Identifying Cases of Type 2 Diabetes in Heterogeneous Data Sources: Strategy from the EMIF Project
Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data...
Saved in:
| Published in | PloS one Vol. 11; no. 8; p. e0160648 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
31.08.2016
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sources (PCDs), three record linkage of administrative and/or registry data sources (RLDs), one hospital and one biobank. Overall, data from 12 million subjects from six European countries were extracted. Based on a shared event definition, sixteeen standard algorithms (components) useful to identify T2DM cases were generated through a top-down/bottom-up iterative approach. Each component was based on one single data domain among diagnoses, drugs, diagnostic test utilization and laboratory results. Diagnoses-based components were subclassified considering the healthcare setting (primary, secondary, inpatient care). The Unified Medical Language System was used for semantic harmonization within data domains. Individual components were extracted and proportion of population identified was compared across data sources. Drug-based components performed similarly in RLDs and PCDs, unlike diagnoses-based components. Using components as building blocks, logical combinations with AND, OR, AND NOT were tested and local experts recommended their preferred data source-tailored combination. The population identified per data sources by resulting algorithms varied from 3.5% to 15.7%, however, age-specific results were fairly comparable. The impact of individual components was assessed: diagnoses-based components identified the majority of cases in PCDs (93-100%), while drug-based components were the main contributors in RLDs (81-100%). The proposed data derivation procedure allowed the generation of data source-tailored case-finding algorithms in a standardized fashion, facilitated transparent documentation of the process and benchmarking of data sources, and provided bases for interpretation of possible inter-data source inconsistency of findings in future studies. |
|---|---|
| AbstractList | Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sources (PCDs), three record linkage of administrative and/or registry data sources (RLDs), one hospital and one biobank. Overall, data from 12 million subjects from six European countries were extracted. Based on a shared event definition, sixteeen standard algorithms (components) useful to identify T2DM cases were generated through a top-down/bottom-up iterative approach. Each component was based on one single data domain among diagnoses, drugs, diagnostic test utilization and laboratory results. Diagnoses-based components were subclassified considering the healthcare setting (primary, secondary, inpatient care). The Unified Medical Language System was used for semantic harmonization within data domains. Individual components were extracted and proportion of population identified was compared across data sources. Drug-based components performed similarly in RLDs and PCDs, unlike diagnoses-based components. Using components as building blocks, logical combinations with AND, OR, AND NOT were tested and local experts recommended their preferred data source-tailored combination. The population identified per data sources by resulting algorithms varied from 3.5% to 15.7%, however, age-specific results were fairly comparable. The impact of individual components was assessed: diagnoses-based components identified the majority of cases in PCDs (93-100%), while drug-based components were the main contributors in RLDs (81-100%). The proposed data derivation procedure allowed the generation of data source-tailored case-finding algorithms in a standardized fashion, facilitated transparent documentation of the process and benchmarking of data sources, and provided bases for interpretation of possible inter-data source inconsistency of findings in future studies. Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sources (PCDs), three record linkage of administrative and/or registry data sources (RLDs), one hospital and one biobank. Overall, data from 12 million subjects from six European countries were extracted. Based on a shared event definition, sixteeen standard algorithms (components) useful to identify T2DM cases were generated through a top-down/bottom-up iterative approach. Each component was based on one single data domain among diagnoses, drugs, diagnostic test utilization and laboratory results. Diagnoses-based components were subclassified considering the healthcare setting (primary, secondary, inpatient care). The Unified Medical Language System was used for semantic harmonization within data domains. Individual components were extracted and proportion of population identified was compared across data sources. Drug-based components performed similarly in RLDs and PCDs, unlike diagnoses-based components. Using components as building blocks, logical combinations with AND, OR, AND NOT were tested and local experts recommended their preferred data source-tailored combination. The population identified per data sources by resulting algorithms varied from 3.5% to 15.7%, however, age-specific results were fairly comparable. The