Spatial modelling of malaria cases associated with environmental factors in South Sumatra, Indonesia

  • Background: Malaria, a parasitic infection, is a life-threatening disease in South Sumatra Province, Indonesia. This study aimed to investigate the spatial association between malaria occurrence and environmental risk factors. Methods: The number of confirmed malaria cases was analysed for the year 2013 from the routine reporting of the Provincial Health Office of South Sumatra. The cases were spread over 436 out of 1613 villages. Six potential ecological predictors of malaria cases were analysed in the different regions using ordinary least square (OLS) and geographically weighted regression (GWR). The global pattern and spatial variability of associations between malaria cases and the selected potential ecological predictors was explored. Results: The importance of different environmental and geographic parameters for malaria was shown at global and village-level in South Sumatra, Indonesia. The independent variables altitude, distance from forest, and rainfall in global OLS were significantly associated with malaria cases. However, as shown by GWR model and in line with recent reviews, the relationship between malaria and environmental factors in South Sumatra strongly varied spatially in different regions. Conclusions: A more in-depth understanding of local ecological factors influencing malaria disease as shown in present study may not only be useful for developing sustainable regional malaria control programmes, but can also benefit malaria elimination efforts at village level.

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Author:Hamzah Hasyim, Afi Nursafngi, Ubydul Haque, Doreen Montag, Jan David Alexander GronebergORCiDGND, Meghnath Dhimal, Ulrich KuchORCiDGND, Ruth MüllerORCiD
URN:urn:nbn:de:hebis:30:3-463892
DOI:https://doi.org/10.1186/s12936-018-2230-8
ISSN:1475-2875
Pubmed Id:https://pubmed.ncbi.nlm.nih.gov/29463239
Parent Title (English):Malaria journal
Publisher:BioMed Central
Place of publication:London
Document Type:Article
Language:English
Year of Completion:2018
Date of first Publication:2018/02/20
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2018/05/17
Tag:Akaike information criterion (AIC); Distance to water; Elevation; Geographically weighted regression (GWR); Local climate; Ordinary least squares (OLS); Physical environment; Rainfall; Sumatra
Volume:17
Issue:1, Art. 87
Page Number:15
First Page:1
Last Page:15
Note:
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
HeBIS-PPN:434442631
Institutes:Medizin / Medizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Open-Access-Publikationsfonds:Medizin
Licence (German):License LogoCreative Commons - Namensnennung 4.0