PERSPECTIVE Figure 2. Time series of child admissions for the three study hospitals, Kenya. Time series of child admissions (<15 years of age) for Kilgoris (a), Kisii (b), and Tabaka (c) for 1980–1999, 1987–2000, and 1981–2000 time periods, respectively. The bold line is 25-point (month) moving average of the same data for child admissions. infection in the community corroborate the view that the high- altitude areas served by the hospitals in our study support unstable transmission. Transmission is better characterized as seasonal and meso-endemic (43). “Highland” malaria is either a new phenomena (16–18,23– 25,30) or a reemergence of a previous prevailing epidemiology (21,44). Our data confirm significant surges in malaria cases, requiring intensive clinical management during specific years of the 1990s because of substantial overall increases in the number of cases at each hospital. To provide a series of expla- nations for these increases is tempting, invoking arguments for and against climate change, drug resistance, and land use changes; various authors discuss these arguments elsewhere (16–18,20,23–26,30,45–48). We emphasize in these argu- ments, however, the importance of considering population growth as the simplest explanation and note the close corre- spondence between the percentage increases in the popula- tion’s growth rates in the districts served by each facility and the percentage rises in malaria cases. Characteristic of much of sub-Saharan Africa over the last 3 decades, including the high- lands of western Kenya, has been a high rate of increase in population size, resulting from high fertility rates and increas- ing child survival. In the populations served by the hospitals in our study, annual growth rates averaged 3.9%. Under such cir- cumstances, without any change in disease incidence, the increase in disease would be expected to have doubled over approximately an 18-year period. Clearly, without a concomi- tant investment in essential clinical services, beds, staff, and supporting infrastructure, the changing requirements for clinical management will have been perceived by most dis- trict-level public health officials as a crisis. Defining true epidemics is difficult (32). For most public health workers, epidemics represent exacerbations of disease out of proportion to the normal level to which that facility is subject; these increases overwhelm the facility’s ability to cope. Therefore, a slow but pervasive epidemic of clinical malaria may have emerged in the highlands of western Kenya, where lack of investment in the physical capacity to manage an increasing population has resulted inevitably in more malaria cases that require a basic clinical service. In addition to this demographic-to-service determinant, the western high- lands are subject to acute seasonal transmission, as evidenced by the temporal distribution of cases (Figure 1a,b). These sea- sonal peaks in clinical disease exhibit marked between-year variations, and several years exhibit dramatic rises in severe and complicated disease (Figure 2a,b,c). Moreover, years of exceptional cases can be very different between health centers separated by no more than 10 km. With limited resources and bed capacities, these acute rises in disease incidence within a given year will undoubtedly put a considerable strain on any clinical service and represent a crisis (32). We used a crude measure of transmission stability based largely on our understanding of patterns of acquired functional immunity (8,49). The ACR was derived from hospitalized patients diagnosed with malaria. Many of the cases would not have been confirmed with any degree of reliability through microscopy or careful clinical exclusion of alternative causes for fever (50). Our data and approach must therefore be Table 2. Deseasonalized child admissions at the three study hospitals, Kenyaa Site Kilgoris Kisii Tabaka Data span 1980–1999 (n=20) 1987–2000 (n=14) 1981–2000 (n=20) Constant 56.2 433.8 166.4 Slope 7.2 10.0 7.6 Change 144 140 152 t value 6.956 2.362 6.980 Significance p<0.001 p=0.019 p<0.001 Adjusted r2 0.165 0.027 0.174 a The data span shows the range of complete years for which data are available (parenthesis indicate the number of observation years). The adjusted r2 (sometimes called the coefficient of determination) is goodness-of-fit measure of a linear regression model and varies between 0 and 1. The measure is the proportion of variation in the dependent variable (in this case, malaria admissions) explained by the regression model (in this case, the trend line). The t value is the value of a t-test used to determine if the adjusted r2 value is significantly different from 0. The result is shown in the significance column where p values <0.05 are significant. 546 Emerging Infectious Diseases • Vol. 8, No. 6, June 2002