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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]

Background: The measurement and valuation of health-related quality of life for and by young people are increasingly important, yet research on the impact of study perspective and validity of preferences obtained from young populations remains limited.Objective: The objective of this study was to evaluate the feasibility and validity of collecting EQ-5D Youth version (EQ-5D-Y) preferences from adolescents, adults, and adults from a child perspective.Methods: A profile case best-worst scaling (BWS) online survey was administered to representative Australian and Spanish adult (age ≥ 18 years) and child (age 11-17 years) samples. Adults were told to either answer from their own perspective or for a hypothetical 10-year-old child. Marginal best- and worst-choice frequencies, analysis of dominant choices, self-reported difficulty completing the tasks, and time to complete tasks were used to determine the validity of responses.Results: In Australia, 2134 adults and 1010 adolescents completed the survey. In Spain, 2007 adults and 1000 adolescents completed it. Analysis of marginal choice frequencies and dominant choices indicated that the pattern of responses between adolescents and adults was similar. For Australian respondents, having no mobility problems was rated as best by adolescents, while adults rated having no pain and discomfort as 'best'. In Spain, both adults and adolescents rated no pain or discomfort as 'best'. Australian adolescents rated very worried, sad or unhappy as 'worst', while Spanish adolescents, Spanish adults and Australian adults rated a lot of pain and discomfort as 'worst'.Conclusions: Results suggest preferences from adolescents using direct BWS are valid. Our descriptive analysis also suggest that there are age-related and country-specific differences in elicitation values for the EQ-5D-Y. [ABSTRACT FROM AUTHOR]

Background and Objective: Unplanned pregnancies can lead to poorer maternal and child health outcomes. The Australian Therapeutic Goods Administration committee rejected reclassifying a range of oral contraceptive pills (OCPs) from prescription to pharmacist-only medicines in 2015, mainly based on safety concerns. Improving access to OCPs may encourage some women to use contraceptives or switch from other contraceptive methods. However, some adverse events may increase and some women may stop using condoms, increasing their risk of sexually transmitted infections. This study aimed to estimate the cost effectiveness of reclassifying OCPs from prescription to pharmacist-only.Perspective: Healthcare system.Setting: Australian primary care.Methods: A Markov model was used to synthesise data from a variety of sources. The model included all Australian women aged 15-49 years (N = 5,644,701). The time horizon was 35 years. Contraceptive use before reclassification was estimated using data from the Household, Income and Labour Dynamics in Australia (HILDA) survey, while survey data informed use after reclassification. Health outcomes included pregnancies, pregnancy outcomes (live birth, miscarriage, stillbirth, ectopic pregnancy and abortion), sexually transmitted infections, adverse events (venous thromboembolism, depression, myocardial infarction and stroke), ovarian cancer cases and quality-adjusted life-years. Costs included those related to general practitioner and specialist consultations, contraceptives and other medicines, pharmacist time, hospitalisations and adverse events. All costs were reported in 2016 Australian Dollars. A 5% discount rate was applied to health outcomes and costs.Results: Reclassifying OCPs resulted in 85.70 million quality-adjusted life-years experienced and costs of $46,910.14 million over 35 years, vs. 85.68 million quality-adjusted life-years experienced and costs of $50,274.95 million with OCPs remaining prescription-only. Thus, reclassifying OCPs was more effective and cost saving. However, a sensitivity analysis found that more research on the probability of pregnancy in women not using contraception and not trying to conceive is needed.Conclusion: Reclassifying OCPs is likely to be considered cost effective by Australian decision makers. [ABSTRACT FROM AUTHOR]

