Hossein Haji Ali Afzali

A critical review of model-based economic studies of depression: modelling techniques, model structure and data sources.

Depression is the most common mental health disorder and is recognized as a chronic disease characterized by multiple acute episodes/relapses. Although modelling techniques play an increasingly important role in the economic evaluation of depression interventions, comparatively little attention has been paid to issues around modelling studies with a focus on potential biases. This, however, is important as different modelling approaches, variations in model structure and input parameters may produce different results, and hence different policy decisions.This paper presents a critical review of literature on recently published model-based cost-utility studies of depression. Taking depression as an illustrative example, through this review, we discuss a number of specific issues in relation to the use of decision-analytic models including the type of modelling techniques, structure of models and data sources.The potential benefits and limitations of each modelling technique are discussed and factors influencing the choice of modelling techniques are addressed. This review found that model-based studies of depression used various simulation techniques. We note that a discrete-event simulation may be the preferred technique for the economic evaluation of depression due to the greater flexibility with respect to handling time compared with other individual-based modelling techniques.Considering prognosis and management of depression, the structure of the reviewed models are discussed. We argue that a few reviewed models did not include some important structural aspects such as the possibility of relapse or the increased risk of suicide in patients with depression. Finally, the appropriateness of data sources used to estimate input parameters with a focus on transition probabilities is addressed. We argue that the above issues can potentially bias results and reduce the comparability of economic evaluations.

Improving the accuracy and comparability of model-based economic evaluations of health technologies for reimbursement decisions: a methodological framework for the development of reference models.

Increasingly, decision analytic models are used within economic evaluations of health technologies (e.g., pharmaceuticals) submitted to national reimbursement bodies in countries like Australia and UK, where such models play a fundamental role in informing public funding decisions. Concerns regarding the accuracy of model outputs and hence the credibility of national reimbursement decisions are frequently raised. We propose a framework for developing reference models for specific diseases to inform economic evaluations of health technologies and their appraisal. The structure of a reference model reflects the natural history of the condition under study and defines the clinical events to be represented, the relationships between the events, and the effect of patient characteristics on the probability and timing of events. We contend that the use of reference models will improve the accuracy and comparability of public funding decisions. This can lead to the more efficient allocation of public funds.

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.

A proposed model for economic evaluations of major depressive disorder

In countries like UK and Australia, the comparability of model-based analyses is an essential aspect of reimbursement decisions for new pharmaceuticals, medical services and technologies. Within disease areas, the use of models with alternative structures, type of modelling techniques and/or data sources for common parameters reduces the comparability of evaluations of alternative technologies for the same condition. The aim of this paper is to propose a decision analytic model to evaluate long-term costs and benefits of alternative management options in patients with depression. The structure of the proposed model is based on the natural history of depression and includes clinical events that are important from both clinical and economic perspectives. Considering its greater flexibility with respect to handling time, discrete event simulation (DES) is an appropriate simulation platform for modelling studies of depression. We argue that the proposed model can be used as a reference model in model-based studies of depression improving the quality and comparability of studies.

Community, parental and adolescent awareness and knowledge of meningococcal disease.

OBJECTIVE To assess knowledge of invasive meningococcal disease (IMD) and concern about the disease in the South Australian Community including adolescents, adults, parents and non-parents. METHODS This cross-sectional study was conducted by face to face interviews in South Australia in 2012. Participants were scored on their knowledge and concern about IMD. Univariate and multivariate regression analyses were performed with the survey data weighted by age and gender in accordance with 2011 Census data. RESULTS Of 5200 households randomly selected and stratified by metropolitan or rural location, 3055 participants were interviewed with a response rate of 60.3%. The majority were Australian born (74.2%, n=2267) with 31.8% (n=972) of those interviewed being parents, and 15.9% (n=487) adolescents (15-24 years). Almost a quarter of participants (23.5%, n=717) do not know what meningococcal disease is, with 9.1% (n=278) believing incorrectly that IMD is a viral infection. 36.6% (n=1114) had low overall knowledge of IMD. Adolescents (p<0.050), non-Australian born (p<0.001), low educational attainment (p=0.019), low household income (p=0.011), low/medium socio-economic status (p<0.050) or living in a metropolitan area (p=0.006) were more likely to have lower overall knowledge of IMD. Participants who were not parents (p<0.001), male gender (p<0.001), single (p<0.001), highly educated (p=0.022) or had high household income (p=0.015), had lower concern about IMD. CONCLUSION Large community knowledge gaps for IMD were observed, particularly amongst adolescents and adults with low educational attainment and low socio-economic status. Improving community knowledge of IMD could help ensure optimal uptake of a new meningococcal vaccine. Our study results can help guide development of community tailored immunisation education programs.

Addressing the challenge for well informed and consistent reimbursement decisions: the case for reference models.

