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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.
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.
This Working Paper reflects the contribution of the fpb to the second work package of the agir project, work package organized by the German diw. It collects in a first attempt a lot of data to approach the volume and evolution of the use of health and nursing care by the elderly. Yet the authors are well aware of the limitations of the present study which can certainly be improved by more detailed data and refinement of the concepts.