One group often identified as having low socioeconomic status, those living in remote or rural areas, are often recognised as bearing an unequal burden of illness in society. This paper aims to examine equity of utilisation of general practitioner services in Australia.

Although we do know that out-of-pocket healthcare expenditure is relatively high in Australia, little is known about what health conditions are associated with the highest out-of-pocket expenditure, and whether the cost of healthcare acts as a barrier to care for people with different chronic conditions. Cross-sectional analysis using linear and logistic regression models applied to the Commonwealth Fund international health policy survey of adults aged 18 years and over was conducted in 2013. Adults with asthma, emphysema and chronic obstructive pulmonary disease (COPD) had 109% higher household out-of-pocket healthcare expenditure than did those with no health condition (95% CI: 50–193%); and adults with depression, anxiety and other mental health conditions had 95% higher household out-of-pocket expenditure (95% CI: 33–187%). People with a chronic condition were also more likely to forego care because of cost. People with depression, anxiety and other mental health conditions had 7.65 times higher odds of skipping healthcare (95% CI: 4.13–14.20), and people with asthma, emphysema and chronic obstructive pulmonary disease had 6.16 times higher odds of skipping healthcare (95% CI: 3.30–11.50) than did people with no health condition. People with chronic health conditions in Canada, the United Kingdom, Germany, France, Norway, Sweden and Switzerland were all significantly less likely to skip healthcare because of cost than were people with a condition in Australia. The out-of-pocket cost of healthcare in Australia acts as a barrier to accessing treatment for people with chronic health conditions, with people with mental health conditions being likely to skip care. Attention should be given to the accessibility and affordability of mental health services in Australia.

Poor health increases the likelihood of experiencing poverty by reducing a person’s ability to work and imparting costs associated with receiving medical treatment. Universal health care is a means of protecting against the impoverishing impact of high healthcare costs. This study aims to document the recent trends in the amount paid by Australian households out-of-pocket for healthcare, identify any inequalities in the distribution of this expenditure, and to describe the impact that healthcare costs have on household living standards in a high-income country with a long established universal health care system. We undertook this analysis using a longitudinal, nationally representative dataset – the Household Income and Labour Dynamics in Australia Survey, using data collected annually from 2006 to 2014. Out of pocket payments covered those paid to health practitioners, for medication and in private health insurance premiums; catastrophic expenditure was defined as spending 10% or more of household income on healthcare. Average total household expenditure on healthcare items remained relatively stable between 2006 and 2014 after adjusting for inflation, changing from $3133 to $3199. However, after adjusting for age, self-reported health status, and year, those in the lowest income group (decile one) had 15 times the odds (95% CI, 11.7–20.8) of having catastrophic health expenditure compared to those in the highest income group (decile ten). The percentage of people in income decile 2 and 3 who had catastrophic health expenditure also increased from 13% to 19% and 7% to 13% respectively. Ongoing monitoring of out of pocket healthcare expenditure is an essential part of assessing health system performance, even in countries with universal health care.

The rapid uptake of supervised machine learning (ML) in clinical prediction modelling, particularly for binary outcomes based on tabular data, has sparked debate about its comparative advantage over traditional statistical logistic regression. Although ML has demonstrated superiority in unstructured data domains, its performance gains in structured, tabular clinical datasets remain inconsistent and context dependent. This viewpoint synthesizes recent comparative studies and simulation findings to argue that there is no universal best modelling approach. Model performance depends heavily on dataset characteristics (eg, linearity, sample size, number of candidate predictors, minority class proportion) and data quality (eg, completeness, accuracy). Consequently, we argue that efforts to improve data quality, not model complexity, are more likely to enhance the reliability and real-world utility of clinical prediction models.

Mental health conditions have the ability to interrupt an individual's ability to participate in the labour force, and this can have considerable follow on impacts to both the individual and the state. Cross-sectional analysis of the base population of Health&WealthMOD, a microsimulation model built on data from the Australian Bureau of Statistics' Survey of Disability, Ageing and Carers and STINMOD, an income and savings microsimulation model was used to quantify the personal cost of lost income and the cost to the state from lost income taxation, increased benefits payments and lost GDP as a result of early retirement due to mental health conditions in Australians aged 45-64 in 2009. Individuals aged 45 to 64 years who have retired early due to depression personally have 73% lower income then their full time employed counterparts and those retired early due to other mental health conditions have 78% lower incomes. The national aggregate cost to government due to early retirement from these conditions equated to $278 million (£152.9 million) in lost income taxation revenue, $407 million (£223.9 million) in additional transfer payments and around $1.7 billion in GDP in 2009 alone. The costs of mental health conditions to the individuals and the state are considerable. While individuals has to bear the economic costs of lost income in addition to the burden of the conditions itself, the impact on the state is loss of productivity from reduced workforce participation, lost income taxation revenue, and increased government support payments - in addition to direct health care costs.