• Bmc Health Serv Res · May 2018

    Revisiting out-of-pocket requirements: trends in spending, financial access barriers, and policy in ten high-income countries.

    • Thomas Rice, Wilm Quentin, Anders Anell, Andrew J Barnes, Pauline Rosenau, Lynn Y Unruh, and van GinnekenEwoutEEuropean Observatory on Health Systems and Policies, Berlin University of Technology, Straße des 17. Juni 135, 10623, Berlin, Germany. ewout.vanginneken@tu-berlin.de..
    • University of California, Los Angeles, USA.
    • Bmc Health Serv Res. 2018 May 18; 18 (1): 371.

    BackgroundCountries rely on out-of-pocket (OOP) spending to different degrees and employ varying techniques. The article examines trends in OOP spending in ten high-income countries since 2000, and analyzes their relationship to self-assessed barriers to accessing health care services. The countries are Australia, Canada, France, Germany, the Netherlands, New Zealand, Norway, Sweden, Switzerland, the United Kingdom, and the United States.MethodsData from three sources are employed: OECD statistics, the Commonwealth Fund survey of individuals in each of ten countries, and country-specific documents on health care policies. Based on trends in OOP spending, we divide the ten countries into three groups and analyze both trends and access barriers accordingly. As part of this effort, we propose a conceptual model for understanding the key components of OOP spending.ResultsThere is a great deal of variation in aggregate OOP spending per capita spending but there has been convergence over time, with the lowest-spending countries continuing to show growth and the highest spending countries showing stability. Both the level of aggregate OOP spending and changes in spending affect perceived access barriers, although there is not a perfect correspondence between the two.ConclusionsThere is a need for better understanding the root causes of OOP spending. This will require data collection that is broken down into OOP resulting from cost sharing and OOP resulting from direct payments (due to underinsurance and lacking benefits). Moreover, data should be disaggregated by consumer groups (e.g. income-level or health status). Only then can we better link the data to specific policies and suggest effective solutions to policy makers.

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