GIS ANALYSIS OF 2006–2007 KENYA RVF OUTBREAK DATA Table 3 Variables in the multivariable model predicting RVF incidence Likelihood ratio 95% confidence limits Parameter Intercept Soil type Solonetz Calcisols Solonchaks Planosols Other Land use Dense bush Other Landform Plains Other Ecological zone Somali acacia Other Rain* (cm) November 1–10 November 11–20 November 21–30 December 11–20 January 1–10 February 1–10 NDVI† November 1–10 November 11–20 January 11–20 Elevation per 100 m * Per centimeter change. † Per 0.1 unit change. Relative risk 1.08E-05 3.26 4.17 96.44 10.70 Reference 1.79 Reference 2.04 Reference 3.88 Reference 1.08 1.24 0.72 1.18 0.37 2.42 0.83 1.25 1.09 0.68 Lower 4.3E-06 2.02 1.85 48.14 6.49 1.28 1.10 2.17 1.05 1.19 0.65 1.13 0.27 1.88 0.72 1.08 1.04 0.59 Upper 2.5E-05 5.37 8.78 195.72 17.96 2.53 4.14 6.75 1.12 1.30 0.80 1.23 0.48 3.17 0.96 1.43 1.14 0.76 Wald x2 656.47 22.49 13.09 163.8 83.56 11.29 4.49 22.18 22.54 82.69 40.45 50.74 48.97 43.74 6.73 9.14 16.24 36.13 379 Pr > x2 < 0.0001 < 0.0001 0.0003 < 0.0001 < 0.0001 0.0008 0.0341 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0095 0.0025 < 0.0001 < 0.0001 10% of Kenya), resulting in the incidence for those areas being significantly greater than the reference areas. Similarly, the differences in the percent of case and non-case locations that were solonetz soil types were relatively small. As with calcisols, few people lived in these areas, which when com- bined with the number of cases occurring, resulted in a high incidence and increased RRs during the outbreak period. One of the strengths of this model is that it uses geographic information that is commonly available. Many countries have rich geographical, geological, and meteorological data avail- able in georeferenced format that could serve as a foundation for similar investigations of the occurrence of RVF or other diseases. Although considerable effort was spent geocoding the case locations, the specificity of this information allowed optimal use of the available reference data on the other geo- graphic variables. This effort is the first that takes place on a scale between individual risk factors3,4,9 and factors affecting multiple countries or regions.23 It improves on earlier efforts4 by using methods that allow simultaneous assessment of multiple variables and incidences for the epidemic period to be computed. In conclusion, our models suggest that RVF incidence dur- ing the outbreak period in Kenya was related to a number of geological, geographical, and meteorological factors, some previously recognized and others not recognized. We have shown that the Kilifi and North Eastern clusters could be linked by rainfall in early November and that the Baringo and North Eastern clusters could be linked by soil types and land cover. The model notes that all cases occurred in gener- ally flat areas and at lower altitudes (always 1,100 m or less). The findings suggest that, although rainfall and associated measures are important predictors of RVF outbreaks, there are additional factors that better define the optimal environ- ment for RVF occurrence. Such findings have the potential to improve current outbreak prediction models by limiting the geographic ranges of prediction to areas that are at risk of having outbreaks occur. Received July 12, 2011. Accepted for publication November 21, 2011. Acknowledgments: The authors would like to thank Dr. Solomon Mpoke, Director of the Kenya Medical Research Institute, John Vulule, Director of the Centre for Global Health Research, Kenya Medical Research Institute and the GIS Users Group of the Centers for Disease Control and Prevention for their support of this work. Disclaimer: The findings and conclusions in this report are the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention. Authors’ addresses: Allen Hightower (retired), Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, GA, E-mail: awh1953@gmail.com. Carl Kinkade, Epidemiol- ogy and Analysis Program Office, Office of Surveillance Epidemiology and Laboratory Systems, Centers for Disease Control and Prevention, Atlanta, GA, E-mail: mke5@cdc.gov. Patrick M. Nguku and David Mutonga, Division of Disease Surveillance and Response, Ministry of Public Health and Sanitation, Nairobi, Kenya, E-mails: drnguku@ yahoo.com and davidmutonga@yahoo.com. Amwayi Anyangu, Field Epidemiology and Laboratory Training Program (FELTP), Depart- ment of Disease Prevention and Control, Ministry of Public Health and Sanitation, Nairobi, Kenya, E-mail: amwayi2004@yahoo.com. Jared Omolo, Field Epidemiology and Training Program, Ministry of Public Health and Sanitation, Nairobi, Kenya, E-mail: jaredom2000@yahoo .com. M. Kariuki Njenga, International Emerging Infections Program, Centers for Disease Control and Prevention–Kenya, Kenya Medical