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Analysis of Hospital Mortality Data: The Role of DRG’s

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Document pages: 12 pages

Abstract: Background: Factors associated with hospital mortality are usually identified andtheir effects are quantified through statistical modeling. To guide the choiceof the best statistical model, we first quantify the predictive ability of eachmodel and then use the CIHI index to see if the hospital policy needs anychange. Objectives: The main purpose of this study compared threestatistical models in the evaluation of the association between hospitalmortality and two risk factors, namely subject’s age at admission and thelength of stay, adjusting for the effect of Diagnostic Related Groups (DRG). Methods:We use several SAS procedures to quantify the effect of DRG on the variabilityin hospital mortality. These procedures are the Logistic Regression model (ignoringthe DRG effect), the Generalized Estimating Equation (GEE) that takes intoaccount the within DRG clustering effect (but the within cluster correlation istreated as nuisance parameter), and the Generalized Linear Mixed Model(GLIMMIX). We showed that the GLIMMIX is superior to other models as itproperly accounts for the clustering effect of “Diagnostic Related Groups” denoted by DRG. Results: The GLM procedure showed that the proportionalcontribution of DRG is 16 . All three models showed significant and increasingtrend in mortality (P

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