In the context of ever-increasing healthcare expenditures and resource-limited health services, the comparative analysis of costs and benefits of competing healthcare technologies (e.g. pharmaceuticals) is essential to support public funding decisions. Australia has been at the forefront of the integration of economic evidence with reimbursement decisions to fund new pharmaceutical products through a national-level body, the Pharmaceutical Benefits Advisory Committee (PBAC). Pharmaceutical companies submit applications to have their products considered for reimbursement. The PBAC recommendations form the basis of Australian Government decisions about public funding and listing of medicines on the Pharmaceutical Benefits Scheme. The UK National Institute for Health and Clinical Excellence (NICE), amongst others, has adopted guidelines to use economic evidence for reimbursement decision processes. Decision analytic models are an expected framework for the economic evaluation of healthcare technologies submitted to national regulatory bodies such as the PBAC and NICE for public funding. They provide an explicit process for synthesising data from a variety of sources, linking intermediate outcomes to final endpoints (e.g. QALYs), extrapolating beyond the data observed in a clinical trial, and allowing for the appropriate handling and representation of uncertainty around outputs. It is now well accepted that uncertainty is inherent within any model-based economic evaluation and needs to be handled appropriately so that policy makers can have confidence in themodel’s results and/ormake decisions regarding the need for additional information. In model-based evaluations, the sources of uncertainty have been classified into three broad categories. Parameter uncertainty concerns models’ input values (e.g. probabilities of moving between states, resource use estimates) and the fact that the true values of most parameters are unknown. A further source of uncertainty, methodological, relates to the choice of analytic methods such as the perspective taken (e.g. society, government). Structural uncertainty arises from the assumptions imposed by the modelling framework and refers to the structure of the chosen model, i.e. the choice of clinical events represented in a model, and the possible transitions between them. Issues around parameter and methodological uncertainties are generally well understood and continually refined in guidelines developed by the national reimbursement bodies such as the PBAC and NICE (e.g. sensitivity analysis to address parameter uncertainty and using a ‘reference case’ to deal withmethodological uncertainty). Although concerns have been raised regarding the impact of assumptions incorporated in model structures on the quality of models, and conjecture that structural uncertainty may have a greater impact on the model’s results than other sources of uncertainty, relatively little guidance has been EDITORIAL Pharmacoeconomics 2011; doi: 10.2165/11593000-000000000-0000

A conceptual framework for selecting the most appropriate variables for measuring hospital efficiency with a focus on Iranian public hospitals.

Over the past few decades, there has been an increasing interest in the measurement of hospital efficiency in developing countries and in Iran. While the choice of measurement methods in hospital efficiency assessment has been widely argued in the literature, few authors have offered a framework to specify variables that reflect different hospital functions, the quality of the process of care and the effectiveness of hospital services. However, without the knowledge of hospital objectives and all relevant functions, efficiency studies run the risk of making biased comparisons, particularly against hospitals that provide higher quality services requiring the use of more resources. Undertaking an in-depth investigation regarding the multi-product nature of hospitals, various hospital functions and the values of various stakeholders (patient, staff and community) with a focus on the Iranian public hospitals, this study has proposed a conceptual framework to select the most appropriate variables for measuring hospital efficiency using frontier-based techniques. This paper contributes to hospital efficiency studies by proposing a conceptual framework and incorporating a broader set of variables in Iran. This can enhance the validity of hospital efficiency studies using frontier-based methods in developing countries.

Exploring Structural Uncertainty in Model-Based Economic Evaluations

Given the inherent uncertainty in estimates produced by decision analytic models, the assessment of uncertainty in model-based evaluations is an essential part of the decision-making process. Although the impact of uncertainty around the choice of model structure and making incorrect structural assumptions on model predictions is noted, relatively little attention has been paid to characterising this type of uncertainty in guidelines developed by national funding bodies such as the Australian Pharmaceutical Benefits Advisory Committee (PBAC). The absence of a detailed description and evaluation of structural uncertainty can add further uncertainty to the decision-making process, with potential impact on the quality of funding decisions. This paper provides a summary of key elements of structural uncertainty describing why it matters and how it could be characterised. Five alternative approaches to characterising structural uncertainty are discussed, including scenario analysis, model selection, model averaging, parameterization and discrepancy. We argue that the potential effect of structural uncertainty on model predictions should be considered in submissions to national funding bodies; however, the characterisation of structural uncertainty is not well defined within the guidelines of these bodies. There has been little consideration of the forms of structural sensitivity analysis that might best inform applied decision-making processes, and empirical research in this area is required.

The clinical burden and predictors of sequelae following invasive meningococcal disease in Australian children.

Of 109 children admitted to a tertiary paediatric hospital with a diagnosis of invasive meningococcal disease from 2000–2011, 37.6% (n = 41) developed sequelae; for serogroup B, 41.3% developed sequelae. Independent predictors of sequelae included: fever ≥ 39°C on hospital presentation [odds ratio (OR): 4.5; P = 0.012] and a diagnosis of septicemia with meningitis compared with septicemia alone (OR: 15.5; P < 0.001) and meningitis alone (OR: 7.8; P = 0.002).

Practice nurse involvement in general practice clinical care: policy and funding issues need resolution

In Australia, primary care-based funding initiatives have been implemented to encourage general practices to employ practice nurses. The aim of this paper is to discuss limitations of the current funding and policy arrangements in enhancing the clinical role of practice nurses in the management of chronic conditions. This paper draws on the results of a real-world economic evaluation, the Primary Care Services Improvement Project (PCSIP). The PCSIP linked routinely collected clinical and resource use data to undertake a risk-adjusted cost-effectiveness analysis of increased practice nurse involvement in clinical-based activities for the management of diabetes and obesity. The findings of the PCSIP suggested that the active involvement of practice nurses in collaborative clinical-based activities is cost-effective, as well as addressing general practice workforce issues. Although primary healthcare organisations (e.g. Medicare Locals) can play a key role in supporting enhanced practice nurse roles, improvements to practice nurse funding models could further encourage more efficient use of an important resource.