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    Figure 1.

    Thirty study sites along the coast of Kenya. 1 = Dabaso; 2 = Garithe; 3 = Kagombani; 4 = Majenjeni; 5 = Masheheni; 6 = Maziwani; 7 = Mbarak Chembe; 8 = Mjanaheri; 9 = Paziani; 10 = Mijomboni; 11 = Barani; 12 = Chasimba; 13 = Dindiri; 14 = Jaribuni; 15 = Kitsoeni; 16 = Kitengwani; 17 = Majajani; 18 = Mtepeni; 19 = Shariani; 20 = Takaungu; 21 = Amani; 22 = Dumbule; 23 = Gazi; 24 = Magaoni; 25 = Moyeni; 26 = Mwaroni; 27 = Tsuini; 28 = Vinuni; 29 = Vuga; 30 = Ziwani.

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    Figure 2.

    Spatial distribution of Anopheles gambiae, An. arabiensis, An. merus, and An. funestus at 30 sites along the coast of Kenya. For identification of sites, see Figure 1.

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    Figure 3.

    Temporal distribution dynamics for Anopheles gambiae s.l. and An. funestus relative to monthly rainfall in the Malindi, Kilifi, and Kwale Districts of Kenya.

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    Figure 4.

    Cross-correlation between mean density of Anopheles gambiae, An. funestus, and rainfall in the Malindi, Kilifi, and Kwale Districts of Kenya. The x-axis denotes lags in months between mosquito samples and rainfall.

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    Figure 5.

    Relationship between annual entomological inoculation rates and the prevalence of Plasmodium falciparum malaria at 30 sites along the coast of Kenya.

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SPATIAL AND TEMPORAL HETEROGENEITY OF ANOPHELES MOSQUITOES AND PLASMODIUM FALCIPARUM TRANSMISSION ALONG THE KENYAN COAST

CHARLES M. MBOGOCentre for Geographic Medicine Research-Coast, Kenya Medical Research Institute, Kilifi, Kenya; Department of International Heath and Development, Department of Pharmacology, and Department of Tropical Medicine, Tulane University, New Orleans, Louisiana; Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana; Institute for Science and Public Policy, and School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma; Department of Biology, State University of New York, Buffalo, New York; Human Health Division, International Centre of Insect Physiology and Ecology, Nairobi, Kenya

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JOSEPH M. MWANGANGICentre for Geographic Medicine Research-Coast, Kenya Medical Research Institute, Kilifi, Kenya; Department of International Heath and Development, Department of Pharmacology, and Department of Tropical Medicine, Tulane University, New Orleans, Louisiana; Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana; Institute for Science and Public Policy, and School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma; Department of Biology, State University of New York, Buffalo, New York; Human Health Division, International Centre of Insect Physiology and Ecology, Nairobi, Kenya

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JOSEPH NZOVUCentre for Geographic Medicine Research-Coast, Kenya Medical Research Institute, Kilifi, Kenya; Department of International Heath and Development, Department of Pharmacology, and Department of Tropical Medicine, Tulane University, New Orleans, Louisiana; Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana; Institute for Science and Public Policy, and School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma; Department of Biology, State University of New York, Buffalo, New York; Human Health Division, International Centre of Insect Physiology and Ecology, Nairobi, Kenya

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WEIDONG GUCentre for Geographic Medicine Research-Coast, Kenya Medical Research Institute, Kilifi, Kenya; Department of International Heath and Development, Department of Pharmacology, and Department of Tropical Medicine, Tulane University, New Orleans, Louisiana; Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana; Institute for Science and Public Policy, and School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma; Department of Biology, State University of New York, Buffalo, New York; Human Health Division, International Centre of Insect Physiology and Ecology, Nairobi, Kenya

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GUIYAN YANCentre for Geographic Medicine Research-Coast, Kenya Medical Research Institute, Kilifi, Kenya; Department of International Heath and Development, Department of Pharmacology, and Department of Tropical Medicine, Tulane University, New Orleans, Louisiana; Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana; Institute for Science and Public Policy, and School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma; Department of Biology, State University of New York, Buffalo, New York; Human Health Division, International Centre of Insect Physiology and Ecology, Nairobi, Kenya

