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To estimate the productivity costs of premature mortality due to cancer in Australia, in aggregate and for the 26 most prevalent cancer sites. A human capital approach was adopted to estimate the long term impacts of Australian cancer deaths in 2003. Using population mortality data, the labour force participation and the present value of lifetime income (PVLI) forgone due to premature mortality was estimated based on individual characteristics at the time of death including age, sex and socioeconomic status. Outcomes were modelled to the year 2030 using economic data from a national microsimulation model. A discount rate of 3% was applied and costs were reported in 2016 Australian dollars. Premature deaths from cancer in 2003 resulted in 88,000 working years lost and a cost of $4.2 billion in the PVLI forgone. Costs were close to three times higher in males than females due to the higher number of premature deaths in men, combined with higher levels of workforce participation and income. Lung, colorectal and brain cancers accounted for the highest proportion of costs, while testicular cancer was the most costly cancer site per death. The productivity costs of premature mortality due to cancer are significant. These results provide an economic measure of the cancer burden which may assist decision makers in allocating scare resources amongst competing priorities.

Background Despite the rapid uptake of genomic technologies within cancer care, few studies provide detailed information on the costs of sequencing across different applications. The objective of the study was to examine and categorise the complete costs involved in genomic sequencing for a range of applications within cancer settings. Methods We performed a cost-analysis using gross and micro-costing approaches for genomic sequencing performed during 2017/2018 across different settings in Brisbane, Australia. Sequencing was undertaken for patients with lung, breast, oesophageal cancers, melanoma or mesothelioma. Aggregated resource data were captured for a total of 1433 patients and point estimates of per patient costs were generated. Deterministic sensitivity analyses addressed the uncertainty in the estimates. Estimated costs to the public health system for resources were categorised into seven distinct activities in the sequencing process: sampling, extraction, library preparation, sequencing, analysis, data storage and clinical reporting. Costs were also aggregated according to labour, consumables, testing, equipment and ‘other’ categories. Results The per person costs were AU$347–429 (2018 US$240–297) for targeted panels, AU$871–$2788 (2018 US$604–1932) for exome sequencing, and AU$2895–4830 (2018 US$2006-3347) for whole genome sequencing. Cost proportions were highest for library preparation/sequencing materials (average 76.8% of total costs), sample extraction (8.1%), data analysis (9.2%) and data storage (2.6%). Capital costs for the sequencers were an additional AU$34–197 (2018 US$24–67) per person. Conclusions Total costs were most sensitive to consumables and sequencing activities driven by commercial prices. Per person sequencing costs for cancer are high when tumour/blood pairs require testing. Using the natural steps involved in sequencing and categorising resources accordingly, future evaluations of costs or cost-effectiveness of clinical genomics across cancer projects could be more standardised and facilitate easier comparison of cost drivers.

To estimate the productivity impacts of a policy intervention on the prevention of premature mortality due to obesity. A simulation model of the Australian population over the period from 2003 to 2030 was developed to estimate productivity gains associated with premature deaths averted due to an obesity prevention intervention that applied a 10% tax on unhealthy foods. Outcome measures were the total working years gained, and the present value of lifetime income (PVLI) gained. Impacts were modelled over the period from 2003 to 2030. Costs are reported in 2018 Australian dollars and a 3% discount rate was applied to all future benefits. Premature deaths averted due to a junk food tax accounted for over 8,000 additional working years and a $307 million increase in PVLI. Deaths averted in men between the ages of 40 to 59, and deaths averted from ischaemic heart disease, were responsible for the largest gains. The productivity gains associated with a junk food tax are substantial, accounting for almost twice the value of the estimated savings to the health care system. The results we have presented provide evidence that the adoption of a societal perspective, when compared to a health sector perspective, provides a more comprehensive estimate of the cost-effectiveness of a junk food tax.

ObjectivesTo estimate the economic burden of informal caregivers not in the labour force (NILF) due to caring for a person with arthritis in Australia, with projections of these costs from 2015 to 2030.DesignStatic microsimulation modelling using national survey data.SettingAustralia nationwide survey.ParticipantsParticipants include respondents to the Survey of Disability, Ageing and Carers who are informal carers of a person who has arthritis as their main chronic condition and non-carers.Outcome measuresEstimating the economic impact and national aggregated costs of informal carers NILF to care for a person with arthritis and projecting these costs from 2015 to 2030 in 5-year intervals.ResultsOn a per-person basis, when adjusted for age, sex and highest education attained, the difference in average weekly total income between informal carers and non-carers employed in the labour force is $A1051 (95% CI: $A927 to $A1204) in 2015 and projected to increase by up to 22% by 2030. When aggregated, the total national annual loss of income to informal carers NILF is estimated at $A388.2 million (95% CI: $A324.3 to $A461.9 million) in 2015, increasing to $A576.9 million (95% CI: $A489.2 to $A681.8 million) by 2030. The national annual tax revenue lost to the government of the informal carers NILF is estimated at $A99 million (95% CI: $A77.9 to $A126.4 million) in 2015 and is projected to increase 49% by 2030.ConclusionInformal carers NILF are economically worse off than employed non-carers, and the aggregated national annual costs are substantial. The future economic impact of informal carers NILF to care for a person with arthritis in Australia is projected to increase, with the estimated differences in income between informal carers and employed non-carers increasing by 22% from 2015 to 2030.

