitamin deficiencies, maltreatment, pollution, and economic recessions. On the other hand, studies from the 20th century have shown that early interventions of various content and delivery formats improve life course health. Further, given that the most socioeconomically disadvantaged groups show the greatest gains, such measures can potentially reduce health inequalities. However, studies of long-term impacts as well as the mechanisms via which shocks or policies affect health, and the dynamic interaction among them, are still lacking. Mapping the complexities of those early event dynamics is an important avenue for future research.
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.