374 HIGHTOWER AND OTHERS Institute (ILRI).15 Soil-type data on the ILRI website were obtained from the Kenya Soil Survey, Report E1, 1982. Soils were classified by physical and chemical properties using the Food and Agriculture Organization (FAO) scheme.16 The land use data were obtained from Land-Use Satellite (LANDSAT) images obtained in 1987 by Japan International Co-operation Agency (JICA) for the Kenya National Water Master Plan. The ecological zone data were also obtained from the ILRI website. The Somali acacia ecozone is characterized by short grasses, shrubs, and acacia trees that can survive for extended periods without water.17 The 250-m digital elevation model data file was obtained from the World Resources Institute website.18 Normalized difference vegetation indices (NDVIs) and rainfall maps for 10-day periods from November 1, 2006 to February 28, 2007 were obtained from the Africa Data Dissemination Service.19 The NDVI is a commonly used mea- sure of remotely sensed vegetation cover or greenness. It has a range from 1, where there is little or no vegetation (and therefore, greenness), to +1, which corresponds to intense greenness. In areas where there are distinct rainy and dry sea- sons, the NDVI often increases after the rainy season begins, and therefore, the 10-day NDVI can be more closely related to a 10 -day rainfall measure in the recent past. As a result, NDVI is considered to be a lagging indicator of rainfall in the past month. For longer periods of rainfall, vegetation levels often level off, decreasing the relationship between rainfall and NDVI.20 Two types of rainfall measures were used. One was an estimated annual rainfall in centimeters.15 The second was a series of rainfall estimates for each 10-day period (called dekads) from November 1, 2006 to February 28, 2007.19 Pop- ulation and population density data for sublocations were based on the 1999 Kenya Census.21 The sublocation is the smallest administrative unit in Kenya. There were 6,625 sublocations in 1999. The population was considered to be at risk from November 1, 2006 to February 28, 2007. GIS methods. To create the analysis database of grid cells with the associated geographic information, the country was divided into a grid containing 46,200 cells (or squares) with a length of 3.5 km on each side. Each of these cells became the basis of a record in a database table, with the longitude and latitude of the center of each cell being stored as the reference location. GIS data maps for all of the variables noted above were all converted to raster format. The meteorological, geo- logical, geographical, and demographic information for the center of each of the grid cells was extracted and added to the matching record in the database table. The estimated person-time at risk and the number of RVF cases occurring within each cell were added to the database records for each cell. The cell size of 3.5 km was chosen to match the spatial resolution of the majority of the data sources. Cell sizes for the 10-day NDVI and rainfall maps were 8 km. The centroids for these maps were overlaid on the 3.5-km cell map, and the 8-km centroid value closest to the 3.5-km centroid value was used for NDVI and rainfall. These maps, created for the East Africa horn region,19 were trimmed using the Kenya national border as a mask for these analyses. ArcView 9.3 was used for all geographical analyses.22 Total person-time at risk for all people living within a grid cell was estimated in two steps: first, by estimating the population in the cell, and second, by multiplying this esti- mated population by the time that it was considered to be at risk. The population of the grid cell was estimated by multi- plying the population density of the sublocation in which the centroid of grid cell resided by the area of the grid cell (12.25 km2). Case locations were geocoded (assigned to specific grid cells) based on the location of the village or the centroid of the sublocation, location, or division in which the village was located if the location of the village could not be located. An iterative procedure similar to the procedure described in the work by Anyamba and others23 was used to geocode case locations from three online gazetteers. Locations for 214 cases were available and assigned global positioning system (GPS) locations (63% of 340 confirmed and probable cases). Reasons for cases not being geocoded were that either the village name could not be found in data- bases of village names or the case did not have a village, sub- location, location, or division reported. Statistical methods. Statistical models were produced using Poisson regression. The number of cases (N = 214) in each 3.5-km-sided grid cell was the outcome variable, and geo- graphic, geologic, and meteorological data for that cell were the explanatory variables. Offset values were the natural logarithm of the estimated person-time at risk for the cell defined as the estimated population in each 3.5-km cell multi- plied by the time at risk (approximately 4 months). Estimates from these models can be interpreted as relative risks. Combined with population information and risk factors for each grid cell, the model can be used to produce estimated incidences for each grid cell during the epidemic period. Model- based estimates of incidence per million person-years were obtained for each grid cell for the period at risk (Figure 1). Figure 1 insets show the Baringo and Kilifi districts. Backwards elimination was used to select significant variables in the mul- tivariable model. However, because of colinearity with soil types, factors that were subcharacteristics of soil types were assessed together in models without soil types as a predictor. Fits of these models were compared using the Akaike Infor- mation Criterion.24 The Poisson regression model estimated a relative risk per 0.1 unit change in the NDVI and per centime- ter change in rainfall. All models were examined for overdispersion; however, none existed. SAS V9.2 was used for statistical analyses.25 The x2 test of independence (uncorrected) was used to com- pare case (N = 70) and non-case cells (N = 46,130) categorized by geologic and geographic variables. These bivariable results are comparable with the analytic methods used for the soil type results presented previously.4 However, because the population is unevenly spread across the country with respect to these analysis variables, there are differences between the location- and incidence-based analyses. Table 1 presents the percent distribution of case and non- case locations with respect to potential explanatory variables, the number of cases, person-time during the outbreak period, and bivariable relative risks based on the levels of each vari- able used in the Poisson regression models. RESULTS The 214 cases occurred in 70 distinct grid cells; 171 cases in the North Eastern province occurred in 51 grid cells, 30 Baringo district cases occurred in 8 distinct grid cells, and 13 Kilifi district cases occurred in 11 distinct grid cells. Case