An Analysis of Hospital Costs for Childhood Cancer Care.

BACKGROUND: This study used a linked dataset consisting of all childhood cancers recorded over the course of 10 years in New South Wales (NSW), Australia, to evaluate the hospital and emergency department costs (from a payer perspective) and resources used by patients with childhood cancer. We also analyzed determinants responsible for high-frequency hospital admissions, hospital length of stay (LoS), and hospital costs. METHODS: We analyzed linked data at the individual patient level for a retrospective cohort of 2,966 patients with cancer aged <18 years with a diagnosis date between 2001 and 2012 from the NSW Central Cancer Registry, Australia. We reported costs and use of hospitalization and emergency department presentation 1 year before the date of diagnosis, 1 year after diagnosis, and 2 to 5 years after diagnosis. We also examined the association between cancer types and hospital admission and hospital costs from the payer perspective. Patient characteristics associated with the frequency of hospital admissions, hospital LoS, and hospital costs were also determined using a generalized linear model. RESULTS: Most hospital admission costs occurred in the first year after diagnosis, accounting for >70% of hospital costs within 5 years after diagnosis. The estimated median annual cost of hospitalization in the first year after diagnosis was A$88,964 (interquartile range [IQR], A$34,399-A$163,968) for patients diagnosed at age 0 to 14 years and A$23,384 (IQR, A$5,585-A$91,565) for those diagnosed at age 15 to 17 years. Higher frequency of hospital admissions, hospital LoS, and hospital costs were significantly associated with younger age at cancer diagnosis, cancer metastases, and living in remote/disadvantaged socioeconomic areas. CONCLUSIONS: Our study represents one of the first in Australia to include detailed hospitalization cost information for all childhood cancer cases. This study highlights the high hospital use by pediatric patients and the importance of early diagnosis. Our findings also demonstrate the health inequities experienced by patients from remote areas and the lowest socioeconomic areas.

Estimating the costs of genomic sequencing in cancer control.

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.

The Productivity Costs of Premature Mortality Due to Cancer in Australia: Evidence from a Microsimulation Model.

AIM: To estimate the productivity costs of premature mortality due to cancer in Australia, in aggregate and for the 26 most prevalent cancer sites. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Is there a mismatch between who gets iron supplementation and who needs it? A cross-sectional study of iron supplements, iron deficiency anaemia and socio-economic status in Australia.

Fe deficiency anaemia (IDA) is more prevalent in lower socio-economic groups; however, little is known about who actually receives Fe supplements. This paper aims to determine whether the groups most likely to have IDA are the most likely to be taking Fe supplements. Logistic regression analysis was conducted using the cross-sectional, nationally representative National Nutrition and Physical Activity Survey and National Health Measures Survey. After adjusting for other factors, those whose main language spoken at home was not English had twice the odds of having IDA compared with those whose main language spoken at home was English (95% CI 1·00, 4·32). Those who were not in the labour force also had twice the odds of having IDA as those who were employed (95% CI 1·16, 3·41). Those in income quintile 1 had 3·7 times the odds of having IDA compared with those in income quintile 5 (95% CI 1·42, 9·63). Those whose main language spoken at home was not English were significantly less likely to take Fe supplements (P=0·002) than those whose main language spoken at home was English. There was no significant difference in the likelihood of taking Fe supplements between those who were not in the labour force and those who were employed (P=0·618); between those who were in income quintile 1 and in higher income quintiles; and between males and females (P=0·854), after adjusting for other factors. There is a mismatch between those who are most in need of Fe supplements and those who currently receive them.

The cost effectiveness of bevacizumab when added to capecitabine, with or without mitomycin-C, in first line treatment of metastatic colorectal cancer: results from the Australasian phase III MAX study.

BACKGROUND: Based on the clinical data, bevacizumab has been approved in Australia and globally for the treatment of advanced colorectal cancer. However, limited evidence exists for its cost-effectiveness. The purpose of this study was to evaluate the cost effectiveness of adding bevacizumab to capecitabine monotherapy in patients with metastatic colorectal cancer, using data from the prospective economic evaluation conducted alongside the MAX trial. METHODS: Individual patient level data on resource use and progression free survival were prospectively collected in the phase III MAX trial. Resource use data were collected for the period between randomisation and disease progression, and unit costs were assigned from the perspective of the Australian health care funder. Effectiveness was measured in quality adjusted progression free survival years, with utility scores obtained from both the community valued EQ-5D questionnaire and the patient valued UBQ-C questionnaire. Progression free survival was used as a secondary effectiveness measure. RESULTS: The addition of bevacizumab to capecitabine monotherapy cost approximately $192,156 (95% confidence interval [CI], $135,619 to $326,894) per quality adjusted progression free survival year gained when using publicly listed pharmaceutical prices and utility values from the EQ-5D questionnaire. This decreased to $149,455 (95% CI, $100,356 to $245,910) when values from the UBQ-C questionnaire were applied. The incremental cost per progression free survival year was $145,059 (95% CI, $106,703 to $233,225). CONCLUSIONS: Bevacizumab was not found to be cost effective at its listed price, based on results from the MAX trial.

Where are older workers with chronic conditions employed?

OBJECTIVE: To determine which industries and occupational groups are associated with employment of older workers with chronic work-limiting health conditions in Australia. DESIGN AND PARTICIPANTS: Analysis of data from the 2005 National Health Survey for 4228 workers aged 45-64 years. MAIN OUTCOME MEASURES: Rate of employment by industry and occupation of older workers with specific chronic conditions. RESULTS: Compared with the reference industry of property and business services, workers in the retail trade industry were found to be more likely to suffer from musculoskeletal conditions (relative risk ratio [RRR], 1.56; 95% CI, 1.04-2.36), while those in health and community services had higher rates of cardiovascular disease (RRR, 2.17; 95% CI, 1.11-4.24). Compared with the reference occupation group of professionals, managers and administrators were less likely to suffer neoplasms (RRR, 0.25; 95% CI, 0.07-0.97). Similar rates of chronic disease were seen across other occupations. CONCLUSION: Increasing rates of chronic health conditions are unlikely to have an even impact across the workforce, as the rate of employment of older workers with these conditions varies between industries.

Demographic change and the future demand for public hospital care in Australia, 2005 to 2050.

BACKGROUND: Over the next 45 years the Australian population will age rapidly as the baby boomer cohort moves into retirement and then old age. As the population ages there will be substantial growth in the demand for hospital bed-days, placing a corresponding demand on infrastructure and staffing. METHODS: Australian Bureau of Statistics population projections to 2050 and Australian Institute of Health and Welfare public hospital bed-day data from 1993-94 to 2003-04 were used to develop models of future demand and examine the sensitivity of the results to model assumptions. RESULTS: Over the long term, demand for public hospital bed-days was projected to grow faster than population growth. By 2050, ageing will increase the demand for bed-days by between 70% and 130% depending on the underlying assumptions, and the proportion of bed-days devoted to older people will increase from under 50% in 2005 to over 70%. CONCLUSIONS: Ageing of the population will increase the demand for health services just as it will become harder to recruit health professionals as the large baby boomer cohort retires from the health workforce. Accordingly, we need to plan now to ensure future needs of the ageing population are met.