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IntroductionIn observational health data, phenotyping algorithms are needed to process raw information into clinically relevant features. Validation studies traditionally estimate sensitivity and specificity by comparing the phenotyping algorithm with a reference standard on a population sample. There are challenges to conduct validation studies for conditions with low prevalence.
Objectives and ApproachWe propose a novel and efficient method for conducting validation studies to indirectly estimate the sensitivity and specificity. We simulated datasets with different levels of disease prevalence and phenotyping algorithms with different sensitivities and specificity. We applied both the traditional (direct) and new (indirect) method on simulated data to estimate the sensitivity and specificity and compare the performance of the two methods. We also designed a gate to exclude true negatives to improve study efficiency on conditions with low prevalence and sensitive analysis was conducted on the imperfect gate.
ResultsThe new (indirect) method provided better or comparable accuracy in estimating both sensitivity and specificity compared to the traditional (direct) method. Applying a gate enabled us to conduct validation study in conditions with very low prevalence. An imperfect gate results in the overestimation of sensitivity but has minimal effect on specificity.
Conclusion/ImplicationsThe new (indirect) method provides an alternative way to conduct validation studies in observational health data with improvement in estimating accuracy.

ABSTRACT
ObjectivesThe International Methodology Consortium for Coded Health Information (IMECCHI), an international collaboration of health services researchers, launched the IMECCHI-DATANETWORK initiative in October 2015. Its main objective is to enable replication of observational studies across countries through a distributed data infrastructure.
ApproachIn a distributed data infrastructure, individual raw data are not shared. Instead, data are converted locally using a common data model (CDM) and loaded into a common software for data processing and analysis. Whenever a study protocol is agreed upon and ethically approved, an ad hoc procedure is programmed - using that software - including the data processing steps needed to create the analytical dataset from the CDM: record linkage, case selection, sampling, matching, etc. The procedure is then shared and locally run by each partner to generate an analytical dataset of integrated data. Analytical datasets may then be shared and pooled for statistical analyses.
ResultsSix partners of the IMECCHI collaboration, located in countries across 4 continents (Canada, Denmark, Italy, New Zealand, South Korea, and Switzerland), currently participate in the initiative. They first conducted a survey to describe the origin, content, completeness and main attributes of each table in their original databases. Based on the results of the survey, a CDM was created, encompassing 4 tables of coded or structured data to be linked at the individual level using a common personal identifier: (1) characteristics of the subjects with dates of birth and death; (2) hospital discharge summaries with diagnosis and procedure codes, and admission, discharge and procedure dates; (3) drug dispensing information with date of dispensing, drug name, duration of the amount of active principle according to the Defined Daily Dose of the World Health Organization; (4) causes of death. In each table, additional attributes describe the coding systems in which the other attributes are coded. Using such specific attributes facilitates interoperability across multiple coding systems. The open source Java-based software, TheMatrix, which operates on flat csv files using a domain-specific programming language, was chosen to embed the ad hoc procedure.
ConclusionWithin the IMECCHI-DATANETWORK initiative, databases from various countries will be locally converted in a CDM which will facilitate study replication in a distributed fashion while granting interoperability across coding systems. Through such international data networks, data are empowered for creating results which are generalizable to multiple countries. Cross-border data sharing and international comparisons are also facilitated.

