title: A study of the environmental determinants of malaria and schistosomiasis in the Philippines using Remote Sensing and Geographic Information Systems.
abstract: Malaria and schistosomiasis are two water-related parasitic diseases affecting millions of people worldwide particularly tropical and subtropical countries. In the Philippines, malaria is found in 72 out of 78 provinces while schistosomiasis is endemic in 24 provinces. The Anopheles mosquito and the Oncomelania snail involved in the transmission of these diseases depend on certain environmental determinants that support mosquito and snail populations. This study, done for the first time in the Philippines, successfully showed how Remote Sensing (RS) and Geographical Information Systems (GIS) can be effectively used in showing how these environmental factors affect the spatial distribution of these two diseases. The study sites, i.e. the municipalities of Asuncion and Kapalong, are known endemic sites for both malaria and schistosomiasis. Georeferenced data enabled visualization of prevalence data in relation to physical maps thus facilitating assessment of disease situation in the two municipalities. RS and GIS data proved that other factors aside from climate influence the epidemiology of the diseases in the two sites. Topography and slope as main physical factors influence the vegetation cover, land use and soil type prevailing in particular areas. In addition, water sources especially irrigation networks differed in various places in the study sites in turn affecting the magnitude and distribution of malaria and schistosomiasis. Significant correlations found between the diseases and the environmental variables formed the basis for development of models to predict the disease prevalence in the two municipalities. Proximity to snail breeding sites and irrigation networks and the highly agricultural nature of the barangays were identified as the most common factors that define the high prevalence areas for schistosomiasis confirming the fact that conditions that support the snail populations will in turn favor the presence of the disease. For malaria, the predictive models included temperature, humidity, soil type, predominance of reproduction brush, presence of cultivated areas, distance from deep wells and distance from conventional water source which are in turn influenced by the factor of elevation.
authors.affiliation: College of Public Health, University of the Philippines Manila, Ermita, Manila, Philippines. lydiarl2002@yahoo.com
authors.forename: L R
authors.initials: LR
authors.lastname: Leonardo
authors.forename: P T
authors.initials: PT
authors.lastname: Rivera
authors.forename: B A
authors.initials: BA
authors.lastname: Crisostomo
authors.forename: J N
authors.initials: JN
authors.lastname: Sarol
authors.forename: N C
authors.initials: NC
authors.lastname: Bantayan
authors.forename: W U
authors.initials: WU
authors.lastname: Tiu
authors.forename: N R
authors.initials: NR
authors.lastname: Bergquist
mesh_terms: D000383:Agriculture; D000818:Animals; D000852:Anopheles / Q000469:parasitology; D002980:Climate; D004199:Disease Vectors; D017753:Ecosystem; D019353:Endemic Diseases; D004784:Environmental Monitoring / Q000295:instrumentation / Q000379:methods*; D062665:Epidemiological Monitoring; D040362:Geographic Information Systems; D006801:Humans; D008288:Malaria / Q000453:epidemiology* / Q000635:transmission; D010679:Philippines / Q000453:epidemiology; D010944:Plants; D015995:Prevalence; D017215:Satellite Communications / Q000295:instrumentation; D012552:Schistosomiasis / Q000453:epidemiology* / Q000635:transmission; D012908:Snails / Q000469:parasitology; D012987:Soil; D019371:Topography, Medical / Q000295:instrumentation / Q000379:methods*; D014881:Water Supply
chemical_list: D012987:Soil