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Background  Out of pocket expenditure (OOPE) on healthcare is related to the burden of illness and the number of chronic conditions a patient experiences, but the relationship of these costs to particular conditions and groups of conditions is less studied. This study examines the effect on OOPE of various morbidity groupings, and explores the factors associated with a ‘heavy financial burden of OOPE’ defined by an expenditure of over 10% of equivalised household income on healthcare.    Methods  Data were collected from 4,574 senior Australians using a stratified sampling procedure by age, rurality and state of residence. Natural clusters of chronic conditions were identified using cluster analysis and clinically relevant clusters based on expert opinion. We undertook logistic regression to model the probability of incurring OOPE, and a heavy financial burden; linear regression to explore the significant factors of OOPE; and two-part models to estimate the marginal effect of factors on OOPE.    Results  The mean OOPE in the previous three months was AU$353; and 14% of respondents experienced a heavy financial burden. Medication and medical service expenses were the major costs. Those who experienced cancer, high blood pressure, diabetes or depression were likely to report higher OOPE. Patients with cancer or diabetes were more likely than others to face a heavy burden of OOPE relative to income. Total number of conditions and some specific conditions predict OOPE but neither the clusters nor pairs of conditions were good predictors of OOPE.    Conclusions  Total number of conditions and some specific conditions predict both OOPE and heavy financial burden but particular comorbid groupings are not useful in predicting OOPE. Low-income patients pay a higher proportion of income than the well-off as OOPE for healthcare. Interventions targeting those who are likely to face severe financial burdens due to their health could address some of these differences.

Out-of-pocket expenditure by Australian seniors with chronic disease: the effect of specific diseases and morbidity clusters