Skip directly to search Skip directly to A to Z list Skip directly to page options Skip directly to site content

Volume 8, Number 6—June 2002


Clinical Epidemiology of Malaria in the Highlands of Western Kenya



Article Metrics

citations of this article
EID Journal Metrics on Scopus
Simon I. Hay*†Comments to Author , Abdisalan M. Noor†, Milka Simba†, Millie Busolo‡, Helen L. Guyatt*†, Sam A. Ochola‡, and Robert W. Snow*†‡
Author affiliations: *University of Oxford, Oxford, UK; †Kenya Medical Research Institute/Wellcome Trust Collaborative Programme, Nairobi, Kenya; ‡Ministry of Health, Nairobi, Kenya;

Cite This Article


Highlight and copy the desired format.

EID Hay SI, Noor AM, Simba M, Busolo M, Guyatt HL, Ochola SA, et al. Clinical Epidemiology of Malaria in the Highlands of Western Kenya. Emerg Infect Dis. 2002;8(6):543-548.
AMA Hay SI, Noor AM, Simba M, et al. Clinical Epidemiology of Malaria in the Highlands of Western Kenya. Emerging Infectious Diseases. 2002;8(6):543-548. doi:10.3201/eid0806.010309.
APA Hay, S. I., Noor, A. M., Simba, M., Busolo, M., Guyatt, H. L., Ochola, S. A....Snow, R. W. (2002). Clinical Epidemiology of Malaria in the Highlands of Western Kenya. Emerging Infectious Diseases, 8(6), 543-548.


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 conditions 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 catchment population of a health facility. Examples of ACR are collected from inpatient admission data at facilities 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 unstable 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.

The temperate highlands of western Kenya were regarded by colonial settlers as safe havens from the surrounding 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 (26), and malaria epidemics were frequently reported by the early 1930s (711). These epidemics in the highlands caused concern to those in the colonial administration because of the economic importance of agricultural exports. During the 1950s and 1960s, control efforts such as indoor residual house-spraying, mass drug administration, or chemoprophylaxis effectively contained or prevented epidemics in some of these high-altitude areas (1215).

In the late 1980s and early 1990s, a series of malaria “epidemics” were reported in Kenya and other communities located at high altitudes in the subregion (11,1626). Some authors have labeled these resurgences as a new typology variant, “highland malaria,” demanding special attention in the new global commitment to Roll Back Malaria (2729). A generally accepted view has been that the transmission of Plasmodium falciparum in high-altitude communities is limited by low ambient temperature. Small changes in climate may therefore provide transiently suitable conditions for unstable transmission 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 time series of age-structured clinical malaria data derived from three hospitals with inpatient admission facilities in the highlands 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 epidemics in these high-altitude locations and tests a variety of epidemic surveillance algorithms on the monthly malaria admissions series abstracted from these facilities (32).


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 populations 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 estimates from Kenyan national censuses in 1979 (34), 1989 (35), and 1999 (36). District and lower level administrative boundaries 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 population estimate of the second.

Data manipulation and statistical transformation were performed in Excel 2000 (Microsoft Corp., Seattle, WA) unless otherwise stated. Monthly mean adult (>15 years) and child (<15 years) admissions were calculated and displayed as spider plots. The time series of admissions at each site was also plotted with a 25-point (month) moving average of the series to show more clearly the long-term movement in these data. For each hospital, we performed trend analysis through linear regression of the malaria admission data against a trend variable (observation month number/12). The coefficient of trend therefore indicates the annual trend (positive = increasing in time, negative = decreasing in time, zero = a stationary series). Such regression models are sensitive to seasonal variation, outliers, and heteroscedasticity (a term which refers to situations in which the variability of the residuals is not constant). To show the long-term trend unambiguously, seasonality was removed from each series by using an additive seasonal decomposition procedure (37,38) and the residuals were checked for normality and heteroscedasticity in SPSS version 11 (SPSS Inc., Chicago, IL).

