Modeling of high-cost patient distribution within renal failure diagnosis related group

Journal of Clinical Epidemiology

Volumn 52, Issue 3, 1999, Pages 251-258

  Quantin, C ^a   Sauleau, E ^b   Bolard, P ^a   Mousson, C ^a   Kerkri, M ^a   Lecomte, P Brunet ^a   Moreau, T ^c   Dusserre, L ^a  

^a DIJON UNIVERSITY HOSPITAL   (France)

^b CENTRE HOSPITALIER DE MULHOUSE   (France)

^c INSERM   (France)

Author keywords

Cost; DRGs; EM algorithm; Mixed distribution model; Renal failure

Indexed keywords

ALGORITHM; ARTICLE; COST EFFECTIVENESS ANALYSIS; DATA BASE; HEALTH CARE COST; HEALTH CARE SYSTEM; HEALTH ECONOMICS; INFECTIOUS COMPLICATION; KIDNEY FAILURE; LENGTH OF STAY; PRIORITY JOURNAL; STATISTICAL MODEL;

COSTS AND COST ANALYSIS; DIAGNOSIS-RELATED GROUPS; FRANCE; HUMANS; KIDNEY FAILURE; LENGTH OF STAY; MODELS, STATISTICAL; ODDS RATIO;

EID: 0033012658     PISSN: 08954356     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0895-4356(98)00164-4     Document Type: Article

References (28)

1. Fetter R.B., Youngsoo S., Freeman J.L., Averill R.F., Thomson J.D. Case-mix; definition by diagnosis related groups. Med Care. 18(Suppl. 2):1980;1-53.
2. De Pouvourville G., Comar L., Bouvier V. Prospective case-based payment for French hospitals [in French]. Rev Epidemiol Sante Publique. 42:1994;68-78.
3. Gonnella J.S., Hornbrook M.C., Louis D.Z. Staging of disease. A case mix group measurement JAMA. 263:1984;241-246.
4. Young W.W., Swinkola R.B., Hutton M.A. Assessment of the Autogroup patient classification system. Med Care. 18:1980;228-244.
5. Froehlich H., Jarvis W.R. Economic impact of diagnosis-related groups and severity of illness on reimbursement for central nervous system infections. J Pediatr. 118:1991;693-697.
6. Kelleher C. Relationship of physician ratings of severity of illness and difficulty of clinical management of stay. Health Serv Res. 27:1993;841-855.
7. Naessens J.M., Leibson C.L., Krishan I., Ballard D.J. Contribution of a measure of disease complexity (complex) to prediction of outcome and charges among hospitalized patients. Mayo Clin Proc. 67:1992;1140-1149.
8. Dayhoff D., Cromwell J. Measuring differences and similarities in hospital caseloads. A conceptual and empirical analysis Health Serv Res. 28:1993;293-312.
9. Averill R.F., McGuire T.E., Manning B.E., Fowler D.A., Horn S.D., Dickson P.S.et al. A study of the relationship between severity of illness and hospital costs in New Jersey hospitals. Health Serv Res. 27:1992;587-606.
10. Horn S.D., Horn R.A., Sharkey P.D., Chambers A.F. Severity of illness within DRGs. Homogeneity study Med Care. 24:1986;225-235.
11. Horn S.D., Sharkey P.D., Chambers A.F., Horn R.A. Severity of illness within DRGs. Impact on prospective payment Am J Public Health. 75:1985;1195-1199.
12. Thomas J.W., Aschraft M.L.F. Measuring severity of illness. Six severity systems and their ability to explain cost variations Inquiry. 28:1991;39-55.
13. Horn S.D., Sharkey P.D., Buckle J.M., Backofen J.E., Averill R.F., Horn R.A. The relationship between severity of illness and hospital length of stay and mortality. Med Care. 29:1991;305-317.
14. Knaus W.A., Draper E.A., Wagner D.P., Zimmerman J.E. APACHE II. A severity of disease classification system Crit Care Med. 13:1985;818-829.
15. Iezzoni LI, Ash AS, Moskowitz MA. MedisGroups: A clinical and Analytic Assessment. Health POLICY Research Consortium. Prepared for the health care financing administration under cooperative agreement no. 18-C-98526/1-03. Baltimore, MD, July 1987.
16. Calore KA, Iezzoni L. Disease staging and PMCs: Can they improve DRGs? Med Care 1987; 824-837.
17. Voss G., Hasman A., Rutten F., De Zwaan C., Carpay J. Explaining cost variations in DRGs Acute Myocardial Infraction by severity of illness. Health Policy. 28:1994;37-50.
18. Freeman J.L., Fetter R.B., Schneider K.C., Lichtenstein J.L., Hughes J.S., Bauman W.A.et al. Diagnosis-related group refinement with diagnosis- and procedure-specific comorbidities and complications. Med Care. 33:1995;806-827.
19. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion). J R Stat Soc B. 39:1997;1-38.
20. Everitt B.S. Maximum likelihood estimation of the parameters in a mixture of two univariate normal distributions, a comparison of different algorithms. Statistician. 33:1984;205-215.
21. Wolfe JH. A Monte Carlo study of the sampling distribution of the likelihood ratio for mixtures of multinomial distributions. Tech Bull Nav Pers Tran Res Lab San Diego 1971; STB 72-82.
22. Dussaucy A., Viel J.F., Mulin B., Euvrard J. L'outil PMSI. Basis, sources d'erreurs et conséquences Rev Epidemiol Sante Publications. 42:1994;345-358.
23. Report of management of renal failure in Europe, XXV, 1994. Nephrol Dial Transplant 1996; 11: 1-18.
24. Munoz E., Thies H., Maesaka J.K., Angus G., Goldstein J., Wise L. Diagnosis related groups, resource utilization, age, and outcome for hospitalized nephrology patients. Am J Kidney Dis. 11:1988;481-488.
25. Shwartz M., Iezzoni L., Ash A., Mackiernan Y. Do severity measures explain differences in length of hospital stay? The case of hip fracture. Health Serv Res. 31:1996;365-385.
26. Mozes B., Easterling M.J., Sheiner L., Melmon K., Kline R., Godman E.et al. Case-mix adjustment using objective measures of severity. The case for laboratory data Health Serv Res. 28:1994;686-712.
27. Ruffieux C., Marazzi A., Paccaud F. Exploring models for the length of stay distribution. Soz Praventivmed. 38:1993;77-82.
28. Quantin C., Entezam F., Bourdais C., Moreau T., Brunet-Lecomte P., Bouzelat H.et al. Modelization of long-stay and high cost patient distribution within bronchopneumopathies and leukemia or lymphoma diagnosis related groups [in French]. Rev Epidemiol Sante Publications. 45:1997;117-130.