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Purpose: This paper responds to the call for more research to fully understand out of pocket (OOP) costs for cancer patients by providing a detailed discussion of such costs for adult haematology patients from Queensland, Australia.Methods: The descriptive qualitative research was based on in-depth interviews with 45 (n = 45) haematology patients from regional, rural and remote areas.Results: The discussion itemises the OOP costs under four categories including the costs associated with travel and accommodation, the costs associated with family and friends during relocation, the costs associated with diagnosis and treatment, and the costs of parking.Conclusions: The paper provides evidence of the categories of financial burden experienced by haematology patients who have to relocate for specialist treatment by detailing the extensive range of OOP costs. The expectation is that the itemisation of OOP cost variables will contribute to future efforts of quantification. [ABSTRACT FROM AUTHOR]

There is a growing appetite for large complex databases that integrate a range of personal, socio-demographic, health, genetic and financial information on individuals. It has been argued that 'Big Data' will provide the necessary catalyst to advance both biomedical research and health economics and outcomes research. However, it is important that we do not succumb to being data rich but information poor. This paper discusses the benefits and challenges of building Big Data, analysing Big Data and making appropriate inferences in order to advance cancer care, using Cancer 2015 (a prospective, longitudinal, genomic cohort study in Victoria, Australia) as a case study. Cancer 2015 has been linked to State and Commonwealth reimbursement databases that have known limitations. This partly reflects the funding arrangements in Australia, a country with both public and private provision, including public funding of private healthcare, and partly the legislative frameworks that govern data linkage. Additionally, linkage is not without time delays and, as such, achieving a contemporaneous database is challenging. Despite these limitations, there is clear value in using linked data and creating Big Data. This paper describes the linked Cancer 2015 dataset, discusses estimation issues given the nature of the data and presents panel regression results that allow us to make possible inferences regarding which patient, disease, genomic and treatment characteristics explain variation in health expenditure. [ABSTRACT FROM AUTHOR]

Background: The Pediatric Quality of Life Inventory™ 4.0 Short Form 15 Generic Core Scales (hereafter the PedsQL) and the Child Health Utility-9 Dimensions (CHU9D) are two generic instruments designed to measure health-related quality of life in children and adolescents in the general population and paediatric patient groups living with specific health conditions. Although the PedsQL is widely used among paediatric patient populations, presently it is not possible to directly use the scores from the instrument to calculate quality-adjusted life-years (QALYs) for application in economic evaluation because it produces summary scores which are not preference-based.Objective: This paper examines different econometric mapping techniques for estimating CHU9D utility scores from the PedsQL for the purpose of calculating QALYs for cost-utility analysis.Methods: The PedsQL and the CHU9D were completed by a community sample of 755 Australian adolescents aged 15-17 years. Seven regression models were estimated: ordinary least squares estimator, generalised linear model, robust MM estimator, multivariate factorial polynomial estimator, beta-binomial estimator, finite mixture model and multinomial logistic model. The mean absolute error (MAE) and the mean squared error (MSE) were used to assess predictive ability of the models.Results: The MM estimator with stepwise-selected PedsQL dimension scores as explanatory variables had the best predictive accuracy using MAE and the equivalent beta-binomial model had the best predictive accuracy using MSE.Conclusions: Our mapping algorithm facilitates the estimation of health-state utilities for use within economic evaluations where only PedsQL data is available and is suitable for use in community-based adolescents aged 15-17 years. Applicability of the algorithm in younger populations should be assessed in further research. [ABSTRACT FROM AUTHOR]

Purpose: Different health state utility (HSU) instruments produce different utilities for the same individuals, thereby compromising the intended comparability of economic evaluations of health care interventions. When developing crosswalks, previous studies have indicated nonlinear relationships. This paper inquires into the degree of nonlinearity across the four most widely used HSU-instruments and proposes exchange rates that differ depending on the severity levels of the health state utility scale.Methods: Overall, 7933 respondents from six countries, 1760 in a non-diagnosed healthy group and 6173 in seven disease groups, reported their health states using four different instruments: EQ-5D-5L, SF-6D, HUI-3 and 15D. Quantile regressions investigate the degree of nonlinear relationships between these instruments. To compare the instruments across different disease severities, we split the health state utility scale into utility intervals with 0.2 successive decrements in utility starting from perfect health at 1.00. Exchange rates (ERs) are calculated as the mean utility difference between two utility intervals on one HSU-instrument divided by the difference in mean utility on another HSU-instrument.Results: Quantile regressions reveal significant nonlinear relationships across all four HSU-instruments. The degrees of nonlinearities differ, with a maximum degree of difference in the coefficients along the health state utility scale of 3.34 when SF-6D is regressed on EQ-5D. At the lower end of the health state utility scale, the exchange rate from SF-6D to EQ-5D is 2.11, whilst at the upper end it is 0.38.Conclusion: Comparisons at different utility levels illustrate the fallacy of using linear functions as crosswalks between HSU-instruments. The existence of nonlinear relationships between different HSU-instruments suggests that level-specific exchange rates should be used when converting a change in utility on the instrument used, onto a corresponding utility change had another instrument been used. Accounting for nonlinearities will increase the validity of the comparison for decision makers when faced with a choice between interventions whose calculations of QALY gains have been based on different HSU-instruments. [ABSTRACT FROM AUTHOR]

Objectives: In Australia, no therapeutic agents were subsidised for the treatment of idiopathic pulmonary artery hypertension (iPAH), a rare progressive and severe disease with short life expectancy, until 1 March 2004, when bosentan (a dual endothelin receptor antagonist of high cost) was listed on the Pharmaceutical Benefits Scheme (PBS). Bosentan, in addition to conventional therapy, has been shown to slow iPAH progression and improve clinical and haemodynamic status and symptomatology, compared with placebo and conventional therapy. The objective of this paper is to describe the process of the Australian Pharmaceutical Benefits Scheme listing for bosentan (Tracleer), which included a health economic model assessing the cost effectiveness of bosentan from a healthcare payer perspective, and a risk-sharing arrangement based on the establishment of a patient registry.Methods: The health economic model predicted the cost, hospitalisation and mortality rates of a population of iPAH patients treated with either the conventional therapy regimen used in Australia or bosentan plus the conventional therapy regimen. The model was implemented as a first-order Monte Carlo simulation with mortality modelled directly as the main clinical outcome. The impacts of proposed continuation criteria, restricting the ongoing use of the drug, were evaluated. Costs and outcomes were discounted at 5% and a sensitivity analysis examined the robustness of the key assumptions.Results: The model predicted that after 5, 10 and 15 years, the difference in average cumulative costs between bosentan plus conventional therapy and conventional therapy alone would be 116,929 Australian dollars (A dollars), A181,808 dollars and A216,331 dollars for each patient, respectively. There would be an associated increase in average life expectancy of 1.39, 2.93 and 3.87 years at 5, 10 and 15 years, respectively, with an incremental cost-effectiveness ratio at 15 years of A55,927 dollars for each life-year gained. Removing the continuation criteria from the model increased the incremental cost-effectiveness ratio to A62,267 dollars (1996-2002 values).Conclusions: Economic modelling based on improved survival suggests bosentan to be a potentially cost-effective treatment for iPAH. However, the structure of the model and its inputs should be reviewed and updated as more data become available. [ABSTRACT FROM AUTHOR]