Furthermore, we applied a proximate measure of transmission stability through a comparison of the numbers of adults to children with malaria admitted to the three hospitals. This adult:child ratio (ACR) of cases was calculated from total adult and child admissions for the duration of the available records. In areas of stable transmission, we assumed that the risks for complicated malaria in adulthood would be significantly lower than the risks in childhood; this assumption was based on expectations of the age distribution of malaria cases under varying transmission intensities (3941). Conversely, areas of infrequent parasite exposure lend themselves to equivalent risks in adults and children. As such, an ACR derived from hospital admissions approaching unity would suggest an increasing tendency toward unstable transmission, assuming that the typical age-structured population pyramid for developing country and rural communities prevailed (36) and that there were no age-dependent biases in attendance rates.


Figure 1

Thumbnail of Spider plots of adult, child, and total admissions and time series of adult:child ratio for three study hospitals in Kenya. Spider plots of malaria admissions in Kilgoris (a), Kisii (c), and Tabaka (e). The data are monthly averages for the 1980–1999, 1987–2000, and 1981–2000 time periods, respectively. Adult cases (&gt;15 years of age) are shown in blue, child cases (&lt;15 years) are shown in red, and total cases in black. Time series plots of the monthly adult:child ratio data are also shown for Kilgoris (b), Kisii (d), and Tabaka (f) as the continuous black line. The dashed line represents the value of 1 where adult and child admissions are equal, as is to be expected in true epidemic conditions (39–41). The bold line is a 25-point (month) moving average of the adult:child ratio.

Figure 1. Spider plots of adult, child, and total admissions and time series of adult:child ratio for three study hospitals in Kenya. Spider plots of malaria admissions in Kilgoris (a), Kisii (c), and...

Time series of malaria admissions from 1980 to 1999, 1987 to 2000, and 1981 to 2000 were recorded for Kilgoris, Kisii, and Tabaka hospitals, respectively. These clinical data represent 171,312 admissions with a primary, coprimary, or coincidental diagnosis of complicated malaria over a total of 54 admission years. The Kilgoris, Kisii, and Tabaka hospitals managed an average of 2,243; 9,191; and 3,929 malaria admissions per year, respectively, for the duration over which records were available (Table 1). Throughout the study period, the frequency of childhood admissions was on average twice that of adult admissions (Table 1; Figure 1a,c,e). The average ACR calculated for all months was 0.46 for Kilgoris (14,079/30,793), 0.52 for Kisii (44,043/84,648), and 0.42 for Tabaka (23,692/55,871). The ACRs derived from monthly admissions are relatively constant throughout the duration of observation (Figure 1b,d, f). Several anomalies in these data were evident, however, particularly at Tabaka in 1985 and Kisii in 1999; we did not identify any obvious explanation for these exceptions in the time series, although they did not occur during periods of major epidemics at the sites (32). In further analysis, we focused on the primary pediatric clinical case data. We considered data from children to be more likely to give an accurate picture of local malaria transmission, as they are less likely to have developed functional immunity or to have traveled and acquired infections elsewhere.

The long-term data used in this analysis indicate that clinical cases of malaria occur every month at each hospital; acute seasonal peaks occur in June and July (Figure 1a,c,e). On average, one third of the total annual child malaria admissions were concentrated in these 2 months (35%, 32%, and 27% for Kilgoris, Kisii, and Tabaka, respectively).

Figure 2

Thumbnail of Time series of child admissions for the three study hospitals, Kenya. Time series of child admissions (&lt;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.

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,...

The trends and interannual variation in pediatric malaria admissions at each facility are shown in Figure 2a–c. These graphs demonstrate clear, substantial between-year variation in child malaria admissions. The 2 years of highest case presentations were 1994 and 1998 for Kilgoris, 1996 and 1997 for Kisii, and 1997 and 1996 for Tabaka (the moving average line in Figure 2a–c clearly shows these years). Although Kisii and Tabaka showed similarities, little coherence occurred in peak years of child admissions between these sites and Kilgoris, despite their close geographic proximity. At each facility, pediatric malaria admissions rose substantially over the period of observation (Table 2). In Kilgoris, deseasonalized child malaria admissions rose from 56 in January 1980 to 200 in December 1999, an increase of 256% over 20 years (p<0.001). Similar trends were observed at Kisii (32% increase from January 1987 through December 1999; p=0.019) and Tabaka (91% increase from January 1980 through December 1999; p<0.001).

