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Explaining the Health Costs Associated with Managing Intracranial Aneurysms in Italy

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Abstract

Background

The clinical management of intracranial aneurysms is debated in many countries because of the associated disability risk and costs. Therefore, estimating the costs and explaining their variability will provide important information for decision makers.

Objective

We aimed to evaluate the acute and post-acute health costs of intracranial aneurysm management and to explain the variability in these costs in the Italian National Health System.

Methods

An observational study was conducted on 145 patients who were affected by a (single) ruptured or an unruptured intracranial aneurysm. They were consecutively admitted to 14 Italian hospitals between October 2005 and March 2007. The data collected during the initial hospitalization and three follow-up visits were used to assess the 1-year health costs and the patients’ health status after discharge. Two multivariate regression models were used to explain the variability in the acute and post-acute costs.

Results

The average total cost per patient was €30,813 (evaluation year: 2012). The first model explained the acute costs fairly well and showed that the severity of illness, the admission unit (i.e., intensive care unit vs. another unit of the hospital), and mortality were associated with large, significant (p < 0.05) coefficients. The second model outperformed the first one in explaining the post-acute costs and showed that health status assessed 30 days after discharge was a significant (p < 0.05) predictor of costs.

Conclusion

Policies aimed at containing health costs should focus on interventions that help to reduce disability, which is a key predictor of long-term costs.

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Notes

  1. ICU versus another unit in the hospital.

  2. The mRS is a disease-specific scale that is widely used with stroke patients to define clinically discrete disability categories. This outcome measure does not include the ‘death’ status.

  3. The model explains about 48 % of the variability by including the health status assessed 12 months after discharge, though this time period would make the model’s estimates strictly retrospective.

  4. We refer to discharge records of the Ministry of Health (http://www.salute.gov.it).

References

  1. WHO. The top ten causes of death. Fact Sheet. www.who.int/mediacentre/factsheets/. Accessed 28 Sep 2009; N° 310 (Nov 2008).

  2. Leal J, Luengo-Fernández R, Gray A, Petersen S, Rayner M. Economic burden of cardiovascular diseases in the enlarged European Union. Eur Heart J. 2006;27(13):1610–9.

    Article  PubMed  Google Scholar 

  3. Laloux P. Cost of acute stroke: a review. Acta Neurol Belg. 2003;103(2):71–7.

    PubMed  Google Scholar 

  4. Payne KA, Huybrechts KF, Caro JJ, Craig Green TJ, Klittich WS. Long term cost-of-illness in stroke: an international review. Pharmacoeconomics. 2002;20(12):813–25.

    Article  PubMed  Google Scholar 

  5. Evers SM, Struijs JN, Ament AJ, van Genugten ML, Jager JH, van den Bos GA. International comparison of stroke cost studies. Stroke. 2004;35(5):1209–15.

    Article  PubMed  Google Scholar 

  6. Dawson J, Lees JS, Chang TP, Walters MR, Ali M, Davis SM, et al. Association between disability measures and healthcare costs after initial treatment for acute stroke. Stroke. 2007;38(6):1893–8.

    Article  PubMed  Google Scholar 

  7. Spieler JF, Lanoe JL, Amarenco P. Costs of stroke care according to handicap levels and stroke subtypes. Cerebrovasc Dis. 2004;17(2–3):134–42.

    Article  PubMed  Google Scholar 

  8. Spratt N, Wang Y, Levi C, Ng K, Evans M, Fisher J. A prospective study of predictors of prolonged hospital stay and disability after stroke. J Clin Neurosci. 2003;10(6):665–9.

    Article  PubMed  Google Scholar 

  9. Christensen MC, Morris S. Association between disability measures and short-term health care costs following intracerebral hemorrhage. Neurocrit Care. 2008;9(3):313–8.

    Article  PubMed  Google Scholar 

  10. Rinkel GJE, Djibuti M, Algra A, Van Gijn J. Prevalence and risk of rupture of intracranial aneurysms: a systematic review. Stroke. 1998;29:251–6.

    Article  CAS  PubMed  Google Scholar 

  11. Higashida RT, Lahue BJ, Torbey MT, Hopkins LN, Leip E, Hanley DF. Treatment of unruptured intracranial aneurysms: a nationwide assessment of effectiveness. Am J Neuroradiol. 2007;28:146–51.

    CAS  PubMed  Google Scholar 

  12. Dodel R, Winter Y, Ringel F, Spottke A, Gharevi N, Müller I, et al. Cost of illness in subarachnoid hemorrhage: a German longitudinal study. Stroke. 2010;41(12):2918–23.

