Applied Health Economics and Health Policy最新文献

The characteristics and relative strengths and weaknesses of partitioned survival models (PSMs) and state transition models (STMs) for three state oncology cost-effectiveness models have previously been studied. Despite clear and longstanding economic modeling guidelines, more than one structure is rarely presented, and the choice of structure appears correlated more with audience or precedent than disease, decision problem, or available data. One reason may be a lack of guidance and tools available to readily compare measures of internal validity such as the model fit and efficiency of different structures, or sensitivity of results to those choices. To address this gap, methods are presented to evaluate the fit and efficiency of three structures, with an accompanying R software package, psm3mkv. The methods are illustrated by analyzing interim and final analysis datasets of the KEYNOTE-826 randomized controlled trial. At both interim and final analyses, the STM Clock Reset structure provided the best and most efficient fit. Structural uncertainties had been reduced from interim to final analysis. Beyond measures of internal validity, guidelines highlight the importance of reflecting all available data, avoiding model selection purely on the basis of goodness of fit and strongly considering external validity. The method and software allow modelers to more easily evaluate and report model fit and efficiency, examine implicit assumptions, and reveal sensitivities to structural choices.

Objective

This study evaluated, in a Swedish setting, the cost effectiveness of fenfluramine (FFA) as an add-on to standard of care (SoC) for reducing seizure frequency in Dravet syndrome, a severe developmental epileptic encephalopathy.

Methods

Cost effectiveness of FFA+SoC compared with SoC only was evaluated using a patient-level simulation model with a lifetime horizon. Patient characteristics and treatment effects, including convulsive seizures, seizure-free days and mortality, were derived from FFA clinical trials. Resource use and costs included cost of drug acquisition, routine care and monitoring, as well as ongoing and emergency resources. Quality of life (QoL) estimates for patients and their caregivers were derived from clinical trial data. Robustness was evaluated by one-way sensitivity analysis, probabilistic sensitivity analysis and scenario analyses.

Results

Lifetime cost of FFA+SoC was ~3 million SEK per patient compared with ~1.5 million SEK for SoC only. FFA+SoC generated 15% more QALYs than SoC only (21.2 vs 18.5 over a lifetime), resulting in an incremental cost-effectiveness ratio (ICER) of ~540,000 SEK. Moreover, FFA+SoC had a higher probability of being cost effective than SoC only from a willingness-to-pay threshold of 710,000 SEK. Results remained generally consistent across scenario analyses, with only few exceptions (exclusions of carer utility or FFA effect on sudden unexpected death in epilepsy).

Conclusion

Due to better seizure control, FFA is a clinically meaningful add-on therapy and was estimated to be a cost-effective addition to current SoC for patients with this rare disease in Sweden at a willingness-to-pay threshold of 1,000,000 SEK.

Background

Decision-makers require knowledge of the strengths and weaknesses of decision-analytic models used to evaluate healthcare interventions to be able to confidently use the results of such models to inform policy. A number of aspects of model validity have previously been described, but no systematic approach to assessing the validity of a model has been proposed. This study aimed to consolidate the different aspects of model validity into a step-by-step approach to assessing the strengths and weaknesses of a decision-analytic model.

Methods

A pre-defined set of steps were used to conduct the validation process of an exemplar early decision-analytic-model-based cost-effectiveness analysis of a risk-stratified national breast cancer screening programme [UK healthcare perspective; lifetime horizon; costs (£; 2021)]. Internal validation was assessed in terms of descriptive validity, technical validity and face validity. External validation was assessed in terms of operational validation, convergent validity (or corroboration) and predictive validity.

Results

The results outline the findings of each step of internal and external validation of the early decision-analytic-model and present the validated model (called ‘MANC-RISK-SCREEN’). The positive aspects in terms of meeting internal validation requirements are shown together with the remaining limitations of MANC-RISK-SCREEN.

Conclusion

Following a transparent and structured validation process, MANC-RISK-SCREEN has been shown to have satisfactory internal and external validity for use in informing resource allocation decision-making. We suggest that MANC-RISK-SCREEN can be used to assess the cost-effectiveness of exemplars of risk-stratified national breast cancer screening programmes (NBSP) from the UK perspective.

Implications

A step-by-step process for conducting the validation of a decision-analytic model was developed for future use by health economists. Using this approach may help researchers to fully demonstrate the strengths and limitations of their model to decision-makers.

Introduction

The escalating burden of catastrophic health expenditure (CHE) poses a significant threat to individuals and households in India, where out-of-pocket expenditure (OOP) constitutes a substantial portion of healthcare financing. With rising OOP in India, a proper measurement to track and monitor CHE due to health expenditure is of utmost important. This study focuses on synthesizing findings, understanding measurement variations, and estimating the pooled incidence of CHE by health services, reported diseases, and survey types.

Method

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a thorough search strategy was employed across multiple databases, between 2010 and 2023. Inclusion criteria encompassed observational or interventional studies reporting CHE incidence, while exclusion criteria screened out studies with unclear definitions, pharmacy revenue-based spending, or non-representative health facility surveys. A meta-analysis, utilizing a random-effects model, assessed the pooled CHE incidence. Sensitivity analysis and subgroup analyses were conducted to explore heterogeneity.