impact of individual components was assessed: diagnoses-based components identified the majority of cases in PCDs (93-100%), while drug-based components were the main contributors in RLDs (81-100%). The proposed data derivation procedure allowed the generation of data source-tailored case-finding algorithms in a standardized fashion, facilitated transparent documentation of the process and benchmarking of data sources, and provided bases for interpretation of possible inter-data source inconsistency of findings in future studies. The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking (http://www.imi.europa.eu/) under European Medical Information Framework grant agreement no. 115372, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and European Federation of Pharmaceutical Industries and Association companies’ in kind contribution. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Pfizer Worldwide Research and Development, GlaxoSmithKline, Cegedim Strategic Data Medical Research Ltd and Janssen provided support in the form of salaries for AKL, PE, DA and MJS, respectively, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sources (PCDs), three record linkage of administrative and/or registry data sources (RLDs), one hospital and one biobank. Overall, data from 12 million subjects from six European countries were extracted. Based on a shared event definition, sixteeen standard algorithms ( components) useful to identify T2DM cases were generated through a top-down/bottom-up iterative approach. Each component was based on one single data domain among diagnoses, drugs, diagnostic test utilization and laboratory results. Diagnoses-based components were subclassified considering the healthcare setting (primary, secondary, inpatient care). The Unified Medical Language System was used for semantic harmonization within data domains. Individual components were extracted and proportion of population identified was compared across data sources. Drug-based components performed similarly in RLDs and PCDs, unlike diagnoses-based components. Using components as building blocks, logical combinations with AND, OR, AND NOT were tested and local experts recommended their preferred data source-tailored combination. The population identified per data sources by resulting algorithms varied from 3.5% to 15.7%, however, age-specific results were fairly comparable. The impact of individual components was assessed: diagnoses-based components identified the majority of cases in PCDs (93–100%), while drug-based components were the main contributors in RLDs (81–100%). The proposed data derivation procedure allowed the generation of data source-tailored case-finding algorithms in a standardized fashion, facilitated transparent documentation of the process and benchmarking of data sources, and provided bases for interpretation of possible inter-data source inconsistency of findings in future studies. Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sources (PCDs), three record linkage of administrative and/or registry data sources (RLDs), one hospital and one biobank. Overall, data from 12 million subjects from six European countries were extracted. Based on a shared event definition, sixteeen standard algorithms (components) useful to identify T2DM cases were generated through a top-down/bottom-up iterative approach. Each component was based on one single data domain among diagnoses, drugs, diagnostic test utilization and laboratory results. Diagnoses-based components were subclassified considering the healthcare setting (primary, secondary, inpatient care). The Unified Medical Language System was used for semantic harmonization within data domains. Individual components were extracted and proportion of population identified was compared across data sources. Drug-based components performed similarly in RLDs and PCDs, unlike diagnoses-based components. Using components as building blocks, logical combinations with AND, OR, AND NOT were tested and local experts recommended their preferred data source-tailored combination. The population identified per data sources by resulting algorithms varied from 3.5% to 15.7%, however, age-specific results were fairly comparable. The impact of individual components was assessed: diagnoses-based components identified the majority of cases in PCDs (93-100%), while drug-based components were the main contributors in RLDs (81-100%). The proposed data derivation procedure allowed the generation of data source-tailored case-finding algorithms in a standardized fashion, facilitated transparent documentation of the process and benchmarking of data sources, and provided bases for interpretation of possible inter-data source inconsistency of findings in future studies.Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sources (PCDs), three record linkage of administrative and/or registry data sources (RLDs), one hospital and one biobank. Overall, data from 12 million subjects from six European countries were extracted. Based on a shared event definition, sixteeen standard algorithms (components) useful to identify T2DM cases were generated through a top-down/bottom-up iterative approach. Each component was based on one single data domain among diagnoses, drugs, diagnostic test utilization and laboratory results. Diagnoses-based components were subclassified considering the healthcare setting (primary, secondary, inpatient care). The Unified Medical Language System was used for semantic harmonization within data domains. Individual components were extracted and proportion of population identified was compared across data sources. Drug-based components performed similarly in RLDs and PCDs, unlike diagnoses-based components. Using components as building blocks, logical combinations with AND, OR, AND NOT were tested and local experts recommended their preferred data source-tailored combination. The population identified per data sources by resulting algorithms varied from 3.5% to 15.7%, however, age-specific results were fairly comparable. The impact of individual components was assessed: diagnoses-based components identified the majority of cases in PCDs (93-100%), while drug-based components were the main contributors in RLDs (81-100%). The proposed data derivation procedure allowed the generation of data source-tailored case-finding algorithms in a standardized fashion, facilitated transparent documentation of the process and benchmarking of data sources, and provided bases for interpretation of possible inter-data source inconsistency of findings in future studies. |
| Audience | Academic |
| Author | Coloma, Preciosa Sturkenboom, Miriam van der Lei, Johan Roberto, Giuseppe Gini, Rosa Herings, Ron Tammesoo, Mari-Liis van Wijngaarden, Rients Ansell, David Loomis, A. Katrina Lapi, Francesco Rijnbeek, Peter Sattar, Naveed Pasqua, Alessandro Mayer, Miguel A. Alavere, Helene Cunningham, James Tramontan, Lara Reisberg, Sulev Schuemie, Martijn J. Leal, Ingrid Pedersen, Lars Egger, Peter Avillach, Paul |
| AuthorAffiliation | 16 Hospital del Mar Medical Research Institute (IMIM) and Universitat Pompeu Fabra, Barcelona, Spain 2 Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands 6 GlaxoSmithKline, Worldwide Epidemiology GSK, Stockley Park West, Uxbridge, United Kingdom 8 The Health Improvement Network, Cegedim Strategic Data Medical Research Ltd, London, United Kingdom 10 Estonian Genome Center, University of Tartu, Tartu, Estonia 12 Health Search, Italian College of General Practitioners and Primary Care, Firenze, Italy 1 Regional Agency for Healthcare Services of Tuscany, Epidemiology unit, Florence, Italy 5 Department of Biomedical Informatics, Harvard Medical School & Children’s Hospital Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America 14 University of Manchester, Manchester, United Kingdom 3 British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom 4 Pfizer Worldwide Resea |
| AuthorAffiliation_xml | – name: 3 British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom – name: 1 Regional Agency for Healthcare Services of Tuscany, Epidemiology unit, Florence, Italy – name: 13 Department of Clinical Epidemiology, Aarhus University Hosptial, Aarhus, Denmark – name: 2 Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands – name: 9 Quretec, Software Technology and Applications Competence Center, University of Tartu, Tartu, Estonia – name: 12 Health Search, Italian College of General Practitioners and Primary Care, Firenze, Italy – name: 7 PHARMO Institute for Drug Outcomes Research, Utrecht, Netherlands – name: 16 Hospital del Mar Medical Research Institute (IMIM) and Universitat Pompeu Fabra, Barcelona, Spain – name: 10 Estonian Genome Center, University of Tartu, Tartu, Estonia – name: 18 Observational Health Data Sciences and Informatics, New York, New York, United States of America – name: 6 GlaxoSmithKline, Worldwide Epidemiology GSK, Stockley Park West, Uxbridge, United Kingdom – name: 8 The Health Improvement Network, Cegedim Strategic Data Medical Research Ltd, London, United Kingdom – name: 11 Tartu University Hospital, Tartu, Estonia – name: 4 Pfizer Worldwide Research and Development, Groton, Connecticut, United States of America – name: 17 Janssen Research & Development, Epidemiology, Titusville, New Jersey, United States of America – name: 14 University of Manchester, Manchester, United Kingdom – name: 5 Department of Biomedical Informatics, Harvard Medical School & Children’s Hospital Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America – name: 15 Arsenàl.IT Consortium, Veneto's Research Centre for eHealth Innovation, Treviso, Italy – name: Universita degli Studi di Firenze, ITALY |
| Author_xml | – sequence: 1 givenname: Giuseppe orcidid: 0000-0001-6478-6442 surname: Roberto fullname: Roberto, Giuseppe – sequence: 2 givenname: Ingrid surname: Leal fullname: Leal, Ingrid – sequence: 3 givenname: Naveed surname: Sattar fullname: Sattar, Naveed – sequence: 4 givenname: A. Katrina surname: Loomis fullname: Loomis, A. Katrina – sequence: 5 givenname: Paul surname: Avillach fullname: Avillach, Paul – sequence: 6 givenname: Peter surname: Egger fullname: Egger, Peter – sequence: 7 givenname: Rients surname: van Wijngaarden fullname: van Wijngaarden, Rients – sequence: 8 givenname: David surname: Ansell fullname: Ansell, David – sequence: 9 givenname: Sulev surname: Reisberg fullname: Reisberg, Sulev – sequence: 10 givenname: Mari-Liis surname: Tammesoo fullname: Tammesoo, Mari-Liis – sequence: 11 givenname: Helene surname: