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ORIGINAL ARTICLE Table of Contents   
Year : 2015  |  Volume : 40  |  Issue : 3  |  Page : 188-192
Probability mapping to determine the spatial risk pattern of acute gastroenteritis in Coimbatore District, India, using Geographic Information Systems (GIS)


1 Associate Professor in Zoology, Nirmala College for Women (Autonomous), Redfields, Coimbatore, India
2 Research Scholar, Department of Environmental Management, Bharathidasan University, Tiruchirapalli, Tamil Nadu, India
3 Assistant Professor, Department of Environmental Management, Bharathidasan University, Tiruchirapalli, Tamil Nadu, India
4 Director of Research, Jagadguru Sri Shivarathreeswara Medical University, Mysore, Karnataka, India

Correspondence Address:
Pawlin Vasanthi Joseph
Assistant Professor in Zoology, Nirmala College for Women (Autonomous), Redfields, Coimbatore - 641 018, Tamil Nadu
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0970-0218.158865

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Background: Maps show well the spatial configuration of information. Considerable effort is devoted to the development of geographical information systems (GIS) that increase understanding of public health problems and in particular to collaborate efforts among clinicians, epidemiologists, ecologists, and geographers to map and forecast disease risk. Objectives: Small populations tend to give rise to the most extreme disease rates, even if the actual rates are similar across the areas. Such situations will follow the decision-maker's attention on these areas when they scrutinize the map for decision making or resource allocation. As an alternative, maps can be prepared using P-values (probabilistic values). Materials and Methods: The statistical significance of rates rather than the rates themselves are used to map the results. The incidence rates calculated for each village from 2000 to 2009 is used to estimate λ, the expected number of cases in the study area. The obtained results are mapped using Arc GIS 10.0. Results: The likelihood of infections from low to high is depicted in the map and it is observed that five villages namely, Odanthurai, Coimbatore Corporation, Ikkaraiboluvampatti, Puliakulam, and Pollachi Corporation are more likely to have significantly high incidences. Conclusion: In the probability map, some of the areas with exceptionally high or low rates disappear. These are typically small unpopulated areas, whose rates are unstable due to the small numbers problem. The probability map shows more specific regions of relative risks and expected outcomes.


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  2007 - Indian Journal of Community Medicine | Published by Wolters Kluwer - Medknow
  Online since 15th September, 2007