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The European Journal of Health Economics
Published in: The European Journal of Health Economics 6/2016

01-07-2016 | Original Paper

Forecasting the use of elderly care: a static micro-simulation model

Authors: Evelien Eggink, Isolde Woittiez, Michiel Ras

Published in: The European Journal of Health Economics | Issue 6/2016

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Abstract

This paper describes a model suitable for forecasting the use of publicly funded long-term elderly care, taking into account both ageing and changes in the health status of the population. In addition, the impact of socioeconomic factors on care use is included in the forecasts. The model is also suitable for the simulation of possible implications of some specific policy measures. The model is a static micro-simulation model, consisting of an explanatory model and a population model. The explanatory model statistically relates care use to individual characteristics. The population model mimics the composition of the population at future points in time. The forecasts of care use are driven by changes in the composition of the population in terms of relevant characteristics instead of dynamics at the individual level. The results show that a further 37 % increase in the use of elderly care (from 7 to 9 % of the Dutch 30-plus population) between 2008 and 2030 can be expected due to a further ageing of the population. However, the use of care is expected to increase less than if it were based on the increasing number of elderly only (+70 %), due to decreasing disability levels and increasing levels of education. As an application of the model, we simulated the effects of restricting access to residential care to elderly people with severe physical disabilities. The result was a lower growth of residential care use (32 % instead of 57 %), but a somewhat faster growth in the use of home care (35 % instead of 32 %).
Footnotes
1
They also include informal care in their model. This can be easily done in our set-up as well, see for instance [1, 2].
 
2
Correlations between variables are preserved as far as possible. This is done by applying iterative weighting. A variant that creates equal personal weights within each separate household is applied, to ensure consistent values for household characteristics such as household composition and household income.
 
3
The survey was conducted during 2007 and 2008. Our base year is 2008, which is the first year for which we have made a forecast based on the population model.
 
4
Little information is available on the education level of this group of elderly persons. Our method is suitable for this age group, since education is completed at a young age for most people, and the education level does not change for elderly persons.
 
5
For elderly persons living in residential homes, we use the OII which is available every 4 years. For people living at home we use the Periodic Life Situation Survey (POLS), which is conducted annually by Statistics Netherlands. Both surveys contain sample information on health status.
 
6
In fact this is done at the individual level in the base data (AVO/OII). An increasing weight for a highly educated individual, caused by increasing educational levels, will lead to an increased weight for the corresponding income level of that individual.
 
7
‘No care’ does not imply that they receive no care at all, but they will have to rely on informal care rather than formal (home) care.
 
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Metadata
Title
Forecasting the use of elderly care: a static micro-simulation model
Authors
Evelien Eggink
Isolde Woittiez
Michiel Ras
Publication date
01-07-2016
Publisher
Springer Berlin Heidelberg
Published in
The European Journal of Health Economics / Issue 6/2016
Print ISSN: 1618-7598
Electronic ISSN: 1618-7601
DOI
https://doi.org/10.1007/s10198-015-0714-9

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