Patterns of multi-morbidity and prediction of hospitalisation and all-cause mortality in advanced age

Background: multi-morbidity is associated with poor outcomes and increased healthcare utilisation. We aim to identify multi-morbidity patterns and associations with potentially inappropriate prescribing (PIP), subsequent hospitalisation and mortality in octogenarians.

Methods: life and Living in Advanced Age; a Cohort Study in New Zealand (LiLACS NZ) examined health outcomes of 421 Māori (indigenous to New Zealand), aged 80-90 and 516 non-Māori, aged 85 years in 2010. Presence of 14 chronic conditions was ascertained from self-report, general practice and hospitalisation records and physical assessments. Agglomerative hierarchical cluster analysis identified clusters of participants with co-existing conditions. Multivariate regression models examined the associations between clusters and PIP, 48-month hospitalisations and mortality.

Results: six clusters were identified for Māori and non-Māori, respectively. The associations between clusters and outcomes differed between Māori and non-Māori. In Māori, those in the complex multi-morbidity cluster had the highest prevalence of inappropriately prescribed medications and in cluster 'diabetes' (20% of sample) had higher risk of hospitalisation and mortality at 48-month follow-up. In non-Māori, those in the 'depression-arthritis' (17% of the sample) cluster had both highest prevalence of inappropriate medications and risk of hospitalisation and mortality.

Conclusions: in octogenarians, hospitalisation and mortality are better predicted by profiles of clusters of conditions rather than the presence or absence of a specific condition. Further research is required to determine if the cluster approach can be used to target patients to optimise resource allocation and improve outcomes.