Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-22T16:37:43.145Z Has data issue: false hasContentIssue false
  • English
  • Français

Development and application of Model of Resource Utilization, Costs, and Outcomes for Stroke (MORUCOS): An Australian economic model for stroke

Published online by Cambridge University Press:  25 October 2005

Catherine Mihalopoulos ,
Dominique A. Cadilhac ,
Marjory L. Moodie ,
Helen M. Dewey ,
Amanda G. Thrift ,
Geoffrey A. Donnan  and
Robert C. Carter
Catherine Mihalopoulos
Affiliation:
The University of Melbourne
Dominique A. Cadilhac
Affiliation:
The University of Melbourne and National Stroke Research Institute
Marjory L. Moodie
Affiliation:
The University of Melbourne
Helen M. Dewey
Affiliation:
The University of Melbourne and National Stroke Research Institute
Amanda G. Thrift
Affiliation:
The University of Melbourne and National Stroke Research Institute
Geoffrey A. Donnan
Affiliation:
The University of Melbourne and National Stroke Research Institute
Robert C. Carter
Affiliation:
The University of Melbourne
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: To outline the development, structure, data assumptions, and application of an Australian economic model for stroke (Model of Resource Utilization, Costs, and Outcomes for Stroke [MORUCOS]).

Methods: The model has a linked spreadsheet format with four modules to describe the disease burden and treatment pathways, estimate prevalence-based and incidence-based costs, and derive life expectancy and quality of life consequences. The model uses patient-level, community-based, stroke cohort data and macro-level simulations. An interventions module allows options for change to be consistently evaluated by modifying aspects of the other modules. To date, model validation has included sensitivity testing, face validity, and peer review. Further validation of technical and predictive accuracy is needed. The generic pathway model was assessed by comparison with a stroke subtypes (ischemic, hemorrhagic, or undetermined) approach and used to determine the relative cost-effectiveness of four interventions.

Results: The generic pathway model produced lower costs compared with a subtypes version (total average first-year costs/case AUD$15,117 versus AUD$17,786, respectively). Optimal evidence-based uptake of anticoagulation therapy for primary and secondary stroke prevention and intravenous thrombolytic therapy within 3 hours of stroke were more cost-effective than current practice (base year, 1997).

Conclusions: MORUCOS is transparent and flexible in describing Australian stroke care and can effectively be used to systematically evaluate a range of different interventions. Adjusting results to account for stroke subtypes, as they influence cost estimates, could enhance the generic model.

Keywords

Cerebrovascular disordersEconomic modelCost benefit analysis
Type
RESEARCH REPORTS
Information
International Journal of Technology Assessment in Health Care , Volume 21 , Issue 4 , October 2005 , pp. 499 - 505
Copyright
© 2005 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson CS, Jamrozik KD, Burvil PV, et al. 1993 Ascertaining the true incidence of stroke: Experience from the Perth Community Stroke Study, 1989–1990. Med J Aust. 158: 80– 84.Google Scholar
Australian Bureau of Statistics. 1998. Australian Historical Population Statistics. Catalogue No. 3105.0.65.001. Canberra: Australian Government Publishers;
Australian Bureau of Statistics. 1998 Australian population projections statistics (base year 1996), series B. Catalogue No 3222.0. Canberra: Australian Government Publishers;
Australian Bureau of Statistics. 1997. Average weekly earnings, states and Australia. ABS Catalogue 6302.00. Canberra: Australian Government Publishers;
Australian Bureau of Statistics. 1997. Deaths Australia 1996. Catalogue No. 3302.0. Canberra: Australian Government Publishers;
Australian Bureau of Statistics. 1997. Labour force. ABS Catalogue 6203.00. Canberra: Australian Government Publishers;
Australian Institute of Health and Welfare. 1997 Health Expenditure Bulletin, No13. AIHW cat no. HWE 4. Canberra: Australian Government Publishers;
Bamford J, Sandercock P, Dennis M, et al. 1991 Classification and natural history of clinically identifiable subtypes of cerebral infarction. Lancet. 337: 1521– 1526.Google Scholar
Bonita R. 1993 Stroke trends in Australia and New Zealand: Mortality, morbidity and risk factors. Ann Epidemiol. 3: 529– 533.Google Scholar
Buxton JM, Drummond MF, Van Hout BA, et al. 1997 Modelling in economic evaluation: An unavoidable fact of life. Health Econ. 6: 217– 227.Google Scholar
Caro JJ, Huybrechts KF. 1999 Stroke treatment economic model (STEM): Predicting long-term costs from functional status. Stroke. 30: 2574– 2579.Google Scholar
Chambers MG, Koch P, Hutton J. 2002 Development of a decision-analytic model of stroke care in the United States and Europe. Value Health. 5: 82– 97.Google Scholar
Dennis MS, Burn JP, Sandercock PA, et al. 1993 Long-term survival after first-ever stroke: The Oxfordshire Community Stroke Project. Stroke. 24: 796– 800.Google Scholar
Dewey HM, Thrift AG, Mihalopoulos C, et al. 2004 ‘Out of pocket’ costs to stroke patients during the first year after stroke—results from the North East Melbourne Stroke Incidence Study (NEMESIS). J Clin Neurosci. 11: 134– 137.Google Scholar
Dewey HM, Thrift AG, Mihalopoulos C, et al. 2001 Cost of stroke in Australia from a societal perspective: Results from the North East Melbourne Stroke Incidence Study (NEMESIS). Stroke. 32: 2409– 2416.Google Scholar
Dewey HM, Thrift AG, Mihalopoulos C, et al. 2002 Informal care for stroke survivors: Results from the North East Melbourne Stroke Incidence Study (NEMESIS). Stroke. 33: 1028– 1033.Google Scholar
Dewey HM, Thrift AG, Mihalopoulos C, et al. 2003 Lifetime cost of stroke subtypes in Australia. Findings from the North East Melbourne Stroke Incidence Study (NEMESIS). Stroke. 34: 2502– 2507.Google Scholar
Drummond M, McGuire A. 2001. Economic evaluation in health care: Merging theory with practice. New York: Oxford University Press;
Drummond MF, Stoddart GL, Torrance GW. 1997. Methods for the economic evaluation of health care programmes: Second edition. Oxford: Oxford Medical Publications;
Matcher DB, Samsa GP, Matthews JR, et al. 1997 The stroke policy model: Linking evidence and clinical decisions. Ann Intern Med. 127: 704– 711.Google Scholar
Mathers C, Stevenson C, Carter R, et al. 1998 Disease costing methodology used in the Disease Costs and Impact Study 1993–94. AIHW cat. No. HWE 7. Canberra: Australian Institute of Health and Welfare (Health and Welfare Expenditure Series no. 3),
Moodie ML, Carter R, Mihalopoulos C, et al. 2004 Trial application of a Model of Resource Utilization, Costs and Outcomes for Stroke (MORUCOS) to assist priority setting in stroke. Stroke. 35: 1041– 1046.Google Scholar
Stouthard MEA, Essink-Bot M, Bonsel GJ, et al. 1997. Disability weights for diseases in the Netherlands. Rotterdam: Department of Public Health, Erasmus University;
Sunberg G, Bagust A, Terent A. 2003 A model for costs of stroke services. Health Policy. 63: 81– 94.Google Scholar
Thrift AG, Dewey HM, MacDonnell RA, et al. 2000 Stroke incidence on the east coast of Australia: The North East Melbourne Stroke Incidence Study (NEMESIS). Stroke. 31: 2087– 2092.Google Scholar