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JAMES T. GUNTERCentre for Geographic Medicine Research-Coast, Kenya Medical Research Institute, Kilifi, Kenya; Department of International Heath and Development, Department of Pharmacology, and Department of Tropical Medicine, Tulane University, New Orleans, Louisiana; Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana; Institute for Science and Public Policy, and School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma; Department of Biology, State University of New York, Buffalo, New York; Human Health Division, International Centre of Insect Physiology and Ecology, Nairobi, Kenya

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CHRIS SWALMCentre for Geographic Medicine Research-Coast, Kenya Medical Research Institute, Kilifi, Kenya; Department of International Heath and Development, Department of Pharmacology, and Department of Tropical Medicine, Tulane University, New Orleans, Louisiana; Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana; Institute for Science and Public Policy, and School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma; Department of Biology, State University of New York, Buffalo, New York; Human Health Division, International Centre of Insect Physiology and Ecology, Nairobi, Kenya

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JOSEPH KEATINGCentre for Geographic Medicine Research-Coast, Kenya Medical Research Institute, Kilifi, Kenya; Department of International Heath and Development, Department of Pharmacology, and Department of Tropical Medicine, Tulane University, New Orleans, Louisiana; Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana; Institute for Science and Public Policy, and School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma; Department of Biology, State University of New York, Buffalo, New York; Human Health Division, International Centre of Insect Physiology and Ecology, Nairobi, Kenya

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JAMES L. REGENSCentre for Geographic Medicine Research-Coast, Kenya Medical Research Institute, Kilifi, Kenya; Department of International Heath and Development, Department of Pharmacology, and Department of Tropical Medicine, Tulane University, New Orleans, Louisiana; Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana; Institute for Science and Public Policy, and School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma; Department of Biology, State University of New York, Buffalo, New York; Human Health Division, International Centre of Insect Physiology and Ecology, Nairobi, Kenya

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JOSEPHAT I. SHILILUCentre for Geographic Medicine Research-Coast, Kenya Medical Research Institute, Kilifi, Kenya; Department of International Heath and Development, Department of Pharmacology, and Department of Tropical Medicine, Tulane University, New Orleans, Louisiana; Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana; Institute for Science and Public Policy, and School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma; Department of Biology, State University of New York, Buffalo, New York; Human Health Division, International Centre of Insect Physiology and Ecology, Nairobi, Kenya

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JOHN I. GITHURECentre for Geographic Medicine Research-Coast, Kenya Medical Research Institute, Kilifi, Kenya; Department of International Heath and Development, Department of Pharmacology, and Department of Tropical Medicine, Tulane University, New Orleans, Louisiana; Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana; Institute for Science and Public Policy, and School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma; Department of Biology, State University of New York, Buffalo, New York; Human Health Division, International Centre of Insect Physiology and Ecology, Nairobi, Kenya

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JOHN C. BEIERCentre for Geographic Medicine Research-Coast, Kenya Medical Research Institute, Kilifi, Kenya; Department of International Heath and Development, Department of Pharmacology, and Department of Tropical Medicine, Tulane University, New Orleans, Louisiana; Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana; Institute for Science and Public Policy, and School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma; Department of Biology, State University of New York, Buffalo, New York; Human Health Division, International Centre of Insect Physiology and Ecology, Nairobi, Kenya