Alavere fullname: Alavere, Helene – sequence: 12 givenname: Alessandro surname: Pasqua fullname: Pasqua, Alessandro – sequence: 13 givenname: Lars surname: Pedersen fullname: Pedersen, Lars – sequence: 14 givenname: James surname: Cunningham fullname: Cunningham, James – sequence: 15 givenname: Lara surname: Tramontan fullname: Tramontan, Lara – sequence: 16 givenname: Miguel A. surname: Mayer fullname: Mayer, Miguel A. – sequence: 17 givenname: Ron surname: Herings fullname: Herings, Ron – sequence: 18 givenname: Preciosa surname: Coloma fullname: Coloma, Preciosa – sequence: 19 givenname: Francesco surname: Lapi fullname: Lapi, Francesco – sequence: 20 givenname: Miriam surname: Sturkenboom fullname: Sturkenboom, Miriam – sequence: 21 givenname: Johan surname: van der Lei fullname: van der Lei, Johan – sequence: 22 givenname: Martijn J. surname: Schuemie fullname: Schuemie, Martijn J. – sequence: 23 givenname: Peter surname: Rijnbeek fullname: Rijnbeek, Peter – sequence: 24 givenname: Rosa surname: Gini fullname: Gini, Rosa |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27580049$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNk11r2zAUhs3oWD-2fzA2QWFsF8kkWZasXgxKP9ZAR8fa7VbIsuQoOFYmyWP591MStySljGIby0fPe2S9R-cw2-tcp7PsLYJjlDP0eeZ638l2vEjhMUQUUlK-yA4Qz_GIYpjvbY33s8MQZhAWeUnpq2wfs6KEkPCDrJrUuovWLG3XgDMZdADOgLvlQgMMzq2sdEwh24GrNPCu0Z12fQDnMkpwm_5A6XACbqOXUTdLYLybgzjV4OLb5BJ8926mVXydvTSyDfrN8D7Kfl5e3J1dja5vvk7OTq9HilEWRybPuapZxWWNlYFFAVlFDDVVaSpWGlVqQk1NFOQGGVNVqESmqNMNK0gYZ_lR9n6Td9G6IAZ7gkhcwRDOc5qIyYaonZyJhbdz6ZfCSSvWAecbIX20qtWizjlR0kjMuSE0J5LAClMl67Kmybgq5foyrNZXc12r5KKX7U7S3ZnOTkXj_ogCQsoZTAnQJoEKvRJeK-2VjGvhw8fqwZBhkSNSFCRpPg6Leve71yGKuQ1Kt61clyXtFbOSY8zhM1BEKebJ9IQeP0KfNm-gGpn8sZ1xaVtqlVScknTIOKNkVYTxE1S6aj23Kh1VY1N8R_BpR5CYqP_GRvYhiMntj-ezN7922Q9b7FTLNk6Da_toXRd2wXfbhXyo4H2PJIAMhfIuBK_NA4KgWLXivV1i1YpiaMUkO3kkUzbK1fLJEdv-X_wP4L44cQ |
| CitedBy_id | crossref_primary_10_1136_bmjopen_2022_066057 crossref_primary_10_1002_cpt_2476 crossref_primary_10_2196_16922 crossref_primary_10_1002_lrh2_10214 crossref_primary_10_1002_cpt_1833 crossref_primary_10_1136_bmjopen_2018_023090 crossref_primary_10_2217_pme_2018_0120 crossref_primary_10_1186_s12911_019_0844_6 crossref_primary_10_3389_fonc_2023_1059109 crossref_primary_10_1016_j_vaccine_2019_07_045 crossref_primary_10_1136_bmjopen_2020_038753 crossref_primary_10_1111_dom_13468 crossref_primary_10_1016_j_numecd_2019_08_017 crossref_primary_10_1002_pds_5787 crossref_primary_10_1371_journal_pone_0226015 crossref_primary_10_3390_ijerph18147679 |
| Cites_doi | 10.1016/j.jclinepi.2014.02.020 10.15265/IY-2014-0009 10.1111/joim.12159 10.1007/s00125-008-1156-z 10.1097/EDE.0b013e318281e48a 10.1136/amiajnl-2011-000439 10.1093/eurheartj/eht108 10.1136/amiajnl-2013-002428 10.1136/amiajnl-2013-001952 10.1186/1471-2458-13-15 10.1136/amiajnl-2012-001145 10.1371/journal.pone.0110900 10.1002/pds.2053 10.1097/EDE.0b013e3182114039 10.1136/amiajnl-2013-001930 10.1093/ije/dyt268 10.1136/amiajnl-2012-000933 10.13063/2327-9214.1189 10.1371/journal.pmed.1001885 10.2147/CLEP.S37587 10.1002/pds.2205 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2016 Public Library of Science 2016 Roberto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2016 Roberto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess 2016 Roberto et al 2016 Roberto et al |
| Copyright_xml | – notice: COPYRIGHT 2016 Public Library of Science – notice: 2016 Roberto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2016 Roberto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess – notice: 2016 Roberto et al 2016 Roberto et al |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM IOV ISR 3V. 7QG 7QL 7QO 7RV 7SN 7SS 7T5 7TG 7TM 7U9 7X2 7X7 7XB 88E 8AO 8C1 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AEUYN AFKRA ARAPS ATCPS AZQEC BBNVY BENPR BGLVJ BHPHI C1K CCPQU D1I DWQXO FR3 FYUFA GHDGH GNUQQ H94 HCIFZ K9. KB. KB0 KL. L6V LK8 M0K M0S M1P M7N M7P M7S NAPCQ P5Z P62 P64 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PTHSS PYCSY RC3 7X8 XX2 5PM DOA |
| DOI | 10.1371/journal.pone.0160648 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale in Context. Opposing Viewpoints Gale In Context: Science ProQuest Central (Corporate) Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Biotechnology Research Abstracts Nursing & Allied Health Database Ecology Abstracts Entomology Abstracts (Full archive) Immunology Abstracts Meteorological & Geoastrophysical Abstracts Nucleic Acids Abstracts Virology and AIDS Abstracts Agricultural Science Collection Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Public Health Database Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Aerospace Collection Agricultural & Environmental Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central Technology Collection Natural Science Collection Environmental Sciences and Pollution Management ProQuest One ProQuest Materials Science Collection ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student AIDS and Cancer Research Abstracts SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Materials Science Database Nursing & Allied Health Database (Alumni Edition) Meteorological & Geoastrophysical Abstracts - Academic ProQuest Engineering Collection Biological Sciences Agriculture