An exploration of healthcare use in older people waiting for and receiving Australian community-based aged care services.

AIM: Home care packages (HCPs) facilitate older individuals to remain at home, with longer HCP wait times associated with increased mortality risk. We analyze healthcare cost data pre- and post-HCP access to inform hypotheses around the effects of healthcare use and mortality risk. METHODS: Regression models were used to assess the impact of delayed HCP access on healthcare costs and to compare costs whilst waiting and in the 6- and 12 month periods post-HCP access for 16 629 older adults. RESULTS: Average wait time for a HCP was 89.7 days (SD = 125.6) during the study period. Wait-time length had no impact on any healthcare cost category or time period. However, total per day healthcare costs were higher in the 6 and 12 months post-receipt of a HCP (AU$61.5, AU$63, respectively) compared with those in the time waiting for a HCP (AU$48.1). Inpatient care accounted for a higher proportion of total healthcare costs post-HCP (AU$45.1, AU$46.3, respectively) compared with in the wait time (AU$30.6), whilst spending on medical services and pharmaceuticals reduced slightly in the 6 month (AU$7.1, AU$6.3) and 12 month (AU$7.2, AU$6.3) post-HCP periods compared with in the wait time (AU$7.9, AU$7.1). CONCLUSIONS: Increased spending post-HCP on inpatient care or non-health support afforded by HCPs may offer protective effects for mortality and risk of admission to aged care. Further research should explore the association between delayed access to inpatient care for geriatric syndromes and mortality to inform recommendations on extensions to residential care outreach services into the community to improve the timely identification of the need for inpatient care. Geriatr Gerontol Int 2023; 23: 899-905.

Challenges in the Evaluation of Emerging Highly Specialised Technologies: Is There a Role for Living HTA?

There is currently deep uncertainty about the clinical benefits and cost effectiveness of highly specialised technologies (HSTs), like gene and cell therapies. These treatments are novel, typically have high upfront costs, the patient populations are small and heterogenous, there is minimal information on their long-term safety and effectiveness, and data are limited and often of poor quality. With the increasing number of these technologies and their high cost burden on governments and health care providers, policy makers are currently walking a decision tightrope. On the one hand, an unfavourable funding decision could potentially limit patient access to life-saving treatments, while on the other, a favourable decision could result in unsustainable budget impacts and perhaps poorer patient health outcomes. Health technology assessment (HTA) is meant to determine the value of a health technology in order to promote an equitable, efficient, and high-quality health system. However, standard HTA processes have failed to mitigate the deep uncertainties associated with these technologies. In this paper, we propose a Living HTA framework to address these challenges. This framework includes a one-off process for making explicit the societal values associated with HSTs. These would inform the decision-making approach, data collection and the development of disease-specific reference models to be used by industry sponsors as the basis for their submissions for public funding. Coverage with an evidence development mechanism is also proposed by which data can be collected in real time to update the reference model on a rolling basis, thereby allowing re-assessment of the clinical and cost effectiveness of individual HSTs. The HTA would be 'live' until the results indicate there is sufficient certainty for the funding decision to be confirmed, the price changed or the funding removed.

The representation of public values in health technology assessment to inform funding decisions: the case of Australia's national funding bodies.

Over the past few years, there has been an increasing recognition of the value of public involvement in health technology assessment (HTA) to ensure the legitimacy and fairness of public funding decisions [Street J, Stafinski T, Lopes E, Menon D. Defining the role of the public in Health Technology Assessment (HTA) and HTA-informed decision-making processes. Int J Technol Assess Health Care. 2020;36:87-95]. However, important challenges remain, in particular, how to reorient HTA to reflect public priorities. In a recent international survey of thirty HTA agencies conducted by the International Network of Agencies for HTA (INAHTA), public engagement in HTA was listed as one of the "Top 10" challenges for HTA agencies [O'Rourke B, Werko SS, Merlin T, Huang LY, Schuller T. The "Top 10" challenges for health technology assessment: INAHTA viewpoint. Int J Technol Assess. 2020;36:1-4].Historically, Australia has been at the forefront of the application of HTA for assessing the effectiveness and cost-effectiveness of new health technologies to inform public funding decisions. However, current HTA processes in Australia lack meaningful public inputs. Using Australia as an example, we describe this important limitation and discuss the potential impact of this gap on the health system and future directions.

Lifetime costs of invasive meningococcal disease: A Markov model approach.