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The seasonal dynamics and spatial distributions of Anopheles mosquitoes and Plasmodium falciparum parasites were studied for one year at 30 villages in Malindi, Kilifi, and Kwale Districts along the coast of Kenya. Anopheline mosquitoes were sampled inside houses at each site once every two months and malaria parasite prevalence in local school children was determined at the end of the entomologic survey. A total of 5,476 Anopheles gambiae s.l. and 3,461 An. funestus were collected. Species in the An. gambiae complex, identified by a polymerase chain reaction, included 81.9% An. gambiae s.s., 12.8% An. arabiensis, and 5.3% An. merus. Anopheles gambiae s.s. contributed most to the transmission of P. falciparum along the coast as a whole, while An. funestus accounted for more than 50% of all transmission in Kwale District. Large spatial heterogeneity of transmission intensity (< 1 up to 120 infective bites per person per year) resulted in correspondingly large and significantly related variations in parasite prevalence (range = 38–83%). Thirty-two percent of the sites (7 of 22 sites) with malaria prevalences ranging from 38% to 70% had annual entomologic inoculation rates (EIR) less than five infective bites per person per year. Anopheles gambiae s.l. and An. funestus densities in Kwale were not significantly influenced by rainfall. However, both were positively correlated with rainfall one and three months previously in Malindi and Kilifi Districts, respectively. These unexpected variations in the relationship between mosquito populations and rainfall suggest environmental heterogeneity in the predominant aquatic habitats in each district. One important conclusion is that the highly non-linear relationship between EIRs and prevalence indicates that the consistent pattern of high prevalence might be governed by substantial variation in transmission intensity measured by entomologic surveys. The field-based estimate of entomologic parameters on a district level does not provide a sensitive indicator of transmission intensity in this study.

INTRODUCTION

Integrated entomologic and epidemiologic studies are required for the identification and analysis of relationships between transmission intensity and malaria disease burden over large areas where heterogeneous malaria prevalence has been documented.1–5 With national malaria control programs being guided by the Roll Back Malaria Program of the World Health Organization, the development of sound control strategies for malaria transmission requires a solid understanding of vector dynamics and the factors influencing their spatial and temporal distribution.4,6 Such information would help to develop early warning systems for predicting malaria epidemics, and for planning control programs based on accurate predictions of their likely effects. Moreover, identification of spatial and temporal variations in vector bionomics and transmission within and among sites, on a district-wide scale, provides useful information for designing effective control programs.

Since 1990, we have been conducting research on the Anopheles gambiae complex and An. funestus mosquitoes, the predominant vectors of human malaria on the coast of Kenya. These mosquito species are widely distributed along the coast.7–12 Previously, we studied the relationship between the entomologic inoculation rate (EIR) and the incidence of severe malaria at two sites13 and at nine sites9 in Kilifi District. Studies on the feeding behavior of malaria vectors were also investigated.8 The results of these studies indicate that even within small geographic areas, high rates of severe malaria can be associated with extremely low transmission intensity.

In this paper, we report the results of an integrated ecologic and epidemiologic investigation into the variation of malaria transmission intensity at 30 villages in three Districts along the coast of Kenya. The objectives of this study were to evaluate Plasmodium falciparum transmission dynamics, and investigate the relationship between mosquito populations, malaria prevalence, and climatic variables along the coast of Kenya.

MATERIALS AND METHODS

Study area.

This study was carried out at 30 villages located in Malindi, Kilifi, and Kwale Districts on the Kenyan coast (Figure 1). These three districts account for more than two-thirds of the rural population in coastal Kenya. The coastal plain is made up of dense forest, savanna type vegetation, seasonal swamps, dry thorn bush, and a number of plantations interspersed with uncultivated land. Sisal, coconut, and cashew nut plantations are prominent along the coast, although subsistence farming is abundant further inland. Altitudes range from 0 to 400 meters above sea level. Coastal Kenya has two rainy seasons: April to June and October to November. Mean annual precipitation range from 750 mm along the western border of Coast province to 1,200 mm along the coast of Kwale District. Several rivers and seasonal estuaries transect and drain the area. In Kwale, the Umba and Ramisi rivers and smaller permanent and seasonal streams drain the area. The Jaribuni and Sabaki rivers drain the Kilifi and Malindi areas, respectively.

Along the coast, houses in the rural area are mainly of two types: the Mijikenda and the Swahili style house. The traditional Mijikenda house consists of framed poles and branches from top to bottom covered with grass. Mud is often used to support the upper structure, while palm leaves often replace grass as roofing material. The Swahili house also consists of a wooden frame, but the walls are filled with mud and small coral stones. The roof is thatched with dried coconut palm leaves. Most houses have several rooms and share a common verandah. Many households keep goats, chickens, and cattle. The study area has been previously described in greater detail.9,13

Mosquito sampling.

