Session: Econometric Modelling
Room: Upson 215
Time: Mon 13:15-14:45

Presenter: Katharina Hauck (Monash University. Department of Econometrics and Business Statistics)

Discussant: Stephen Mennemeyer (University of Alabama at Birmingham)

Abstract

Adverse events in hospitals cause significant morbidity and mortality, and considerable effort has been invested into analysing their incidence and preventability. An unresolved issue in models of medical adverse events is potential endogeneity of length of stay (LOS): whilst the probability of suffering a medical adverse event during the episode is likely to increase as a patient stays longer, there are a range of unobservable patient and hospital factors affecting both the occurrence of adverse events and LOS, such as unobserved patient complexity and hospital management. Therefore, statistical models of adverse events which do not account for the potential endogeneity of LOS may generate inconsistent and biased estimates.
Our objective is to examine the factors impacting on the incidence of adverse events, accounting for endogeneity of LOS by estimating structural equation models. We estimate separate models for three of the most common and serious types of medical adverse events: Adverse drug reactions, hospital acquired infections, and pressure ulcers. We use episode level administrative hospital data from public hospitals in the state of Victoria, Australia, for the years 2004/05 and 2005/06. These data contain detailed information on patients, in particular medical complexity and adverse events suffered during admission. We use instrumental variable probit and conditional mixed process methods, with days and months of discharge as instruments for LOS. Results show that LOS is endogenous in models of adverse events, and that LOS increases the probability of adverse events at comparable magnitudes to other risk factors such as age, being an emergency patient, or suffering of significant comorbidities.

In contrast to patient risk factors, LOS is a hospital-level risk factors which is directly amenable to the actions of hospital management; patients can be discharged earlier, and part or all of the stay in hospital can be substituted by stays at alternative care providers, or at home. This may be beneficial if it significantly lowers risk of adverse events. Our econometric model of adverse events can inform on the expected cost of days spent in hospital, of which the expected cost of AEs is one component. Although it is more satisfactory to address hospital-level causal reasons for adverse events, such as poor safety procedures, LOS may be the only factor which can be influenced in the short run and under relatively low costs. Our results provide hospital managers with the quantitative evidence to take a pragmatic approach towards the reduction of adverse events, and make informed discharge and care decisions. Considering the large costs of adverse events to patients and the health care system, it seems timely that they are factored into discharge and treatment decisions in a quantitative way.

Key Terms

quality of care, hospital performance, instrumental variable methods

Authors:

Katharina Hauck (Monash University. Department of Econometrics and Business Statistics) and Xuey Zhao