Science Database Health & Medical Collection (Alumni) PML(ProQuest Medical Library) Algology Mycology and Protozoology Abstracts (Microbiology C) Biological Science Database Engineering Database Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Environmental Science Database Materials Science Collection ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection Environmental Science Collection Genetics Abstracts MEDLINE - Academic Recercat PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Agricultural Science Database Publicly Available Content Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials Nucleic Acids Abstracts SciTech Premium Collection ProQuest Central China Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Meteorological & Geoastrophysical Abstracts Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Engineering Collection Advanced Technologies & Aerospace Collection Engineering Database Virology and AIDS Abstracts ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Agricultural Science Collection ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database Ecology Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Environmental Science Collection Entomology Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Environmental Science Database ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic Meteorological & Geoastrophysical Abstracts - Academic ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) Materials Science Collection ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Genetics Abstracts ProQuest Engineering Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) Agricultural & Environmental Science Collection AIDS and Cancer Research Abstracts Materials Science Database ProQuest Materials Science Collection ProQuest Public Health ProQuest Nursing & Allied Health Source ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Animal Behavior Abstracts Materials Science & Engineering Collection Immunology Abstracts ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE Agricultural Science Database Engineering Research Database MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) |
| DocumentTitleAlternate | Type 2 Diabetes Identification in Heterogeneous Data Sources |
| EISSN | 1932-6203 |
| ExternalDocumentID | 1815712336 oai_doaj_org_article_d394cafa299f4634a40b26cad8d6049b PMC5006970 oai_recercat_cat_2072_314554 4166761591 A462097647 27580049 10_1371_journal_pone_0160648 |
| Genre | Journal Article |
| GeographicLocations | United States New York Netherlands United Kingdom--UK United States--US Denmark Italy Aarhus Denmark Estonia Spain |
| GeographicLocations_xml | – name: United States – name: New York – name: Netherlands – name: Estonia – name: United Kingdom--UK – name: Italy – name: Aarhus Denmark – name: Denmark – name: Spain – name: United States--US |
| GroupedDBID | --- 123 29O 2WC 53G 5VS 7RV 7X2 7X7 7XC 88E 8AO 8C1 8CJ 8FE 8FG 8FH 8FI 8FJ A8Z AAFWJ AAUCC AAWOE AAYXX ABDBF ABIVO ABJCF ABUWG ACGFO ACIHN ACIWK ACPRK ACUHS ADBBV ADRAZ AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHMBA ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS APEBS ARAPS ATCPS BAWUL BBNVY BCNDV BENPR BGLVJ BHPHI BKEYQ BPHCQ BVXVI BWKFM CCPQU CITATION CS3 D1I D1J D1K DIK DU5 E3Z EAP EAS EBD EMOBN ESX EX3 F5P FPL FYUFA GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE IAO IEA IGS IHR IHW INH INR IOV IPY ISE ISR ITC K6- KB. KQ8 L6V LK5 LK8 M0K M1P M48 M7P M7R M7S M~E NAPCQ O5R O5S OK1 OVT P2P P62 PATMY PDBOC PHGZM PHGZT PIMPY PQQKQ PROAC PSQYO PTHSS PV9 PYCSY RNS RPM RZL SV3 TR2 UKHRP WOQ WOW ~02 ~KM BBORY CGR CUY CVF ECM EIF IPNFZ NPM RIG PMFND 3V. 7QG 7QL 7QO 7SN 7SS 7T5 7TG 7TM 7U9 7XB 8FD 8FK AZQEC C1K DWQXO FR3 GNUQQ H94 K9. KL. M7N P64 PJZUB PKEHL PPXIY PQEST PQGLB PQUKI PRINS RC3 7X8 PUEGO XX2 5PM - 02 AAPBV ABPTK ADACO BBAFP KM |
| ID | FETCH-LOGICAL-c767t-f339cd7b9ad2cf05507b4f6fb8fb78fc8e46fd4c09f1ffbb181f5df5d0b047973 |
| IEDL.DBID | M48 |
| ISSN | 1932-6203 |
| IngestDate | Sun Feb 06 00:55:09 EST 2022 Wed Aug 27 01:25:15 EDT 2025 Thu Aug 21 13:47:27 EDT 2025 Fri Aug 29 12:29:22 EDT 2025 Fri Jul 11 10:21:51 EDT 2025 Mon Jul 21 10:33:15 EDT 2025 Fri Jul 25 10:14:37 EDT 2025 Tue Jun 17 21:15:03 EDT 2025 Tue Jun 10 20:22:09 EDT 2025 Fri Jun 27 04:01:02 EDT 2025 Fri Jun 27 03:56:58 EDT 2025 Thu May 22 20:57:48 EDT 2025 Thu Apr 03 06:49:56 EDT 2025 Thu Apr 24 23:00:13 EDT 2025 Tue Jul 01 01:55:52 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 8 |
| Language | English |
| License | This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Creative Commons Attribution License |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c767t-f339cd7b9ad2cf05507b4f6fb8fb78fc8e46fd4c09f1ffbb181f5df5d0b047973 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceptualization: RG GR IL PR MJS. Data curation: GR RG PR. Formal analysis: RG GR. Investigation: RG IL PA RvW DA SR AP LP LT MAM PC PR. Methodology: RG GR IL PR. Software: RG PR. Supervision: RG. Visualization: GR RG. Writing – original draft: GR RG IL. Writing – review & editing: GR IL NS AKL PA PE RvW DA SR M-LT HA AP LP JC LT MAM RH PC FL MS JvdL MJS PR RG. Competing Interests: The authors AKL, PE, DA and MJS are employed by Pfizer, GlaxoSmithKline, Cegedim and Janssen that are commercial companies. However, this did not have any influence on the reporting or discussion of the results presented in this manuscript and does not alter our adherence to PLOS ONE policies on sharing data and materials. |