Latitude and longitude data were recorded for the primary school at each site using a hand-held navigational system (Global Positioning System; Garman International, Inc., Olathe, KS). Mosquitoes were collected inside 10 houses within a 2-km radius of each school at each site. The same houses were sampled at each site once every two months throughout the study period, although on some occasions, circumstances required that a nearby house be substituted. The sampling logistics were such that at least 15 sites (five from each district) were sampled for mosquitoes every month from June 1997 to May 1998. To collect enough half-gravid An. gambiae s.l. for cytogenetic studies, houses were sampled in the afternoons (noon to 3:00 pm) using the pyrethrum spray collection (PSC) method.14 Collected mosquitoes were preserved in Carnoy’s fluid in the field and brought to the laboratory for further processing. At the time of mosquito collection, the number of individuals who slept in the house the previous night was also recorded.

Meteorologic data.

Daily minimum and maximum temperatures, relative humidity, and rainfall data were collected from three meteorologic stations: Malindi airport, Mtwapa in Kilifi District, and Moi International Airport in Mombasa.

Mosquito processing.

Mosquitoes were sorted and identified to species on the basis of morphologic characters.15 A proportion of females in the An. gambiae complex were identified to sibling species by a polymerase chain reaction (PCR).16,17 Primers used were specific for An. gambaie s.s., An. arabiensis, and An. merus members of the An. gambiae complex. The heads and thoraces of all anopheline mosquitoes were tested using a P. falciparum sporozoite enzyme-linked immunosorbent assay.18 Tests were assessed visually for positivity.19

Entomologic inoculation rate.

The EIR, a standard measure of transmission intensity, is expressed as the number of infective bites per person per unit time (e.g., daily, monthly, yearly). It was obtained by multiplying the human-biting rate by the proportion of sporozoite positive mosquitoes. The human-biting rates (the number of biting mosquitoes per human-night), was calculated by dividing the total number of blood-fed and half-gravid mosquitoes caught in PSC catches by the number of persons sleeping in the house the night preceding the collection.

Parasitologic examinations.

After obtaining informed consent from parents and teachers, a cross-sectional parasitologic survey was carried out at 30 primary schools, one per site, in May 1998. The study received ethical clearance from the National Ethical Review Committee of the Kenya Medical Research Institute. Thick and thin blood smears were prepared for approximately 100 school children per site between six and 12 years of age. The blood slides were stained with Giemsa, parasites were counted per 200 leukocytes in the thick blood films, and the total number of parasites was calculated based on a mean leukocyte count of 8,000 per microliter of blood. The thin films were used for species identification. A blood film was considered negative if no parasites were found in 200 fields of the thick smear.

Statistical analyses.

Data from pre-coded forms were checked for accuracy, logic, and range using SPSS version 10 (SPSS, Inc., Chicago, IL). All omissions and errors were corrected. The relationships between mosquito density, as well as EIR, and area-specific rainfall were analyzed using cross-correlation with various time lags. The differences in mosquito abundance and EIR among the three districts were examined using analysis of variance. The statistical analyses were accomplished using SPSS version 10. The geometric mean density of parasites per microliter was calculated after log transformation of parasite numbers to normalize the distribution and minimize the standard error. The outcome was then back transformed. To determine the relationship between malaria prevalence and the annual EIR for each site, data were plotted on a linear scale.

RESULTS

Human biting rate, sporozoite rate, and EIR.

The spatial variation in mean daily human biting rates (HBRs), mean daily EIRs, and P. falciparum sporozoite rates for An. gambiae s.l. and An. funestus at the 30 sites is shown in Table 1. Daily human biting rates ranged from 0.25 to 2.45 bites per person per night in Malindi, 0.00 to 1.07 in Kilifi, and 0.14 to 2.7 in Kwale. Overall, the HBR was highest in Malindi and lowest in Kilifi for An. gambiae s.l., while in Kwale it was highest for An. funestus. The P. falciparum sporozoite rate in An. gambiae s.l. was 3.9% in Malindi, 6.7% in Kilifi, and 8.9% in Kwale, while in An. funestus it was 1.7%, 4.9%, and 5.7% in Malindi, Kilifi, and Kwale, respectively. The mean annual EIR at the 30 sites ranged from 0 to 120 infective bites per person. Overall, P. falciparum transmission intensity increased gradually from Malindi towards Kwale, south of Mombasa.