Background Higher educational attainment is important for economic wellbeing and associated with better health and longevity. Previous research focused on intelligence, socioeconomic status and mental health or individual risk behaviours as predictors of educational attainment, but the role of multiple domains of adolescent risk behaviours is less clear. This study examined the association between multiple domains of risk behaviour in adolescence and educational attainment by 22 years-of-age. Methods Young people (Generation 2, Gen2) and their parents (Generation 1, Gen1) participating in the Raine Study completed questionnaires at years 1, 5, 8, 10 (Gen1 only), 14, 17 (both) and 22 (Gen2 only). The Raine Study is an ongoing longitudinal study initiated in Perth, Western Australia, between 1989 and 1991. The 1,102 Gen2 participants who responded to questions about highest educational attainment were included in this study. The association between Gen2 self-reported risk behaviours (including age at commencement of drinking alcohol, smoking, sexual intercourse and drug use) and educational attainment (defined as self-reported years of completed high school: ≤ 10, 11, 12 or tertiary education (> 12)) at year 22, after adjusting for mother’s age and combined parental education level, participant sex, and family income, educational performance and adolescent mental health, was explored using ordinal regression models and presented as odds ratios (OR) with 95% confidence intervals (CI). Results Ordinal models suggested that never smoking or starting older than 18 compared with smoking before age 15 (OR 2.02, 95%CI: 1.28–2.14); first drinking alcohol between 15 and 17 years compared with younger than 15 (OR 1.52, 95%CI: 1.08–2.14); and, first sexual intercourse aged ≥ 18 years compared with under 15 (OR 1.67, 95%CI: 1.08–2.57) were associated with higher levels of educational attainment at 22-year follow-up. Additionally, lower (“better”) behavioural scores increased the odds of higher levels of attainment. Conclusions Absence of health risk behaviours at a younger age or later commencement was associated with higher educational attainment. Evidence-based interventions that address the societal influences underpinning risk behaviours in adolescents may support longer school retention.

Embedding precision medicine in paediatric oncology shows promise to have a positive impact on how children with cancer will be treated in the future. While there are a number of studies of precision medicine in childhood cancer, there is limited evidence available on the cost of implementing the related testing. This is the first Australian study that systematically measures the cost of using precision medicine in the care of high-risk childhood cancers, through the Zero Childhood Cancer Precision Medicine Programme. In 2021 Australian dollars, the estimated costs inclusive of genomic and preclinical testing were: (A) $12,743 per patient for access; (B) $14,262 per identification of molecular cause; and (C) $21,769 per MTB recommendation. The information gained supports the understanding of the cost of reporting clinically significant outcomes relevant to the biology of the tumour, diagnosis, prognosis and potentially improving clinical management for a child.

Background To increase the uptake of key clinical recommendations for non-surgical management of knee osteoarthritis (OA) and improve patient outcomes, we developed a new model of service delivery (PARTNER model) and an intervention to implement the model in the Australian primary care setting. We will evaluate the effectiveness and cost-effectiveness of this model compared to usual general practice care. Methods We will conduct a mixed-methods study, including a two-arm, cluster randomised controlled trial, with quantitative, qualitative and economic evaluations. We will recruit 44 general practices and 572 patients with knee OA in urban and regional practices in Victoria and New South Wales. The interventions will target both general practitioners (GPs) and their patients at the practice level. Practices will be randomised at a 1:1 ratio. Patients will be recruited if they are aged ≥45 years and have experienced knee pain ≥4/10 on a numerical rating scale for more than three months. Outcomes are self-reported, patient-level validated measures with the primary outcomes being change in pain and function at 12 months. Secondary outcomes will be assessed at 6 and 12 months. The implementation intervention will support and provide education to intervention group GPs to deliver effective management for patients with knee OA using tailored online training and electronic medical record support. Participants with knee OA will have an initial GP visit to confirm their diagnosis and receive management according to GP intervention or control group allocation. As part of the intervention group GP management, participants with knee OA will be referred to a centralised multidisciplinary service: the PARTNER Care Support Team (CST). The CST will be trained in behaviour change support and evidence-based knee OA management. They will work with patients to develop a collaborative action plan focussed on key self-management behaviours, and communicate with the patients’ GPs. Patients receiving care by intervention group GPs will receive tailored OA educational materials, a leg muscle strengthening program, and access to a weight-loss program as appropriate and agreed. GPs in the control group will receive no additional training and their patients will receive usual care. Discussion This project aims to address a major evidence-to-practice gap in primary care management of OA by evaluating a new service delivery model implemented with an intervention targeting GP practice behaviours to improve the health of people with knee OA. Trial Registration Australian New Zealand Clinical Trials Registry: ACTRN12617001595303 , date of registration 1/12/2017.

