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(International journal of health geographics[TA]) articles in PubMed
700 results
  • The spatial structure of chronic morbidity: evidence from UK census returns. [Journal Article]
  • Int J Health Geogr 2016; 15(1):30IJ
  • 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]
  • Int J Health Geogr 2016; 15(1):28IJ
  • 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).
  • Individual level covariate adjusted conditional autoregressive (indiCAR) model for disease mapping. [Journal Article]
  • Int J Health Geogr 2016; 15(1):25IJ
  • Huque MH, Anderson C, … Ryan L
  • CONCLUSIONS: Incorporating individual covariate data in disease mapping studies improves the estimation of fixed and random effect parameters by utilizing information from multiple sources. Health registries routinely collect individual and area level information and thus could benefit by using indiCAR for mapping disease rates. Moreover, the natural applicability of indiCAR in a distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. CI NSW Study Reference Number: 2012/07/410. Dated: July 2012.
  • Mapping intra-urban malaria risk using high resolution satellite imagery: a case study of Dar es Salaam. [Journal Article]
  • Int J Health Geogr 2016; 15(1):26IJ
  • Kabaria CW, Molteni F, … Linard C
  • CONCLUSIONS: The predictive maps produced can serve as valuable resources for municipal councils aiming to shrink the extents of malaria across cities, target resources for vector control or intensify mosquito and disease surveillance. The semi-automated modelling process developed can be replicated in other urban areas to identify factors that influence heterogeneity in malaria risk patterns and detect vulnerable zones. There is a definite need to expand research into the unique epidemiology of malaria transmission in urban areas for focal elimination and sustained control agendas.
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