IDEAS home Printed from https://ideas.repec.org/a/spr/pharme/v34y2016i2d10.1007_s40273-015-0339-y.html
   My bibliography  Save this article

Calculating Total Health Service Utilisation and Costs from Routinely Collected Electronic Health Records Using the Example of Patients with Irritable Bowel Syndrome Before and After Their First Gastroenterology Appointment

Author

Listed:
  • Caroline Canavan

    (University of Nottingham)

  • Joe West

    (University of Nottingham)

  • Timothy Card

    (University of Nottingham)

Abstract

Introduction Health economic models are increasingly important in funding decisions but most are based on data, which may therefore not represent the general population. We sought to establish the potential of real-world data available within the Clinical Practice Research Datalink (CPRD) and linked Hospital Episode Statistics (HES) to determine comprehensive healthcare utilisation and costs as input variables for economic modelling. Methods A cohort of patients with irritable bowel syndrome (IBS) who first saw a gastroenterologist in 2008 or 2009, and with 3 years of data before and after their appointment, was created in the CPRD. Primary care, outpatient, inpatient, prescription and colonoscopy data were extracted from the linked CPRD and HES. The appropriate cost to the NHS was attached to each event. Total and stratified annual healthcare utilisation rates and costs were calculated before and after the gastroenterology appointment with distribution parameters. Absolute differences were calculated with 95 % confidence intervals. Results Total annual healthcare costs over 3 years increase by £935 (95 % CI £928–941) following a gastroenterology appointment for IBS. We derived utilisation and cost data with parameter distributions stratified by demographics and time. Women, older patients, smokers and patients with greater comorbidity utilised more healthcare resources, which generated higher costs. Conclusions These linked datasets provide comprehensive primary and secondary care data for large numbers of patients, which allows stratification of outcomes. It is possible to derive input parameters appropriate for economic models and their distributions directly from the population of interest.

