Modelling Population Growth, Shrinkage and Aging using a Hybrid Simulation Approach: Application to Healthcare

Bożena Mielczarek, Jacek Zabawa

Abstract

This paper describes a hybrid simulation model that integrates the System Dynamic approach with discrete time control to formulate the projections of population evolution. The study relies on historical demographic data and the officially formulated scenarios for the most likely population projections developed for the region. The results of the simulation experiments provide valuable insights into dynamics of regional demographic trends and offer a well-defined starting point for future research in the health policy field. The intensity and structure of the demand for healthcare services depend heavily on age-gender profiles that change due to ongoing extensions of the average expected length of life, the aging of population, the continuing trend of declining number of births and the steadily growing number of deaths. The preliminary findings show promise in using the hybrid simulation approach for more advanced exploration of demography dependent health policy issues.

References

  1. Alfonesca, M., Martinez-Bravo, M.T., Torrea, J.L., 2000. Mathematical models for the analysis of Hepatitis B and AIDS epidemics. Simulation - transactions of the society for modeling and simulation international, 74(4), pp. 219-226.
  2. Ansah, J.P., Eberlein, R.L., Love, S.R., Bautista, M.A., Thompson, J.P., Malhotra, R. and Matchar, D.B., 2014. Implications of long-term care capacity response policies for an aging population: A simulation analysis. Health Policy, 116(1), pp. 105-113.
  3. Ashton, R., Hague, L., Brandreth, M., Worthington, D. and Cropper, S., 2005. A simulation-based study of a NHS walk-in centre. The Journal of the Operational Research Society, 56(2), pp. 153-161.
  4. Barber, P. and Lopez-Valcarcel, B., 2010. Forecasting the need for medical specialists in Spain: application of a system dynamics model. Human Resources for Health, 8(1), pp. 24.
  5. Caro, J.J., Guo, S., Ward, A., Shajil, C., Malik, F. and Leyva, F., 2006. Modelling the economic and health consequences of cardiac resynchronization therapy in the UK. Current medical research and opinion, 22(6), pp. 1171-9.
  6. Davis, P., Lay-Yee, R. and Pearson, J., 2010. Using microsimulation to create a synthesised data set and test policy options: The case of health service effects under demographic ageing. Health Policy, 97(2-3), pp. 267- 274.
  7. Eberlein, R.L., Thompson, J.P. and Matchar, D.B., 2011. Chronological aging in continuous time, E. Husemann and D. Lane, eds. In: Proceedings of the 30th International Conference of the System Dynamics Society, July 22-26, 2011 St. Gallen, Switzerland 2011.
  8. Forrester, J.W., 1968. Industrial dynamics - after the first decade. Management Science, 14(7), pp. 398-415.
  9. GUS, 2015, Glówny Urzad Statystyczny. Available: www.stat.gov.pl [December, 2015].
  10. Homer, J.B. and Hirsch, G.B., 2006. System dynamics modeling for public health: background and opportunities. American Journal of Public Health, 96(3), pp. 452-458.
  11. Hughes, G.R., Currie, C.S.M. and Corbett, E.L., 2006. Modeling tuberculosis in areas of high HIV prevalence. Proceedings of the 38th conference on Winter Simulation. Monterey, California, pp. 459-465.
  12. Jun, J.B., Jacobson, S.H. and Swisher, J.R., 1999. Application of discrete-event simulation in health care clinics: A survey. The Journal of the Operational Research Society, 50(2), pp. 109-123.
  13. Krahl, D., 2009. ExtendSim advanced techology: discrete rate simulation, M.D. Rossetti, R.R. Hill, B. Johansson, A. Dunkin and R.G. Ingalls, eds. In: Proceedings of the 2009 Winter Simulation Conference 2009, pp. 333-338.
  14. Lane, D.C., Monefeldt, C. and Rosenhead, J.V., 2000. Looking in the wrong place for healthcare improvements: A system dynamics study of an accident and emergency department. The Journal of the Operational Research Society, 51(5), pp. 518-531.
  15. Lassila, J., Valkonen, T. and Alho, J.M., 2014. Demographic forecasts and fiscal policy rules. International Journal of Forecasting, 30(4), pp. 1098- 1109.
  16. Lauf, S., Haase, D. and Kleinschmit, B., 2016. The effects of growth, shrinkage, population aging and preference shifts on urban development-A spatial scenario analysis of Berlin, Germany. Land Use Policy, 52, pp. 240-254.
  17. Lisenkova, K., Mérette, M. and Wright, R., 2013. Population ageing and the labour market: Modelling size and age-specific effects. Economic Modelling, 35, pp. 981-989.
  18. Lutz, W., Sanderson, W. and Scherbov, S., 2001. The end of world population growth. Nature, 412(6846), pp. 543-545.
  19. Masnick, K. and Mcdonnell, G., 2010. A model linking clinical workforce skill mix planning to health and health care dynamics. Human Resources for Health, 8(1), pp. 11.
  20. Mielczarek, B., Zabawa, J. and Lubicz, M., 2014. A system dynamics model to study the impact of an age pyramid on emergency demand, M.S. Obaidat, J. Kacprzyk and T. Oren, eds. In: SIMULTECH 2014 - Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications 2014, SciTePress, pp. 879-888.
  21. Mielczarek, B. and Uzialko-Mydlikowska, J., 2012. Application of computer simulation modeling in the health care sector: a survey. Simulation - transactions of the society for modeling and simulation international, 88(2), pp. 197-216.
  22. Mustafee, N., Katsaliaki, K. and Taylor, S.J.E., 2010. Profiling literature in healthcare simulation. Simulation, 86(8-9), pp. 543-558.
  23. Testi, A., Tanfani, E. and Torre, G., 2007. A three-phase approach for operating theatre schedules. Health Care Management Science, 10(2), pp. 163-72.
  24. Tian, Y. and Zhao, X., 2016. Stochastic Forecast of the Financial Sustainability of Basic Pension in China. Sustainability, 8(1), pp. 46.
  25. Waligórska, M., Kostrzewa, Z., Potyra, M. and Rutkowska, L., 2014. Population projection 2014- 2050. CSO, Demographic Surveys and Labour Market Department.
Download


Paper Citation


in Harvard Style

Mielczarek B. and Zabawa J. (2016). Modelling Population Growth, Shrinkage and Aging using a Hybrid Simulation Approach: Application to Healthcare . In Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-758-199-1, pages 75-83. DOI: 10.5220/0005960800750083


in Bibtex Style

@conference{simultech16,
author={Bożena Mielczarek and Jacek Zabawa},
title={Modelling Population Growth, Shrinkage and Aging using a Hybrid Simulation Approach: Application to Healthcare},
booktitle={Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2016},
pages={75-83},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005960800750083},
isbn={978-989-758-199-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - Modelling Population Growth, Shrinkage and Aging using a Hybrid Simulation Approach: Application to Healthcare
SN - 978-989-758-199-1
AU - Mielczarek B.
AU - Zabawa J.
PY - 2016
SP - 75
EP - 83
DO - 10.5220/0005960800750083