IntroductionThe International Methodology Consortium for Coded Health Information (IMeCCHI) is a collaboration of health services researchers who promote methodological advances in coded health information. The IMeCCHI-DATANETWORK initiative focuses on developing a multi-purpose distributed data infrastructure and common data model (CDM) to enable cross-border data sharing and international comparisons.
MethodsIMeCCHI consortium partners from six different countries – Canada, Denmark, Italy, New Zealand, South Korea, and Switzerland – used a questionnaire to describe their original databases which differ in size, structure, content and coding systems. To standardize these data, they agreed on a CDM and mapped their population-based databases to meet the CDM specifications. At the end of this process, local data had a more homogenous content and structure, which made them syntactically and semantically interoperable. Data transformation was performed using a common data management software called TheMatrix.
ResultsThe CDM encompasses four tables of structured data (person characteristics, hospitalizations, outpatient prescription medication and death), linked at the individual level through a person identifier. It can be used to answer research questions across countries using locally converted databases, which facilitates study replication in a distributed fashion. As a proof-of-concept study, an initial research question was addressed using an agreed protocol. Local data were transformed in csv files in the CDM structure and TheMatrix was tested to transform the standardized data from each partner into local analytical datasets. This allowed results to be shared between countries, whilst maintaining local control over each region's data.
ConclusionThe IMeCCHI-DATANETWORK, a model of a distributed data network, demonstrated that it is feasible to analyze international data using standardized analytical methods that enable independent analyses by regions, without relocating datasets thereby protecting local confidentiality obligations. The distributed data infrastructure can produce results that can be generalized to several countries, while facilitating cross-border data sharing and international comparisons.

Background: The Accelerated Development of VAccine beNefit-risk Collaboration in Europe (ADVANCE) is a public-private collaboration aiming to develop and test a system for rapid benefit-risk monitoring of vaccines using existing healthcare databases in Europe. We estimated vaccine coverage from electronic healthcare databases as part of a fit-for-purpose assessment for vaccine benefit-risk studies. Methods: A retrospective dynamic cohort study was conducted through a distributed network approach. Coverage with measles-vaccine for birth year 2006, human papillomavirus (HPV)-vaccine for birth years 1990-2000 and influenza-vaccine for birth years 1920-1950 was estimated using period-prevalence and inverse probability weighting methods. Seven databases from four countries participated: Italy (Pedianet, Val Padana), Spain (BIFAP, SIDIAP), UK (RCGP-RSC, THIN), Denmark (SSI/AUH). Database access providers extracted the data, transformed it into a common structure and ran an R-script locally. The created output tables were shared and pooled at a central server. Results: The total study population comprised 274,616 persons for measles-vaccine, 2,011,666 persons for HPV-vaccine and 14,904,033 persons for influenza-vaccine. Measles-vaccine coverage varied from 84.3% (Denmark) to 96.5% (Italy, Val Padana) for the first dose and from 82.8% (Italy, Val Padana) to 90.9% (UK) for the second dose at the age of 7 years. The HPV-vaccine coverage, aggregated over birth years 1997-2000, ranged from 60% (UK) to 88.3% (Denmark) at the age of 15 years. The influenza-vaccine coverage for the influenza seasons from 2009 to 2015 for persons aged 65 years and more was roughly stable around 43% in Denmark and around 68% in the UK while a decrease from 58 to 50% was observed in Catalonia (Spain). Conclusions: We obtained detailed, age-specific coverage estimates though a common procedure. We discussed between database comparability and comparability to published national estimates. ; The Innovative Medicines Initiative Joint Undertaking funded this project under ADVANCE grant agreement no 115557, resources of which were composed of a financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and in kind contributions from EFPIA member companies. ; Braeye, T (reprint author), Sciensano, Rue Juliette Wytsman 14, B-1050 Brussels, Belgium. toon.braeye@sciensano.be

INTRODUCTION: The European Medical Information Framework consortium has assembled electronic health record (EHR) databases for dementia research. We calculated dementia prevalence and incidence in 25 million persons from 2004 to 2012. METHODS: Six EHR databases (three primary care and three secondary care) from five countries were interrogated. Dementia was ascertained by consensus harmonization of clinical/diagnostic codes. Annual period prevalences and incidences by age and gender were calculated and meta-analyzed. RESULTS: The six databases contained 138,625 dementia cases. Age-specific prevalences were around 30% of published estimates from community samples and incidences were around 50%. Pooled prevalences had increased from 2004 to 2012 in all age groups but pooled incidences only after age 75 years. Associations with age and gender were stable over time. DISCUSSION: The European Medical Information Framework initiative supports EHR data on unprecedented number of people with dementia. Age-specific prevalences and incidences mirror estimates from community samples in pattern at levels that are lower but increasing over time. ; The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under European Medical Informatics Framework (EMIF) grant agreement no. 115372, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies' in kind contribution. R.S. and G.P. are partly funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London.