Old-age care prevalence in Switzerland: drivers and future development

Abstract

Long-term care (LTC) delivered to elderly persons in need of assistance in activities of daily living is a topic of increasing importance. The financing of LTC, the needs for specialized infrastructure and the limited number of caregivers will pose a systemic threat in many developed countries. In this paper, we analyze the factors influencing the old-age care prevalence rates in Switzerland through a log-linear regression model. Based on a cross-sectional dataset covering the LTC needs from 1995 to 2014, we statistically support the effect of key drivers such as the age, the gender and the region of residence. We distinguish the prevalence by the mild, moderate and severe frailty levels and by care received either at home or in an institution. Our regression results evidence that prevalence rates exponentially increase with the age yielding significantly higher values for women. These effects are emphasized for moderate and severe dependence and for institutional care. Finally, we forecast the number of dependent persons until 2045. Our projections reveal an important increase in the future numbers. While we observe that the dependent population more than doubles over the 30-year horizon, we report significant cantonal differences. Our results are relevant to governments, practitioners and academics alike and help to better understand the factors affecting the demand of LTC and predicting future needs.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Notes

  1. 1.

    Three linguistic regions are distinguished in Switzerland. These regions are (1) the German-speaking region comprising the cantons of Aargau (AG), Appenzell Innerrhoden (AI), Appenzell Ausserrhoden (AR), Bern (BE), Basel-Landschaft (BL), Basel-Stadt (BS), Glarus (GL), Graubünden (GR), Lucerne (LU), Nidwalden (NW), Obwalden (OW), St.Gallen (SG), Schaffhausen (SH), Solothurn (SO), Schwyz (SZ), Thurgau (TG), Uri (UR), Zug (ZG), and Zurich (ZH); (2) the French-speaking region comprising the cantons of Fribourg (FR), Geneva (GE), Jura (JU), Neuchâtel (NE), Vaud (VD), and Valais (VS); and (3) the Italian-speaking region formed by the canton of Ticino (TI).

  2. 2.

    http://www.zas.admin.ch.

  3. 3.

    http://www.bfs.admin.ch.

  4. 4.

    Differences in the numbers may arise from the exact registration dates of the acuity levels, how up-to-date the sources are, if former years are revised over time, the processes for aggregation used, and the cleaning of incomplete entries.

  5. 5.

    http://www.bfs.admin.ch/bfs/en/home/statistics/population.html.

  6. 6.

    The historical census on the population for the years 1995–2014 is built upon the aggregation of two publicly available datasets, the first one covering the years of interest from 1995 to 2010 and the second one covering the years from 2010 to 2014. For the year 2010 which appears in both datasets, we take the average values between the datasets. Furthermore, the first dataset covers the ages from 65 to 98 years separately and comprises a \(99+\)-category while the second dataset covers the ages from 65 to 99 and has a \(100+\)-category. We merge the datasets and build a \(99+\)-category corresponding to the sum of the \(99+\) in the first dataset and the classes 99 and 100+ from the second dataset.

  7. 7.

    When adding the age–gender interaction in the forecasts, the robustness analysis in Sect. 5.3 would yield increased relative errors.

  8. 8.

    For years before 2014, we omit the hat “\({}^{\wedge }\)” in our notation since the numbers are reported statistics and do not represent forecast estimates.

References

  1. 1.

    Ai C, Norton EC (2008) A semiparametric derivative estimator in log transformation models. Econom J 11(3):538–553

    MathSciNet  Article  Google Scholar 

  2. 2.

    Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B (2012) Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet 380(9836):37–43

    Article  Google Scholar 

  3. 3.

    Biessy G (2016) A semi-Markov model with pathologies for long-term care insurance, working paper, Université d’Évry Val d’Essonne

  4. 4.

    Black MA, Craig BA (2002) Estimating disease prevalence in the absence of a gold standard. Stat Med 21(18):2653–2669

    Article  Google Scholar 

  5. 5.

    Bonsang E (2009) Does informal care from children to their elderly parents substitute for formal care in Europe? J Health Econ 28(1):143–154

    Article  Google Scholar 

  6. 6.

    Brown J, Coe N, Finkelstein A (2007) Medicaid crowd-out of private long-term care insurance demand: evidence from the health and retirement survey, technical report August, National Bureau of Economic Research, Cambridge, MA

  7. 7.

    Callahan CM, Arling G, Tu W, Rosenman MB, Counsell SR, Stump TE, Hendrie HC (2012) Transitions in care for older adults with and without dementia. J Am Geriatr Soc 60(5):813–820

    Article  Google Scholar 

  8. 8.

    Colombo F (2012) Typology of public coverage for long-term care in OECD countries. In: Costa-Font J, Courbage C (eds) Financing long-term care in Europe, chapter 2. Palgrave Macmillan, New York, pp 17–40

    Google Scholar 

  9. 9.

    Cook RD, Weisberg S (1983) Diagnostics for heteroscedasticity in regression. Biometrika 70(1):1–10

    MathSciNet  Article  Google Scholar 

  10. 10.

