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Modelling the dynamics of a public health care system: evidence from time-series data

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Modelling the dynamics of a public health care system : evidence from time-series data. / Iacone, Fabrizio; Martin, Stephen; Siciliani, Luigi; Smith, Peter.

In: Applied economics, Vol. 44, No. 23, 08.2012, p. 2955-2968.

Research output: Contribution to journalArticlepeer-review

Harvard

Iacone, F, Martin, S, Siciliani, L & Smith, P 2012, 'Modelling the dynamics of a public health care system: evidence from time-series data', Applied economics, vol. 44, no. 23, pp. 2955-2968. https://doi.org/10.1080/00036846.2011.568407

APA

Iacone, F., Martin, S., Siciliani, L., & Smith, P. (2012). Modelling the dynamics of a public health care system: evidence from time-series data. Applied economics, 44(23), 2955-2968. https://doi.org/10.1080/00036846.2011.568407

Vancouver

Iacone F, Martin S, Siciliani L, Smith P. Modelling the dynamics of a public health care system: evidence from time-series data. Applied economics. 2012 Aug;44(23):2955-2968. https://doi.org/10.1080/00036846.2011.568407

Author

Iacone, Fabrizio ; Martin, Stephen ; Siciliani, Luigi ; Smith, Peter. / Modelling the dynamics of a public health care system : evidence from time-series data. In: Applied economics. 2012 ; Vol. 44, No. 23. pp. 2955-2968.

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@article{0ba6c91dbbb84f8bbfe9f0ce4ee99971,
title = "Modelling the dynamics of a public health care system: evidence from time-series data",
abstract = "The English National Health Service (NHS) was established in 1948, and has therefore yielded some long time series data on health system performance. Waiting time for inpatient care have been a persistent policy concern since the creation of the NHS. After developing a simple theoretical framework of the dynamic interaction between key indicators of health system performance, we investigate empirically the relationship between hospital activity, waiting time and population characteristics using aggregate time-series data for the NHS over the period 1952 to 2003. Structural Vector Autoregression (S-VAR) suggests that in the long run: higher activity is associated with lower waiting times (elasticity=-0.9); an increase in the elderly population is associated with higher waiting time (elasticity=1.3). In the short run, higher lagged waiting time leads to higher activity (elasticity=0.12). We also find that shocks in waiting times are countered by higher activity, so the effect is only temporary. ",
keywords = "waiting times, dynamics, vector auto-regression",
author = "Fabrizio Iacone and Stephen Martin and Luigi Siciliani and Peter Smith",
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language = "English",
volume = "44",
pages = "2955--2968",
journal = "Applied economics",
issn = "0003-6846",
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RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Modelling the dynamics of a public health care system

T2 - evidence from time-series data

AU - Iacone, Fabrizio

AU - Martin, Stephen

AU - Siciliani, Luigi

AU - Smith, Peter

PY - 2012/8

Y1 - 2012/8

N2 - The English National Health Service (NHS) was established in 1948, and has therefore yielded some long time series data on health system performance. Waiting time for inpatient care have been a persistent policy concern since the creation of the NHS. After developing a simple theoretical framework of the dynamic interaction between key indicators of health system performance, we investigate empirically the relationship between hospital activity, waiting time and population characteristics using aggregate time-series data for the NHS over the period 1952 to 2003. Structural Vector Autoregression (S-VAR) suggests that in the long run: higher activity is associated with lower waiting times (elasticity=-0.9); an increase in the elderly population is associated with higher waiting time (elasticity=1.3). In the short run, higher lagged waiting time leads to higher activity (elasticity=0.12). We also find that shocks in waiting times are countered by higher activity, so the effect is only temporary.

AB - The English National Health Service (NHS) was established in 1948, and has therefore yielded some long time series data on health system performance. Waiting time for inpatient care have been a persistent policy concern since the creation of the NHS. After developing a simple theoretical framework of the dynamic interaction between key indicators of health system performance, we investigate empirically the relationship between hospital activity, waiting time and population characteristics using aggregate time-series data for the NHS over the period 1952 to 2003. Structural Vector Autoregression (S-VAR) suggests that in the long run: higher activity is associated with lower waiting times (elasticity=-0.9); an increase in the elderly population is associated with higher waiting time (elasticity=1.3). In the short run, higher lagged waiting time leads to higher activity (elasticity=0.12). We also find that shocks in waiting times are countered by higher activity, so the effect is only temporary.

KW - waiting times

KW - dynamics

KW - vector auto-regression

UR - http://www.scopus.com/inward/record.url?scp=80051984134&partnerID=8YFLogxK

U2 - 10.1080/00036846.2011.568407

DO - 10.1080/00036846.2011.568407

M3 - Article

VL - 44

SP - 2955

EP - 2968

JO - Applied economics

JF - Applied economics

SN - 0003-6846

IS - 23

ER -