Extraordinary disease burden: an analysis of multi-morbidity

The situation in Tasmania represents a unique perspective. Within the public system, the entire patient population is available for examination in a linked and costed dataset covering the past 10 years. This dataset provides the opportunity to examine the effect of high levels of co-morbidity on patient-care pathways, as well as the associated cost estimates.

For the present analysis, a subset of the existing casemix dataset - encompassing admitted and emergency department care - was examined. An approach to identifying multi-morbidity cases was created based on the occurrence of distinct conditions in the coded dataset. A further analysis of PCCL scores was made by using a modified version of the AR_DRG-V5.1 grouper.

The primary dataset comprised 191,000 individuals with 433,000 episodes of admitted data over a four-year period. The 3,376 individuals having multi-morbidity were identified. These individuals encompass nearly 48,000 episodes with 1,550 deaths.

The paper outlines an approach to identifying these individuals, and analyzing some aspects in terms of occurrence, cost, and outcome. It builds on work already undertaken in identifying the impact of tertiary care using Australian and European data. The high level of disease burden, and disproportionate use of hospital resources, requires that this analysis be undertaken to identify the scope for improvement in the care of these individuals, as well as creating a model for a better allocation of resources.