    Cosandey J (2016) De Nouvelles Mesures pour les Soins des Personnes Agées, Avenir Suisse

  11. 11.

    Costa-Font J, Wittenberg R, Patxot C, Comas-Herrera A, Gori C, di Maio A, Pickard L, Pozzi A, Rothgang H (2008) Projecting long-term care expenditure in four European union member States: the influence of demographic scenarios. Soc Indic Res 86(2):303–321

    Article  Google Scholar 

  12. 12.

    Courbage C, Montoliu-Montes G, Wagner J (2018) Informal care, long term-care insurance and intra-family moral hazard: empirical evidence from Southern Europe, working paper, University of Lausanne

  13. 13.

    Courbage C, Zweifel P (2011) Two-sided intergenerational moral hazard, long-term care insurance, and nursing home use. J Risk Uncertain 43(1):65–80

    Article  Google Scholar 

  14. 14.

    Crimmins EM, Kim JK, Solé-Auró A (2011) Gender differences in health: results from SHARE, ELSA and HRS. Eur J Public Health 21(1):81–91

    Article  Google Scholar 

  15. 15.

    Czado C, Rudolph F (2002) Application of survival analysis methods to long-term care insurance. Insur Math Econ 31(3):395–413

    MathSciNet  Article  Google Scholar 

  16. 16.

    De Meijer C, Koopmanschap M, D’ Uva TB, van Doorslaer E (2011) Determinants of long-term care spending: age, time to death or disability? J Health Econ 30(2):425–438

    Article  Google Scholar 

  17. 17.

    Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 7(1):1–26

    MathSciNet  Article  Google Scholar 

  18. 18.

    Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Springer, Boston

    Google Scholar 

  19. 19.

    Eggink E, Woittiez I, Ras M (2016) Forecasting the use of elderly care: a static micro-simulation model. Eur J Health Econ 17(6):681–691

    Article  Google Scholar 

  20. 20.

    El Bernoussi R, Rockinger M (2017) Besoin en Logements pour Personnes Agées en Suisse. Technical Report, Lausanne

  21. 21.

    Eugster B, Lalive R, Steinhauer A, Zweimüller J (2011) The demand for social insurance: does culture matter? Econ J 121(556):413–448

    Article  Google Scholar 

  22. 22.

    European Commission (2015) The 2015 ageing report: economic and budgetary projections for the 28 EU Member States (2013–2060), vol 3

  23. 23.

    Federal Assembly (2017) Loi Fédérale sur l’Assurance-Vieillesse et Survivants (LAVS) 831.10

  24. 24.

    Federal Statistical Office (1998) Deux Siècles d’Histoire Démographique Suisse. Berne

  25. 25.

    Federal Statistical Office (2015) Les Scénarios de l’Evolution de la Population de la Suisse 2015–2045. Neuchâtel

  26. 26.

    Federal Statistical Office (2017) Tables de Mortalité pour la Suisse 2008/2013. Neuchâtel

  27. 27.

    Fong JH (2017) Old-age frailty patterns and implications for long-term care programmes. Geneva Pap Risk Insur Issues Pract 42(1):114–128

    Article  Google Scholar 

  28. 28.

    Fong JH, Sherris M, Yap J (2017) Forecasting disability: application of a frailty model. Scand Actuar J 2:125–147

    MathSciNet  Article  Google Scholar 

  29. 29.

    Fuino M, Wagner J (2018) Long-term care models and dependence probability tables by acuity level: new empirical evidence from Switzerland. Insur Math Econ 81(5):51–70

    MathSciNet  Article  Google Scholar 

  30. 30.

    Gentili E, Masiero G, Mazzonna F (2017) The role of culture in long-term care arrangement decisions. J Econ Behav Organ 143(2017):186–200

    Article  Google Scholar 

  31. 31.

    Han L, Allore H, Murphy T, Gill T, Peduzzi P, Lin H (2013) Annals of epidemiology dynamics of functional aging based on latent-class trajectories of activities of daily living. Ann Epidemiol 23(2):87–92

    Article  Google Scholar 

  32. 32.

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

    Article  Google Scholar 

  33. 33.

    Katz PR (2011) An international perspective on long term care: focus on nursing homes. J Am Med Dir Assoc 12(7):487–492

    Article  Google Scholar 

  34. 34.

    Kwon H-S, Lee C-S, Hur J-S (2012) Projecting the cost of long-term care insurance in Korea. Asia Pacific J Risk Insur. https://doi.org/10.1515/2153-3792.1163

  35. 35.

    Le Corre P-Y (2012) Long-term care insurance: building a successful development. In: Costa-Font J, Courbage C (eds) Financing long-term care in Europe, chapter 4. Palgrave Macmillan, New York, pp 53–72

  36. 36.

    Lopes R (2011) Kolmogorov–Smirnov test. In: Costa-Font J, Courbage C (eds) International encyclopedia of statistical science. Springer, Berlin

    Google Scholar 

  37. 37.

