International and national bodies have identified tackling ageism in health care as an urgent goal. However, health professionals, researchers, and policy makers recognize that it is not easy to identity and fight ageism in practice, as the identification of multiple manifestations of ageism is dependent on the way it is defined and operationalized. This article reports on a systematic review of the operational definitions and inductive conceptualizations of ageism in the context of health care. We reviewed scientific articles published from January 1995 to June 2015 and indexed in the electronic databases Web of Science, PubMed, and Cochrane. Electronic searches were complemented with visual scanning of reference lists and hand searching of leading journals in the field of ageing and social gerontology. The review reveals that the predominant forms of operationalization and inductive conceptualization of ageism in the context of health care have neglected some components of ageism, namely the self-directed and implicit components. Furthermore, the instruments used to measure ageism in health care have as targets older people in general, not older patients in particular. The results have important implications for the advancement of research on this topic, as well as for the development of interventions to fight ageism in practice. There is a need to take into account underexplored forms of operationalization and inductive conceptualizations of ageism, such as self-directed ageism and implicit ageism. In addition, ageism in health care should be measured by using context-specific instruments.

Introduction Progress towards effective service coverage and financial protection—the two dimensions of Universal Health Coverage (UHC)—has been limited in Kenya in the last decade. The government of Kenya has embarked on a highly ambitious reform programme currently being piloted in four Kenyan counties and aiming at national rollout by 2022. This study provides an updated assessment of the performance of the Kenyan health system in terms of financial protection allowing to monitor trends over time. In light of the UHC initiative, the study provides a baseline to assess the impact of the UHC pilot programme and inform scale-up plans. It also investigates household characteristics associated with catastrophic payments. Methods Using data from the Kenya Household Health Expenditure and Utilization Survey (KHHEUS) 2018, we investigated the incidence and intensity of catastrophic and impoverishing health expenditure. We used a logistic regression analysis to assess households’ characteristics associated with the probability of incurring catastrophic health expenditures. Results The results show that the incidence of catastrophic payments is more severe for the poorest households and in the rural areas and mainly due to outpatient services. Results for the impoverishing effect suggest that after accounting for out-of-pocket(OOP) payments, the proportion of poor people increases by 2.2 percentage points in both rural and urban areas. Thus, between 1 and 1.1 million individuals are pushed into poverty due to OOP payments. Among the characteristics associated with the probability of incurring OOP expenditures, socioeconomic conditions, the presence of elderly and of people affected by chronic conditions showed significant results. Conclusion Kenya is still lagging behind in terms of protecting its citizens against financial risks associated with ill health and healthcare seeking behaviour. More effort is needed to protect the most vulnerable population groups from the high costs of illness.

Background: Kenya is experiencing persistently high levels of inequity in health and access to care services. In 2018, decades of sustained policy efforts to promote equitable, affordable and quality health services have culminated in the launch of a universal health coverage scheme, initially piloted in four Kenyan counties and planned for national rollout by 2022. Our study aims to contribute to monitoring and evaluation efforts alongside policy implementation, by establishing a detailed, baseline assessment of socio-economic inequality and inequity in health care utilization in Kenya shortly before the policy launch. Methods: We use concentration curves and corrected concentration indexes to measure socio-economic inequality in care use and the horizontal inequity index as a measure of inequity in care utilization for three types of care services: outpatient care, inpatient care and preventive and promotive care. Further insights into the individual and household level characteristics that determine observed inequality are derived through decomposition analysis. Results: We find significant inequality and inequity in the use of all types of care services favouring richer population groups, with particularly pronounced levels for preventive and inpatient care services. These are driven primarily by differences in living standards and educational achievement, while the region of residence is a key driver for inequality in preventive care use only. Pro-rich inequalities are particularly pronounced for care provided in privately owned facilities, while public providers serve a much larger share of individuals from lower socio-economic groups. Conclusions: Through its focus on increasing affordability of care for all Kenyans, the newly launched universal health coverage scheme represents a crucial step towards reducing disparities in health care utilization. However in order to achieve equity in health and access to care such efforts must be paralleled by multi-sectoral approaches to address all key drivers of inequity: persistent poverty, disparities in living standards and educational achievement, as well as regional differences in availability and accessibility of care.

In contrast with the case of health care, distributional fairness of long-term care (LTC) services in Europe has received limited attention. Given the increased relevance of LTC in the social policy agenda it is timely to evaluate the evidence on inequality and horizontal inequity by socio-economic status (SES) in the use of LTC and to identify the socio-economic factors that drive them. We address both aspects and reflect on the sensitivity of inequity estimates to adopting different definitions of legitimate drivers of care need. Using Survey of Health, Ageing and Retirement in Europe (SHARE)data collected in 2013, we analyse differences in home care utilization between community-dwelling Europeans in nine countries. We present concentration indexes and horizontal inequity indexes for each country and results from a decomposition analysis across income, care needs, household structures, education achievement and regional characteristics. We find pro-poor inequality in home care utilization but little evidence of inequity when accounting for differential care needs. Household characteristics are an important contributor to inequality, while education and geographic locations hold less explanatory power. We discuss the findings in light of the normative assumptions surrounding different definitions of need in LTC and the possible regressive implications of policies that make household structures an eligibility criterion to access services.