Abstract
The inclusion of a patient's illness experience as outcome in the assessment of health care technology has revealed methodological limitations such as the interpretation of multi-attribute scores and lack of knowledge about the association between illness and disease information. In an attempt to overcome these limitations, a cross-sectional study is performed to search for patterns of illness severity and investigate the association between illness measures and between illness patterns and disease factors. A sample of 211 patients with ulcerative colitis is studied using the sickness impact profile (SIP) and the rating form for inflammatory bowel disease patient concerns (RFIPC) as illness measures. SIP and RFIPC scores show low association, suggesting that they provide complementary information about the patient's illness status. Cluster analysis is performed using the two measures of illness separately to identify groups of patients with different degrees of severity of illness (clusters). The cluster description covers illness, disease and social and demographic variables. The RFIPC, clusters show a general pattern of ascendant rank scores for the RFIPC items. SIP clusters differ not only in the level of severity, but also in specific types of disability. The patients in the clusters with the highest degree of disability (reflected by SIP) show a non-linear relationship with patients' concerns (reflected by RFIPC) and disease factors.
Similar content being viewed by others
References
Almeida, R. T. andCarlsson, P. (1996): ‘Severity of a case for outcome assessment in health care—definitions and classifications of instruments’,Health Policy,37, pp. 35–52
Anderberg, M. R. (1973):Cluster analysis for application. Probability and mathematical statistics:19, New York: (Academic Press)
Bergner, M., Bobbitt, R. A., Carter W. B. andGilson B. S. (1981): ‘The sickness impact profile: development and final revision of a health status measure’,Med. Care,19, pp. 787–805
Berzon, R., Donnelly, M. Simpson Jr, R.,Simeon, R. andTilson, H. (1995): ‘Quality of life bibliography and indexes’: 1994 Update.Quality of Life Research,4: pp. 547–569
Bender, V. (1992): ‘Quality, of life’, In:Järnerot G., Lennard-Jones J., Truelove S., (Eds.), ‘Inflammatory bowel disease’, Malmö, Sweden: Corona AB, pp. 581–593
de Dombal, F. T. (1986): Measuring and quantifying the status of patients with inflammatory bowel disease, InDeDombal FT, Myren J, Bouchier IAD, Watkinson, G (Eds.) ‘Inflammatory bowel disease: some international data and reflections’, (Oxford University Press), pp. 267–285
Dilts, D., Khamalah, J. andPlotkin, A. (1995): ‘Using cluster analysis for medical resource decision making’,Med. Decis. Making’,15, pp. 333–347
Drossman, D. A., Leserman, J., Zhiming, L., Mitchell, C. M., Zagami, E. A. andPatrick, D. L. (1991): ‘The rating form of IBD patient concerns: a new measure of health status’,Psychosom. Med.,53, pp. 701–712.
Drummond, M. F. andDavies, L. (1991): ‘Economic analysis alongside clinical trials. Revisiting the methodological issues’,Int. J. Technol. Assess. Health Care,7, pp. 561–573
Irvine, E. J. (1995): ‘Quality of life in inflammatory bowel disease: Biases and other factors affecting scores’,Scand. J. Gastroenterol. Suppl.,30 (Suppl 208), pp. 136–140
McDowell, I. andNewell, C. (1987): ‘Measuring health: A guide to rating scales and questionnaires’. (Oxford University Press)
McHorney, C. A. andTarlov, A. R. (1995): ‘Individual-patient monitoring in clinical practice: are available health status surveys adequate?’Quality of Life Research,4, pp. 293–307
Neter, J. andWasserman, W. (1974): ‘Applied linear statistical models—regression, analysis of variance, and experimental designs’. Homewood, Illinois: Richard D. Irwin, Inc
Siegel, S. (1988): ‘Nonparametric statistics for the behavioural sciences’. (2nd. ed.),Castellan Jr N. J., ed. Statistics Series, New York: McGraw-Hill
SPSS. (1994): SPSS Professional StatisticsTM 6.1. Chicago: SPSS Inc.
Streiner, D. L. andNorman, G. R. (1995): ‘Health measurement scales—a practical guide to their development and use, (2nd. Ed.)’ (Oxford University Press), pp. 4–27
Sullivan, M., Ahlmén, M., Archenholtz, B. andSvensson, G. (1986): ‘Measuring health in rheumatic disorders by means of a Swedish version of the sickness impact profile’.Scand. J. Rheumatol.’,15, pp. 193–200
Wilson, I. B. andCleary, P. D. (1995): ‘Linking clinical variables with health-related quality of life’.JAMA,273, pp. 59–65.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Almeida, R.T., Hjortswang, H., Ström, M. et al. Technology assessment using the association between outcome measures and patterns of illness severity. Med. Biol. Eng. Comput. 35, 386–390 (1997). https://doi.org/10.1007/BF02534095
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF02534095