    Article  PubMed  Google Scholar 

  13. Raja PV, Huang J, Germanwala AV, Gailloud P, Murphy KP, Tamargo RJ. Microsurgical clipping and endovascular coiling of intracranial aneurysms: a critical review of the literature. Neurosurgery 2008;62(6):1187–202; discussion 202–3.

    Google Scholar 

  14. Birkmeyer J, Siewers A, Finlayson E, Stukel T, Lucas F, Batista I, et al. Hospital volume and surgical mortality in the United States. N Engl J Med. 2002;346(15):1128–37.

    Article  PubMed  Google Scholar 

  15. Tarricone R. Cost-of-illness analysis: what room in health economics. Health Policy. 2006;77(1):51–63.

    Article  PubMed  Google Scholar 

  16. Roos YBWEM, Dijkgraaf MGW, Albrecht KW, Beenen LFM, Groen RJM, De Haan RJ, et al. Direct costs of modern treatment of aneurysmal subarachnoid haemorrhage in the first year after diagnosis. Stroke. 2002;33:1595–9.

    Article  CAS  PubMed  Google Scholar 

  17. Caro JJ, Huybrechts KF, Kelley HE. Predicting treatment costs after acute ischemic stroke on the basis of patient characteristics at presentation and early dysfunction. Stroke. 2001;32:100–6.

    Article  CAS  PubMed  Google Scholar 

  18. Marques de Sa J. Applied statistics using SPSS, STATISTICA and MATLAB. Berlin: Springer; 2003.

  19. Fox J. Applied regression analysis, linear models and related methods. Thousand Oaks: Sage; 1997.

    Google Scholar 

  20. Zivot E, Wang J. Modelling financial time series with S-PLUS. Boston: Birkhauser; 2006.

    Google Scholar 

  21. Baum C. An introduction to modern econometrics using Stata. College Station: Stata Press; 2006.

    Google Scholar 

  22. White H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica. 1980;48(4):817–38.

    Article  Google Scholar 

  23. Manning WG, Basu A, Mullahy J. Generalized modeling approaches to risk adjustment of skewed outcomes data. J Health Econ. 2005;24(3):465–88.

    Article  PubMed  Google Scholar 

  24. Manning WG, Mullahy J. Estimating log models: to transform or not to transform. J Health Econ. 2001;20(4):461–94.

    Article  CAS  PubMed  Google Scholar 

  25. Buccoliero L, Calciolari S, Marsilio M. A methodological and operative framework for the evaluation of an e-health project. Int J Health Plan Manag. 2008;23(1):3–20.

    Article  Google Scholar 

  26. Drummond M, Griffin A, Tarricone R. Economic evaluation for devices and drugs—same or different? Value Health. 2009;12(4):402–4.

    Article  PubMed  Google Scholar 

  27. Fattore G, Torbica A. Economic evaluation in health care: the point of view of informed physicians. Value Health. 2006;9(3):157–67.

    Article  PubMed  Google Scholar 

  28. Sorenson C, Tarricone R, Siebert M, Drummond M. Applying health economics for policy decision making: do devices differ from drugs? Europace. 2011;13(Suppl 2):1154–8.

    Article  Google Scholar 

  29. Anand SS, Yusuf S. Stemming the global tsunami of cardiovascular disease. Lancet. 2011;377(9765):529–32.

    Article  PubMed  Google Scholar 

  30. Ciani O, Tarricone R, Torbica A. Diffusion and use of health technology assessment in policy making: What lessons for decentralised healthcare systems? Health Policy. 2012;108(2-3):194-202.

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Authors’ contributions

All authors were involved in drafting the manuscript and revising it critically for important intellectual content, and they all gave final approval of the version to be published. Rosanna Tarricone and Aleksandra Torbica contributed equally to the conception and design of the cost analysis. Stefano Calciolari made substantial contributions to the data collection, analysis, and interpretation, and he is the guarantor of the overall content.

Funding: The study was supported by institutional funds from CERGAS Università Bocconi.

Conflicts of interest

The authors have no conflicts of interest that are directly relevant to the content of this paper.

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Correspondence to Stefano Calciolari.

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Calciolari, S., Torbica, A. & Tarricone, R. Explaining the Health Costs Associated with Managing Intracranial Aneurysms in Italy. Appl Health Econ Health Policy 11, 427–435 (2013). https://doi.org/10.1007/s40258-013-0041-1

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