A generic method for improving the spatial ...
Type de document :
Article dans une revue scientifique: Article original
PMID :
URL permanente :
Titre :
A generic method for improving the spatial interoperability of medical and ecological databases
Auteur(s) :
Ghenassia, A [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
BEUSCART, Jean-Baptiste [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Ficheur, Gregoire [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Occelli, Florent [Auteur]
IMPact de l'Environnement Chimique sur la Santé humaine (IMPECS) - EA 4483
Impact de l'environnement chimique sur la santé humaine - ULR 4483 [IMPECS]
Babykina, Génia [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Chazard, Emmanuel [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Genin, Michaël [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
BEUSCART, Jean-Baptiste [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Ficheur, Gregoire [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Occelli, Florent [Auteur]
IMPact de l'Environnement Chimique sur la Santé humaine (IMPECS) - EA 4483
Impact de l'environnement chimique sur la santé humaine - ULR 4483 [IMPECS]
Babykina, Génia [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Chazard, Emmanuel [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Genin, Michaël [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Titre de la revue :
International journal of health geographics
Nom court de la revue :
Int. J. Health Geogr.
Numéro :
16
Date de publication :
2017-10-03
ISSN :
1476-072X
Mot(s)-clé(s) en anglais :
Data reuse
Interoperability
Change-of-support problem
Spatial analysis
Mesh:Geographic Mapping*
Mesh:France/epidemiology
Mesh:Ecological and Environmental Phenomena*
Mesh:Databases
Mesh:Factual/trends
Mesh:Databases
Mesh:Factual/standards*
Mesh:Birth Rate/trends*
Mesh:Spatial Analysis*
Mesh:Humans
Interoperability
Change-of-support problem
Spatial analysis
Mesh:Geographic Mapping*
Mesh:France/epidemiology
Mesh:Ecological and Environmental Phenomena*
Mesh:Databases
Mesh:Factual/trends
Mesh:Databases
Mesh:Factual/standards*
Mesh:Birth Rate/trends*
Mesh:Spatial Analysis*
Mesh:Humans
Discipline(s) HAL :
Sciences du Vivant [q-bio]
Résumé en anglais : [en]
The availability of big data in healthcare and the intensive development of data reuse and georeferencing have opened up perspectives for health spatial analysis. However, fine-scale spatial studies of ecological and medical ...
Lire la suite >The availability of big data in healthcare and the intensive development of data reuse and georeferencing have opened up perspectives for health spatial analysis. However, fine-scale spatial studies of ecological and medical databases are limited by the change of support problem and thus a lack of spatial unit interoperability. The use of spatial disaggregation methods to solve this problem introduces errors into the spatial estimations. Here, we present a generic, two-step method for merging medical and ecological databases that avoids the use of spatial disaggregation methods, while maximizing the spatial resolution. Firstly, a mapping table is created after one or more transition matrices have been defined. The latter link the spatial units of the original databases to the spatial units of the final database. Secondly, the mapping table is validated by (1) comparing the covariates contained in the two original databases, and (2) checking the spatial validity with a spatial continuity criterion and a spatial resolution index. We used our novel method to merge a medical database (the French national diagnosis-related group database, containing 5644 spatial units) with an ecological database (produced by the French National Institute of Statistics and Economic Studies, and containing with 36,594 spatial units). The mapping table yielded 5632 final spatial units. The mapping table's validity was evaluated by comparing the number of births in the medical database and the ecological databases in each final spatial unit. The median [interquartile range] relative difference was 2.3% [0; 5.7]. The spatial continuity criterion was low (2.4%), and the spatial resolution index was greater than for most French administrative areas. Our innovative approach improves interoperability between medical and ecological databases and facilitates fine-scale spatial analyses. We have shown that disaggregation models and large aggregation techniques are not necessarily the best ways to tackle the change of support problem.Lire moins >
Lire la suite >The availability of big data in healthcare and the intensive development of data reuse and georeferencing have opened up perspectives for health spatial analysis. However, fine-scale spatial studies of ecological and medical databases are limited by the change of support problem and thus a lack of spatial unit interoperability. The use of spatial disaggregation methods to solve this problem introduces errors into the spatial estimations. Here, we present a generic, two-step method for merging medical and ecological databases that avoids the use of spatial disaggregation methods, while maximizing the spatial resolution. Firstly, a mapping table is created after one or more transition matrices have been defined. The latter link the spatial units of the original databases to the spatial units of the final database. Secondly, the mapping table is validated by (1) comparing the covariates contained in the two original databases, and (2) checking the spatial validity with a spatial continuity criterion and a spatial resolution index. We used our novel method to merge a medical database (the French national diagnosis-related group database, containing 5644 spatial units) with an ecological database (produced by the French National Institute of Statistics and Economic Studies, and containing with 36,594 spatial units). The mapping table yielded 5632 final spatial units. The mapping table's validity was evaluated by comparing the number of births in the medical database and the ecological databases in each final spatial unit. The median [interquartile range] relative difference was 2.3% [0; 5.7]. The spatial continuity criterion was low (2.4%), and the spatial resolution index was greater than for most French administrative areas. Our innovative approach improves interoperability between medical and ecological databases and facilitates fine-scale spatial analyses. We have shown that disaggregation models and large aggregation techniques are not necessarily the best ways to tackle the change of support problem.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
CHU Lille
Institut Pasteur de Lille
Université de Lille
Institut Pasteur de Lille
Université de Lille
Date de dépôt :
2019-12-09T18:17:13Z
2020-05-05T13:15:00Z
2020-05-06T08:00:14Z
2020-05-05T13:15:00Z
2020-05-06T08:00:14Z
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