Author(s)

Mohamed M. Shoukri^1*, Sara N. Algahtani², Abdelmoneim M. Eldali³, Manal R. AlMarzouqi³, Saleh M. Al-Ageel³

¹Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada.
²King Fahd Medical City, Riyadh, KSA.
³Department of Biostatistics, Epidemiology, and Scientific Computing, King Faisal Specialist Hospital and Research Center, Riyadh, KSA.

Background: Factors associated with hospital mortality are usually identified and their effects are quantified through statistical modeling. To guide the choice of the best statistical model, we first quantify the predictive ability of each model and then use the CIHI index to see if the hospital policy needs any change. Objectives: The main purpose of this study compared three statistical models in the evaluation of the association between hospital mortality and two risk factors, namely subject’s age at admission and the length 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 variability in hospital mortality. These procedures are the Logistic Regression model (ignoring the DRG effect), the Generalized Estimating Equation (GEE) that takes into account the within DRG clustering effect (but the within cluster correlation is treated as nuisance parameter), and the Generalized Linear Mixed Model (GLIMMIX). We showed that the GLIMMIX is superior to other models as it properly accounts for the clustering effect of “Diagnostic Related Groups” denoted by DRG. Results: The GLM procedure showed that the proportional contribution of DRG is 16%. All three models showed significant and increasing trend in mortality (P < 0.0001) with respect to the two risk factors (age at admission, and hospital length of stay). It was also clear that the CIHI index was not different under the three models. We re-estimated the models parameters after dichotomizing the risk factors at the optimal cut-off points, using the ROC curve. The parameters estimates and their significance did not change.

Diagnostic Related Groups, Intra-Cluster Correlation, GEE Models, GLIMMIX Models, Odds Ratios, ROC Curves

Shoukri, M.M., Algahtani, S.N., Eldali, A.M., AlMarzouqi, M.R. and Al-Ageel, S.M. (2019) Analysis of Hospital Mortality Data: The Role of DRG’s. Open Journal of Statistics, 9, 62-73. https://doi.org/10.4236/ojs.2019.91006