Record linkage of human biomonitoring (HBM) survey data with administrative register data can be used to enhance available datasets and complement the possible shortcomings of both data sources. Through record linkage, valuable information on medical history (diagnosed diseases, medication use, etc.) and follow-up information on health and vital status for established cohorts can be obtained. In this study, we investigated the availability of health registers in different EU Member States and EEA countries and assessed whether they could be linked to HBM studies. We found that the availability of administrative health registers varied substantially between European countries as well as the availability of unique personal identifiers that would facilitate record linkage. General protocols for record linkage were similar in all countries with ethical and data protections approval, informed consent, approval by administrative register owner, and linkage conducted by the register owner. Record linkage enabled cross-sectional survey data to be used as cohort study data with available follow-up and health endpoints. This can be used for extensive exposure-health effect association analysis. Our study showed that this is possible for many, but not all European countries.

• Klíčová slova
• HBM4EU, administrative registers, biomonitoring, chemicals, health, health registers,
• MeSH
• biologický monitoring * MeSH
• chorobopisy - spojování MeSH
• kohortové studie MeSH
• lidé MeSH
• monitorování životního prostředí * metody   MeSH
• průřezové studie MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Evropa MeSH

BACKGROUND: The capacity to use data linkage and artificial intelligence to estimate and predict health indicators varies across European countries. However, the estimation of health indicators from linked administrative data is challenging due to several reasons such as variability in data sources and data collection methods resulting in reduced interoperability at various levels and timeliness, availability of a large number of variables, lack of skills and capacity to link and analyze big data. The main objective of this study is to develop the methodological guidelines calculating population-based health indicators to guide European countries using linked data and/or machine learning (ML) techniques with new methods. METHOD: We have performed the following step-wise approach systematically to develop the methodological guidelines: i. Scientific literature review, ii. Identification of inspiring examples from European countries, and iii. Developing the checklist of guidelines contents. RESULTS: We have developed the methodological guidelines, which provide a systematic approach for studies using linked data and/or ML-techniques to produce population-based health indicators. These guidelines include a detailed checklist of the following items: rationale and objective of the study (i.e., research question), study design, linked data sources, study population/sample size, study outcomes, data preparation, data analysis (i.e., statistical techniques, sensitivity analysis and potential issues during data analysis) and study limitations. CONCLUSIONS: This is the first study to develop the methodological guidelines for studies focused on population health using linked data and/or machine learning techniques. These guidelines would support researchers to adopt and develop a systematic approach for high-quality research methods. There is a need for high-quality research methodologies using more linked data and ML-techniques to develop a structured cross-disciplinary approach for improving the population health information and thereby the population health.

• Klíčová slova
• Artificial intelligence, Data linkage, Guidelines, Health indicators, Linked data, Machine learning techniques, Methodological guidelines, Population health research, Statistical techniques,
• Publikační typ
• časopisecké články MeSH