INTRODUCTION: Invasive meningococcal disease (IMD) is an uncommon but life-threatening infectious disease associated with high sequelae rates in young children and an increased risk of mortality in adolescents and young adults. Funding decisions to reject inclusion of new meningococcal serogroup B vaccines on national immunisation schedules have been criticised by IMD patients, their families, paediatricians and charity organisations. We aim to estimate the lifetime costs of IMD with the best available evidence to inform cost-effectiveness analyses. METHODS: A Markov model was developed taking healthcare system and societal perspectives. A range of data including age-specific mortality rates, and probabilities of IMD-related sequelae were derived from a systematic review and meta-analysis. All currencies were inflated to year 2017 prices by using consumer price indexes in local countries and converted to US dollars by applying purchasing power parities conversion rates. Expert panels were used to inform the model development process including key structural choices and model validations. RESULTS: The estimated lifetime societal cost is US$319,896.74 per IMD case including the direct healthcare cost of US$65,035.49. Using a discount rate of 5%, the costs are US$54,278.51 and US$13,968.40 respectively. Chronic renal failure and limb amputation result in the highest direct healthcare costs per patient. Patients aged < 5 years incur the higher healthcare expenditure compared with other age groups. The costing results are sensitive to the discount rate, disease incidence, acute admission costs, and sequelae rates and costs of brain injuries and epilepsy. CONCLUSIONS: IMD can result in substantial costs to the healthcare system and society. Understanding the costs of care can assist decision-making bodies in evaluating cost-effectiveness of new vaccine programs.

Structuring a conceptual model for cost-effectiveness analysis of frailty interventions.

BACKGROUND: Frailty is a major health issue which impacts the life of older people, posing a significant challenge to the health system. One of the key emerging areas is the development of frailty interventions to halt or reverse the progression of the condition. In many countries, economic evidence is required to inform public funding decisions for such interventions, and cost-effectiveness models are needed to estimate long-term costs and effects. Such models should capture current clinical understanding of frailty, its progression and its health consequences. The objective of this paper is to present a conceptual model of frailty that can be used to inform the development of a cost-effectiveness model to evaluate frailty interventions. METHODS: After critical analysis of the clinical and economic literature, a Delphi study consisting of experts from the disciplines of clinical medicine and epidemiology was undertaken to inform the key components of the conceptual model. We also identified relevant databases that can be used to populate and validate the model. RESULTS: A list of significant health states/events for which frailty is a strong independent risk factor was identified (e.g., hip fracture, hospital admission, delirium, death). We also identified a list of important patient attributes that may influence disease progression (e.g., age, gender, previous hospital admissions, depression). A number of large-scale relevant databases were also identified to populate and validate the cost-effectiveness model. Face validity of model structure was confirmed by experts. DISCUSSION AND CONCLUSIONS: The proposed conceptual model is being used as a basis for developing a new cost-effectiveness model to estimate lifetime costs and outcomes associated with a range of frailty interventions. Using an appropriate model structure, which more accurately reflects the natural history of frailty, will improve model transparency and accuracy. This will ultimately lead to better informed public funding decisions around interventions to manage frailty.

Case fatality rates of invasive meningococcal disease by serogroup and age: A systematic review and meta-analysis.

INTRODUCTION: Invasive meningococcal disease (IMD) is uncommon but still causes considerable public health burden due to its high mortality and morbidity. This review aims to quantitatively synthesise all published evidence pertinent to mortality caused by IMD and assess the effect of age and serogroup on case fatality rates (CFRs). METHODS: The PubMed and Embase databases, and the Cochrane Library were searched. Articles reporting national CFRs and published in English between January 2000 and May 2018 were eligible. The studies reporting mortality resulting from a specific symptom of IMD (e.g. meningococcal meningitis) were excluded. Mixed-effects logistic regression with a restricted cubic spline was used to analyse CFRs as a function of age. Random-effects meta-analyses were performed to estimate an overall CFR and CFRs by serogroup. RESULTS: Among 48 eligible studies reporting national CFRs, 40 studies were included in meta-analyses representing 163,758 IMD patients. CFRs ranged from 4.1% to 20.0% with the pooled overall CFR of 8.3% (95% confidence interval (CI): 7.5-9.1%). Serogroup B was associated with a lower pooled CFR (6.9% (95%CI: 6.0-7.8%)) than other serogroups (W: 12.8% (95%CI: 10.7-15.0%); C: 12.0% (95%CI: 10.5-13.5%); Y: 10.8% (95%CI: 8.2-13.4%)). The meta-analysis was not performed for serogroup A (MenA) cases due to a small number of MenA patients who were enrolled in eligible studies. For laboratory confirmed IMD cases, the predicted CFR was 9.0% in infants, gradually decreased to 7.0% in 7-year olds, subsequently increased to 15.0% in young adults aged 28 years, stabilised between 15 and 20% in mid-aged adults and reached a high in elderly people. CONCLUSIONS: Our findings can provide useful information for better understanding the mortality risks, and quantifying the burden associated with IMD mortality.

