Abstract
BackgroundThe availability of data generated from different sources is increasing with the possibility to link these data sources with each other. However, linked administrative data can be complex to use and may require advanced expertise and skills in statistical analysis. The main objectives of this study were to describe the current use of data linkage at the individual level and artificial intelligence (AI) in routine public health activities, to identify the related estimated health indicators (i.e., outcome and intervention indicators) and health determinants of non-communicable diseases and the obstacles to linking different data sources.MethodWe performed a survey across European countries to explore the current practices applied by national institutes of public health, health information and statistics for innovative use of data sources (i.e., the use of data linkage and/or AI).ResultsThe use of data linkage and AI at national institutes of public health, health information and statistics in Europe varies. The majority of European countries use data linkage in routine by applying a deterministic method or a combination of two types of linkages (i.e., deterministic & probabilistic) for public health surveillance and research purposes. The use of AI to estimate health indicators is not frequent at national institutes of public health, health information and statistics. Using linked data, 46 health outcome indicators, 34 health determinants and 23 health intervention indicators were estimated in routine. The complex data regulation laws, lack of human resources, skills and problems with data governance, were reported by European countries as obstacles to routine data linkage for public health surveillance and research.ConclusionsOur results highlight that the majority of European countries have integrated data linkage in their routine public health activities but only a few use AI. A sustainable national health information system and a robust data governance framework allowing to link different data sources are essential to support evidence-informed health policy development. Building analytical capacity and raising awareness of the added value of data linkage in national institutes is necessary for improving the use of linked data in order to improve the quality of public health surveillance and monitoring activities.
Highlights
The availability of data generated from different sources is increasing with the possibility to link these data sources with each other
Building analytical capacity and raising awareness of the added value of data linkage in national institutes is necessary for improving the use of linked data in order to improve the quality of public health surveillance and monitoring activities
The results of this study showed the variability in the use of data linkage and artificial intelligence (AI) at national institutes of public health, health information and statistics across European countries
Summary
The availability of data generated from different sources is increasing with the possibility to link these data sources with each other. The possibility to link these data sources with other databases offers unique opportunities to answer those research questions, which require a large sample size or detailed data on hard-toreach populations This methodology link available information from different sources and can generate evidence at population level with a high level of external validity and relevance for policy making [1]. Data linkage ensures a high statistical power, thereby reducing methodological issues relating to attrition, recall bias and lost-to-follow up [2] This technique allows performing more detailed stratified analyses of subgroups according to age, or specific geographical regions, and providing rapid access to data collected in a standardized format [3,4,5]. Traditional data sources (e.g., health interview and examination surveys, diseasespecific registries, etc.) and administrative data sources (e.g., hospital discharge, health insurance claims, causes of mortality data, etc.) complement each other and can increase the completeness and comprehensiveness of health information by taking into account various dimensions of health and risk factors influencing health status directly and indirectly
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