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Modeling public health care expenditure using patient level data: Empirical evidence from Italy

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Abstract

In this work we present some results obtained with a unique database of patient level data collected through GPs. The availability of such data opens new scenarios and paradigms for the planning and management of the health care system and for policy impact evaluation studies. The dataset, representative of the Italian population, contains detailed information on prescribed drugs, laboratory tests, outpatient visits and hospitalizations of more than 2 millions patients, managed by 900 GPs overtime. This pool of registers has produced a stock of information on about 25 millions of medical diagnosis, 100 millions of laboratory and diagnostic tests, 10 millions of blood pressure measurements and 50 millions of drug prescriptions. Using this novel dataset we analyze the expenditures of the Italian NHS over time, across age and geographical areas for the period from 2004 to 2011.

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  • Vincenzo Atella & Federico Belotti & Valentina Conti & Claudio Cricelli & Joanna Kopinska & Andrea Piano Mortari, 2016. "Modeling public health care expenditure using patient level data: Empirical evidence from Italy," CEIS Research Paper 367, Tor Vergata University, CEIS, revised 10 Feb 2016.
  • Handle: RePEc:rtv:ceisrp:367
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    1. William Encinosa & Didem Bernard & Avi Dor, 2010. "Does Prescription Drug Adherence Reduce Hospitalizations and Costs?," NBER Working Papers 15691, National Bureau of Economic Research, Inc.
    2. Brigitte Dormont & Hélène Huber, 2006. "Causes of Health Expenditure Growth: the Predominance of Changes in Medical Practices Over Population Ageing," Annals of Economics and Statistics, GENES, issue 83-84, pages 187-217.
    3. Spadaro Amedeo (ed.), 2007. "Microsimulation as Tool for the Evaluation of Public Policies: Methods and Applications," Books, Fundacion BBVA / BBVA Foundation, number 201169.
    4. Andreas Werblow & Stefan Felder & Peter Zweifel, 2007. "Population ageing and health care expenditure: a school of 'red herrings'?," Health Economics, John Wiley & Sons, Ltd., vol. 16(10), pages 1109-1126.
    5. Peter Zweifel & Stefan Felder & Markus Meiers, 1999. "Ageing of population and health care expenditure: a red herring?," Health Economics, John Wiley & Sons, Ltd., vol. 8(6), pages 485-496.
    6. Vincenzo Atella & Joanna Kopinska, 2014. "The impact of cost-sharing schemes on drug compliance in Italy: evidence based on quantile regression," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 59(2), pages 329-339, April.
    7. Michael Grossman, 1972. "The Demand for Health: A Theoretical and Empirical Investigation," NBER Books, National Bureau of Economic Research, Inc, number gros72-1, September.
    8. Meena Seshamani & Alastair Gray, 2004. "Ageing and health-care expenditure: the red herring argument revisited," Health Economics, John Wiley & Sons, Ltd., vol. 13(4), pages 303-314.
    9. Seshamani, Meena & Gray, Alastair M., 2004. "A longitudinal study of the effects of age and time to death on hospital costs," Journal of Health Economics, Elsevier, vol. 23(2), pages 217-235, March.
    10. Vincenzo Atella & Furio C. Rosati & Mariacristina Rossi, 2006. "Precautionary Saving and Health Risk. Evidence from Italian Households Using a Time Series of Cross Sections," Rivista di Politica Economica, SIPI Spa, vol. 96(3), pages 113-132, May-June.
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    Keywords

    cost analysis; big data; disease burden; Electronic Medical Records; primary care; cost sharing;

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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