Methodological guidelines to estimate population-based health indicators using linked data and/or machine learning techniques

Archives of Public Health

Rodolphe Thiébaut¹  Romana Haneef²  Anne Gallay²  Hanna Tolenan³  Ondřej Májek⁴  Mariken Tijhuis⁵  Ivan Pristaš⁶ 

[1] Bordeaux University, Bordeaux School of Public Health, Bordeaux, France;INSERM / INRIA SISTM team, Bordeaux Population health, Bordeaux, France;Medical Information Department, Bordeaux University Hospital, Bordeaux, France;Department of Non-Communicable Diseases and Injuries, Santé Publique France, Saint-Maurice, France;Finnish Institute for Health and Welfare (THL), Helsinki, Finland;Institute of Health Information and Statistics of the Czech Republic, Prague, Czech Republic;Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czech Republic;National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands;National Institute of public health, division of health informatics and biostatistics, Zagreb, Croatia;

关键词: Data linkage;    Linked data;    Machine learning techniques;    Artificial intelligence;    Guidelines;    Methodological guidelines;    Statistical techniques;    Population health research;    Health indicators;   

DOI  :  10.1186/s13690-021-00770-6

来源: Springer

【 摘 要 】

BackgroundThe 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.MethodWe 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.ResultsWe 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.ConclusionsThis 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.

【 授权许可】

CC BY