PERSPECTIVE Clinical Epidemiology of Malaria in the Highlands of Wester n Kenya Simon I. Hay,*† Abdisalan M. Noor,† Milka Simba,† Millie Busolo,‡ Helen L. Guyatt,*† Sam A. Ochola,‡ and Robert W. Snow*†‡ Malaria in the highlands of Kenya is traditionally regarded as unstable and limited by low temperature. Brief warm periods may facilitate malaria transmission and are therefore able to generate epidemic condi- tions in immunologically naive human populations living at high altitudes. The adult:child ratio (ACR) of malaria admissions is a simple tool we have used to assess the degree of functional immunity in the catch- ment population of a health facility. Examples of ACR are collected from inpatient admission data at facili- ties with a range of malaria endemicities in Kenya. Two decades of inpatient malaria admission data from three health facilities in a high-altitude area of western Kenya do not support the canonical view of unsta- ble transmission. The malaria of the region is best described as seasonal and meso-endemic. We discuss the implications for malaria control options in the Kenyan highlands. T by colonial settlers as safe havens from the surrounding he temperate highlands of western Kenya were regarded malarious areas of Uganda and Kenya (1,2). After World War I, malaria encroached into these highland communities as a result of wide-scale population settlement linked to transport and agricultural development (2–6), and malaria epidemics were frequently reported by the early 1930s (7–11). These epi- demics in the highlands caused concern to those in the colonial administration because of the economic importance of agricul- tural exports. During the 1950s and 1960s, control efforts such as indoor residual house-spraying, mass drug administration, or chemoprophylaxis effectively contained or prevented epi- demics in some of these high-altitude areas (12–15). In the late 1980s and early 1990s, a series of malaria “epi- demics” were reported in Kenya and other communities located at high altitudes in the subregion (11,16–26). Some authors have labeled these resurgences as a new typology vari- ant, “highland malaria,” demanding special attention in the new global commitment to Roll Back Malaria (27–29). A gen- erally accepted view has been that the transmission of Plasmo- dium falciparum in high-altitude communities is limited by low ambient temperature. Small changes in climate may there- fore provide transiently suitable conditions for unstable trans- mission in populations that have acquired little functional immunity (8,9,30). The highlands of Kenya constitute a densely populated, politically significant area, which serves as a major source of revenue and foreign exchange from agricultural exports. The Kenyan government has recently defined 15 districts in the highlands (31,32) as being prone to epidemics, meriting close inspection, preparation, and intervention (33). We examine a *University of Oxford, Oxford, UK; †Kenya Medical Research Institute/ Wellcome Trust Collaborative Programme, Nairobi, Kenya; and ‡Minis- try of Health, Nairobi, Kenya time series of age-structured clinical malaria data derived from three hospitals with inpatient admission facilities in the high- lands of western Kenya. These data provide an empirical basis for understanding the epidemiology of malaria and consequent strategic approaches to disease management and prevention in this area. A companion paper investigates the epidemiologic and statistical problems associated with defining true epidem- ics in these high-altitude locations and tests a variety of epi- demic surveillance algorithms on the monthly malaria admissions series abstracted from these facilities (32). Methods The location of the three hospitals that provided inpatient clinical care and were identified for use in this study, along with details about the collection of clinical data and the local weather conditions, are provided in our companion paper (32). Providing a precise catchment area of the population for admission’s data was not possible as such information is not routinely collected in the hospitals; we assumed therefore that most inpatients came from the immediate surrounding high- elevation catchment area. Typically, long-term, facility-based data are difficult to interpret without some estimate of the pop- ulations served and how the population may have changed over time. To provide demographic information on the number of people served by the hospitals, we used population esti- mates from Kenyan national censuses in 1979 (34), 1989 (35), and 1999 (36). District and lower level administrative bound- aries changed with each census, so population growth rates were defined for three contiguous administrative areas, as close to the hospital as possible, which had not been subjected to boundary redefinition. Intercensal population growth rates (r) were calculated by using the formula r=loge(t2-t1), where t1 is the population estimate of the first census and t2 the popula- tion estimate of the second. Emerging Infectious Diseases • Vol. 8, No. 6, June 2002 543