Are the benefits of new health services greater than their opportunity costs?

The Australian health system performs well compared with other developed countries, but there is potential for improved health outcomes through the consideration of the opportunity costs of funding new health services. The opportunity costs of funding a new health service are the benefits forgone from the activities that would be funded if the new health service was not funded. When the forgone activity cannot be observed directly, the expected opportunity costs have been estimated as the expected gain in quality-adjusted life years (QALYs) associated with marginal increases in government expenditure on health. We have previously estimated that a gain of 1 QALY is expected for every additional A$28033 of government expenditure on health. This paper discusses the relevance and proposed use of this estimate of opportunity costs to inform decisions around the public funding of new health services in Australia.

Model Structuring for Economic Evaluations of New Health Technologies.

In countries such as Australia, the UK and Canada, decisions on whether to fund new health technologies are commonly informed by decision analytic models. While the impact of making inappropriate structural choices/assumptions on model predictions is well noted, there is a lack of clarity about the definition of key structural aspects, the process of developing model structure (including the development of conceptual models) and uncertainty associated with the structuring process (structural uncertainty) in guidelines developed by national funding bodies. This forms the focus of this article. Building on the reports of good modelling practice, and recognising the fundamental role of model structuring within the model development process, we specified key structural choices and provided ideas about model structuring for the future direction. This will help to further standardise guidelines developed by national funding bodies, with potential impact on transparency, comprehensiveness and consistency of model structuring. We argue that the process of model structuring and structural sensitivity analysis should be documented in a more systematic and transparent way in submissions to national funding bodies. Within the decision-making process, the development of conceptual models and presentation of all key structural choices would mean that national funding bodies could be more confident of maximising value for money when making public funding decisions.

The development of funding recommendations for health technologies at the state level: A South Australian case study.

OBJECTIVES: State governments often face capped budgets that can restrict expenditure on health technologies and their evaluation, yet many technologies are introduced to practice through state-funded institutions such as hospitals, rather than through national evaluation mechanisms. This research aimed to identify the criteria, evidence, and standards used by South Australian committee members to recommend funding for high-cost health technologies. METHODS: We undertook 8 semi-structured interviews and 2 meeting observations with members of state-wide committees that have a mandate to consider the safety, effectiveness, and cost-effectiveness of high-cost health technologies. RESULTS: Safety and effectiveness were fundamental criteria for decision makers, who were also concerned with increasing consistency in care and equitable access to technologies. Committee members often consider evidence that is limited in quantity and quality; however, they perceive evaluations to be rigorous and sufficient for decision making. Precise standards for safety, effective, and cost-effectiveness could not be identified. CONCLUSIONS: Consideration of new technologies at the state level is grounded in the desire to improve health outcomes and equity of access for patients. High quality evidence is often limited. The impact funding decisions have on population health is unclear due to limited use of cost-effectiveness analysis and unclear cost-effectiveness standards.

Estimating the Reference Incremental Cost-Effectiveness Ratio for the Australian Health System.

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.

Governments Need Better Guidance to Maximise Value for Money: The Case of Australia's Pharmaceutical Benefits Advisory Committee.

In Australia, the Pharmaceutical Benefits Advisory Committee (PBAC) makes recommendations to the Minister for Health on which pharmaceuticals should be subsidised. Given the implications of PBAC recommendations for government finances and population health, PBAC is required to provide advice primarily on the basis of value for money. The aim of this article is twofold: to describe some major limitations of the current PBAC decision-making process in relation to its implicit aim of maximising value for money; and to suggest what might be done toward overcoming these limitations. This should also offer lessons for the many decision-making bodies around the world that are similar to PBAC. The current PBAC decision-making process is limited in two important respects. First, it features the use of an implicit incremental cost-effectiveness ratio (ICER) threshold that may not reflect the opportunity cost of funding a new technology, with unknown and possibly negative consequences for population health. Second, the process does not feature a means of systematically assessing how a technology may be of greater or lesser value in light of factors that are not captured by standard measures of cost effectiveness, but which are nonetheless important, particularly to the Australian community. Overcoming these limitations would mean that PBAC could be more confident of maximising value for money when making funding decisions.

