Author (year) | GIS application | Geographic factors of malaria or anaemia |
---|---|---|
 | Malaria |  |
Anthony et al., (1992) [37] | Dot map of malaria incidence in one of the study villages. | Malaria point prevalence varied within and across villages in Indonesia. Incidence of malaria infections greatest in Yapimakot (39.1%), followed by Dabolding (34.95), Kabiding (31.9%) and Kutdol (28.6%). Prevalence of malaria ~50% lower among populations living in areas of forest-covered mountain slopes above the valley compared to villagers. |
Giardina et al. (2012) Giardina et al. [28] | Geospatial analysis, remote sensing data, and choropleth maps used to estimate environmental/climatic predictors of malaria. | Prevalence of malaria varied across survey locations in Senegal (lowest in northern regions, highest in the sourthern regions). High geographical variation in parasitaemia prevalence, including urban (1.3%) vs. rural (8.47%) differences (reduced odds for urban areas by 81%, 95% BCI: 55%-93%). |
Gordon (2004) [38] | Choropleth and symbology maps used to depict worldwide prevalence estimates and other related geographic features such as climate suitability for vector transmission. | Annual deaths from malaria in 2002 by WHO region highest in Africa (978,661) and lowest in Europe (44). |
Hightower et al., (1998) [27] | GIS used to perform spatial analyses and link location information to parasitology and entomology databases. | Prevalence of parasitemia tended to decrease with increasing household distance from larval habitat (p = 0.3437) except during the dry month of September. Average number of trapped An. gambiae mosquitoes was related to the distance of the household to the nearest breeding site for September (p = 0.0039), but not wet month of June (p = 0.1530). Opposite relationship was found for An. funestus(June p = 0.0191, September p = 0.6608). |
MARA/ARMA (1998) MARA/ARMA [31] | Various thematic maps used to depict relevant environmental (e.g. climatic) and population characteristics (e.g. density), and disease prevalence/incidence data. | Childhood (0-4y) population exposed to malaria mortality risk was higher in areas with 50% malaria transmission stability than areas with 90%. In Kenya the number of children < 5y who die or develop clinical malaria varies across areas of high, medium, low, or unstable malaria endemicity. In Mali an inverse U-shaped association found between malaria prevalence and distance to a water source (total population estimate). |
Mbogo (1993) [25] | Vector map of study area. | Prevalence of asymptomatic infections (with or without parasitaemia concentration ≥ 5000/uL) was higher in rural area of Sokoke compared to Kalifi town, Kenya. Higher proportion of children recruited from Sokoke reported to the District Hopsital with febrile illness and high parasitemia. |
Mbogo (1995) [36] | Vector map of study area. | Spatial patterns of severe disease varied across study sites indpendently of transmission intensity and entomological innoculation rate (EIR). |
Root (1999) Root [34] | Choropleth maps to depict spatial patterns of <5 mortality in 20 sub-Saharan African countries. | High mortality rates in East/South Africa and in vicinity of Lake Victoria represented heterogeneity in disease environments, indicating spatial impact and correlation between intensity of malaria transmission and observed mortality patterns. |
Schellenberg et al., (1998) [26] | Choropleth maps used to depict quintiles of severe malaria presenting to District Hospital and layout of all-weather roads. | Admission rates significantly higher in children living within 5 km from hospital (31.6/1000 child-years at risk) compared to those > 25 km away (5.0 per 1000 child-years at risk). Children living > 2.5 km away from nearest road were significantly less likely to be admitted compared to those living < 0.5 km (Adj RR = 0.47, 95%CI: 0.3-0.9). |
Snow (1998a) [32] | Dot density map of projected population distribution according to modelled predictions of regions of stable malaria endemicity. | High transmission intensity conditions identified around Lake Victoria (affecting 677,000 children < 5y). Largest number of children 0-4y exposed to areas of moderate stable malaria endemicity. Highest risk of malaria mortality and hospital admission in areas of high and moderate stable malaria endemicity, respectively. |
Dot density map of population distribution from communities exposed to at least 50% probability of malaria transmission according to a fuzzy logic climate model. | Wide geographical variation in estimates of malaria mortality in childhood. Deaths in hospital due to malaria per 1000 catchment childhood population highest in Sukutu, The Gambia (range 0.33-2.8) compared to other sites. | |
Thematic maps of climate suitability for stable transmission, interpolated population density, and zones of malaria risk in Africa. | Higher median mortality and morbidity rates in areas of stable transmission with ≥ 0.2 climate suitability than malaria risk area in South Africa with ≥ 0.5 climate suitability. | |
WHO (2008b) [7] | Choropleth maps of global incidence of malaria (and malaria related deaths) in 2006. | Variation in estimated burden of malaria (cases and deaths) in 2006 among children < 5y within and across 30 high burden countries. |
WHO (2010) [33] | Choropleth maps of geographical distribution of confirmed malaria cases/1000 population. | Variation in estimated malaria cases among children < 5y across 24 selected countries between 2000–2009. |
 | Anaemia |  |
Mainardi ( 2012) [21] | Spatial distribution of anaemia prevalence, including comparison between countries, and association with urabanizaiton. | Geographical variation in average proporiton of children with moderate or severe anaemia. Localization/urbanization was inversely associated with moderate and severe anaemia (OLS). Increased median time to a water source was significantly associated with lower prevalence of moderate (p < 0.01), but not severe anaemia (GWR). Widespread anaemia prevalence observed in mainly inland regions in West Africa, and a few specific areas in Eastern and central Africa. |
Snow 1994 [22] | Vector map of study areas in Kenya and Tanzania. | Higher prevalence of parasitaemia among children 0-4y in Ifakara compared to Kilifi. Higher prevalence of severe anaemia among children 0-4y in Kilifi than Ifakara. |
WHO (2008a) [2] | Choropleth maps of global anaemia prevalence and public health significance by country. | Prevalence of anaemia among pre-school aged children (0.5-4.99y) highest in Africa (global range 23.1 to 67.6%). |
Tanzanian NBS and ICF International 2012 [39] | Choropleth maps of anaemia prevalence by region. | Anaemia prevalence ranged from 42% among two in-land regions (Rukwa and Kilimanjaro) to 78% in the northern island region of Unguja. |
Greenwell 2006 [29] | Choropleth maps of anaemia or malaria prevalence, as well as malaria transmision by country (vector) or overall (raster). Overlayed dot density maps were used to show cluster locations. | Children in areas of moderate malaria prevalence were at highest risk of severe anaemia. The validity of haemoglobin measurements was dependent on whether the assessment was conducted during a high malaria transmission season. |
Magalhaes 2011 [24] | Dot density map of anaemia prevalence by DHS location. Choropleth maps of predictive geogrpahical risk or variation of anaemia or Hb concentration. | Mean haemoglobin was lowest in Burkina Faso, and a large spatial cluster of low mean haemoglobin and high anaemia risk was predicted for an area shared by Burkina Faso and Mali. |