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预算影响分析——良好实践原则:ISPOR 2012 预算影响分析良好实践 II 工作组报告。

Budget impact analysis-principles of good practice: report of the ISPOR 2012 Budget Impact Analysis Good Practice II Task Force.

机构信息

Pharmaceutical Outcomes Research and Policy Program, University of Washington, Seattle, WA, USA.

RTI Health Solutions, Research Triangle Park, NC, USA.

出版信息

Value Health. 2014 Jan-Feb;17(1):5-14. doi: 10.1016/j.jval.2013.08.2291. Epub 2013 Dec 13.

Abstract

BACKGROUND

Budget impact analyses (BIAs) are an essential part of a comprehensive economic assessment of a health care intervention and are increasingly required by reimbursement authorities as part of a listing or reimbursement submission.

OBJECTIVES

The objective of this report was to present updated guidance on methods for those undertaking such analyses or for those reviewing the results of such analyses. This update was needed, in part, because of developments in BIA methods as well as a growing interest, particularly in emerging markets, in matters related to affordability and population health impacts of health care interventions.

METHODS

The Task Force was approved by the International Society for Pharmacoeconomics and Outcomes Research Health Sciences Policy Council and appointed by its Board of Directors. Members were experienced developers or users of BIAs; worked in academia and industry and as advisors to governments; and came from several countries in North America and South America, Oceania, Asia, and Europe. The Task Force solicited comments on the drafts from a core group of external reviewers and, more broadly, from the membership of the International Society for Pharmacoeconomics and Outcomes Research.

RESULTS

The Task Force recommends that the design of a BIA for a new health care intervention should take into account relevant features of the health care system, possible access restrictions, the anticipated uptake of the new intervention, and the use and effects of the current and new interventions. The key elements of a BIA include estimating the size of the eligible population, the current mix of treatments and the expected mix after the introduction of the new intervention, the cost of the treatment mixes, and any changes expected in condition-related costs. Where possible, the BIA calculations should be performed by using a simple cost calculator approach because of its ease of use for budget holders. In instances, however, in which the changes in eligible population size, disease severity mix, or treatment patterns cannot be credibly captured by using the cost calculator approach, a cohort or patient-level condition-specific model may be used to estimate the budget impact of the new intervention, accounting appropriately for those entering and leaving the eligible population over time. In either case, the BIA should use data that reflect values specific to a particular decision maker's population. Sensitivity analysis should be of alternative scenarios chosen from the perspective of the decision maker. The validation of the model should include at least face validity with decision makers and verification of the calculations. Data sources for the BIA should include published clinical trial estimates and comparator studies for the efficacy and safety of the current and new interventions as well as the decision maker's own population for the other parameter estimates, where possible. Other data sources include the use of published data, well-recognized local or national statistical information, and, in special circumstances, expert opinion. Reporting of the BIA should provide detailed information about the input parameter values and calculations at a level of detail that would allow another modeler to replicate the analysis. The outcomes of the BIA should be presented in the format of interest to health care decision makers. In a computer program, options should be provided for different categories of costs to be included or excluded from the analysis.

CONCLUSIONS

We recommend a framework for the BIA, provide guidance on the acquisition and use of data, and offer a common reporting format that will promote standardization and transparency. Adherence to these good research practice principles would not necessarily supersede jurisdiction-specific BIA guidelines but may support and enhance local recommendations or serve as a starting point for payers wishing to promulgate methodology guidelines.

摘要

背景

预算影响分析(BIA)是医疗保健干预综合经济评估的重要组成部分,越来越多的报销机构要求作为上市或报销申请的一部分进行此类分析。

目的

本报告旨在为进行此类分析的人员或审查此类分析结果的人员提供方法方面的最新指南。之所以需要进行此更新,部分原因是 BIA 方法的发展,以及特别是在新兴市场中,对医疗保健干预措施的可负担性和人群健康影响相关问题的日益关注。

方法

该工作组由国际药物经济学与结果研究学会卫生科学政策理事会批准,并由其理事会任命。成员是 BIA 的经验丰富的开发者或使用者;在学术界和工业界工作,以及作为政府顾问;并来自北美和南美、大洋洲、亚洲和欧洲的多个国家。工作组就草案征求了核心外部评审员以及国际药物经济学与结果研究学会成员的意见。

结果

工作组建议,新医疗保健干预措施的 BIA 设计应考虑医疗保健系统的相关特征、可能的准入限制、新干预措施的预期采用情况,以及当前和新干预措施的使用和效果。BIA 的关键要素包括估算合格人群的规模、当前的治疗组合以及新干预措施引入后的预期治疗组合、治疗组合的成本以及与病情相关的成本的任何预期变化。在可能的情况下,应使用简单的成本计算器方法进行 BIA 计算,因为其易于预算持有者使用。但是,在无法通过成本计算器方法可靠地捕获合格人群规模、疾病严重程度组合或治疗模式的变化的情况下,可以使用队列或患者水平的特定于病情的模型来估算新干预措施的预算影响,适当考虑随时间进出合格人群的人员。在任何一种情况下,BIA 都应使用反映特定决策者人群特定值的数据。应从决策者的角度选择替代方案进行敏感性分析。模型的验证应至少包括决策者的表面有效性和计算的验证。BIA 的数据源应包括当前和新干预措施的疗效和安全性的已发表临床试验估计值和对照研究,以及决策者自身人群的其他参数估计值(在可能的情况下)。其他数据源包括使用已发表的数据、公认的本地或国家统计信息,以及在特殊情况下,专家意见。BIA 的报告应提供有关输入参数值和计算的详细信息,详细程度应足以允许另一位建模人员复制分析。BIA 的结果应以符合医疗保健决策者利益的格式呈现。在计算机程序中,应为包括或排除分析的不同类别的成本提供选项。

结论

我们建议采用 BIA 框架,提供关于数据获取和使用的指导,并提供通用报告格式,以促进标准化和透明度。遵守这些良好研究实践原则不一定会取代特定于司法管辖区的 BIA 指南,但可能会支持和增强地方建议,或作为希望颁布方法指南的支付者的起点。

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