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. 2022 Jan 4;80(1):9.
doi: 10.1186/s13690-021-00770-6.

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

Romana Haneef  1 , Mariken Tijhuis  2 , Rodolphe Thiébaut  3   4   5 , Ondřej Májek  6   7 , Ivan Pristaš  8 , Hanna Tolonen  9 , Anne Gallay  10
Affiliations

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

Romana Haneef et al. Arch Public Health. .

Erratum in

Abstract

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.

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

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Conflict of interest statement

R. Haneef is the first author of this paper and the section editor of “health information system” of “Archives of Public Health”. All other authors declare that they have no competing interests related to the work.

Figures

Fig. 1
Fig. 1
Flow diagram of search strategy used to identify studies using data linkage and/or machine learning techniques for health surveillance and health care performance to develop the methodological guidelines to estimate population-based indicators, a study performed under InfAct project, May 2021
See this image and copyright information in PMC

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