| ORCID | 0000-0001-6478-6442 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1371/journal.pone.0160648 |
| PMID | 27580049 |
| PQID | 1815712336 |
| PQPubID | 1436336 |
| PageCount | e0160648 |
| ParticipantIDs | plos_journals_1815712336 doaj_primary_oai_doaj_org_article_d394cafa299f4634a40b26cad8d6049b pubmedcentral_primary_oai_pubmedcentral_nih_gov_5006970 csuc_recercat_oai_recercat_cat_2072_314554 proquest_miscellaneous_1827892290 proquest_miscellaneous_1816629339 proquest_journals_1815712336 gale_infotracmisc_A462097647 gale_infotracacademiconefile_A462097647 gale_incontextgauss_ISR_A462097647 gale_incontextgauss_IOV_A462097647 gale_healthsolutions_A462097647 pubmed_primary_27580049 crossref_primary_10_1371_journal_pone_0160648 crossref_citationtrail_10_1371_journal_pone_0160648 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2016-08-31 |
| PublicationDateYYYYMMDD | 2016-08-31 |
| PublicationDate_xml | – month: 08 year: 2016 text: 2016-08-31 day: 31 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: San Francisco – name: San Francisco, CA USA |
| PublicationTitle | PloS one |
| PublicationTitleAlternate | PLoS One |
| PublicationYear | 2016 |
| Publisher | Public Library of Science Public Library of Science (PLoS) |
| Publisher_xml | – name: Public Library of Science – name: Public Library of Science (PLoS) |
| References | AE Vlug (ref16) 1999; 38 BA Nexo (ref18) 2013; 24 VE Valkhoff (ref9) 2014; 67 MA Mayer (ref21) 2015; 210 B Carstensen (ref25) 2008; 51 P Avillach (ref3) 2013; 20 R Gini (ref28) 2015; 24 RL Richesson (ref4) 2013; 20 L Ryden (ref14) 2013; 34 MA Hernan (ref30) 2011; 22 MP Herk-Sukel (ref20) 2012; 21 AN Kho (ref26) 2012; 19 KI Morley (ref5) 2014; 9 R Gini (ref10) 2016; 4 R Gini (ref15) 2013; 13 PM Coloma (ref2) 2011; 20 ref24 G Hripcsak (ref8) 2013; 20 RL Richesson (ref1) 2014; 9 SA Johannesdottir (ref19) 2012; 4 BT Blak (ref17) 2011; 19 ref29 M Conway (ref12) 2011; 2011 JJ Sancho (ref22) 1998; 56 R Gini (ref27) 2015; 24 L Leitsalu (ref23) 2015; 44 CL Overby (ref7) 2013; 20 EI Benchimol (ref11) 2015; 12 G Trifiro (ref13) 2014; 275 J Pathak (ref6) 2013; 20 |
| References_xml | – volume: 56 start-page: 35 year: 1998 ident: ref22 article-title: IMASIS. A multicenter hospital information system—experience in Barcelona publication-title: Stud Health Technol Inform – volume: 67 start-page: 921 issue: 8 year: 2014 ident: ref9 article-title: Validation study in four health-care databases: upper gastrointestinal bleeding misclassification affects precision but not magnitude of drug-related upper gastrointestinal bleeding risk publication-title: J Clin Epidemiol doi: 10.1016/j.jclinepi.2014.02.020 – volume: 9 start-page: 215 issue: 1 year: 2014 ident: ref1 article-title: Clinical research informatics and electronic health record data publication-title: Yearb Med Inform doi: 10.15265/IY-2014-0009 – volume: 275 start-page: 551 issue: 6 year: 2014 ident: ref13 article-title: Combining multiple healthcare databases for postmarketing drug and vaccine safety surveillance: why and how? publication-title: J Intern Med doi: 10.1111/joim.12159 – volume: 51 start-page: 2187 issue: 12 year: 2008 ident: ref25 article-title: The Danish National Diabetes Register: trends in incidence, prevalence and mortality publication-title: Diabetologia doi: 10.1007/s00125-008-1156-z – volume: 24 start-page: 331 issue: 2 year: 2013 ident: ref18 article-title: Treatment of HIV and risk of multiple sclerosis publication-title: Epidemiology doi: 10.1097/EDE.0b013e318281e48a – volume: 19 start-page: 212 issue: 2 year: 2012 ident: ref26 article-title: Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study publication-title: J Am Med Inform Assoc doi: 10.1136/amiajnl-2011-000439 – volume: 34 start-page: 3035 issue: 39 year: 2013 ident: ref14 article-title: ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD publication-title: Eur Heart J doi: 10.1093/eurheartj/eht108 – volume: 20 start-page: e206 issue: e2 year: 2013 ident: ref6 article-title: Electronic health records-driven phenotyping: challenges, recent advances, and perspectives publication-title: J Am Med Inform Assoc doi: 10.1136/amiajnl-2013-002428 – volume: 38 start-page: 339 issue: 4–5 year: 1999 ident: ref16 article-title: Postmarketing surveillance based on electronic patient records: the IPCI project publication-title: Methods Inf Med – ident: ref29 – ident: ref24 – volume: 20 start-page: e319 issue: e2 year: 2013 ident: ref4 article-title: A comparison of phenotype definitions for diabetes mellitus publication-title: J Am Med Inform Assoc doi: 10.1136/amiajnl-2013-001952 – volume: 13 start-page: 15 year: 2013 ident: ref15 article-title: Chronic disease prevalence from Italian administrative databases in the VALORE project: a validation through comparison of population estimates with general practice databases and national survey publication-title: BMC Public Health doi: 10.1186/1471-2458-13-15 – volume: 24 start-page: 1 year: 2015 ident: ref27 article-title: Automatic identification of stages of type 2 