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

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

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

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.

Original languageEnglish
Article number87
JournalMalaria Journal
Volume17
Issue number1
DOIs
StatePublished - 20 Feb 2018

Keywords

  • Akaike information criterion (AIC)
  • Distance to water
  • Elevation
  • Geographically weighted regression (GWR)
  • Local climate
  • Ordinary least squares (OLS)
  • Physical environment
  • Rainfall
  • Sumatra

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