Vectorial system and EIR.

The distribution of species in the An. gambiae complex and An. funestus, and their relative contribution to transmission is shown in Table 2. Analysis of 1,961 An. gambiae complex mosquitoes by a PCR showed that the relative proportion of each species was 81.9% An. gambiae s.s., 12.8% An. arabiensis, and 5.3% An. merus. Anopheles gambiae s.s. was the predominant vector species at the 30 sites, accounting for more than 60% of all the mosquito collections. This was followed closely by An. funestus, which was most abundant in Kilifi and Kwale Districts. Anopheles arabiensis was present at most sites, but was most abundant in Malindi District. Anopheles merus was also common in Malindi District.

The relative contribution to annual EIR by each of the four species varied from site to site. The relative contribution of malaria transmission attributable to An. gambiae s.s. in Malindi, Kilifi and Kwale was 65%, 48%, and 45%, respectively. Anopheles funestus accounted for more than 50% of all malaria transmission in Kwale, while its relative contribution was 20.5% in Kilifi, and only 6.4% in Malindi District.

Spatial distribution of Anopheles.

The spatial distribution map for the three species in the An. gambiae complex and An. funestus along the Kenya coast is shown in Figure 2. More An. arabiensis were found in the Malindi area than in Kilifi or Kwale, while An. merus was found near the coastline but mostly in Malindi. Interestingly, higher proportions of An. funestus densities were detected in Kwale than in Kilifi and Malindi, and these decreased toward the north. Likewise, vector abundance of An. gambiae s.s. decreased from the north in Malindi to the south in Kwale. Similarly, the proportions of An. funestus in Kwale decreased from the coastline moving inland. There were fewer An. funestus collected at the inland sites of Moyeni, Dumbule, and Amani compared with sites closer to the coast.

Temporal dynamics of transmission.

The relationship between mosquito density and rainfall was highly variable among districts. The temporal distribution of An. gambiae s.l. and An. funestus relative to monthly rainfall is shown in Figure 3. In Malindi, the density of An. gambiae s.l. correlated closely with the seasonal rainfall patterns, while An. funestus showed little variation. Plasmodium falciparum transmission was dominated by An. gambiae s.l. in Malindi, while both An. gambiae s.l. and An. funestus contributed significantly to transmission in Kilifi and Kwale. Dramatic variations in rainfall patterns were observed among the three areas. For example, Kilifi received unusually heavy rains in September 1997 (Figure 3), while the other two districts had less rain than expected. District-wide, mean monthly temperature and humidity showed little variation. At two of the three districts, cross-correlation analysis revealed significant associations in the abundance of An. gambiae s.l. and An. funestus and rainfall in previous months (Figure 4). Variations in time lags were detected in Malindi and Kilifi districts. In Kilifi, mosquito density was associated with rainfall three months prior, whereas the lag in Malindi was equal to one month.

Parasitologic parameters.

The parasite prevalence and parasite densities from the cross-sectional survey conducted at the 30 sites in May 1998 are shown in Table 3. Of 2,976 school children examined for malaria parasites, 60%, 62%, and 64% were positive for P. falciparum in Malindi, Kilifi, and Kwale Districts, respectively. Infections with P. falciparum accounted for more than 85% of all infections along the coast. The prevalence of P. falciparum ranged from 38% at the Dabaso site in Malindi to 83% at the Mtepeni site in Kilifi District. Significantly higher geometric mean parasite densities for P. falciparum were found in Kilifi (1.43/μL) compared with Malindi (1.05/μL) and Kwale (1.28/μL) Districts. Other parasites identified and recorded include P. malariae (1.0%), P. ovale (0.2%), and mixed infections of P. falciparum with P.malariae and/or P. ovale, and P. malariae with P. ovale. The P. falciparum gametocyte prevalence was 1.4% in Kilifi, 1.1% in Malindi, and 1.3% in Kwale, but with low geometric mean densities among carriers of 5.3, 3.0, and 4.0 gametocytes per microliter, respectively.

