A Methodological Approach for Measuring the Impact of HTA

There is a lack of evidence concerning the link between HTA and outcomes in terms of health improvements. This work proposes a framework for assessing the impact of HTA. This impact assessment is a necessary step in then better understanding the value for money of HTA bodies. We emphasis that this is still a work in progress. iDSI has developed a theory of change-based framework in order to evaluate the impact the iDSI has on institutional strengthening – leading to ‘better decisions’ for ‘better health’. This framework recognises that there is a complex translation process between better decisions and better health dependent on many assumptions about local factors and systems, including linkage between decisions and budgets, delivery, implementation, and data accuracy. Work has been undertaken over the last 6 months developing a methodological approach for measuring the impact of health technology assessment (HTA). Two case studies are used to illustrate the approach. At the core of impact assessment is a requirement to link causes and effects, to explain ‘how’ and ‘why’ and to identify – and thus improve or adapt – mechanisms leading to impact. Policy makers also want to know ‘to what extent’ or ‘the magnitude of impact’. The framework developed adopts an economic approach nested in theory of change as a means of both quantifying the magnitude of impact (utilising economic models) as well as explaining why and how impact happens (drawing on theory based approaches) in order to reinforce learning as to how to improve our response and optimise the use of HTA to have the greatest impact in a given context. This should also enable us to capture and explain wider impact – perhaps more intangible aspects which cannot be easily quantified. This may also possibly increase policy-makers’ ‘buy-in’

Protecting children from faith-based abuse through accusations of witchcraft and spirit possession: understanding contexts and informing practice

Faith-based abuse relating to the practice of witchcraft and spirit possession is a controversial and not well-understood form of child abuse. From its ‘discovery’ in the UK as a cause of abuse, serious injury and death for children, in 2000 to the present, the recent history of witchcraft and spirit possession involves some high-profile cases, involving serious harm and death for some children, which attracted significant publicity. This article reviews research and commentary, including grey literature, and the emerging policy framework. It discusses the underpinning relationship between faith-based practices and abuse, and takes a post-colonial perspective to discuss the social explanations for the continuing practice of witchcraft and spirit possession in contemporary society. These discussions are then shown to inform practice. Practice priorities are informed assessment of suspected cases, through early and statutory interventions, care for survivors, and an important focus on community engagement to prevent this form of child abuse

Improving the efficiency and relevance of health technology assessent: the role of iterative decision analytic modelling

Decision making in health care involves two sets of related decisions: those concerning appropriate service provision on the basis of existing information; and those concerned with whether to fund additional research to reduce the uncertainty relating to the decision. Information acquisition is not costless, and the allocation of funds to the enhancement of the decision makers’ information set, in a budgetconstrained health service, reduces the ‘pot’ of resources available for health service provision. Hence, a framework is necessary to unify these decisions and ensure that HTA is subject to the same evaluation of efficiency as service provision. A framework is presented which addresses these two sets of decisions through the employment of decision analytic models and Bayesian value of information analysis, early and regularly within the health technology assessment process. The model becomes the vehicle of health technology assessment, managing and directing future research effort on an iterative basis over the lifetime of the technology. This ensures consistency in decision making between service provision, research and development priorities and research methods. Fulfilling the aim of the National Health Service HTA programme, that research is “produced in the most economical way” using “cost effective research protocols”. The proposed framework is applied to the decision concerning the appropriate management of female patients with symptoms of urinary tract infection, which was the subject of a recent NHS HTA call for proposals. A probabilistic model is employed to fully characterise and assess the uncertainty surrounding the decision. The expected value of perfect information (EVPI) is then calculated for the full model, for each individual management strategy and for particular model parameters. Research effort can then be focused on those areas where the cost of uncertainty is high and where additional research is potentially cost-effective. The analysis can be used to identify the most appropriate research protocol and to concentrate research upon particular parameters where more precise estimates would be of most value.assessment

How do diabetes models measure up? A review of diabetes economic models and ADA guidelines

Introduction: Economic models and computer simulation models have been used for assessing short-term cost-effectiveness of interventions and modelling long-term outcomes and costs. Several guidelines and checklists have been published to improve the methods and reporting. This article presents an overview of published diabetes models with a focus on how well the models are described in relation to the considerations described by the American Diabetes Association (ADA) guidelines. Methods: Relevant electronic databases and National Institute for Health and Care Excellence (NICE) guidelines were searched in December 2012. Studies were included in the review if they estimated lifetime outcomes for patients with type 1 or type 2 diabetes. Only unique models, and only the original papers were included in the review. If additional information was reported in subsequent or paired articles, then additional citations were included. References and forward citations of relevant articles, including the previous systematic reviews were searched using a similar method to pearl growing. Four principal areas were included in the ADA guidance reporting for models: transparency, validation, uncertainty, and diabetes specific criteria. Results: A total 19 models were included. Twelve models investigated type 2 diabetes, two developed type 1 models, two created separate models for type 1 and type 2, and three developed joint type 1 and type 2 models. Most models were developed in the United States, United Kingdom, Europe or Canada. Later models use data or methods from earlier models for development or validation. There are four main types of models: Markov-based cohort, Markov-based microsimulations, discrete-time microsimulations, and continuous time differential equations. All models were long-term diabetes models incorporating a wide range of compilations from various organ systems. In early diabetes modelling, before the ADA guidelines were published, most models did not include descriptions of all the diabetes specific components of the ADA guidelines but this improved significantly by 2004. Conclusion: A clear, descriptive short summary of the model was often lacking. Descriptions of model validation and uncertainty were the most poorly reported of the four main areas, but there exist conferences focussing specifically on the issue of validation. Interdependence between the complications was the least well incorporated or reported of the diabetes-specific criterion

Hospital expenditure at the end-of-life: what are the impacts of health status and health risks?