Results

Out of 501 initially relevant articles, 36 studies met inclusion criteria. The review identified significant variations in CHE measurements, with incidence ranging from 5.1% to 69.9%. Meta-analysis indicated the estimated incidence of CHE at a 10% threshold is 0.30 [0.25–0.35], indicating a significant prevalence of financial hardship due to health expenses. The pooled incidence is estimated by considering different sub-groups. No statistical differences were found between inpatient and outpatient CHE. However, disease-specific estimates were significantly higher (52%) compared to combined diseases (21%). Notably, surveys focusing on health reported higher CHE (33%) than consumption surveys (14%).

Discussion

The study highlights the intricate challenges in measuring CHE, emphasizing variations in recall periods, components considered in out-of-pocket expenditure, and diverse methods for defining capacity to pay. Notably, the findings underscore the need for standardized definitions and measurements across studies. The lack of uniformity in reporting exacerbates the challenge of comparing and comprehensively understanding the financial burden on households.

Multiple, accelerating and interacting ecological crises are increasingly understood as constituting a major threat to human health and well-being. Unconstrained economic growth is strongly implicated in these growing crises, and it has been argued that this growth has now become “uneconomic growth”, which is a situation where the size of the economy is still expanding, but this expansion is causing more harm than benefit. This article summarises the multiple pathways by which uneconomic growth can be expected to harm human health. It describes how health care systems—especially through overuse, low value and poor quality care—can themselves drive uneconomic growth. Health economists need to understand not only the consequences of environmental impacts on health care, but also the significance of uneconomic growth, and pay closer attention to the growing body of work by heterodox economists, especially in the fields of ecological and feminist economics. This will involve paying closer heed to the existence and consequences of diminishing marginal returns to health care consumption at high levels; the central importance of inequalities and injustice in health; and the need to remedy health economists’ currently limited ability to deal effectively with low value care, overdiagnosis and overtreatment.

Objectives

This study aimed to systematically assess global economic evaluation studies on COVID-19 vaccination, offer valuable insights for future economic evaluations, and assist policymakers in making evidence-based decisions regarding the implementation of COVID-19 vaccination.

Methods

Searches were performed from January 2020 to September 2023 across seven English databases (PubMed, Web of Science, MEDLINE, EBSCO, KCL-Korean Journal Dataset, SciELO Citation Index, and Derwent Innovations Index) and three Chinese databases (Wanfang Data, China Science and Technology Journal, and CNKI). Rigorous inclusion and exclusion criteria were applied. Data were extracted from eligible studies using a standardized data collection form, with the reporting quality of these studies assessed using the Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022).

Results

Of the 40 studies included in the final review, the overall reporting quality was good, evidenced by a mean score of 22.6 (ranging from 10.5 to 28). Given the significant heterogeneity in fundamental aspects among the studies reviewed, a narrative synthesis was conducted. Most of these studies adopted a health system or societal perspective. They predominantly utilized a composite model, merging dynamic and static methods, within short to medium-term time horizons to simulate various vaccination strategies. The research strategies varied among studies, investigating different doses, dosages, brands, mechanisms, efficacies, vaccination coverage rates, deployment speeds, and priority target groups. Three pivotal parameters notably influenced the evaluation results: the vaccine's effectiveness, its cost, and the basic reproductive number (R₀). Despite variations in model structures, baseline parameters, and assumptions utilized, all studies identified a general trend that COVID-19 vaccination is cost-effective compared to no vaccination or intervention.

Conclusions

The current review confirmed that COVID-19 vaccination is a cost-effective alternative in preventing and controlling COVID-19. In addition, it highlights the profound impact of variables such as dose size, target population, vaccine efficacy, speed of vaccination, and diversity of vaccine brands and mechanisms on cost effectiveness, and also proposes practical and effective strategies for improving COVID-19 vaccination campaigns from the perspective of economic evaluation.

Background

There has been an increase in model-based economic evaluations of interventions for dementia. The most recent systematic review of economic evaluations for dementia highlighted weaknesses in studies, including lack of justification for model assumptions and data inputs.

Objective

This study aimed to update the last published systematic review of model-based economic evaluations of interventions for dementia, including Alzheimer’s disease, with a focus on any methodological improvements and quality assessment of the studies.

Methods

Systematic searches in eight databases, including PubMed, Cochrane, Embase, CINAHL, PsycINFO, EconLit, international HTA database, and the Tufts Cost-Effectiveness Analysis Registry were undertaken from February 2018 until August 2022. The quality of the included studies was assessed using the Philips checklist and the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 checklist. The findings were summarized through narrative analysis.

Results

This review included 23 studies, comprising cost-utility analyses (87%), cost-benefit analyses (9%) and cost-effectiveness analyses (4%). The studies covered various interventions, including pharmacological (n = 10, 43%), non-pharmacological (n = 4, 17%), prevention (n = 4, 17%), diagnostic (n = 4, 17%) and integrated (n = 1, 4%) [diagnostics-pharmacologic] strategies. Markov transition models were commonly employed (65%), followed by decision trees (13%) and discrete-event simulation (9%). Several interventions from all categories were reported as being cost effective. The quality of reporting was suboptimal for the Methods and Results sections in almost all studies, although the majority of studies adequately addressed the decision problem, scope, and model-type selection in their economic evaluations. Regarding the quality of methodology, only a minority of studies addressed competing theories or clearly explained the rationale for model structure. Furthermore, few studies systematically identified key parameters or assessed data quality, and uncertainty was mostly addressed partially.

Conclusions

This review informs future research and resource allocation by providing insights into model-based economic evaluations for dementia interventions and highlighting areas for improvement.