Background Those with a low income are known to have a higher risk of developing heart disease. However, the inverse relationship – falling into income poverty after developing heart disease has not been explored with longitudinal data. This paper aims to determine if those with heart disease have an elevated risk of falling into poverty. Methods Survival analysis was conducted using the longitudinal Household Income and Labour Dynamics in Australia survey, between the years 2007 and 2012. The study focused on the Australian population aged 21 years and over in 2007 who were not already in poverty and did not already have heart disease, who were followed from 2007 to 2012. Cox regression models adjusting for age, sex and time-varying co-variates (marital status, home ownership and remoteness of area of residence) were constructed to assess the risk of falling into poverty. Results For those aged 20 who developed heart disease, the hazard ratio for falling into income poverty was 9.24 (95 % CI: 8.97–9.51) and for falling into multidimensional poverty the hazard ratio was 14.21 (95 % CI: 13.76–14.68); for those aged 40 the hazard ratio for falling into income poverty was 3.45 (95 % CI: 3.39–3.51) and for multidimensional poverty, 5.20 (95 % CI: 5.11–5.29); and for those aged 60 the hazard ratio for falling into income poverty was 1.29 (95 % CI: 1.28–1.30) and for multidimensional poverty, 1.52 (95 % CI: 1.51–1.54), relative those who never developed heart disease. The risk for both income and multidimensional poverty decreases with age up to the age of 70, over which, those who developed heart disease had a reduced risk of poverty. Conclusion For those under the age of 70, developing heart disease is associated with an increased risk of falling into both income poverty and multidimensional poverty.

Background This paper aimed to identify whether high psychological distress is associated with an increased risk of income and multidimensional poverty amongst older adults in Australia. Methods We undertook longitudinal analysis of the nationally representative Household Income and Labour Dynamics in Australian (HILDA) survey using modified Poisson regression models to estimate the relative risk of falling into income poverty and multidimensional poverty between 2010 and 2012 for males and females, adjusting for age, employment status, place of residence, marital status and housing tenure; and Population Attributable Risk methodology to estimate the proportion of poverty directly attributable to psychological distress, measured by the Kessler 10 scale. Results For males, having high psychological distress increased the risk of falling into income poverty by 1.68 (95% CI: 1.02 to 2.75) and the risk of falling into multidimensional poverty by 3.40 (95% CI: 1.91 to 6.04). For females, there was no significant difference in the risk of falling into income poverty between those with high and low psychological distress ( p  = 0.1008), however having high psychological distress increased the risk of falling into multidimensional poverty by 2.15 (95% CI: 1.30 to 3.55). Between 2009 and 2012, 8.0% of income poverty cases for people aged 65 and over (95% CI: 7.8% to 8.4%), and 19.5% of multidimensional poverty cases for people aged 65 and over (95% CI: 19.2% to 19.9%) can be attributed to high psychological distress. Conclusions The elevated risk of falling into income and multidimensional poverty has been an overlooked cost of poor mental health.

To assess the labour force participation and quantify the economic status of older Australian workers with multiple health conditions. Many older people suffer from multiple health conditions. While multiple morbidities have been highlighted as an important research topic, there has been limited research in this area to date, particularly on the economic status of those with multiple morbidities. Cross sectional analysis of Health&WealthMOD, a microsimulation model of Australians aged 45 to 64 years. People with one chronic health condition had 0.59 times the odds of being employed compared to those with no condition (OR 0.59, 95% CI: 0.49, 0.71), and those with four or more conditions had 0.14 times the odds of being employed compared to those with no condition (OR 0.14, 95% CI: 0.11, 0.18). People with one condition received a weekly income 32% lower than those with no health condition, paid 49 % less tax, and received 37% more in government transfer payments; those with four or more conditions received a weekly income 94% lower, paid 97% less in tax and received over 2,000% more in government transfer payments per week than those with no condition. While having a chronic health condition is associated with lower labour force participation and poorer economic status, having multiple conditions compounds the affect - with these people being far less likely to be employed and having drastically lower incomes.