Literature DB >> 24438712

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

Sean D Sullivan1, Josephine A Mauskopf2, Federico Augustovski3, J Jaime Caro4, Karen M Lee5, Mark Minchin6, Ewa Orlewska7, Pete Penna8, Jose-Manuel Rodriguez Barrios9, Wen-Yi Shau10.   

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.
© 2013 International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Published by International Society for Pharmacoeconomics and Outcomes Research (ISPOR) All rights reserved.

Entities:  

Keywords:  budget impact analysis; cost calculator; economic evaluation; methodology; modeling

Mesh:

Year:  2013        PMID: 24438712     DOI: 10.1016/j.jval.2013.08.2291

Source DB:  PubMed          Journal:  Value Health        ISSN: 1098-3015            Impact factor:   5.725


  279 in total

1.  Economic Value of Improved Accuracy for Self-Monitoring of Blood Glucose Devices for Type 1 Diabetes in Canada.

Authors:  R Brett McQueen; Marc D Breton; Markus Ott; Helena Koa; Bruce Beamer; Jonathan D Campbell
Journal:  J Diabetes Sci Technol       Date:  2015-08-14

2.  Predicting the long-term impact of voluntary medical male circumcision on HIV incidence among men who have sex with men in Beijing, China.

Authors:  Chen Zhang; Glenn F Webb; Jie Lou; Brian E Shepherd; Han-Zhu Qian; Yu Liu; Sten H Vermund
Journal:  AIDS Care       Date:  2019-10-16

3.  Enhancing the Budget Impact Model for Institutional Use: A Tool with Practical Applications for the Hospital Oncology Pharmacy.

Authors:  Lisa M Hess; Frank N Cinfio; Stewart Wetmore; Collin Churchill; Christopher Fausel; Amine Ale-Ali; Steven Gelwicks; Christopher A Bly; Sinem Perk; Robert W Klein
Journal:  Hosp Pharm       Date:  2016-06

4.  A budget impact analysis of a magnetic sphincter augmentation device for the treatment of medication-refractory mechanical gastroesophageal reflux disease: a United States payer perspective.

Authors:  John Pandolfino; John Lipham; Amarpreet Chawla; Nicole Ferko; Andrew Hogan; Rana A Qadeer
Journal:  Surg Endosc       Date:  2019-09-26       Impact factor: 4.584

5.  Cost-Effectiveness Analyses of the 21-Gene Assay in Breast Cancer: Systematic Review and Critical Appraisal.

Authors:  Shi-Yi Wang; Weixiong Dang; Ilana Richman; Sarah S Mougalian; Suzanne B Evans; Cary P Gross
Journal:  J Clin Oncol       Date:  2018-04-16       Impact factor: 44.544

6.  Economic Analysis of Veterans Affairs Initiative to Prevent Methicillin-Resistant Staphylococcus aureus Infections.

Authors:  Richard E Nelson; Vanessa W Stevens; Karim Khader; Makoto Jones; Matthew H Samore; Martin E Evans; R Douglas Scott; Rachel B Slayton; Marin L Schweizer; Eli L Perencevich; Michael A Rubin
Journal:  Am J Prev Med       Date:  2016-05       Impact factor: 5.043

Review 7.  Current challenges in health economic modeling of cancer therapies: a research inquiry.

Authors:  Jeffrey D Miller; Kathleen A Foley; Mason W Russell
Journal:  Am Health Drug Benefits       Date:  2014-05

8.  Budget impact and cost-effectiveness analyses of direct-acting antivirals for chronic hepatitis C virus infection in Hong Kong.

Authors:  X Li; N S Chan; A W Tam; I F N Hung; E W Chan
Journal:  Eur J Clin Microbiol Infect Dis       Date:  2017-05-17       Impact factor: 3.267

9.  Cost-Effectiveness Analysis of Tyrosine Kinase Inhibitors for Patients with Advanced Gastrointestinal Stromal Tumors.

Authors:  Virginie Nerich; Camille Fleck; Loïc Chaigneau; Nicolas Isambert; Christophe Borg; Elsa Kalbacher; Marine Jary; Pauline Simon; Xavier Pivot; Jean-Yves Blay; Samuel Limat
Journal:  Clin Drug Investig       Date:  2017-01       Impact factor: 2.859

10.  The Cost-effectiveness and Budget Impact of 2-Drug Dolutegravir-Lamivudine Regimens for the Treatment of HIV Infection in the United States.

Authors:  Michael P Girouard; Paul E Sax; Robert A Parker; Babafemi Taiwo; Kenneth A Freedberg; Roy M Gulick; Milton C Weinstein; A David Paltiel; Rochelle P Walensky
Journal:  Clin Infect Dis       Date:  2015-12-09       Impact factor: 9.079

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.