The inpatient costs and hospital service use associated with invasive meningococcal disease in South Australian children.

BACKGROUND: Invasive meningococcal disease (IMD) remains a serious public health concern due to a sustained high case fatality rate and morbidity in survivors. This study aimed to estimate the hospital service costs associated with IMD and variables associated with the highest costs in Australian children admitted to a tertiary paediatric hospital. METHODS: Clinical details were obtained from medical records and associated inpatient costs were collected and inflated to 2011 Australian dollars using the medical and hospital services component of the Australian Consumer Price Index. Both unadjusted and adjusted analyses were undertaken. Multivariate regression models were used to adjust for potential covariates and determine independent predictors of high costs and increased length of hospital stay. RESULTS: Of 109 children hospitalised with IMD between May 2000 and April 2011, the majority were caused by serogroup B (70.6%). Presence of sequelae, serogroup B infection, male gender, infants less than one year of age, and previous medical diagnosis were associated with higher inpatient costs and length of stay (LOS) in hospital (p<0.001) during the acute admissions. Children diagnosed with septicaemia had a longer predicted LOS (p=0.033) during the acute admissions compared to those diagnosed with meningitis alone or meningitis with septicaemia. Serogroup B cases incurred a significantly higher risk of IMD related readmissions (IRR: 21.1, p=0.008) for patients with sequelae. Serogroup B infection, male gender, diagnosis of septicaemia, infants less than one year of age, and no previous medical diagnosis were more likely to have higher inpatient costs and LOS during the IMD related readmissions for patients with sequelae (p<0.05). CONCLUSION: Although IMD is uncommon, the disease severity and associated long-term sequelae result in high health care costs, which should be considered in meningococcal B vaccine funding considerations.

When to use discrete event simulation (DES) for the economic evaluation of health technologies? A review and critique of the costs and benefits of DES.

Modelling in economic evaluation is an unavoidable fact of life. Cohort-based state transition models are most common, though discrete event simulation (DES) is increasingly being used to implement more complex model structures. The benefits of DES relate to the greater flexibility around the implementation and population of complex models, which may provide more accurate or valid estimates of the incremental costs and benefits of alternative health technologies. The costs of DES relate to the time and expertise required to implement and review complex models, when perhaps a simpler model would suffice. The costs are not borne solely by the analyst, but also by reviewers. In particular, modelled economic evaluations are often submitted to support reimbursement decisions for new technologies, for which detailed model reviews are generally undertaken on behalf of the funding body. This paper reports the results from a review of published DES-based economic evaluations. Factors underlying the use of DES were defined, and the characteristics of applied models were considered, to inform options for assessing the potential benefits of DES in relation to each factor. Four broad factors underlying the use of DES were identified: baseline heterogeneity, continuous disease markers, time varying event rates, and the influence of prior events on subsequent event rates. If relevant, individual-level data are available, representation of the four factors is likely to improve model validity, and it is possible to assess the importance of their representation in individual cases. A thorough model performance evaluation is required to overcome the costs of DES from the users' perspective, but few of the reviewed DES models reported such a process. More generally, further direct, empirical comparisons of complex models with simpler models would better inform the benefits of DES to implement more complex models, and the circumstances in which such benefits are most likely.

Practice nurse involvement in primary care depression management: an observational cost-effectiveness analysis.