Suggested Citation

  • Caroline Canavan & Joe West & Timothy Card, 2016. "Calculating Total Health Service Utilisation and Costs from Routinely Collected Electronic Health Records Using the Example of Patients with Irritable Bowel Syndrome Before and After Their First Gastr," PharmacoEconomics, Springer, vol. 34(2), pages 181-194, February.
  • Handle: RePEc:spr:pharme:v:34:y:2016:i:2:d:10.1007_s40273-015-0339-y
    DOI: 10.1007/s40273-015-0339-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40273-015-0339-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s40273-015-0339-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Manca, A & Austin, P. C, 2008. "Using propensity score methods to analyse individual patient-level cost-effectiveness data from observational studies," Health, Econometrics and Data Group (HEDG) Working Papers 08/20, HEDG, c/o Department of Economics, University of York.
    2. Alan Brennan & Stephen E. Chick & Ruth Davies, 2006. "A taxonomy of model structures for economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 15(12), pages 1295-1310, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Caroline Canavan & Joe West & Timothy Card, 2016. "Calculating Total Health Service Utilisation and Costs from Routinely Collected Electronic Health Records Using the Example of Patients with Irritable Bowel Syndrome Before and After Their First Gastr," PharmacoEconomics, Springer, vol. 34(2), pages 181-194, February.
    2. Bernhard Ultsch & Oliver Damm & Philippe Beutels & Joke Bilcke & Bernd Brüggenjürgen & Andreas Gerber-Grote & Wolfgang Greiner & Germaine Hanquet & Raymond Hutubessy & Mark Jit & Mirjam Knol & Rüdiger, 2016. "Methods for Health Economic Evaluation of Vaccines and Immunization Decision Frameworks: A Consensus Framework from a European Vaccine Economics Community," PharmacoEconomics, Springer, vol. 34(3), pages 227-244, March.
    3. Becky Pennington & Alex Filby & Lesley Owen & Matthew Taylor, 2018. "Smoking Cessation: A Comparison of Two Model Structures," PharmacoEconomics, Springer, vol. 36(9), pages 1101-1112, September.
    4. Peter J. Dodd & Jeff J. Pennington & Liza Bronner Murrison & David W. Dowdy, 2018. "Simple Inclusion of Complex Diagnostic Algorithms in Infectious Disease Models for Economic Evaluation," Medical Decision Making, , vol. 38(8), pages 930-941, November.
    5. Jonathan Karnon & James Stahl & Alan Brennan & J. Jaime Caro & Javier Mar & Jörgen Möller, 2012. "Modeling Using Discrete Event Simulation," Medical Decision Making, , vol. 32(5), pages 701-711, September.
    6. Matthew Franklin & James Lomas & Gerry Richardson, 2020. "Conducting Value for Money Analyses for Non-randomised Interventional Studies Including Service Evaluations: An Educational Review with Recommendations," PharmacoEconomics, Springer, vol. 38(7), pages 665-681, July.
    7. Stuart J. Wright & William G. Newman & Katherine Payne, 2019. "Accounting for Capacity Constraints in Economic Evaluations of Precision Medicine: A Systematic Review," PharmacoEconomics, Springer, vol. 37(8), pages 1011-1027, August.
    8. Arielle Anderer & Hamsa Bastani & John Silberholz, 2022. "Adaptive Clinical Trial Designs with Surrogates: When Should We Bother?," Management Science, INFORMS, vol. 68(3), pages 1982-2002, March.
    9. Eren Demir & David Southern, 2017. "Enabling better management of patients: discrete event simulation combined with the STAR approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(5), pages 577-590, May.
    10. Jen Kruger & Daniel Pollard & Hasan Basarir & Praveen Thokala & Debbie Cooke & Marie Clark & Rod Bond & Simon Heller & Alan Brennan, 2015. "Incorporating Psychological Predictors of Treatment Response into Health Economic Simulation Models," Medical Decision Making, , vol. 35(7), pages 872-887, October.
    11. Sarah Bates & Thomas Bayley & Paul Norman & Penny Breeze & Alan Brennan, 2020. "A Systematic Review of Methods to Predict Weight Trajectories in Health Economic Models of Behavioral Weight-Management Programs: The Potential Role of Psychosocial Factors," Medical Decision Making, , vol. 40(1), pages 90-105, January.
    12. William Crown, 2014. "Propensity-Score Matching in Economic Analyses: Comparison with Regression Models, Instrumental Variables, Residual Inclusion, Differences-in-Differences, and Decomposition Methods," Applied Health Economics and Health Policy, Springer, vol. 12(1), pages 7-18, February.
    13. F. Tomini & F. Prinzen & A. D. I. Asselt, 2016. "A review of economic evaluation models for cardiac resynchronization therapy with implantable cardioverter defibrillators in patients with heart failure," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(9), pages 1159-1172, December.
    14. Luis Hernandez & Asli Ozen & Rodrigo DosSantos & Denis Getsios, 2016. "Systematic Review of Model-Based Economic Evaluations of Treatments for Alzheimer’s Disease," PharmacoEconomics, Springer, vol. 34(7), pages 681-707, July.
    15. Sarang Deo & Sameer Mehta & Charles J. Corbett, 2022. "Optimal Scale‐Up of HIV Treatment Programs in Resource‐Limited Settings Under Supply Uncertainty," Production and Operations Management, Production and Operations Management Society, vol. 31(3), pages 883-905, March.
    16. Matthew J. Glover & Edmund Jones & Katya L. Masconi & Michael J. Sweeting & Simon G. Thompson, 2018. "Discrete Event Simulation for Decision Modeling in Health Care: Lessons from Abdominal Aortic Aneurysm Screening," Medical Decision Making, , vol. 38(4), pages 439-451, May.
    17. L. B. Standfield & T. A. Comans & P. A. Scuffham, 2017. "An empirical comparison of Markov cohort modeling and discrete event simulation in a capacity-constrained health care setting," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 18(1), pages 33-47, January.
    18. Emma McManus & Tracey Sach & Nick Levell, 2018. "The Use of Decision–Analytic Models in Atopic Eczema: A Systematic Review and Critical Appraisal," PharmacoEconomics, Springer, vol. 36(1), pages 51-66, January.
    19. Marion Rauner & Michaela Schaffhauser-Linzatti & Helmut Niessner, 2012. "Resource planning for ambulance services in mass casualty incidents: a DES-based policy model," Health Care Management Science, Springer, vol. 15(3), pages 254-269, September.
    20. Mehdi Najafzadeh & Carlo A Marra & Larry D Lynd & Mohsen Sadatsafavi & J Mark FitzGerald & Bruce McManus & Don Sin, 2012. "Future Impact of Various Interventions on the Burden of COPD in Canada: A Dynamic Population Model," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-12, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:pharme:v:34:y:2016:i:2:d:10.1007_s40273-015-0339-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.