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ORIGINAL ARTICLE
Year : 2021  |  Volume : 65  |  Issue : 4  |  Page : 362-368

A spatiotemporal geographic information system-based assessment of human immunodeficiency virus/acquired immune deficiency syndrome distribution in Manipur, India


1 Associate Professor, National Institute of Technology, Imphal, Manipur, India
2 Research Scholar, National Institute of Technology, Imphal, Manipur, India
3 Project Assistant, National Institute of Technology, Imphal, Manipur, India

Correspondence Address:
Vicky Anand
Department of Civil Engineering, National Institute of Technology, Imphal, Manipur
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijph.IJPH_1308_20

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Background: Geographic information system (GIS) is a versatile tool that assists in health education, planning, research, monitoring, and evaluation of programs related to health. One of the epidemics which threaten the overall human welfare is human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS). In Manipur, the cases of HIV/AIDS have been reported at significant level. Objective: The study aimed to detect the hotspot regions of HIV/AIDS prevalence in Manipur and to identify the significant factors which influence the HIV prevalence. Methods: This study evaluates the spatial variations of HIV/AIDS prevalence in the state of Manipur, India, from 2011 to 2018. In this study, Getis-Ord Gi* statistic was used to detect the HIV/AIDS prevalent regions. The ordinary least square (OLS) spatial statistics embedded in the ArcGIS were employed for exploring the spatial relation between HIV/AIDS occurrence and the predictors. Results: It was observed from the hotspot results that Churachandpur, Ukhrul, and Thoubal are the blocks where HIV/AIDS is more prevalent. Six factors associated with the prevalence of HIV/AIDS were found to be significant. The most obvious factor influencing HIV in the region is illiteracy. The constructed OLS model has the highest value of adjusted R2 statistic equals to 0.67 and the lowest value of the Akaike Information Criterion statistic equals to 474.55. Conclusion: The use of hotspot analysis, regression analysis, spatial autocorrelation, and GIS can aid health planners in properly assessing and identifying spatial prevalence of diseases among the masses to better guide evidence-based health planning decisions.


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