BACKGROUND: Most evidence on the effect of collaborative care for depression is derived in the selective environment of randomised controlled trials. In collaborative care, practice nurses may act as case managers. The Primary Care Services Improvement Project (PCSIP) aimed to assess the cost-effectiveness of alternative models of practice nurse involvement in a real world Australian setting. Previous analyses have demonstrated the value of high level practice nurse involvement in the management of diabetes and obesity. This paper reports on their value in the management of depression. METHODS: General practices were assigned to a low or high model of care based on observed levels of practice nurse involvement in clinical-based activities for the management of depression (i.e. percentage of depression patients seen, percentage of consultation time spent on clinical-based activities). Linked, routinely collected data was used to determine patient level depression outcomes (proportion of depression-free days) and health service usage costs. Standardised depression assessment tools were not routinely used, therefore a classification framework to determine the patient's depressive state was developed using proxy measures (e.g. symptoms, medications, referrals, hospitalisations and suicide attempts). Regression analyses of costs and depression outcomes were conducted, using propensity weighting to control for potential confounders. RESULTS: Capacity to determine depressive state using the classification framework was dependent upon the level of detail provided in medical records. While antidepressant medication prescriptions were a strong indicator of depressive state, they could not be relied upon as the sole measure. Propensity score weighted analyses of total depression-related costs and depression outcomes, found that the high level model of care cost more (95% CI: -$314.76 to $584) and resulted in 5% less depression-free days (95% CI: -0.15 to 0.05), compared to the low level model. However, this result was highly uncertain, as shown by the confidence intervals. CONCLUSIONS: Classification of patients' depressive state was feasible, but time consuming, using the classification framework proposed. Further validation of the framework is required. Unlike the analyses of diabetes and obesity management, no significant differences in the proportion of depression-free days or health service costs were found between the alternative levels of practice nurse involvement.

A model-based economic evaluation of improved primary care management of patients with type 2 diabetes in Australia.

BACKGROUND: There are few studies investigating the economic value of the Australian practice nurse workforce on the management of chronic conditions. This is particularly important in Australia, where the government needs evidence to inform decisions on whether to maintain or redirect current financial incentives that encourage practices to recruit practice nurses. OBJECTIVE: The objective of this study was to estimate the lifetime costs and quality-adjusted life-years (QALYs) associated with two models of practice nurse involvement in clinical-based activities (high and low level) in the management of type 2 diabetes within the primary care setting. METHODS: A previously validated state transition model (the United Kingdom Prospective Diabetes Study Outcomes Model) was adapted, which uses baseline prognostic factors (e.g. gender, haemoglobin A1c [HbA1c]) to predict the risk of occurrence of diabetes-related complications (e.g. stroke). The model was populated by data from Australian and UK observational studies. Costs and utility values associated with complications were summed over patients' lifetimes to estimate costs and QALY gains from the perspective of the health care system. All costs were expressed in 2011 Australian dollars (AU$). The base-case analysis assumed a 40-year time horizon with an annual discount rate of 5 %. RESULTS: Relative to low-level involvement of practice nurses in the provision of clinical-based activities, the high-level model was associated with lower mean lifetime costs of management of complications (-AU$8,738; 95 % confidence interval [CI] -AU$12,522 to -AU$4,954), and a greater average gain in QALYs (0.3; 95 % CI 0.2-0.4). A range of sensitivity analyses were performed, in which the high-level model was dominant in all cases. CONCLUSION: Our results suggest that the high-level model is a dominant management strategy over the low-level model in all modelled scenarios. These findings indicate the need for effective primary care-based incentives to encourage general practices not only to employ practice nurses, but to better integrate them into the provision of clinical services.

Model performance evaluation (validation and calibration) in model-based studies of therapeutic interventions for cardiovascular diseases : a review and suggested reporting framework.

Decision analytic models play an increasingly important role in the economic evaluation of health technologies. Given uncertainties around the assumptions used to develop such models, several guidelines have been published to identify and assess 'best practice' in the model development process, including general modelling approach (e.g., time horizon), model structure, input data and model performance evaluation. This paper focuses on model performance evaluation. In the absence of a sufficient level of detail around model performance evaluation, concerns regarding the accuracy of model outputs, and hence the credibility of such models, are frequently raised. Following presentation of its components, a review of the application and reporting of model performance evaluation is presented. Taking cardiovascular disease as an illustrative example, the review investigates the use of face validity, internal validity, external validity, and cross model validity. As a part of the performance evaluation process, model calibration is also discussed and its use in applied studies investigated. The review found that the application and reporting of model performance evaluation across 81 studies of treatment for cardiovascular disease was variable. Cross-model validation was reported in 55 % of the reviewed studies, though the level of detail provided varied considerably. We found that very few studies documented other types of validity, and only 6 % of the reviewed articles reported a calibration process. Considering the above findings, we propose a comprehensive model performance evaluation framework (checklist), informed by a review of best-practice guidelines. This framework provides a basis for more accurate and consistent documentation of model performance evaluation. This will improve the peer review process and the comparability of modelling studies. Recognising the fundamental role of decision analytic models in informing public funding decisions, the proposed framework should usefully inform guidelines for preparing submissions to reimbursement bodies.