Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Paper
  • Published:
The European Journal of Health Economics Aims and scope Submit manuscript

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 %).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  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.

References

  1. Hoeymans, N., van Loon, A.J.M., van den Berg, M., Harbers, M.M., Hilderink, H.B.M., van Oers, J.A.M., Schoemaker, C.G.: A healthier Netherlands. Key findings from the Dutch 2014 Public Health Status and Foresight Report. In: RIVM, Bilthoven, (2014)

  2. Besseling, P., Shestalova, V.: Forecasting public health expenditures in the Netherlands. In: Netherlands Bureau for Economic Policy Analysis (CPB), The Hague, (2011)

  3. Pommer, E., Eggink, E.: Publieke dienstverlening in perspectief. SCP-memorandum voor de kabinetsformatie 2010. In: Sociaal en Cultureel Planbureau (SCP-publicatie 2010/16), Den Haag, (2010)

  4. Kennedy, P.: A guide to econometrics, 6th edn. Wiley-Blackwell, New York (2008)

    Google Scholar 

  5. Van den Bosch, K., Willemé, P., Geerts, J., Breda, J., Peeters, S., Van De Sande, S., Vrijens, F., Van de Voorde, C., Stordeur, S.: Toekomstige behoefte aan residentiële ouderenzorg in België: Projecties 2011–2025. In: KCE Reports 167A. D/2011/10.273/63. Brussels, (2011)

  6. Jones, S.A., Patrick, M., Pearson, J.: Forecasting demand of emergency care. Health Care Manag. Sci. 5, 297–305 (2002)

    Article  PubMed  Google Scholar 

  7. European Commission: Ageing report: economic and budgetary projections for the EU-27 member states (2008–2060). Office for Official Publications of the European Communities, Luxembourg (2009)

    Google Scholar 

  8. European Commission: The 2012 Ageing report: economic and budgetary projections for the 27 EU member states (2010–2060). http://ec.europa.eu/economy_finance/publications/european_economy/2012/2012-ageing-report_en.htm (2012). Accessed 27 Nov 2014

  9. Costa-Font, J., Wittenberg, R., Patxot, C., Comas-Herrera, A., Gori, C., di Maio, A., Pickard, L., Pozzi, A., Rothgang, H.: How does demography affect long-term care expenditure projections? Evidence of four European Union member states. Soc. Indicators Res. 86, 303–321 (2008)

    Article  Google Scholar 

  10. Fogl, R.W.: Forecasting the cost of U.S. health care in 2040. In: NBR Working Paper Series 14361. (2008)

  11. Palin, S., Jackson, K.K.: Long-term forecast of medicaid enrollment and spending in Alaska: supplement 2006–2026. Medicaid Budget Group Finance and Management Services Department of Health and Social Services, Alaska (2007)

    Google Scholar 

  12. Pickard, L., Comas-Herrera, A., Costa-Font, J., Gori, C., di Maio, A., Patxot, C., Pozzi, A., Rothgang, H., Wittenberg, R.: Modelling an entitlement to long-term care services for older people in Europe: projections for long-term care expenditure to 2050. J. Eur. Soc. Policy 17, 33–48 (2007)

    Article  Google Scholar 

  13. Hancock, R., Comas-Herrera, A., Wittenberg, R., Pickard, L.: Who will pay for long-term care in the UK? Projections linking macro- and micro-simulation models. Fisc. Stud. 34, 378–426 (2003)

    Google Scholar 

  14. Van Galen, J., Van der Meer, A., Brouwer, J., Sogelée, G.: AZRA, een AWBZ-Ramingsmodel: beschrijving model en gebruikte bronnen. In: Delft, (2007)

  15. Depla, M., Pot, A.M., de Graaf, R., van Dorsselaer, S., Sonnenberg, C., Deeg, D.: Schatting van het gebruik van geestelijke gezondheidszorg en verslavingszorg door ouderen in 2010 en 2020. Trimbos-instituut, Utrecht (2005)

    Google Scholar 

  16. Baroni, E., Richiardi, M.: Orcutt’s vison, 50 years on. In: Working paper 65. Moncalieri, (2007)

  17. Spielauer, M.: Dynamic micro-simulation of health care demand, health care finance and the economic impact of health behavior. Part 1: background and a comparison with cell-based models. report IR-02-032/April. International Institute for Applied Systems Analysis, Laxenburg (2002)

