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(Int J Health Geogr[TA])
703 results
  • smokeSALUD: exploring the effect of demographic change on the smoking prevalence at municipality level in Austria. [Journal Article]
  • IJInt J Health Geogr 2016 Oct 7; 15(1):36
  • Tomintz M, Kosar B, Clarke G
  • CONCLUSIONS: This case study shows the application of smokeSALUD to model the spatial-temporal changes in the smoking population in Austria between 2001 and 2011. This is important as no data on smoking exists at this geographical scale (municipality). However, spatial microsimulation models are useful tools to estimate small area health data and to overcome these problems. The simulations and analysis should support health decision makers to identify hot spots of smokers and this should help to show where to spend health resources best in order to reduce health inequalities.
  • Geographic dimensions of a health network dedicated to occupational and work related diseases. [Journal Article]
  • IJInt J Health Geogr 2016 Sep 27; 15(1):34
  • Delaunay M, Godard V, … Bonneterre V
  • CONCLUSIONS: The geographic approach to this network, enhanced by the possibilities provided by the GIS tool, allow a better understanding of the coverage of this network at a national level, as well as the visualization of capture rates for all OD clinics. Highlighting geographic and thematic shading zones bring new perspectives to the analysis of occupational health data, and should improve occupational health vigilance and surveillance.
  • The spatial structure of chronic morbidity: evidence from UK census returns. [Journal Article]
  • IJInt J Health Geogr 2016 Aug 24; 15(1):30
  • Dutey-Magni PF, Moon G
  • CONCLUSIONS: Systematic investigation of spatial structures and dependency is important to develop model-based estimation tools in chronic disease mapping. Spatial structures reflecting migration interactions are easy to develop and capture autocorrelation in LLTI. Patterns of spatial dependency in the geographical distribution of LLTI are not comparable across ethnic groups. Ethnic stratification of local health information is needed and there is potential to further address complexity in prevalence models by improving access to disaggregated data.
  • Visual analytics of geo-social interaction patterns for epidemic control. [Journal Article]
  • IJInt J Health Geogr 2016 Aug 10; 15(1):28
  • Luo W
  • CONCLUSIONS: The case study shows how GS-EpiViz does support the design and testing of advanced control scenarios in airborne disease (e.g., influenza). The geo-social patterns identified through exploring human interaction data can help target critical individuals, locations, and clusters of locations for disease control purposes. The varying spatial-social scales can help geographically and socially prioritize limited resources (e.g., vaccines).
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