The Quality of Rare Disease Registries: Evaluation and Characterization

被引:17
作者
Coi, Alessio [1 ]
Santoro, Michele [1 ]
Villaverde-Hueso, Ana [4 ,5 ]
Di Paola, Michele Lipucci [6 ]
Gainotti, Sabina [3 ]
Taruscio, Domenica [3 ]
de la Paz, Manuel Posada [4 ,5 ]
Bianchi, Fabrizio [1 ,2 ]
机构
[1] CNR, Inst Clin Physiol, Pisa, Italy
[2] Fdn Toscana Gabriele Monasterio, Pisa, Italy
[3] NIH, Natl Ctr Rare Dis, Rome, Italy
[4] Spain RDR, Inst Rare Dis Res, Madrid, Spain
[5] Inst Hlth Carlos III, CIBERER, Madrid, Spain
[6] European Org Rare Dis EURORDIS, Paris, France
关键词
Rare disease registries; Quality; Survey; CLASSIFICATION;
D O I
10.1159/000444476
中图分类号
Q3 [遗传学];
学科分类号
071007 [遗传学];
摘要
Background: The focus on the quality of the procedures for data collection, storing, and analysis in the definition and implementation of a rare disease registry (RDR) is the basis for developing a valid and long-term sustainable tool. The aim of this study was to provide useful information for characterizing a quality profile for RDRs using an analytical approach applied to RDRs participating in the European Platform for Rare Disease Registries 2011-2014 (EPIRARE) survey. Methods: An indicator of quality was defined by choosing a small set of quality-related variables derived from the survey. The random forest method was used to identify the variables best defining a quality profile for RDRs. Fisher's exact test was employed to assess the association with the indicator of quality, and the Cochran-Armitage test was used to check the presence of a linear trend along different levels of quality. Results: The set of variables found to characterize high-quality RDRs focused on ethical and legal issues, governance, communication of activities and results, established procedures to regulate access to data and security, and established plans to ensure long-term sustainability. Conclusions: The quality of RDRs is usually associated with a good oversight and governance mechanism and with durable funding. The results suggest that RDRs would benefit from support in management, information technology, epidemiology, and statistics. (C) 2016 S. Karger AG, Basel
引用
收藏
页码:108 / 115
页数:8
相关论文
共 17 条
[1]
[Anonymous], 2015, Orphanet Report Series
[2]
Rare diseases in ICD11: making rare diseases visible in health information systems through appropriate coding [J].
Ayme, Segolene ;
Bellet, Bertrand ;
Rath, Ana .
ORPHANET JOURNAL OF RARE DISEASES, 2015, 10
[3]
Dispelling myths about rare disease registry system development (vol 8, pg 21, 2013) [J].
Bellgard, Matthew ;
Beroud, Christophe ;
Parkinson, Kay ;
Harris, Tess ;
Ayme, Segolene ;
Baynam, Gareth ;
Weeramanthri, Tarun ;
Dawkins, Hugh ;
Hunter, Adam .
SOURCE CODE FOR BIOLOGY AND MEDICINE, 2014, 9 (01)
[4]
Cross sectional survey of multicentre clinical databases in the United Kingdom [J].
Black, N ;
Barker, M ;
Payne, M .
BMJ-BRITISH MEDICAL JOURNAL, 2004, 328 (7454) :1478-1481
[5]
Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[6]
DEcIDE Center, 7 DECIDE CTR
[7]
Gliklich RichardE., 2010, Registries for evaluating patient outcomes: a user's guide
[8]
Survival ensembles [J].
Hothorn, Torsten ;
Buehlmann, Peter ;
Dudoit, Sandrine ;
Molinaro, Annette ;
Van der Laan, Mark J. .
BIOSTATISTICS, 2006, 7 (03) :355-373
[9]
Kole A, 2014, ORPHANET J RARE D S4, V1, P13
[10]
Posada M, GUIDELINES DATA SOUR