A generic method for improving the spatial interoperability of medical and ecological databases

Abstract

BackgroundThe 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.MethodsFirstly, 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.ResultsWe 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.ConclusionsOur 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12942-017-0109-5) contains supplementary material, which is available to authorized users.

Fine‐scale geographical distribution and ecological risk factors for Crohn's disease in France (2007‐2014)

“…The total surface area is 543 965 km 2 , giving an average of 118 inhabitants per km 2 . The hybrid spatial unit described in our previous work has been used to divide metropolitan France area into 5610 spatial analysis units . Briefly, these spatial units were defined to match data from the French national hospital discharge database (measured on a spatial scale called the PMSI geographical code) and population data from the French National Institute of Statistics and Economic Studies (measured at the municipal level), in order to construct epidemiological indicators.…”

“…The hybrid spatial unit described in our previous work has been used to divide metropolitan France area into 5610 spatial analysis units. 9 Briefly, these spatial units were defined to match data from the French national hospital discharge database (measured on a spatial scale called the PMSI geographical code) and population data from the French National Institute of Statistics and Economic Studies (measured at the municipal level), in order to construct epidemiological indicators. These 5610 units have a median (interquartile range [IQR]) surface area of 70.0 (21.6-147.6) km 2 , and a median population of 6300 (3500-11 800) inhabitants.…”

Association Between COVID-19 and Self-Harm: Nationwide Retrospective Ecological Spatiotemporal Study in Metropolitan France

“…This retrospective, observational study involved patients over the age of 10 hospitalized in a medical, surgical or obstetrics ward for self-harm from January 2019 to December 2021 and/or for COVID-19 from March 2020 to December 2021 in metropolitan France (mainland France and Corsica, an area covering a total of 543,940 km² and 57,249,208 inhabitants aged 10 and over in 2018) [16]. A hybrid spatial unit described in previous work, was used to divide metropolitan France into 5,535 spatial units [17]. The time unit was the month.…”