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|a (JST)24774193
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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|a Eggink, Evelien
|e verfasserin
|4 aut
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|a Forecasting the use of elderly care: a static micro-simulation model
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|c 2016
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|a Text
|b txt
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|a 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 %).
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|a © Springer-Verlag Berlin Heidelberg 2016
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|a Mathematics
|x Applied mathematics
|x Analytics
|x Predictive analytics
|x Analytical forecasting
|x Forecasting models
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|a Health sciences
|x Medical treatment
|x Inpatient treatment
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4 |
|a Health sciences
|x Health care industry
|x Health care services
|x Home care services
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4 |
|a Social sciences
|x Population studies
|x Human populations
|x Persons
|x Adults
|x Older adults
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4 |
|a Health sciences
|x Medical conditions
|x Disorders
|x Physical disorders
|x Musculoskeletal disorders
|x Muscular disorders
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650 |
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4 |
|a Health sciences
|x Medical specialties
|x Geriatrics
|x Eldercare
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4 |
|a Behavioral sciences
|x Psychology
|x Clinical psychology
|x Mental illness
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650 |
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4 |
|a Behavioral sciences
|x Sociology
|x Human societies
|x Social structures
|x Social stratification
|x Social classes
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650 |
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4 |
|a Social sciences
|x Population studies
|x Population characteristics
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4 |
|a Mathematics
|x Pure mathematics
|x Probability theory
|x Probabilities
|x Probability forecasts
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|a research-article
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1 |
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|a Woittiez, Isolde
|e verfasserin
|4 aut
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1 |
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|a Ras, Michiel
|e verfasserin
|4 aut
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773 |
0 |
8 |
|i Enthalten in
|t The European Journal of Health Economics
|d Springer Science + Business Media
|g 17(2016), 6, Seite 681-691
|w (DE-627)320494462
|w (DE-600)2011428-X
|x 16187601
|7 nnns
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773 |
1 |
8 |
|g volume:17
|g year:2016
|g number:6
|g pages:681-691
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|u https://www.jstor.org/stable/24774193
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|d 17
|j 2016
|e 6
|h 681-691
|