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


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Article Info

DATE

2018-12

AUTHORS

Hamzah Hasyim, Afi Nursafingi, Ubydul Haque, Doreen Montag, David A. Groneberg, Meghnath Dhimal, Ulrich Kuch, Ruth Müller

ABSTRACT

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. More... »

PAGES

87

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12936-018-2230-8

DOI

http://dx.doi.org/10.1186/s12936-018-2230-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1101134294

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29463239


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    "description": "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.\nMETHODS: 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.\nRESULTS: 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.\nCONCLUSIONS: 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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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s12936-018-2230-8'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12936-018-2230-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12936-018-2230-8'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12936-018-2230-8'


 

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295 Institute for Occupational Medicine, Social Medicine and Environmental Medicine, Faculty of Medicine, Goethe University, Frankfurt am Main, Germany
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297 https://www.grid.ac/institutes/grid.252749.f schema:alternateName Baldwin–Wallace College
298 schema:name Department of Public Health, Baldwin Wallace University, Berea, OH, USA
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300 https://www.grid.ac/institutes/grid.452693.f schema:alternateName Nepal Health Research Council
301 schema:name Institute for Occupational Medicine, Social Medicine and Environmental Medicine, Faculty of Medicine, Goethe University, Frankfurt am Main, Germany
302 Nepal Health Research Council (NHRC), Ramshah Path, Kathmandu, Nepal
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304 https://www.grid.ac/institutes/grid.4868.2 schema:alternateName Queen Mary University of London
305 schema:name Barts and the London School of Medicine, Centre for Primary Care and Public Health, Queen Mary University of London, London, UK
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307 https://www.grid.ac/institutes/grid.7839.5 schema:alternateName Goethe University Frankfurt
308 schema:name Institute for Occupational Medicine, Social Medicine and Environmental Medicine, Faculty of Medicine, Goethe University, Frankfurt am Main, Germany
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310 https://www.grid.ac/institutes/grid.8570.a schema:alternateName Gadjah Mada University
311 schema:name Remote Sensing Program, Faculty of Geography, Gadjah Mada University, Yogyakarta, Indonesia
312 rdf:type schema:Organization
 




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