Geospatial Health (Nov 2017)

The use of geographic information system as a tool for schistosomiasis surveillance in the province of Davao del Norte, the Philippines

  • Vicente Y. Belizario,
  • John Paul Caesar R. delos Trinos,
  • Berne Silawan,
  • Chiqui M. De Veyra,
  • Agapito Hornido,
  • Hansel Amoguis,
  • Dominic Basalo,
  • Cherry Dema-ala,
  • Irenn Mantilla,
  • Rosele Layan

DOI
https://doi.org/10.4081/gh.2017.540
Journal volume & issue
Vol. 12, no. 2

Abstract

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Schistosomiasis (SCH) in The Philippines is caused by Schistosoma japonicum and remains endemic in 28 provinces in 12 regions. Effective SCH control requires describing areas at risk where control efforts may be focused. This study aims at demonstrating the utility of geographical information system (GIS) as a tool for SCH surveillance in the province of Davao del Norte. Qualitative and quantitative data on SCH determinants, obtained from local government offices, partner agencies and institutions, were standardised, formatted and incorporated into a GIS map. Atrisk areas are described in terms of determinants and (variables), which included geography and climate (topography, temperature and flood-prone areas), agriculture (irrigation and land use), poverty (percentage of households with income below the poverty threshold), sanitation level (percentage of households with sanitary toilets), intermediate and reservoir hosts (presence of snail colonies and reservoir hosts) as well as prevalence and treatment coverage. Endemic villages (barangays) were generally found to be located in flood-prone areas in the lowlands near major rivers. New Corella has the highest poverty index among the SCH-endemic areas studied as well as the highest number of confirmed snail colonies. Among known endemic localities in Davao del Norte, Tagum City was found to be the only city meeting the poverty index target of <16.6%. Clustering of SCH cases were reported in six barangays ranging from 0.48% (8 out of 1,655) in Braulio Dujali to 2% (25 out of 1,405) in Asuncion. This study demonstrates the utility of GIS in predicting and assessing SCH risk, which allows prioritisation and allocation of control resources and delivery of services in areas at the highest risk for SCH.

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