Background: The inclusion of future medical costs in cost-effectiveness analyses remains a controversial issue. The impact of capturing future medical costs is likely to be particularly important in patients with cancer where costly lifelong medical care is necessary. The lack of clear, definitive pharmacoeconomic guidelines can limit comparability and has implications for decision making.Objective: The aim of this study was to demonstrate the impact of incorporating future medical costs through an applied example using original data from a clinical study evaluating the cost effectiveness of a sepsis intervention in cancer patients.Methods: A decision analytic model was used to capture quality-adjusted life-years (QALYs) and lifetime costs of cancer patients from an Australian healthcare system perspective over a lifetime horizon. The evaluation considered three scenarios: (1) intervention-related costs (no future medical cost), (2) lifetime cancer costs and (3) all future healthcare costs. Inputs to the model included patient-level data from the clinical study, relative risk of death due to sepsis, cancer mortality and future medical costs sourced from published literature. All costs are expressed in 2017 Australian dollars and discounted at 5%. To further assess the impact of future costs on cancer heterogeneity, variation in survival and lifetime costs between cancer types and the implications for cost-effectiveness analysis were explored.Results: The inclusion of future medical costs increased incremental cost-effectiveness ratios (ICERs) resulting in a shift from the intervention being a dominant strategy (cheaper and more effective) to an ICER of $7526/QALY. Across different cancer types, longer life expectancies did not necessarily result in greater lifetime healthcare costs. Incremental costs differed across cancers depending on the respective costs of managing cancer and survivorship, thus resulting in variations in ICERs.Conclusions: There is scope for including costs beyond intervention costs in economic evaluations. The inclusion of future medical costs can result in markedly different cost-effectiveness results, leading to higher ICERs in a cancer population, with possible implications for funding decisions. [ABSTRACT FROM AUTHOR]

Background: Spending on new healthcare technologies increases net population health when the benefits of a new technology are greater than their opportunity costs-the benefits of the best alternative use of the additional resources required to fund a new technology.Objective: The objective of this study was to estimate the expected incremental cost per quality-adjusted life-year (QALY) gained of increased government health expenditure as an empirical estimate of the average opportunity costs of decisions to fund new health technologies. The estimated incremental cost-effectiveness ratio (ICER) is proposed as a reference ICER to inform value-based decision making in Australia.Methods: Empirical top-down approaches were used to estimate the QALY effects of government health expenditure with respect to reduced mortality and morbidity. Instrumental variable two-stage least-squares regression was used to estimate the elasticity of mortality-related QALY losses to a marginal change in government health expenditure. Regression analysis of longitudinal survey data representative of the general population was used to isolate the effects of increased government health expenditure on morbidity-related, QALY gains. Clinical judgement informed the duration of health-related quality-of-life improvement from the annual increase in government health expenditure.Results: The base-case reference ICER was estimated at AUD28,033 per QALY gained. Parametric uncertainty associated with the estimation of mortality- and morbidity-related QALYs generated a 95% confidence interval AUD20,758-37,667.Conclusion: Recent public summary documents suggest new technologies with ICERs above AUD40,000 per QALY gained are recommended for public funding. The empirical reference ICER reported in this article suggests more QALYs could be gained if resources were allocated to other forms of health spending. [ABSTRACT FROM AUTHOR]

Background: The EORTC QLU-C10D is a new multi-attribute utility instrument derived from the widely used cancer-specific quality-of-life (QOL) questionnaire, EORTC QLQ-C30. The QLU-C10D contains ten dimensions (Physical, Role, Social and Emotional Functioning; Pain, Fatigue, Sleep, Appetite, Nausea, Bowel Problems), each with four levels. To be used in cost-utility analysis, country-specific valuation sets are required.Objective: The aim of this study was to provide Australian utility weights for the QLU-C10D.Methods: An Australian online panel was quota-sampled to ensure population representativeness by sex and age (≥ 18 years). Participants completed a discrete choice experiment (DCE) consisting of 16 choice-pairs. Each pair comprised two QLU-C10D health states plus life expectancy. Data were analysed using conditional logistic regression, parameterised to fit the quality-adjusted life-year framework. Utility weights were calculated as the ratio of each QOL dimension-level coefficient to the coefficient on life expectancy.Results: A total of 1979 panel members opted in, 1904 (96%) completed at least one choice-pair, and 1846 (93%) completed all 16 choice-pairs. Dimension weights were generally monotonic: poorer levels within each dimension were generally associated with greater utility decrements. The dimensions that impacted most on choice were, in order, Physical Functioning, Pain, Role Functioning and Emotional Functioning. Oncology-relevant dimensions with moderate impact were Nausea and Bowel Problems. Fatigue, Trouble Sleeping and Appetite had relatively small impact. The value of the worst health state was -0.096, somewhat worse than death.Conclusions: This study provides the first country-specific value set for the QLU-C10D, which can facilitate cost-utility analyses when applied to data collected with the EORTC QLQ-C30, prospectively and retrospectively. [ABSTRACT FROM AUTHOR]

