Spatial modelling of rheumatoid arthritis in Turkey by geographic information systems (GIS).

We described the recent spatial distribution of rheumatoid arthritis in Turkey and assessed the role of environmental variables in this distribution. We developed an observed rheumatoid arthritis (RA) incidence grid map by using georeferenced rheumatoid arthritis case data (2011) from the centres of 81 provinces and the kriging method with a spherical variogram model in geographic information systems (GIS). We also modelled rheumatoid arthritis incidence in GIS by using complementary spatial database including the grid map layers of 14 environmental variables of Turkey. We conducted principle component analysis and multiple regression to investigate the relationships among variables and develop a model, respectively. The produced model was run in GIS to obtain a predicted (model) RA map. We tested the reliability of the model map by residual statistics and found the model map dependable. Observed and model incidence maps revealed the geographic distribution of rheumatoid arthritis cases in Turkey. The mean temperature, minimum temperature, maximum temperature, water vapour pressure, elevation, potential evapotranspiration, latitude, distance to seas, sunshine fraction, precipitation, longitude and aspect variables were found to have significant impacts on rheumatoid arthritis. Consequently, the model incidence map established a good background to predict rheumatoid arthritis cases following environmental changes.

Spatiotemporal change and ecological modelling of malaria in Turkey by means of geographic information systems.

We described the spatiotemporal change of malaria (Plasmodium vivax) in Turkey over 34 years (1975-2008), and assessed the role of environmental variables in this change. We developed seven 5-year-period raster maps by using geo-referenced malaria case data from the centres of 81 provinces and the kriging method with a spherical variogram model in a geographic information systems (GIS) model. We also modelled malaria incidence in GIS by using our average malaria incidence raster map, and complementary spatial database including the raster map layers of 14 environmental variables. We chose linear regression analysis with backward method to investigate relationships among variables and develop a model. The model was run in GIS to obtain a model incidence raster map. We tested the reliability of the model map by residual statistics, and found the model map dependable. Five-year-period maps revealed that the distribution of malaria cases moved from the East Mediterranean region to the Southeast Anatolia region due to changing human activities. The latitude, minimum temperature, distance to seas and elevation variables were found to have significant impacts on malaria. Consequently, the model incidence map established a good background for early warning systems to predict epidemics of malaria following environmental changes.