    Google Scholar 

  18. Duée, M., Rebillard, C.: La dépendance des personnes âgées: une projection à long terme. In. INSEE, Paris (2004)

    Google Scholar 

  19. Duée, M., Rebillard, C.: La dépendance des personnes âgées: une projection en 2040. Données sociales. La société française. INSEE, Paris (2006)

    Google Scholar 

  20. Johnson, R., Toohey, D., Wiener, J.: Meeting the long-term care needs of the baby boomers. How changing families will affect paid helpers and institutions. In: The retirement project, discussion paper 07-04. The Urban Institute, Washington, (2007)

  21. Karlsson, M., Mayhew, L., Plumb, R., Rickayzen, B.: Future costs for long-term care: cost projections for long-term care for older people in the United Kingdom. Health Pol. 75(2), 187–213 (2006)

    Article  Google Scholar 

  22. Geerts, J., Willemé, P., Mot, E.: Long-term care use and supply in Europe: projections for Germany, the Netherlands, Spain and Poland. In: Enepri Research Report No. 116. (2012)

  23. Woittiez, I., Eggink, E., Jonker, J., Sadiraj, K.: Vergrijzing,verpleging en verzorging.Ramingen,profielen en scenario’s,2005–2030. In: SCP Publication 2009-20. Netherlands Institute for Social Research/SCP, The Hague, (2009)

  24. Timmermans, J., Woittiez, I.: Verklaringsmodel verpleging en verzorging: onderzoeksverantwoording. In: Werkdocument 110. Netherlands Institute for Social Research/SCP, The Hague, (2004)

  25. Eggink, E., Jonker, J., Sadiraj, K., Woittiez, I.: VeVeRa-III, Ramingen verpleging en verzorging 2005–2030, modelbeschrijving. In: SCP-special. Netherlands Institute for Social Research/SCP, The Hague, (2009)

  26. Mot, E.: The Dutch system of long-term care. In: Aouragh, A., de Groot, M., Mannaerts, H. (eds.) CPB Document 204. CPB Netherlands Bureau for Economic Policy Analysis, The Hague (2012)

    Google Scholar 

  27. OECD: OECD statistics (health expenditure and financing). stats.oecd.org (2012)

  28. Eggink, E., Oudijk, D., Sadiraj, K.: VeVeRa-IV, Actualisatie en aanpassing ramingsmodel verpleging en verzorging 2009–2030. In: SCP Report 2012–2015. Netherlands Institute for Social Research/SCP, The Hague, (2012)

  29. Andersen, R., Newman, J.F.: Societal and individual determinants of medical care utilization in the United States. Milbank Mem. Fund Q Health Soc. 51(1), 95–124 (1973)

    Article  CAS  PubMed  Google Scholar 

  30. Andersen, R., Newman, J.F.: Societal and individual determinants of medical care utilization in the United States. Milbank Q. 83 (2005). doi:10.1111/j.1468-0009.2005.00428.x

  31. Babitsch, B., Gohl, D., Von Lengerke, T.: Re-revisiting Andersenʼs behavioral model of health services use: a systematic review of studies from 1998–2011. GMS Psychosoc. Med. 9, 1–15 (2012)

    Google Scholar 

  32. Jonker, J.-J., Sadiraj, K., Woittiez, I., Ras, M., Morren, M.: Verklaringsmodel verpleging en verzorging SCP publication 2007–2031, p. 2007. Netherlands Institute for Social Research/SCP, The Hague (2007)

    Google Scholar 

  33. Netherlands, S.: Bevolking naar huishoudenspositie, 2009–2050. statline.cbs.nl (2009)

  34. OCW: Referentieraming 2008 (reference forecast 2008).Internal publication of the Dutch Ministry of Education, Culture and Science (Ministerie van Onderwijs, Cultuur en Wetenschap). http://www.minocw.nl/begroting/publicaties (2008). Accessed 1 Mar 2009

  35. Perenboom, R.J.M.: Enige prognoses betreffende dementie in de jaren 2007 tot 2030 in Amstedam. TNO, Leiden (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Netherlands Institute for Social Research/SCP, PO Box 16164, 2500 BD, The Hague, The Netherlands

    Evelien Eggink, Isolde Woittiez & Michiel Ras

Authors
  1. Evelien Eggink
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Isolde Woittiez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michiel Ras
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Evelien Eggink.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Eggink, E., Woittiez, I. & Ras, M. Forecasting the use of elderly care: a static micro-simulation model. Eur J Health Econ 17, 681–691 (2016). https://doi.org/10.1007/s10198-015-0714-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10198-015-0714-9

Keywords

JEL Classifications

Search

Navigation