In parallel with these significant rises in number of cases, estimates of the annual rate of natural population growth in the communities around the hospitals suggest that child populations might have increased by 215%, 49%, and 77% during the same period at Kilgoris, Kisii, and Tabaka, respectively.


We examined longitudinal, age-structured, clinical data on the frequency of admission for severe and complicated P. falciparum malaria at the three hospitals located above 1,600 m in the highlands of western Kenya. These data provided an opportunity to explore in more detail several generally accepted positions about the clinical epidemiology of malaria at high altitude in East Africa.

In these time series, the increased malaria admission at each of the three hospitals was concentrated in children <15 years of age (approximately two thirds of all admissions). Given the equivalent sizes of at-risk population below and above 15 years of age (36), one must assume that adults have developed a degree of functional immunity to the severe consequences of P. falciparum infection. The hypothesis that communities located at high altitude are prone to unstable, infrequent parasite exposure limiting the development of functional immunity before adulthood (8,9,30) is, therefore, not reflected in our data.

Complicated malaria warranting intensive clinical management is a problem every year at each hospital. Previous cross-sectional estimates of the prevalence of P. falciparum infection in children from birth to 10 years of age in homesteads in Kisii Central during 1990 suggested infection rates between 4.5% and 13% (42). More recently (July 2000), the prevalence of P. falciparum infection was 10.3% in children from birth to 9 years of age (HL Guyatt, unpub. data). Neither the clinical epidemiology nor estimates of the prevalence of 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 (1618,2325,30) 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 explanations for these increases is tempting, invoking arguments for and against climate change, drug resistance, and land use changes; various authors discuss these arguments elsewhere (1618,20,2326,30,4548). We emphasize in these arguments, however, the importance of considering population growth as the simplest explanation and note the close correspondence between the percentage increases in the population’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 highlands of western Kenya, has been a high rate of increase in population size, resulting from high fertility rates and increasing child survival. In the populations served by the hospitals in our study, annual growth rates averaged 3.9%. Under such circumstances, 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 concomitant investment in essential clinical services, beds, staff, and supporting infrastructure, the changing requirements for clinical management will have been perceived by most district-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 highlands are subject to acute seasonal transmission, as evidenced by the temporal distribution of cases (Figure 1a,b). These seasonal 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 interpreted with this caveat. Nevertheless, in other areas of Kenya where stable transmission is well established (51), notably coastal Kwale (ACR = 4,181/6,692 = 0.63 based on admissions data, 1984–1999) and lakeside Homa Bay (ACR = 18,686/35,703 = 0.52 based on admissions data,1982–1999), many more children than adults are admitted to hospital with a malaria diagnosis, resulting in ACRs similar to those described in the highlands (R. Snow, unpub. data). Conversely, in an arid area of northeastern Kenya (Wajir), where a major malaria epidemic occurred in 1998, more adults than children were admitted to the hospital (ACR = 2,704/1,369 = 1.96 based on admissions data, 1988–2000) (52). Despite poor malaria diagnosis in many routine clinical facilities, we believe that the ACR is one possible tool to rapidly assess the extent to which a community has sufficient parasite exposure to invoke some degree of clinical immunity early in childhood. This tool should be explored further within the context of malaria classification for epidemic-prone areas of Africa.

