Detail
Article
Online article
FT
Medvik - BMC
  • Something wrong with this record ?

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

R. Haneef, M. Tijhuis, R. Thiébaut, O. Májek, I. Pristaš, H. Tolonen, A. Gallay

. 2022 ; 80 (1) : 9. [pub] 20220104

Language English Country Great Britain

Document type Journal Article

Grant support
801553 / InfAct European Commission

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.

References provided by Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc22010325
003      
CZ-PrNML
005      
20220425131808.0
007      
ta
008      
220420s2022 xxk f 000 0|eng||
009      
AR
024    7_
$a 10.1186/s13690-021-00770-6 $2 doi
035    __
$a (PubMed)34983651
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a xxk
100    1_
$a Haneef, Romana $u Department of Non-Communicable Diseases and Injuries, Santé Publique France, Saint-Maurice, France. Romana.HANEEF@santepubliquefrance.fr $1 https://orcid.org/0000000177410268
245    10
$a Methodological guidelines to estimate population-based health indicators using linked data and/or machine learning techniques / $c R. Haneef, M. Tijhuis, R. Thiébaut, O. Májek, I. Pristaš, H. Tolonen, A. Gallay
520    9_
$a 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.
655    _2
$a časopisecké články $7 D016428
700    1_
$a Tijhuis, Mariken $u National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
700    1_
$a Thiébaut, Rodolphe $u Bordeaux University, Bordeaux School of Public Health, Bordeaux, France $u INSERM / INRIA SISTM team, Bordeaux Population health, Bordeaux, France $u Medical Information Department, Bordeaux University Hospital, Bordeaux, France
700    1_
$a Májek, Ondřej $u Institute of Health Information and Statistics of the Czech Republic, Prague, Czech Republic $u Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czech Republic
700    1_
$a Pristaš, Ivan $u National Institute of public health, division of health informatics and biostatistics, Zagreb, Croatia
700    1_
$a Tolonen, Hanna $u Finnish Institute for Health and Welfare (THL), Helsinki, Finland
700    1_
$a Gallay, Anne $u Department of Non-Communicable Diseases and Injuries, Santé Publique France, Saint-Maurice, France
773    0_
$w MED00000561 $t Archives of public health Archives belges de sante publique $x 0778-7367 $g Roč. 80, č. 1 (2022), s. 9
856    41
$u https://pubmed.ncbi.nlm.nih.gov/34983651 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y - $z 0
990    __
$a 20220420 $b ABA008
991    __
$a 20220425131806 $b ABA008
999    __
$a ind $b bmc $g 1784554 $s 1161523
BAS    __
$a 3
BAS    __
$a PreBMC
BMC    __
$a 2022 $b 80 $c 1 $d 9 $e 20220104 $i 0778-7367 $m Archives of public health $n Arch. public health $x MED00000561
GRA    __
$a 801553 / InfAct $p European Commission
LZP    __
$a Pubmed-20220420

Find record

Citation metrics

Loading data ...

Archiving options

Loading data ...