TITLE:
Analysis of Hospital Mortality Data: The Role of DRG’s
AUTHORS:
Mohamed M. Shoukri, Sara N. Algahtani, Abdelmoneim M. Eldali, Manal R. AlMarzouqi, Saleh M. Al-Ageel
KEYWORDS:
Diagnostic Related Groups, Intra-Cluster Correlation, GEE Models, GLIMMIX Models, Odds Ratios, ROC Curves
JOURNAL NAME:
Open Journal of Statistics,
Vol.9 No.1,
January
25,
2019
ABSTRACT: 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