In high-altitude zones of western Kenya, clinical malaria has an acutely seasonal distribution, is comparatively concentrated in the pediatric population, and is a substantial public health problem every year. Occasional, but exceptional, temporal surges of disease occur in some years. We can assume that parasite transmission in this area of Kenya is stable and a degree of functional immunity is acquired during early childhood. Low levels of parasite challenge have been found to be sufficient for early development of functional immunity (53). We argue that large parts of the western highlands, located at a similar altitude, have ecologies similar to many other areas with low, stable, but seasonal malaria in Kenya. Treating the highland districts as special cases; demanding intensive investment in early detection, warning, and forecasting systems; and frequent complex-emergency responses by government or nongovernmental organizations (33) may not be the most appropriate and cost-effective use of limited resources. Investment in sustainable approaches to vector control (spraying households with residual insecticide), promoting individual protection (insecticide-treated bed nets), and effective case management are perhaps more likely to achieve long-term reductions in disease.

Dr. Hay is a research fellow, funded by the Wellcome Trust, in the Department of Zoology at the University of Oxford. He is also a member of the World Health Organization Roll Back Malaria Technical Support Network on Malaria Epidemic Prevention and Control. His research involves applying satellite technologies to the study and control of vector-borne diseases, with a particular emphasis on epidemic warning for malaria and dengue hemorrhagic fever.



The authors thank the staff of Tabaka Mission Hospital, St. Joseph’s Mission Hospital, and the Ministry of Health staff at Kisii District Hospital for assistance and dedication in identifying clinical records for this study. We also thank Lydiah Mogere for her help with abstracting the data from Tabaka Mission Hospital; Lydiah Mwangi and Lucy Muhunyo for data entry; and Dennis Shanks, Sarah Randolph, David Rogers, and Kevin Marsh for comments on the manuscript.

The Wellcome Trust funded this study through grant #056642 to SIH, #055100 to HLG, and #033340 to RWS. We further acknowledge the support of the Kenya Medical Research Institute. This paper is published with the permission of its director.