    Lumley T, Diehr P, Emerson S, Chen L (2002) The importance of the normality assumption in large public health data sets. Annu Rev Public Health 23(1):151–169

    Article  Google Scholar 

  38. 38.

    Manning WG (1998) The logged dependent variable, heteroscedasticity, and the retransformation problem. J Health Econ 17(3):283–295

    MathSciNet  Article  Google Scholar 

  39. 39.

    Manning WG, Mullahy J (2001) Estimating log models: to transform or not to transform? J Health Econ 20(4):461–494

    Article  Google Scholar 

  40. 40.

    Marengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, Meinow B, Fratiglioni L (2011) Aging with multimorbidity: a systematic review of the literature. Ageing Res Rev 10(4):430–439

    Article  Google Scholar 

  41. 41.

    Mathers C (1996) Trends in health expectancies in Australia 1981–1993. J Aust Popul Assoc 13(1):1–15

    Google Scholar 

  42. 42.

    Mathers C, Vos T, Stevenson C (2001) The burden of disease and injury in Australia. World Health Organ 23(1):1076–1084

    Google Scholar 

  43. 43.

    Meguro K, Tanaka N, Kasai M, Nakamura K, Ishikawa H, Nakatsuka M, Satoh M, Ouchi Y (2012) Prevalence of dementia and dementing diseases in the old–old population in Japan: the Kurihara project. Implications for long-term care insurance data. Psychogeriatrics 12(4):226–234

    Article  Google Scholar 

  44. 44.

    Monod-Zorzi S, Seematter-Bagnoud L, Büla C, Pellegrini S, Jaccard Ruedin H (2007) Maladies Chroniques et Dépendance Fonctionnelle des Personnes Agées. Observatoire Suisse de la Santé, Neuchâtel

  45. 45.

    Nichols BL, Davis CR, Richardson DR (2010) An integrative review of global nursing workforce issues. Annu Rev Nurs Res 28:113–32

    Article  Google Scholar 

  46. 46.

    OECD (2015) Health at a glance 2015. OECD Publishing, Paris

  47. 47.

    Ohri A (2012) R for business analytics. Springer, New York

    Google Scholar 

  48. 48.

    Pauly MV (1990) The rational nonpurchase of long-term-care insurance. J Political Econ 98(1):153–168

    Article  Google Scholar 

  49. 49.

    Rockinger M, Wagner J (2016) Les Soins et la Dépendance: Un Risque Systémique, Le Temps, 28 June

  50. 50.

    Schünemann J, Strulik H, Trimborn T (2017) The gender gap in mortality: how much is explained by behavior? J Health Econ 54:79–90

    Article  Google Scholar 

  51. 51.

    Stock JH, Watson MM (2012) Introduction to econometrics, 3rd edn. Pearson Education Limited, Harlow

    Google Scholar 

  52. 52.

    Swiss Re (2014) How will we care? Finding sustainable long-term care solutions for an ageing world, sigma, no 5

  53. 53.

    Trevor B, Adrian P (1979) A simple test for heteroscedasticity and random coefficient variation. Econometrica 47(5):1287–1294

    MathSciNet  Article  Google Scholar 

  54. 54.

    United Nations (2016) Human development report 2016. United Nations Development Programme, New York

  55. 55.

    Weaver F (2012) Long-Term care financing in Switzerland. In: Costa-Font J, Courbage C (eds) Financing Long-Term Care in Europe, chapter 15. Palgrave Macmillan, New York, pp 279–299

    Google Scholar 

  56. 56.

    Wooldridge JM (2013) Introductory econometrics: a modern approach, 5th edn. Cengage Learning

  57. 57.

    World Health Organization (2014) Migration of health workers. WHO Document Production Services, Geneva

  58. 58.

    Xue Q-L (2011) The Frailty syndrome: definition and natural history. Clin Geriatr Med 27(1):1–15

    MathSciNet  Article  Google Scholar 

  59. 59.

    Yip AG, Brayne C, Matthews FE, MRC Cognitive Function and Ageing Study (2006) Risk factors for incident dementia in England and Wales: the medical research council cognitive function and ageing study. A population-based nested case–Control Study. Age Ageing 35(2):154–160

  60. 60.

    Zweifel P, Felder S, Werblow A (2004) Population ageing and health care expenditure: new evidence on the red herring. Geneva Pap Risk Insur Issues Pract 29(4):652–666

    Article  Google Scholar 

Download references

Acknowledgements

Funding was provided by Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (CH) (grant no. 100018_169662) and the Swiss Insurance Association. Support from the Swiss Central Compensation Office for providing the data is kindly acknowledged.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Joël Wagner.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fuino, M., Wagner, J. Old-age care prevalence in Switzerland: drivers and future development. Eur. Actuar. J. 8, 321–362 (2018). https://doi.org/10.1007/s13385-018-0185-3

Download citation

Keywords

  • Long-term care
  • Log-linear regression
  • Prevalence rates
  • Forecast