Although young people are one of Australia's healthiest subgroups, they display age-specific morbidity patterns.
Unfortunately, such data are unavailable, and instead we apply clinically defined morbidity patterns to proxy patients' need.
Morbidity patterns due to filarial infection showed an increase with age (from 30 to 50 years) independent of sex.
Ideally, patients' morbidity patterns should explain inequality in the distribution of health care costs, if costs were allocated exactly according to patients' need.
We do not at this time know how the late life morbidity patterns referred to in the "Application" section change over time or vary between populations, for example.
Our results, however, indicate that the amount of inequality explained by patients' morbidity patterns varies greatly across type of services showing different levels of realized access.
Looking at the decomposition of inequality for in-patient care, (Fig. 2), it is seen that morbidity patterns explain a large part of predicted inequality.
For example, it is of public health interest to document the full range of late-life morbidity patterns over various time measures.
Access to comprehensive data on patients' morbidity patterns is unique, allowing for investigation of novel associations between SES, morbidity and health care utilization patterns investigating the inquiry whether patients' health care usage reflects their need defined through morbidity.
In the decomposition analysis, we included patients' morbidity patterns, degree of complications at time of analysis, and whether the patient was diagnosed or died in the current year (2011).
Big (structured and unstructured) Data can reveal changing mortality and morbidity patterns in time and place and can lead to health policies that take preventive measures rather than more costly curative ones.
