Protocol for the health economic evaluation of increasing the weekend specialist to patient ratio in hospitals in England

Introduction: This protocol concerns the evaluation of increased specialist staffing at weekends in hospitals in England. Seven-day health services are a key policy for the UK government and other health systems trying to improve use of infrastructure and resources. A particular motivation for the 7-day policy has been the observed increase in the risk of death associated with weekend admission, which has been attributed to fewer hospital specialists being available at weekends. However, the causes of the weekend effect have not been adequately characterised; many of the excess deaths associated with the ‘weekend effect’ may not be preventable, and the presumed benefits of improved specialist cover might be offset by the cost of implementation. Methods/design: The Bayesian-founded method we propose will consist of four major steps. First, the development of a qualitative causal model. Specialist presence can affect multiple, interacting causal processes. One or more models will be developed from the results of an expert elicitation workshop and probabilities elicited for each model and relevant model parameters. Second, systematic review of the literature. The model from the first step will provide search limits for a review to identify relevant studies. Third, a statistical model for the effects of specialist presence on care quality and patient outcomes. Fourth, valuation of outcomes. The expected net benefits of different levels of specialist intensity will then be evaluated with respect to the posterior distributions of the parameters

Consolidated health economic evaluation reporting standards (CHEERS) statement

<p>Economic evaluations of health interventions pose a particular challenge for reporting. There is also a need to consolidate and update existing guidelines and promote their use in a user friendly manner. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement is an attempt to consolidate and update previous health economic evaluation guidelines efforts into one current, useful reporting guidance. The primary audiences for the CHEERS statement are researchers reporting economic evaluations and the editors and peer reviewers assessing them for publication.</p> <p>The need for new reporting guidance was identified by a survey of medical editors. A list of possible items based on a systematic review was created. A two round, modified Delphi panel consisting of representatives from academia, clinical practice, industry, government, and the editorial community was conducted. Out of 44 candidate items, 24 items and accompanying recommendations were developed. The recommendations are contained in a user friendly, 24 item checklist. A copy of the statement, accompanying checklist, and this report can be found on the ISPOR Health Economic Evaluations Publication Guidelines Task Force website (www.ispor.org/TaskForces/EconomicPubGuidelines.asp).</p> <p>We hope CHEERS will lead to better reporting, and ultimately, better health decisions. To facilitate dissemination and uptake, the CHEERS statement is being co-published across 10 health economics and medical journals. We encourage other journals and groups, to endorse CHEERS. The author team plans to review the checklist for an update in five years.</p&gt

On the Foundation of Guidelines for Health Economic Evaluation

In recent years, there has been increased interest in setting up guidelines for carrying out cost-effectiveness analysis of medical interventions, and some such guidelines have indeed been established. In the paper, we present a model of information retrievement and use in which we can study the role of guidelines. The main result, which is a version of the well-known theorem of Blackwell (1948), shows that in cases where there are sufficiently many decisions to be made on the basis of the information obtained, there can be no objective ranking of methods, except the trivial one stating that more information is better than less. The consequence is that guidelines, and the very detailed version known as the reference case approach, may have administrative advantages but can be harmful when considered as an aid towards better decisions.cost-effectiveness; guidelines; Blackwell’s theorem

Health economic evaluation in Japan: a case study of one aspect of health technology assessment.

There is a burgeoning literature in health economic evaluation, with this form of analysis becoming increasingly influential at the health policy making level in a number of countries. However, a search of the literature reveals that in Japan, the world's second largest health care market, very little health economic evaluation has been undertaken. The main reason for the lack of interest in economic evaluation is that the fee-for-service and strict price regulation that characterises the system of health care financing in Japan is not conducive to this form of analysis. Moreover, the government and many researchers are satisfied that the current organisation of health care has given long life and low infant mortality at low cost. Even if it is accepted that low health care costs and good health prevail in Japan, slower economic growth rates, an ageing population and the development of new medical technologies will place increasing pressure on health care resources and will necessitate a more rational use of these resources. Good economic evaluation, by weighing benefits against costs, has an important role to play.

