How sick is Austria? – A decision support framework for different evaluations of the burden of disease within the Austrian population based on different data sources
ABSTRACT Objectives In healthcare it is crucial to have a fundamental knowledge of the burden of diseases within the population. Therefore we aimed to develop an Atlas of Epidemiology to gain better insight on the epidemiological situation. Based on primary and secondary health care data, we aime...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Swansea University
2017-04-01
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| Series: | International Journal of Population Data Science |
| Online Access: | https://ijpds.org/article/view/111 |
| _version_ | 1797430892895928320 |
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| author | Barbara Glock Florian Endel Gottfried Endel Klaudia Sandholzer Niki Popper Christoph Rinner Georg Duftschmid Peter Filzmoser Mehmet Can Mert Jürgen Holl Michael Wagner-Pinter |
| author_facet | Barbara Glock Florian Endel Gottfried Endel Klaudia Sandholzer Niki Popper Christoph Rinner Georg Duftschmid Peter Filzmoser Mehmet Can Mert Jürgen Holl Michael Wagner-Pinter |
| author_sort | Barbara Glock |
| collection | DOAJ |
| description | ABSTRACT
Objectives
In healthcare it is crucial to have a fundamental knowledge of the burden of diseases within the population. Therefore we aimed to develop an Atlas of Epidemiology to gain better insight on the epidemiological situation. Based on primary and secondary health care data, we aimed to present results in interactive charts and maps, comprehensible to experts and the general public. The atlas builds a framework for rapid deployment of new data and results in a reproducible and efficient way. As a first use case three methods based on two different databases for the estimation of diabetes prevalence in Austria are compared.
Approach
Datasources: (i) reimbursement data 2006/2007 (GAP-DRG); (ii) national routine health survey (ATHIS) for 2006/2007. Methods for diabetes prevalence estimation: 1) ATC-ICD statistically relates pseudonymized data on medications to data on diagnoses from hospitalizations and sick leaves. 2) With the method Experts, medical experts assign specific medications to diabetes diagnoses. Patients with these medications are identified together with hospitalized diabetes diagnosed patients in GAP-DRG. 3) In ATHIS a sample of 15.000 persons was questioned if they a) ever had diabetes and b) were treated against diabetes in the last 12 months. Results are projected onto the Austrian population. Patients are divided by 10-year age-classes, gender and state. For the publicly online framework, implemented in html and javascript, pre-processed data in different granularity is required and used.
Results
Maps of Austria represent the prevalence of diabetes for each method and granularity level. The difference of the methods can be seen by clicking on the next map. For different age-classes (resp. different gender) the three methods can be compared directly within a bar chart. The technology for a rapid deployment of new data is now developed. For the use case first results have already been presented to decision makers, and feedback has been incorporated.
Conclusion
Besides depicting disease prevalence, the atlas of epidemiology also allows to visualize health care service data and results of simulation models in a fast and efficient way, which is important for decision makers. Soon the results of the ATC-ICD project on the prevalence of different diseases based on ICD9 diagnoses and medication data will be published in an aggregated form. This project is part of the K-Project dexhelpp in COMET – Competence Centers for Excellent Technologies that is funded by BMVIT, BMWGJ and transacted by FFG. |
| first_indexed | 2024-03-09T09:35:03Z |
| format | Article |
| id | doaj.art-a0c0fdb696164889a1831c0a901a4ff3 |
| institution | Directory Open Access Journal |
| issn | 2399-4908 |
| language | English |
| last_indexed | 2024-03-09T09:35:03Z |
| publishDate | 2017-04-01 |
| publisher | Swansea University |
| record_format | Article |
| series | International Journal of Population Data Science |
