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Social protection for the costs of long‐term care (LTC) varies widely between countries, and to date there has been no systematic comparison of the experiences of people with LTC needs in different countries. In response to this information gap, the OECD and the European Commission (EC) have established a project to make quantitative comparisons of social protection for LTC in OECD and EU countries, using the typical cases approach. Social protection encompasses both cash benefits, conditional on long‐term care needs, and long‐term care services offered at no or subsidized cost to the user. A data collection questionnaire has been distributed. This report describes how the data for Belgium have been collected. The following schemes are taken into account: the allowance for the assistance of the elderly; the allowances for incontinence and for the chronically ill; the Flemish care insurance; the sickness and invalidity insurance for home nursing care and care in institutions; home care (not nursing care), regulated and subsidized by regional governments; and service vouchers. The data refer to the year 2015.
This paper presents the models developed at the FPB to project public spending on curative care and long-term care in the medium and long term. The variables explaining curative care spending are income, the age composition of the population, the unemployment rate and technological and medical progress. This variable is approximated using two indicators, the number of new drug approvals (Farmanet data) and the approvals for non-pharmaceutical products (Food and Drug Administration data). With the exception of the latter, all drivers mentioned above increase the cost of curative care. As for long-term care spending, it is explained by income, the proportion of older people in the population and their life expectancy. Long-term care spending is positively impacted by income and ageing. Yet, due to the increase in life expectancy, the impact of ageing shifts gradually towards the oldest age group.
At the occasion of its 50th anniversary, the National Institute for Health and Invalidity Insurance (NIHDI) asked the Federal Planning Bureau to draft a report on the social impact of public health care and health care insurance. We focused on three specific questions. First, what was the contribution of health care to population health during the past half-century? Lacking sufficient data on other dimensions of health, we look at mortality and life expectancy. Two approaches to this question lead to the same conclusion: the expansion of health care has contributed substantially to the increase in life years. The second question concerns the role of health care in the economy. The value added and employment in the branches Health care and Social services has increased vastly between 1970 and making health care an ever more important part of the Belgian economy . The third question is about the impact of health care and public health care insurance on inequalities in health and income. Among other findings, we report that in Belgium there is no social inequality in the use of general practitioner, though there is in specialist consultations. Also, a fairly large number of older persons have to cope with own contributions to health care that exceed 10% of their income.
While rising health care expenditures as a percentage of national income is a well-known and widely documented feature across the industrialized world, it has proved difficult to quantify the effects of the underlying cost drivers. The main difficulty is to find suitable proxies to measure medical technological innovation, which is believed to be a major determinant of steadily increasing health spending. This paper’s main contribution is the use of data on approved medical devices and drugs to proxy for medical technological progress. The effects of these variables on total real per capita health spending are estimated using a panel model for 18 OECD countries covering the period 1981-2009. The results confirm the substantial cost-increasing effect of medical technology, which may account for at least 50% of the explained historical growth of spending. Excluding the approval variables causes a significant upward bias of the estimated income elasticity of health spending and negatively affects some model specification tests. Despite the overall net positive effect of technology, the effect of two subgroups of approvals on expenditure is significantly negative. These subgroups can be thought of as representing ‘incremental medical innovation’, while the positive effects are related to radically innovative pharmaceutical products and devices. The results are consistent with those reported in other studies which suggest that some new products, despite their high price when they are introduced, can ultimately save money by reducing spending on other medical interventions.