Background: It is important for health policy and expenditure projections to understand the relationship between age, death and expenditure on health care (HC). Research has shown that older age groups incur lower hospital costs than previously anticipated and that remaining time to death (TTD) was a much stronger indicator for expenditure than age. How health behaviour or risk factors impact on HC utilisation and costs at the end of life is relatively unknown. Smoking and Body Mass Index (BMI) have featured most prominently and mixed findings exist as to the exact nature of this association.<p></p> Methods: This paper considers the relationship between TTD, age and expenditure for inpatient care in the last 12 quarters of life; and introduces measures of health status and risks. A longitudinal dataset covering 35 years is utilised, including baseline survey data linked to hospital and death records. The effect of age, TTD and health indicators on expenditure for inpatient care is estimated using a two-part model.<p></p> Results: As individuals approach death costs increase. This effect is highly significant (p<0.01) from the last until the 8th quarter before death and influenced by age. Statistically significant effects on costs were found for: smoking status, systolic blood pressure and lung function (FEV1). On average, smokers incurred lower quarterly costs in their last 12 quarters of life than non-smokers (~7%). Participants’ BMI at baseline did show a negative association with probability of HC utilisation however this effect disappeared when costs were estimated.<p></p> Conclusions: Health risk measures obtained at baseline provide a good indication of individuals’ probability of needing medical attention later in life and incurring costs, despite the small size of the effect. Utilising a linked dataset, where such measures are available can add substantially to our ability to explain the relationship between TTD and costs.<p></p&gt

Determination of the most appropriate method for extrapolating overall survival data from a placebo-controlled clinical trial of lenvatinib for progressive, radioiodine-refractory differentiated thyroid cancer

Background: Cost-effectiveness models for the treatment of long-term conditions often require information on survival beyond the period of available data. Objectives: This paper aims to identify a robust and reliable method for the extrapolation of overall survival (OS) in patients with radioiodine-refractory differentiated thyroid cancer receiving lenvatinib or placebo. Methods: Data from 392 patients (lenvatinib: 261, placebo: 131) from the SELECT trial are used over a 34-month period of follow-up. A previously published criterion-based approach is employed to ascertain credible estimates of OS beyond the trial data. Parametric models with and without a treatment covariate and piecewise models are used to extrapolate OS, and a holistic approach, where a series of statistical and visual tests are considered collectively, is taken in determining the most appropriate extrapolation model. Results: A piecewise model, in which the Kaplan–Meier survivor function is used over the trial period and an extrapolated tail is based on the Exponential distribution, is identified as the optimal model. Conclusion: In the absence of long-term survival estimates from clinical trials, survival estimates often need to be extrapolated from the available data. The use of a systematic method based on a priori determined selection criteria provides a transparent approach and reduces the risk of bias. The extrapolated OS estimates will be used to investigate the potential long-term benefits of lenvatinib in the treatment of radioiodine-refractory differentiated thyroid cancer patients and populate future cost-effectiveness analyses

Anticrossings in Foerster Coupled Quantum Dots

We consider two coupled generic quantum dots, each modelled by a simple potential which allows the derivation of an analytical expression for the inter-dot Foerster coupling, in the dipole-dipole approximation. We investigate the energy level behaviour of this coupled two-dot system under the influence of an external applied electric field and predict the presence of anticrossings in the optical spectra due to the Foerster interaction.Comment: 13 pages, 7 figures. Published version. Substantially revised, new sections on decay rates, absorption spectra, and tunnelin

Cost-effectiveness analysis in R using a multi-state modelling survival analysis framework: a tutorial

This tutorial provides a step-by-step guide to performing cost-effectiveness analysis using a multi-state modelling approach. Alongside the tutorial we provide easy-to-use functions in the statistics package R. We argue this multi-state modelling approach using a package such as R has advantages over approaches where models are built in a spreadsheet package. In particular, using a syntax-based approach means there is a written record of what was done and the calculations are transparent. Reproducing the analysis is straightforward as the syntax just needs to be run again. The approach can be thought of as an alternative way to build a Markov decision analytic model, which also has the option to use a state-arrival extended approach if the Markov property does not hold. In the state-arrival extended multi-state model a covariate that represents patients’ history is included allowing the Markov property to be tested. We illustrate the building of multi-state survival models, making predictions from the models and assessing fits. We then proceed to perform a cost-effectiveness analysis including deterministic and probabilistic sensitivity analyses. Finally, we show how to create two common methods of visualising the results, namely cost-effectiveness planes and cost-effectiveness acceptability curves. The analysis is implemented entirely within R. It is based on adaptions to functions in the existing R package mstate, to accommodate parametric multi-state modelling which facilitates extrapolation of survival curves