diabetes, hypertension, ischaemic heart disease and heart failure from Italian General Practioners' electronic medical records: a validation study publication-title: Pharmacoepidemiol Drug Saf – volume: 20 start-page: 117 issue: 1 year: 2013 ident: ref8 article-title: Next-generation phenotyping of electronic health records publication-title: J Am Med Inform Assoc doi: 10.1136/amiajnl-2012-001145 – volume: 19 start-page: 251 issue: 4 year: 2011 ident: ref17 article-title: Generalisability of The Health Improvement Network (THIN) database: demographics, chronic disease prevalence and mortality rates publication-title: Inform Prim Care – volume: 9 start-page: e110900 issue: 11 year: 2014 ident: ref5 article-title: Defining disease phenotypes using national linked electronic health records: a case study of atrial fibrillation publication-title: PLoS One doi: 10.1371/journal.pone.0110900 – volume: 20 start-page: 1 issue: 1 year: 2011 ident: ref2 article-title: Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project publication-title: Pharmacoepidemiol Drug Saf doi: 10.1002/pds.2053 – volume: 22 start-page: 290 issue: 3 year: 2011 ident: ref30 article-title: With great data comes great responsibility: publishing comparative effectiveness research in epidemiology publication-title: Epidemiology doi: 10.1097/EDE.0b013e3182114039 – volume: 20 start-page: e243 issue: e2 year: 2013 ident: ref7 article-title: A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury publication-title: J Am Med Inform Assoc doi: 10.1136/amiajnl-2013-001930 – volume: 44 start-page: 1137 issue: 4 year: 2015 ident: ref23 article-title: Cohort Profile: Estonian Biobank of the Estonian Genome Center, University of Tartu publication-title: Int J Epidemiol doi: 10.1093/ije/dyt268 – volume: 20 start-page: 184 issue: 1 year: 2013 ident: ref3 article-title: Harmonization process for the identification of medical events in eight European healthcare databases: the experience from the EU-ADR project publication-title: J Am Med Inform Assoc doi: 10.1136/amiajnl-2012-000933 – volume: 4 issue: 1 year: 2016 ident: ref10 article-title: Data Extraction And Management In Networks Of Observational Health Care Databases For Scientific Research: A Comparison Among EU-ADR, OMOP, Mini-Sentinel And MATRICE Strategies publication-title: eGEMs doi: 10.13063/2327-9214.1189 – volume: 12 start-page: e1001885 issue: 10 year: 2015 ident: ref11 article-title: The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) Statement publication-title: PLoS Med doi: 10.1371/journal.pmed.1001885 – volume: 4 start-page: 303 year: 2012 ident: ref19 article-title: Existing data sources for clinical epidemiology: The Danish National Database of Reimbursed Prescriptions publication-title: Clin Epidemiol doi: 10.2147/CLEP.S37587 – volume: 2011 start-page: 274 year: 2011 ident: ref12 article-title: Analyzing the heterogeneity and complexity of Electronic Health Record oriented phenotyping algorithms publication-title: AMIA Annu Symp Proc – volume: 21 start-page: 94 issue: 1 year: 2012 ident: ref20 article-title: Record linkage for pharmacoepidemiological studies in cancer patients publication-title: Pharmacoepidemiol Drug Saf doi: 10.1002/pds.2205 – volume: 210 start-page: 224 year: 2015 ident: ref21 article-title: Reuse of EHRs to Support Clinical Research in a Hospital of Reference publication-title: Stud Health Technol Inform – volume: 24 start-page: 1 year: 2015 ident: ref28 article-title: Identifying chronic conditions from data sources with incomplete diagnostic information: the case of Italian administrative databases publication-title: Pharmacoepidemiol Drug Saf |
| SSID | ssj0053866 |
| Score | 2.3247793 |
| Snippet | Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source,... |
| SourceID | plos doaj pubmedcentral csuc proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | e0160648 |
| SubjectTerms | Age Algorithms Biology and Life Sciences Building components Causes of Chronic illnesses Collaboration Data Mining - methods Data sources Databases, Factual Derivation Diabetes Diabetes mellitus Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - epidemiology Diabetis Diagnostic systems Documentation Drugs Electronic health records Epidemiology Europe - epidemiology Female Genomes Health care Health informatics Heart Heterogeneity Hospitals Humans Information systems Male Medical diagnosis Medical informatics Medical records Medical research Medical screening Medicine and Health Sciences Mortality Physical Sciences Physicians Primary care Protocols clínics Research and Analysis Methods Source studies Standard data Type 2 diabetes |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELbQnrggyquBAgYh8ZDSJrFjJ9xKYbUg8RClqDfLjxgqVcmq2T3w75mJvdEGVZQD0u5h1-OsdmY8j2TmG0Ke-dpWVmibVq5gkKDUOjWNzNNa65yVNmeGYzfyx09iccI_nJanW6O-sCYswAMHxh04VnOrvQaz6blgXPPMFMJqVzkB0a1B6ws-b5NMBRsMp1iI2CjHZH4Q5bK_7NpmHzHVBM772XJEM9uvbYTtH43zbHne9ZdFnn8WUG55pPlNciOGkvQw_IUdcq1pb5GdeFh7-iIiSr-8TUxoxx1amugR-K2edp5iCkoLGmtienrW0gUWx3SgU0237ulbvdL0eLi737-mEcf2F8WOFApxIwURzumXcCvnDjmZv_t2tEjjcIXUSiFXqWestk6aWrvC-gxhzQz3wpvKG1l5WzVceMdtVvvce2MgEvClg1dmEJVesrtk1gI7dwlllZTCycZ4XnFnHewuIWrwzuXwA2WZELbhtLIReRwHYJyr4XGahAwkcEyhfFSUT0LScdcyIG9cQf8KhajAUTQXVq8UAmePH_BdZLJQDJHZeULeoKjHCyPt8AWonoqqp65SvYQ8RkVRoWN1NBXqkAtQSSm4TMjTgQKBNlqs5Pmh132v3n_-_g9Ex18nRM8jke-Ad1bH7glgAAJ4TSj3JpRgLuxkeRfVesPCXoFgSwnxCxOwc6Pqly8_GZfxolidN6gj0ggBcSOr_0aDPdc4XSAh98LpGblfQNKKqWpC5ORcTcQzXWnPfg5Y6CVCbcvs_v-Q5wNyHcJhEZ4Y7JHZ6mLdPISQc2UeDdblNxVGgGc priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Technology Collection dbid: 8FG link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1bb9MwFLagvPCCGLeFDTAIiYuULYkdO-EFjUEpSFzEGNqb5Us8Jk1J17QP_HvOSdywoGkgtQ-tj9Pq3G2f85mQp760hRXaxoXLGCxQSh2bSqZxqXXKcpsyw7Eb-dNnMTvkH4_yo7Dh1oayyrVP7By1ayzuke9CJMoluFkmXs_PYrw1Ck9XwxUaV8m1FCINlnQV0_drTwy2LERol2My3Q3S2Zk3dbWDyGoCb_05F44mtl3ZAN4_uOjJ_LRpL8o__y6jPBeXpjfJjZBQ0r1eAzbIlaq-RTaCybb0ecCVfnGbmL4pt2tsovsQvVraeIoLUZrRUBnT0pOazrBEpgHNqppVS9_qpaYH3R5_-4oGNNtfFPtSKGSPFAQ5pV_7DZ075HD67vv-LA5XLMRWCrmMPWOlddKU2mXWJwhuZrgX3hTeyMLbouLCO26T0qfeGwNS8LmDV2IQm16yu2RSAzs3CWWFlMLJynhecGcdzM4hd_DOpfADeR4Rtua0sgF_HK_BOFXdoZqEdUjPMYXyUUE-EYmHWfMef-Mf9C9RiArCRbWweqkQPnv4gO8skZliiM_OI_IGRT08GGm7L5rFsQqWqxwrudVeQ9z2XDCueWIyYbUrnIDllYnII1QU1fetDg5D7XGRJZDscRmRJx0Fwm3UWM9zrFdtqz58-fEfRAffRkTPApFvgHdWhx4KYADCeI0ot0eU4DTsaHgT1XrNwlb9MS-YuVb1i4cfD8P4UKzR69QRaYSA7JGVl9Fg5zXeMRCRe731DNzPYOmKC9aIyJFdjcQzHqlPfnaI6DkCbsvk_uV_fYtch3RX9CcC22SyXKyqB5BSLs3Dzm_8BmeQd2U priority: 102 providerName: ProQuest |
| Title | Identifying Cases of Type 2 Diabetes in Heterogeneous Data Sources: Strategy from the EMIF Project |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/27580049 https://www.proquest.com/docview/1815712336 https://www.proquest.com/docview/1816629339 https://www.proquest.com/docview/1827892290 https://recercat.cat/handle/2072/314554 https://pubmed.ncbi.nlm.nih.gov/PMC5006970 https://doaj.org/article/d394cafa299f4634a40b26cad8d6049b http://dx.doi.org/10.1371/journal.pone.0160648 |
| Volume | 11 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3db9MwELe27oUXxPhaYBSDkPiQMiWxYydICG1lpSBtTBub9mbZTjwmTc1oWom98Ldzl7gRQQMmtZZan93q7uy7i32_I-SFy21mhbZhViQMApRch6aUcZhrHbPUxsxwzEbe2xeTY_75ND1dIcuarZ6B9bWhHdaTOp5dbP34fvUeFvy7pmqDjJeDti6rabmFiGmCZ6tkDWyTxJoGe7w7V4DV3ZxeotcSiiRiPpnub7P0jNXA1gvrof27DXxweVHV13mnf16y_M1qje-Q297dpNutfqyTlXJ6l6z7BV3TVx51-vU9YtqU3SbtiY7AttW0chTDVJpQf2-mpudTOsELNBXoXVktavpBzzU9ak4A6rfUY91eUcxaoeBbUhDzmB60j3vuk-Px7tfRJPQFGEIrhZyHjrHcFtLkukisixD6zHAnnMmckZmzWcmFK7iNchc7Zwx4Cy4t4BUZRK6X7AEZTIGdG4SyTEpRyNI4nvHCFjA6Bc_CFUUMP5CmAWFLTivr0cmxSMaFao7cJEQpLccUykd5-QQk7EZdtugc_6F_g0JUYEzKmdVzheDa3Qd8J5FMFEP0dh6QHRR1NzHSNl9UszPl17UqWM6tdhqsuuOCcc0jkwiri6wQEHyZgDxFRVFtVmu3nahtDuoHriCXAXneUCAYxxRv-5zpRV2rT19ObkB0dNgjeumJXAW8s9pnWAADEOSrR7nZo4Qtxfa6N1CtlyysFQg2leDjMAEjl6p-ffezrhsnxRt8jToijRDgW7L8XzSYl40VCALysF09HfcTCGwxnA2I7K2rnnj6PdPzbw1eeopw3DJ6dIP_9pjcAo9YtIcGm2Qwny3KJ-B1zs2QrMpTCW02irEdfxyStZ3d_YPDYfMcZ9hsNNj-3P0FrYKHhA |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwELdGeYAXxPhaYDCDQHxI2dLYsRMkhMZG6dgHiG1obya24zFpasrSCu2f4m_kLnHCiqbBy6T2ofHFqc7n-4jvfkfIU5eZ1IjchKmNGQQoWR7qQvbDLM_7LDF9pjlWI2_viOE-_3iQHMyRX20tDKZVtjqxVtS2NPiOfAUsUSJBzTLxdvwjxK5ReLrattBoxGKzOP0JIVv1ZmMd1vdZHA_e760NQ99VIDRSyEnoGMuMlTrLbWxchHhemjvhdOq0TJ1JCy6c5SbKXN85reHBLrHwiTTCsUsG814hVzlMg8FeOvjQan7QHUL48jwm-yteGpbH5ahYRiQ3gV2Gzpi_nqmmxjcL6ExCb3xcVuf5u3-nbZ6xg4Ob5IZ3YOlqI3HzZK4Y3SLzXkVU9IXHsX55m-imCLgupKJrYC0rWjqKgS-Nqc_EqejRiA4xJacESS7KaUXX80lOd-szheo19ei5pxTrYCh4qxQEZ0A_Ny-Q7pD9S2H-XdIbATsXCGWplMLKQjuecmss3J2Ar-Ks7cMDkiQgrOW0Mh7vHNtuHKv6EE9C3NNwTOH6KL8-AQm7u8YN3sc_6F_hIiowT8WJyScK4bq7H_iNIxkrhnjwPCDvcKm7iZG2vlCeHCqvKZRlGTe5y8FPcFwwnvNIx8LkNrUCwjkdkCUUFNXUyXYKSq1yEUfgXHIZkCc1BcJ7jDB_6DCfVpXa-PT1P4h2v8wQPfdErgTemdzXbAADEDZshnJxhhKUlJkZXkCxbllYqT_bGe5sRf384cfdME6KOYG1OCKNEOCtsuwiGqz0xp4GAbnX7J6O-zGEyhggB0TO7KuZ5ZkdGR19rxHYEwT4ltH9i__6Erk23NveUlsbO5sPyHVwtUVzGrFIepOTafEQ3NmJflTrEEq-XbbS-g0p7baT |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3rb9MwELdGkRBfEOO1wmAGgXhI2dLYsRMkhMZGtTEY08bQvhk_4jFpasrSCu1f46_jLnHDgqbBl0nth9YXJzqf7xHf_Y6Qpz63mRXaRplLGAQouY5MIQdRrvWApXbADMdq5E_bYmOffzhID-bIr1ktDKZVznRirahdafEd-QpYolSCmmVixYe0iJ314dvxjwg7SOFJ66ydRiMiW8XpTwjfqjeb67DWz5Jk-P7L2kYUOgxEVgo5iTxjuXXS5Nol1seI7WW4F95k3sjM26zgwjtu49wPvDcGHsKnDj6xQWh2yWDeK-SqZDLDPZattekloEeECKV6TA5WgmQsj8tRsYyobgI7Dp0xhT1bTW1oHNCah974uKzO833_TuE8YxOHN8mN4MzS1Ub65slcMbpF5oO6qOiLgGn98jYxTUFwXVRF18ByVrT0FINgmtCQlVPRoxHdwPScEqS6KKcVXdcTTffq84XqNQ1IuqcUa2IoeK4UhGhId5qXSXfI_qUw_y7pjYCdC4SyTErhZGE8z7izDq5OwW_xzg3gBmnaJ2zGaWUD9jm24DhW9YGehBio4ZjC9VFhffokaq8aN9gf_6B_hYuowFQVJ1ZPFEJ3tz_wm8QyUQyx4XmfvMOlbidG2vqP8uRQBa2hHMu51V6Dz-C5YFzz2CTCapc5AaGd6ZMlFBTV1My2ykqtcpHE4Ghy2SdPagqE-hjhpjnU06pSm5-__gfR3m6H6Hkg8iXwzupQvwEMQAixDuVihxIUlu0ML6BYz1hYqT9bG66cifr5w4_bYZwU8wNrcUQaIcBzZflFNFj1jf0N-uRes3ta7icQNmOw3Ceys686y9MdGR19r9HYUwT7lvH9ix99iVwDdaU-bm5vPSDXwesWzcHEIulNTqbFQ_BsJ-ZRrUIo-XbZOus3Hyi6lA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Identifying+Cases+of+Type+2+Diabetes+in+Heterogeneous+Data+Sources%3A+Strategy+from+the+EMIF+Project&rft.jtitle=PloS+one&rft.au=Roberto%2C+Giuseppe&rft.au=Leal%2C+Ingrid&rft.au=Sattar%2C+Naveed&rft.au=Loomis%2C+A+Katrina&rft.date=2016-08-31&rft.issn=1932-6203&rft.eissn=1932-6203&rft.volume=11&rft.issue=8&rft.spage=e0160648&rft_id=info:doi/10.1371%2Fjournal.pone.0160648&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1932-6203&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1932-6203&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1932-6203&client=summon |