  1. James  SP. Report on a visit to Kenya and Uganda to advise on anti-malarial measures. London: His Majesty’s Stationery Office; 1929.
  2. Matson  AT. The history of malaria in Nandi. East Afr Med J. 1957;34:43141.PubMed
  3. Campbell  JM. Malaria in the Uasin Gishu and the Trans Nzoia. Kenya and East Africa Medical Journal. 1929;6:3243.
  4. Chataway  JHH. Report on the malaria epidemic in the Lumbwa reserve. Kenya and East African Medical Journal. 1928;5:3039.
  5. Anderson  FT. Report on an investigation of health conditions on farms in the Trans Nzoia with special reference to malaria. East Afr Med J. 1930;6:274308.
  6. Heisch  RB, Harper  JO. An epidemic of malaria in the Kenya highlands transmitted by Anopheles funestus. Tropical Medicine and Hygiene. 1949;51:18790.
  7. Paterson  AR. A guide to the prevention of malaria in Kenya in 1935 and afterwards. Health pamphlet number 12. Nairobi: Medical Department, Colony & Protectorate of Kenya, Government Printers; 1935.
  8. Garnham  PCC. Malaria epidemics at exceptionally high altitudes in Kenya. BMJ. 1945;11:457. DOI
  9. Garnham  PCC. The incidence of malaria at high altitudes. J Natl Malar Soc. 1948;7:27584.PubMed
  10. DeMello  JP. Some aspects of malaria in Kenya. East Afr Med J. 1947;24:11223.
  11. Snow  RW, Ikoku  A, Omumbo  J, Ouma  J. The epidemiology, politics and control of malaria epidemics in Kenya: 1900–1998. Report prepared for Roll Back Malaria, Resource Network on Epidemics, World Health Organization. Nairobi: KEMRI/Wellcome Trust Collaborative Programme; 1999.
  12. Roberts  JMD. The control of epidemic malaria in the highlands of western Kenya. Part I. Before the campaign. J Trop Med Hyg. 1964;67:1618.PubMed
  13. Roberts  JMD. The control of epidemic malaria in the highlands of western Kenya. Part II. The campaign. J Trop Med Hyg. 1964;67:1919.PubMed
  14. Roberts  JMD. The control of epidemic malaria in the highlands of western Kenya. Part III. After the campaign. J Trop Med Hyg. 1964;67:2307.PubMed
  15. Strangeways-Dixon  D. Paludrine (Proguanil) as a malarial prophylactic amongst African labour in Kenya. East Afr Med J. 1950;27:12730.PubMed
  16. Matola  YG, White  GB, Magayuka  SA. The changed pattern of malaria endemicity and transmission at Amani in the eastern Usambara Mountains, north-eastern Tanzania. J Trop Med Hyg. 1987;90:12734.PubMed
  17. Marimbu  J, Ndayiragije  A, Le Bras  M, Chaperon  J. Environment and malaria in Burundi. Apropos of a malaria epidemic in a non-endemic mountainous region. Bull Soc Pathol Exot. 1993;86:399401.PubMed
  18. Loevinsohn  ME. Climatic warming and increased malaria incidence in Rwanda. Lancet. 1994;343:7148. DOIPubMed
  19. Some  ES. Effects and control of highland malaria epidemic in Uasin-Gishu District, Kenya. East Afr Med J. 1994;71:28.PubMed
  20. Mouchet  J, Laventure  S, Blanchy  S, Fioramonti  R, Rakotonjanabelo  A, Rabarison  P, La reconquête des Hautes Terres de Madagascar par le paludisme. Bull Soc Pathol Exot. 1997;90:1628.PubMed
  21. Malakooti  MA, Biomndo  K, Shanks  GD. Reemergence of epidemic malaria in the highlands of western Kenya. Emerg Infect Dis. 1998;4:6716.PubMed
  22. Cox  J, Craig  M, Le Sueur  D, Sharp  B. Mapping malaria risk in the highlands of Africa. Technical report. Durban: MARA/HIMAL; 1999.
  23. Kilian  AHD, Langi  P, Talisuna  A, Kabagambe  G. Rainfall pattern, El Niño and malaria in Uganda. Trans R Soc Trop Med Hyg. 1999;93:223. DOIPubMed
  24. Lindblade  KA, Walker  ED, Onapa  AW, Katungu  J, Wilson  ML. Highland malaria in Uganda: prospective analysis of an epidemic associated with El Niño. Trans R Soc Trop Med Hyg. 1999;93:4807. DOIPubMed
  25. Lindblade  KA, Walker  ED, Onapa  AW, Katungu  J, Wilson  ML. Land use change alters malaria transmission parameters by modifying temperature in a highland area of Uganda. Trop Med Int Health. 2000;5:26374. DOIPubMed
  26. Hay  SI, Cox  J, Rogers  DJ, Randolph  SE, Stern  DI, Shanks  GD, Climate change and the resurgence of malaria in the East African highlands. Nature. 2002;415:9059. DOIPubMed
  27. Nabarro  DN, Tayler  EM. The Roll Back Malaria campaign. Science. 1998;280:20678. DOIPubMed
  28. World Health Organization. 20th WHO expert committee report on malaria. Technical Report Series, No. 892. Geneva: The Organization; 2000.
  29. World Health Organization. Malaria early warning systems, a framework for field research in Africa: concepts, indicators and partners WHO/CDS/RBM/2001.32. Geneva: The Organization; 2001.
