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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F22%3A00126141" target="_blank" >RIV/00216224:14110/22:00126141 - isvavai.cz</a>

Result on the web

<a href="https://archpublichealth.biomedcentral.com/articles/10.1186/s13690-021-00770-6" target="_blank" >https://archpublichealth.biomedcentral.com/articles/10.1186/s13690-021-00770-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s13690-021-00770-6" target="_blank" >10.1186/s13690-021-00770-6</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30304 - Public and environmental health

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

ARCHIVES OF PUBLIC HEALTH

ISSN

0778-7367

e-ISSN

2049-3258

Volume of the periodical

80

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

12

Pages from-to

1-12

UT code for WoS article

000738623200002

EID of the result in the Scopus database

2-s2.0-85122309409