Objective: Deciding on public funding for pharmaceuticals on the basis of value for money is now widespread. We suggest that evidence-based assessment of value has restricted the availability of medicines in Australia in a way that reflects the relative bargaining power of government and the pharmaceutical industry. We propose a simple informal game-theoretic model of bargaining between the funding agency and industry and test its predictions using a logistic multiple regression model of past funding decisions made by the Pharmaceutical Benefits Advisory Committee in Australia.Method: The model estimates the probability of a drug being recommended for subsidy as a function of incremental cost per quality-adjusted life-year (QALY), as well as other drug and market characteristics. Data are major submissions or resubmissions from 1993 to 2009 where there was a claim of superiority and evidence of a difference in quality of life. Independent variables measure the incremental cost per QALY, the cost to the public budget, the strength and quality of the clinical and economic evidence, need as measured by severity of illness and the availability of alternative treatments, whether or not a resubmission, and newspaper reports as a measure of public pressure. We report the odds ratio for each variable and calculate the ratio of the marginal effect of each variable to the marginal effect of the cost per QALY as a measure of the revealed willingness to pay for each of the variables that influence the decision.Results: The results are consistent with a bargaining model where a 10,000 Australian dollar ($A) fall in value (increase in cost per QALY) reduces the average probability of public funding from 37 to 33% (95% CI 34-32). If the condition is life threatening or the drug has no active comparator, then the odds of a positive recommendation are 3.18 (95% CI 1.00-10.11) and 2.14 (95% CI 0.95-4.83) greater, equivalent to a $A33,000 and a $A21,000 increase in value (fall in cost per QALY). If both conditions are met, the odds are increased by 4.41 (95% CI 1.28-15.24) times, equivalent to an increase in value of $A46,000. Funding is more likely as time elapses and price falls, but we did not find clear evidence that public or corporate pressure influences decisions.Conclusion: Evidence from Australia suggests that the determinants of public funding and pricing decisions for medicines reflect the relative bargaining power of government and drug companies. Value for money depends on the quality of evidence, timing, patient need, perceived benefit and opportunity cost; these factors reflect the potential gains from striking a bargain and the risk of loss from not doing so. [ABSTRACT FROM AUTHOR]

Background and Objective: Many guidelines for clinical decisions are hierarchical and nonlinear. Evaluating if these guidelines are used in practice requires methods that can identify such structures and thresholds. Classification and regression trees (CART) were used to analyse prescribing patterns of Australian general practitioners (GPs) for the primary prevention of cardiovascular disease (CVD). Our aim was to identify if GPs use absolute risk (AR) guidelines in favour of individual risk factors to inform their prescribing decisions of lipid-lowering medications.Methods: We employed administrative prescribing information that is linked to patient-level data from a clinical assessment and patient survey (the AusHeart Study), and assessed prescribing of lipid-lowering medications over a 12-month period for patients (n = 1903) who were not using such medications prior to recruitment. CART models were developed to explain prescribing practice. Out-of-sample performance was evaluated using receiver operating characteristic (ROC) curves, and optimised via pruning.Results: We found that individual risk factors (low-density lipoprotein, diabetes, triglycerides and a history of CVD), GP-estimated rather than Framingham AR, and sociodemographic factors (household income, education) were the predominant drivers of GP prescribing. However, sociodemographic factors and some individual risk factors (triglycerides and CVD history) only become relevant for patients with a particular profile of other risk factors. The ROC area under the curve was 0.63 (95% confidence interval [CI] 0.60-0.64).Conclusions: There is little evidence that AR guidelines recommended by the National Heart Foundation and National Vascular Disease Prevention Alliance, or conditional individual risk eligibility guidelines from the Pharmaceutical Benefits Scheme, are adopted in prescribing practice. The hierarchy of conditional relationships between risk factors and socioeconomic factors identified by CART provides new insights into prescribing decisions. Overall, CART is a useful addition to the analyst's toolkit when investigating healthcare decisions. [ABSTRACT FROM AUTHOR]