Causal Inference Methodology for Comparisons of Hospital Quality of Care

In a national or provincial health care system, where limited financial resources are available to improve patient care, it is necessary to be able to evaluate the current care practices of hospitals to determine where resources are best spent. Assessment of hospital care quality is achieved by comparing each hospital's performance to some reference level of care, often the average care level in the system, termed standardization. Standardization allows adjustment for differences in patient characteristics between hospitals which would unduly penalize hospitals that treat sicker patients. The quality and quantity of information available to make such adjustments, or lack thereof, can bias estimates of a hospital's performance, resulting in misleading assessments of quality. Further, the goal of profiling care is not just to identify areas in which care disparities exist, but ultimately to intervene on care to improve patient outcomes. In this thesis, I take advantage of the causal nature of such comparisons (i.e. poor care leads to poor outcomes) and propose new statistical methods under a causal inference framework. First, I illustrate the current limitations of a standard hospital comparison analysis using U.S. prostate cancer data. Second, I develop a doubly robust estimator for the standardized mortality ratio (SMR) when the reference is to the system average level of care. I show that this estimator will provide unbiased estimates of the SMR as long as one of the component models is correctly specified. Third, I show that one assumption needed for the above estimator can be relaxed only for this reference comparison. Fourth, I adapt causal mediation analysis methods to derive a decomposition of the hospital effect on patient outcomes that may act through a mediating process, and develop two estimators for this decomposition. This allows quantification of the effect an intervention to improve care may have on patient outcomes so that hospitals can be prioritized in terms of those who would benefit most from government resources. Finally, I illustrate the proposed mediation methods on Ontario kidney cancer data. This thesis provides valuable tools to effectively identify and target hospitals in which care improvement is most needed.

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