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.
['Hasyim|Hamzah|H|http://orcid.org/0000-0002-2780-8902', 'Nursafingi|Afi|A|', 'Haque|Ubydul|U|', 'Montag|Doreen|D|', 'Groneberg|David A|DA|', 'Dhimal|Meghnath|M|', 'Kuch|Ulrich|U|', 'Müller|Ruth|R|']
D004777:Environment D005843:Geography D006801:Humans D007214:Indonesia D008288:Malaria D015233:Models, Statistical D012307:Risk Factors D062206:Spatial Analysis D019371:Topography, Medical
Sumatra Ordinary least squares (OLS) Geographically weighted regression (GWR) Rainfall Akaike information criterion (AIC) Distance to water Elevation Local climate Physical environment