Am. J. Trop. Med. Hyg., 86(2), 2012, pp. 373–380 doi:10.4269/ajtmh.2012.11-0450 Copyright © 2012 by The American Society of Tropical Medicine and Hygiene Relationship of Climate, Geography, and Geology to the Incidence of Rift Valley Fever in Kenya during the 2006–2007 Outbreak Allen Hightower,* Carl Kinkade, Patrick M. Nguku, Amwayi Anyangu, David Mutonga, Jared Omolo, M. Kariuki Njenga, Daniel R. Feikin, David Schnabel, Maurice Ombok, and Robert F. Breiman Division of Parasitic Diseases and Malaria, Center for Global Health, Center for Disease Control, Atlanta, Georgia; Epidemiology and Analysis Program Office, Office of Surveillance Epidemiology and Laboratory Systems, Center for Disease Control, Atlanta, Georgia; Division of Disease Surveillance and Response, Ministry of Public Health and Sanitation, Nairobi, Kenya; Field Epidemiology and Laboratory Training Program, Ministry of Public Health and Sanitation, Nairobi, Kenya; United States Army Medical Research Unit–Kenya, Nairobi, Kenya; Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya; Global Disease Detection Division, Centers for Disease Control and Prevention–Kenya, Nairobi, Kenya Abstract. We estimated Rift Valley fever (RVF) incidence as a function of geological, geographical, and climatolog- ical factors during the 2006–2007 RVF epidemic in Kenya. Location information was obtained for 214 of 340 (63%) confirmed and probable RVF cases that occurred during an outbreak from November 1, 2006 to February 28, 2007. Locations with subtypes of solonetz, calcisols, solonchaks, and planosols soil types were highly associated with RVF occurrence during the outbreak period. Increased rainfall and higher greenness measures before the outbreak were associated with increased risk. RVF was more likely to occur on plains, in densely bushed areas, at lower elevations, and in the Somalia acacia ecological zone. Cases occurred in three spatial temporal clusters that differed by the date of associated rainfall, soil type, and land usage. INTRODUCTION Rift Valley fever (RVF) is a mosquito-borne viral zoonosis that causes periodic epidemics and epizootics in sub-Saharan Africa.1 Although outbreaks are often associated with heavy rainfall and flooding2 and have reoccurred in similar loca- tions,3,4 heavy rainfall and flooding contribute to but are not the sole environmental criteria for RVF outbreaks. RVF virus is transmitted by various species of mosquitoes, such as Aedes and Culex, and through the secretions of infected animals, which are also infected by the same range of mosquito vectors.5–9 Thus, the ecological niche of these mosquito species can help define the locations in which human cases might occur. The ecological niche of RVF vec- tors varies widely.1 However, in eastern Africa, outbreaks have occurred in areas where drought-resistant vectors lay eggs that can survive for several years and require flooding events for hatching.1 Additionally, the distance that the live- stock is moved is often reasonably small, because much of the livestock is moved in this region by foot. Inclement weather conditions also limit the mobility of livestock populations during epidemic periods.10 Although considerable work has been conducted to predict outbreaks of RVF,11–14 the possible impact of geographical and geological factors in addition to climatological influences on the incidence of RVF disease in an area have not been characterized. In this paper, we use geocoded case locations and their geographical, geological, and climatological attributes to esti- mate incidence of RVF disease during the outbreak period in Kenya in late 2006 to early 2007. We present both national models of person-time–based incidence covering the entire outbreak period and separate analyses for the three major temporal–spatial clusters. We use sources of climate, geo- graphical, and geological data that are freely available on the *Address correspondence to Allen Hightower (retired), Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Mailstop A06, 1600 Clifton Road, Atlanta, GA 30333. E-mail: awh1953@gmail.com 373 Internet and are available for many countries where RVF cases and outbreaks occur. The findings from these models could be used in outbreak prediction models to reduce the number of false outbreak predictions and serve as a basis for additional investigation for case occurrence in other locations. METHODS Case definition and case detection. A suspect case of RVF was defined as a person presenting between November 1, 2006 and February 28, 2007, with an acute febrile illness (> 37.5 C for > 48 hours) and not responding to antimicrobial drugs or antimalarial therapy in a district where human or livestock RVF was confirmed.4 A probable case was defined as a patient with fever and bleeding manifestations. The RVF cases were reported through the Kenya Ministry of Public Health and Sanitation’s Integrated Disease Surveillance and Response (IDSR), a passive system that is used by most sub- Saharan African nations to monitor and control priority com- municable and non-communicable disease. Patients meeting the probable or suspect case definition were defined as con- firmed cases if immunoglobulin M (IgM) antibodies to RVF were detected by enzyme-linked immunosorbent assay (EIA) and/or RVF ribonucleic acid (RNA) was detected by reverse- transcriptase polymerase chain reaction (PCR). For the pur- pose of this report, all suspect patients (not confirmed by laboratory diagnostics) as well as probable cases from which specimens were negative by laboratory testing for RVF were excluded. Confirmed cases, as well as probable cases without available specimens who died before specimens could be obtained or who did not have access to healthcare during their acute illness (usually because of the widespread flooding), were included.4 Confirmed and probable cases without a geo- graphic location at least at the division level (third adminis- trative level) were excluded. Data sources. Data layers for geologic, geographic, and demographic data, including soil types and land use patterns, were obtained from the publicly available geographic informa- tion system (GIS) site of the International Livestock Research