| spelling | doaj.art-a0c0fdb696164889a1831c0a901a4ff32023-12-02T02:31:11ZengSwansea UniversityInternational Journal of Population Data Science2399-49082017-04-011110.23889/ijpds.v1i1.111111How sick is Austria? – A decision support framework for different evaluations of the burden of disease within the Austrian population based on different data sourcesBarbara Glock0Florian Endel1Gottfried Endel2Klaudia Sandholzer3Niki Popper4Christoph Rinner5Georg Duftschmid6Peter Filzmoser7Mehmet Can Mert8Jürgen Holl9Michael Wagner-Pinter10dwh Simulation Services, dwh GmbHInstitute for Analysis and Scientific Computing, Vienna University of TechnologyMain Association of Austrian Social Security InstitutionsMain Association of Austrian Social Security InstitutionsdexhelppCenter for Medical Statistics, Informatics, and Intelligent Systems, Medical University of ViennaCenter for Medical Statistics, Informatics, and Intelligent Systems, Medical University of ViennaInstitute of Statistics & Mathematical Methods in Economics, Vienna University of TechnologyInstitute of Statistics & Mathematical Methods in Economics, Vienna University of TechnologySynthesis ForschungSynthesis ForschungABSTRACT Objectives In healthcare it is crucial to have a fundamental knowledge of the burden of diseases within the population. Therefore we aimed to develop an Atlas of Epidemiology to gain better insight on the epidemiological situation. Based on primary and secondary health care data, we aimed to present results in interactive charts and maps, comprehensible to experts and the general public. The atlas builds a framework for rapid deployment of new data and results in a reproducible and efficient way. As a first use case three methods based on two different databases for the estimation of diabetes prevalence in Austria are compared. Approach Datasources: (i) reimbursement data 2006/2007 (GAP-DRG); (ii) national routine health survey (ATHIS) for 2006/2007. Methods for diabetes prevalence estimation: 1) ATC-ICD statistically relates pseudonymized data on medications to data on diagnoses from hospitalizations and sick leaves. 2) With the method Experts, medical experts assign specific medications to diabetes diagnoses. Patients with these medications are identified together with hospitalized diabetes diagnosed patients in GAP-DRG. 3) In ATHIS a sample of 15.000 persons was questioned if they a) ever had diabetes and b) were treated against diabetes in the last 12 months. Results are projected onto the Austrian population. Patients are divided by 10-year age-classes, gender and state. For the publicly online framework, implemented in html and javascript, pre-processed data in different granularity is required and used. Results Maps of Austria represent the prevalence of diabetes for each method and granularity level. The difference of the methods can be seen by clicking on the next map. For different age-classes (resp. different gender) the three methods can be compared directly within a bar chart. The technology for a rapid deployment of new data is now developed. For the use case first results have already been presented to decision makers, and feedback has been incorporated. Conclusion Besides depicting disease prevalence, the atlas of epidemiology also allows to visualize health care service data and results of simulation models in a fast and efficient way, which is important for decision makers. Soon the results of the ATC-ICD project on the prevalence of different diseases based on ICD9 diagnoses and medication data will be published in an aggregated form. This project is part of the K-Project dexhelpp in COMET – Competence Centers for Excellent Technologies that is funded by BMVIT, BMWGJ and transacted by FFG.https://ijpds.org/article/view/111 |
| spellingShingle | Barbara Glock Florian Endel Gottfried Endel Klaudia Sandholzer Niki Popper Christoph Rinner Georg Duftschmid Peter Filzmoser Mehmet Can Mert Jürgen Holl Michael Wagner-Pinter How sick is Austria? – A decision support framework for different evaluations of the burden of disease within the Austrian population based on different data sources International Journal of Population Data Science |
| title | How sick is Austria? – A decision support framework for different evaluations of the burden of disease within the Austrian population based on different data sources |
| title_full | How sick is Austria? – A decision support framework for different evaluations of the burden of disease within the Austrian population based on different data sources |
| title_fullStr | How sick is Austria? – A decision support framework for different evaluations of the burden of disease within the Austrian population based on different data sources |
| title_full_unstemmed | How sick is Austria? – A decision support framework for different evaluations of the burden of disease within the Austrian population based on different data sources |
| title_short | How sick is Austria? – A decision support framework for different evaluations of the burden of disease within the Austrian population based on different data sources |
| title_sort | how sick is austria a decision support framework for different evaluations of the burden of disease within the austrian population based on different data sources |
| url | https://ijpds.org/article/view/111 |
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