Spatial clustering of Cholera in Sabah, Malaysia

Cholera epidemics can produce devastating public health outcomes. Cholera distribution is influenced by temperature, precipitation, elevation, distance to the coastline and oceanic environmental factors such as sea surface temperature, sea surface height and ocean chlorophyll concentration. The pu...

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Main Authors: Syed Sharizman Syed Abdul Rahim, Shamsul Azhar Shah, Zahir Izuan Azhar, Mohammad Saffree Jeffree, Mohd Rohaizat Hassan, Nazarudin Safian
Format: Conference or Workshop Item
Language:English
Published: 2019
Subjects:
Online Access:https://eprints.ums.edu.my/id/eprint/24706/1/Spatial%20Clustering%20of%20Cholera%20in%20Sabah%2C%20Malaysia.pdf
https://eprints.ums.edu.my/id/eprint/24706/
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Institution: Universiti Malaysia Sabah
Language: English
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spelling my.ums.eprints.247062020-02-09T23:39:27Z https://eprints.ums.edu.my/id/eprint/24706/ Spatial clustering of Cholera in Sabah, Malaysia Syed Sharizman Syed Abdul Rahim Shamsul Azhar Shah Zahir Izuan Azhar Mohammad Saffree Jeffree Mohd Rohaizat Hassan Nazarudin Safian R Medicine (General) Cholera epidemics can produce devastating public health outcomes. Cholera distribution is influenced by temperature, precipitation, elevation, distance to the coastline and oceanic environmental factors such as sea surface temperature, sea surface height and ocean chlorophyll concentration. The purpose of this study is to describe the spatial epidemiology of cholera in the four districts of Sabah. Methods: This is a retrospective review of 4 years (2011 to 2014) data from the districts of Kota Kinabalu, Penampang, Putatan and Papar, Sabah. All reported cases of cholera from those areas are included. Coordinates for locations of the cases are based on home addresses. SPSS v20, ArcGIS v10 and CrimeStat IV were used for data analysis and mapping. Results: Cholera showed several clustering of cases, such as in 2011 and 2014 in Kota Kinabalu. In the year 2011 and 2013, Penampang and Papar districts had the nearest neighbour index of less than 1, but p value was not significant, meaning the pattern did not appear to be significant. Nearest neighbour hierarchical clustering analysis further revealed cholera had 7 clusters, of those 6 were first order and 1 was a second order cluster. Conclusion: Cholera shows disease clustering which could mean it is due to its common point source or localised human to human transmission. Using GIS as a tool may help in surveillance and control of cholera infections. 2019 Conference or Workshop Item PeerReviewed text en https://eprints.ums.edu.my/id/eprint/24706/1/Spatial%20Clustering%20of%20Cholera%20in%20Sabah%2C%20Malaysia.pdf Syed Sharizman Syed Abdul Rahim and Shamsul Azhar Shah and Zahir Izuan Azhar and Mohammad Saffree Jeffree and Mohd Rohaizat Hassan and Nazarudin Safian (2019) Spatial clustering of Cholera in Sabah, Malaysia. In: Malaysian Journal of Medicine and Health Sciences.
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
topic R Medicine (General)
spellingShingle R Medicine (General)
Syed Sharizman Syed Abdul Rahim
Shamsul Azhar Shah
Zahir Izuan Azhar
Mohammad Saffree Jeffree
Mohd Rohaizat Hassan
Nazarudin Safian
Spatial clustering of Cholera in Sabah, Malaysia
description Cholera epidemics can produce devastating public health outcomes. Cholera distribution is influenced by temperature, precipitation, elevation, distance to the coastline and oceanic environmental factors such as sea surface temperature, sea surface height and ocean chlorophyll concentration. The purpose of this study is to describe the spatial epidemiology of cholera in the four districts of Sabah. Methods: This is a retrospective review of 4 years (2011 to 2014) data from the districts of Kota Kinabalu, Penampang, Putatan and Papar, Sabah. All reported cases of cholera from those areas are included. Coordinates for locations of the cases are based on home addresses. SPSS v20, ArcGIS v10 and CrimeStat IV were used for data analysis and mapping. Results: Cholera showed several clustering of cases, such as in 2011 and 2014 in Kota Kinabalu. In the year 2011 and 2013, Penampang and Papar districts had the nearest neighbour index of less than 1, but p value was not significant, meaning the pattern did not appear to be significant. Nearest neighbour hierarchical clustering analysis further revealed cholera had 7 clusters, of those 6 were first order and 1 was a second order cluster. Conclusion: Cholera shows disease clustering which could mean it is due to its common point source or localised human to human transmission. Using GIS as a tool may help in surveillance and control of cholera infections.
format Conference or Workshop Item
author Syed Sharizman Syed Abdul Rahim
Shamsul Azhar Shah
Zahir Izuan Azhar
Mohammad Saffree Jeffree
Mohd Rohaizat Hassan
Nazarudin Safian
author_facet Syed Sharizman Syed Abdul Rahim
Shamsul Azhar Shah
Zahir Izuan Azhar
Mohammad Saffree Jeffree
Mohd Rohaizat Hassan
Nazarudin Safian
author_sort Syed Sharizman Syed Abdul Rahim
title Spatial clustering of Cholera in Sabah, Malaysia
title_short Spatial clustering of Cholera in Sabah, Malaysia
title_full Spatial clustering of Cholera in Sabah, Malaysia
title_fullStr Spatial clustering of Cholera in Sabah, Malaysia
title_full_unstemmed Spatial clustering of Cholera in Sabah, Malaysia
title_sort spatial clustering of cholera in sabah, malaysia
publishDate 2019
url https://eprints.ums.edu.my/id/eprint/24706/1/Spatial%20Clustering%20of%20Cholera%20in%20Sabah%2C%20Malaysia.pdf
https://eprints.ums.edu.my/id/eprint/24706/
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