Background/Aims: Population-based registries play a key role in the epidemiological surveillance of congenital anomalies (CAs). This study is aimed at improving the epidemiological surveillance and providing prevalence estimates of rare CAs using the Registry of Rare Diseases as an added data source to the Registry of Congenital Anomalies. Methods: Cases of diagnosed rare CAs (2006-2013) were extracted from the Tuscany Registry of Rare Diseases and the Tuscany Registry of Congenital Anomalies in order to set up an integrated dataset. Prevalence (per 100,000 births; 95% confidence interval) was calculated for each rare CA. Results: Overall, 56 rare CAs were analyzed including 656 cases, of whom 121 (18.4%) were retrieved from the Registry of Rare Diseases that provided a major contribution for rare CAs for which a prenatal diagnosis is difficult, or for CAs more easily diagnosed in the postneonatal period. After data integration, an increased prevalence estimate was observed in particular for atresia of bile ducts (6.24; 3.57-10.14), tuberous sclerosis (2.34; 0.86-5.10), Kabuki syndrome (1.95; 0.63-4.55), and some monogenic CAs. Conclusions: This study represents an example of integration of registries operating in the field of rare diseases. Providing the accurate prevalence of rare CAs is a key point to improving surveillance, supporting public health policies, and planning healthcare.

Background/Aims: Population-based registries play a key role in the epidemiological surveillance of congenital anomalies (CAs). This study is aimed at improving the epidemiological surveillance and providing prevalence estimates of rare CAs using the Registry of Rare Diseases as an added data source to the Registry of Congenital Anomalies. Methods: Cases of diagnosed rare CAs (2006-2013) were extracted from the Tuscany Registry of Rare Diseases and the Tuscany Registry of Congenital Anomalies in order to set up an integrated dataset. Prevalence (per 100,000 births; 95% confidence interval) was calculated for each rare CA. Results: Overall, 56 rare CAs were analyzed including 656 cases, of whom 121 (18.4%) were retrieved from the Registry of Rare Diseases that provided a major contribution for rare CAs for which a prenatal diagnosis is difficult, or for CAs more easily diagnosed in the postneonatal period. After data integration, an increased prevalence estimate was observed in particular for atresia of bile ducts (6.24; 3.57-10.14), tuberous sclerosis (2.34; 0.86-5.10), Kabuki syndrome (1.95; 0.63-4.55), and some monogenic CAs. Conclusions: This study represents an example of integration of registries operating in the field of rare diseases. Providing the accurate prevalence of rare CAs is a key point to improving surveillance, supporting public health policies, and planning healthcare.

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.

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.

Background: The European Commission and Patients Organizations identify rare disease registries (RDRs) as strategic instruments to develop research and improve knowledge in the field of rare diseases. Interoperability between RDRs is needed for research activities, validation of therapeutic treatments, and public health actions. Sharing and comparing information requires a uniform and standardized way of data collection, so levels of interconnection between RDRs with similar aims and/or nature of data should be identified. The objective of this study is to define a classification and characterization of RDRs in order to identify different profiles and informative needs. Methods: Exploratory statistical analyses (cluster analysis and random forest) were applied to data derived from the EPIRARE project (‘Building Consensus and Synergies for the EU Rare Disease Patient Registration') survey on the activities and needs of RDRs. Results: The cluster analysis identified 3 main typologies of RDRs: public health, clinical and genetic research, and treatment registries. The analysis of the most informative variables, identified by the random forest method, led to the characterization of 3 types of RDRs and the definition of different profiles and informative needs. Conclusions: These results represent a useful source of information to facilitate the harmonization and interconnection of RDRs in accordance with the different profiles identified. It could help sharing the information between RDRs with similar profiles and, whenever possible, interconnections between registries with different profiles.

Background: The European Commission and Patients Organizations identify rare disease registries (RDRs) as strategic instruments to develop research and improve knowledge in the field of rare diseases. Interoperability between RDRs is needed for research activities, validation of therapeutic treatments, and public health actions. Sharing and comparing information requires a uniform and standardized way of data collection, so levels of interconnection between RDRs with similar aims and/or nature of data should be identified. The objective of this study is to define a classification and characterization of RDRs in order to identify different profiles and informative needs. Methods: Exploratory statistical analyses (cluster analysis and random forest) were applied to data derived from the EPIRARE project (‘Building Consensus and Synergies for the EU Rare Disease Patient Registration') survey on the activities and needs of RDRs. Results: The cluster analysis identified 3 main typologies of RDRs: public health, clinical and genetic research, and treatment registries. The analysis of the most informative variables, identified by the random forest method, led to the characterization of 3 types of RDRs and the definition of different profiles and informative needs. Conclusions: These results represent a useful source of information to facilitate the harmonization and interconnection of RDRs in accordance with the different profiles identified. It could help sharing the information between RDRs with similar profiles and, whenever possible, interconnections between registries with different profiles.