  30. Lindsay  SW, Martens  WJM. Malaria in the African highlands: past, present and future. Bull World Health Organ. 1998;76:3345.PubMed
  31. Ministry of Health. Guidelines for malaria epidemic preparedness and control in Kenya. Nairobi: Ministry of Health, Government of Kenya, 1999.
  32. Hay  SI, Simba  M, Busolo  M, Noor  AM, Guyatt  HL, Ochola  SA, Defining and detecting malaria epidemics in the highlands of Western Kenya. Emerg Infect Dis. 2002;8:54956.PubMed
  33. Ministry of Health. National Malaria Strategy: 2001–2010. Nairobi: Division of Malaria of Control, Ministry of Health, Government of Kenya; 2001.
  34. Central Bureau of Statistics. Kenya population census, 1979. Vol. 1. Nairobi: Central Bureau of Statistics, Ministry of Economic Planning and Development, Government of Kenya; 1981.
  35. Central Bureau of Statistics. Kenya population census, 1989. Vol. 1. Nairobi: Central Bureau of Statistics, Ministry of Economic Planning and Development, Government of Kenya; 1989.
  36. Central Bureau of Statistics. 1999 population and housing census: counting our people for development. Vol. 1. Population distribution by administrative and urban centres. Nairobi: Central Bureau of Statistics, Ministry of Finance, Government of Kenya; 2001.
  37. Makridakis  S, Wheelwright  SC, McGee  VE. Forecasting: methods and applications. New York: John Wiley & Sons; 1983.
  38. McLaughlin  RL. Forecasting techniques for decision making. Rockville (MD): Control Data Management Institute; 1984.
  39. MacDonald  G. Epidemiological basis of malaria control. Bull World Health Organ. 1956;15:61326.PubMed
  40. MacDonald  G. Epidemics. In: The epidemiology and control of malaria. London: Oxford University Press; 1957. p. 45–62.
  41. Gilles  HM. Epidemiology of malaria. In: Gilles HM, Warrell DA, editors. Bruce-Chwatt's essential malariology. London: Edward Arnold, 1993. p. 124–63.
  42. Adungo  NI. Factors affecting malaria transmission by vector mosquito population in western Kenya, with special reference to altitude. Nairobi: University of Nairobi; 1992.
  43. Metselaar  D, Van Theil  PM. Classification of malaria. Tropical and Geographical Malaria 1959;11.
  44. Shanks  GD, Biomndo  K, Hay  SI, Snow  RW. Changing patterns of clinical malaria since 1965 among a tea estate population located in the Kenyan highlands. Trans R Soc Trop Med Hyg. 2000;94:2535. DOIPubMed
  45. Mouchet  J, Manguin  S, Sircoulon  J, Laventure  S, Faye  O, Onapa  AW, Evolution of malaria in Africa for the past 40 years: impact of climatic and human factors. J Am Mosq Control Assoc. 1998;14:12130.PubMed
  46. Reiter  P. Global-warming and vector-borne disease in temperate regions and at high altitude. Lancet. 1998;351:839. DOIPubMed
  47. Hay  SI, Myers  MF, Burke  DS, Vaughn  DW, Endy  T, Ananda  N, Etiology of interepidemic periods of mosquito-borne disease. Proc Natl Acad Sci U S A. 2000;97:93359. DOIPubMed
  48. Rogers  DJ, Randolph  SE, Snow  RW, Hay  SI. Satellite imagery in the study and forecast of malaria. Nature. 2002;415:7105. DOIPubMed
  49. Snow  RW, Marsh  K. The epidemiology of clinical malaria among African children. Bull Inst Pasteur. 1998;96:1523. DOI
  50. Font  F, Gonzalez  MA, Nathan  R, Kimario  J, Lwilla  F, Ascaso  C, Diagnostic accuracy and case management of clinical malaria in the primary health services of a rural area in south-eastern Tanzania. Trop Med Int Health. 2001;6:4238. DOIPubMed
  51. Snow  RW, Gouws  E, Omumbo  J, Rapuoda  B, Craig  MH, Tanser  FC, Models to predict the intensity of Plasmodium falciparum transmission: applications to the burden of disease in Kenya. Trans R Soc Trop Med Hyg. 1998;92:6016. DOIPubMed
  52. Hay  SI, Rogers  DJ, Shanks  GD, Myers  MF, Snow  RW. Malaria early warning in Kenya. Trends Parasitol. 2001;17:959. DOIPubMed
  53. Gupta  S, Snow  RW, Donnelly  C, Marsh  K, Newbold  C. Immunity to non-cerebral severe malaria is acquired after one or two infections. Nat Med. 1999;5:3403. DOIPubMed





Cite This Article

DOI: 10.3201/eid0806.010309

Table of Contents – Volume 8, Number 6—June 2002


Please use the form below to submit correspondence to the authors or contact them at the following address:

Simon I. Hay, TALA Research Group, Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK; fax: 44 1865 271243;

character(s) remaining.

Comment submitted successfully, thank you for your feedback.