A Bayesian framework for health economic evaluation in studies with missing data.

Health economics studies with missing data are increasingly using approaches such as multiple imputation that assume that the data are "missing at random." This assumption is often questionable, as-even given the observed data-the probability that data are missing may reflect the true, unobserved outcomes, such as the patients' true health status. In these cases, methodological guidelines recommend sensitivity analyses to recognise data may be "missing not at random" (MNAR), and call for the development of practical, accessible approaches for exploring the robustness of conclusions to MNAR assumptions. Little attention has been paid to the problem that data may be MNAR in health economics in general and in cost-effectiveness analyses (CEA) in particular. In this paper, we propose a Bayesian framework for CEA where outcome or cost data are missing. Our framework includes a practical, accessible approach to sensitivity analysis that allows the analyst to draw on expert opinion. We illustrate the framework in a CEA comparing an endovascular strategy with open repair for patients with ruptured abdominal aortic aneurysm, and provide software tools to implement this approach

Cancer centre supportive oncology service: health economic evaluation

Objectives: There have been many models of providing oncology and palliative care to hospitals. Many patients will use the hospital non-electively or semielectively, and a large proportion are likely to be in the last years of life. We describe our multidisciplinary service to treatable but not curable cancer patients at University Hospitals Sussex. The team was a mixture of clinical nurse specialists and a clinical fellow supported by dedicated palliative medicine consultant time and oncology expertise. / Methods: We identified patients with cancer who had identifiable supportive care needs and record activity with clinical coding. We used a baseline 2019/2020 dataset of national (secondary uses service) data with discharge code 79 (patients who died during that year) to compare a dataset of patients seen by the service between September 2020 and September 2021 in order to compare outcomes. While this was during COVID-19 this was when the funding was available. / Results: We demonstrated a reduction in length of stay by an average of 1.43 days per admission and a reduction of 0.95 episodes of readmission rates. However, the costs of those admissions were found to be marginally higher. Even with the costs of the service, there is a clear return on investment with a benefit cost ratio of 1.4. / Conclusions: A supportive oncology service alongside or allied to acute oncology but in conjunction with palliative care is feasible and cost-effective. This would support investment in such a service and should be nationally commissioned in conjunction with palliative care services seeing all conditions

Ophthalmology Care in Ethiopia: a Health Economic Evaluation

M.Phil. in Global Health - ThesisINTH395AMAMD-GLO

Non-compliance and missing data in health economic evaluation

Health economic evaluations face the issues of non-compliance and missing data. Here, non-compliance is defined as non-adherence to a specific treatment, and occurs within randomised controlled trials (RCTs) when participants depart from their random assignment. Missing data arises if, for example, there is loss to follow-up, survey non-response, or the information available from routine data sources is incomplete. Appropriate statistical methods for handling non-compliance and missing data have been developed, but they have rarely been applied in health economics studies. Here, we illustrate the issues and outline some of the appropriate methods to handle these with an application to a health economic evaluation that uses data from an RCT. In an RCT the random assignment can be used as an instrument for treatment receipt, to obtain consistent estimates of the complier average causal effect, provided the underlying assumptions are met. Instrumental variable methods can accommodate essential features of the health economic context such as the correlation between individuals' costs and outcomes in cost-effectiveness studies. Methodological guidance for handling missing data encourages approaches such as multiple imputation or inverse probability weighting, that assume the data are Missing At Random, but also sensitivity analyses that recognise the data may be missing according to the true, unobserved values, that is, Missing Not at Random. Future studies should subject the assumptions behind methods for handling non-compliance and missing data to thorough sensitivity analyses. Modern machine learning methods can help reduce reliance on correct model specification. Further research is required to develop flexible methods for handling more complex forms of non-compliance and missing data.Comment: 41 page