The relationship between annual EIRs and the prevalence of P. falciparum infection at the 30 sites along the coast of Kenya is shown in Figure 5. The EIRs ranged from 0 to 120 infective bites per person per year, while the malaria prevalence ranged from 38% to 83%. The results indicate that 26% of the sites (7 of 22 sites) with annual EIRs ≤ 5 had malaria prevalence ranging from 38% to 70%. The rest of the sites (74%) had EIRs ≥ 5 and malaria prevalence greater than 50%.

DISCUSSION

In this study, we found that individuals receive between less than 1 to approximately 120 infective bites per year. We also found EIRs to be greater than what we previously reported in Kilifi (north of Kilifi Creek). In Kilifi, we observed annual EIRs of < 5 infective bites per year in seven of nine sites studied intensively over a one-year period.9 Although we observed that these low EIRs maintain a high incidence of severe malaria, we did not know if this relatively low level of transmission is indicative of the relationship throughout the coast of Kenya.9

Our study shows that relatively high malaria parasite prevalence can occur at low and even non-detectable levels of transmission all along the coast of Kenya. For instance, in Chasimba, we were not able to detect any measurable EIRs, yet the prevalence of P. falciparum infections exceeded 39% and parasite densities were 1,288/μL. This high prevalence in asymptomatic individuals provides a large reservoir of infections, which drives the transmission process.20 It is puzzling that a large percentage of sites have prevalence rates greater than 50% in the presence of very few or no mosquitoes (Tables 1 and 3). Beier and others evaluated data from 31 sites throughout Africa on the relationships between annual EIRs and the prevalence of P. falciparum malaria infection.5 They observed that it is common to find prevalence rates exceeding 30–50%, even when EIRs are very low. When the EIR exceeds 15 infective bites per person per year, the prevalence of P. falciparum never falls below 50%, whereas annual EIRs of 200 or greater were consistently associated with prevalence rates exceeding 80%.5 Clearly, the EIR as a direct measure of malaria transmission or sporozoite exposure varies significantly within small geographic areas and should be taken into consideration during malaria control programs.21,22 However, we note that due the great variability of vector abundance and EIRs even within small geographic areas, the EIR as measurement of transmission intensity has to be used with caution because it seems to be insensitive.

Our study also shows that the primary vectors of malaria along the coast of Kenya include An. funestus and three members of the An. gambiae complex: An. gambiae s.s., An. arabiensis, and An. merus. In some sites, the relative contribution by An. gambiae s.s. to transmission was more than 70%, while An. funestus played a more important role in transmission in the southern coastal area. This is due in part to a higher HBR, a higher sporozoite rate, and the fact that An. funestus was the predominant species throughout the dry season.

Another important consideration is the extent to which environmental heterogeneity affects the abundance and quality of larval habitats. The factors affecting larval survival and the mechanisms controlling adult production are largely unknown for even the most important vector species in Africa. As such, the characterization and comparison of diverse aquatic habitat is essential to elucidate the factors regulating the production of mosquitoes in heterogeneous environments.

We have evaluated P. falciparum malaria prevalence and examined spatial heterogeneity of mosquito species composition in coastal Kenya. One important conclusion is that the highly non-linear relationship between EIRs and prevalence indicates that the consistent pattern of high prevalence might be governed by substantial variation in transmission intensity measured by entomologic surveys. Thus, the field-based estimate of entomologic parameters on a district level does not provide a sensitive indicator of transmission intensity, compared with measures of malaria prevalence. We also have detected significant spatial heterogeneity in the species composition of the malaria vectors in the three districts. However, we did not identify the environmental factors associated with the spatial occurrence of the malaria vectors, especially those related to the larval habitats. Our study demonstrates that P. falciparum malaria transmission in coastal Kenya is very heterogeneous. As such, programs should consider the transmission intensity of specific areas when designing and implementing vector control and malaria prevention in heterogeneous environments. To understand the mechanisms affecting the distribution of the anopheline vectors, continued epidemiologic and entomological studies must consider the complex interplay of human, mosquito, and parasite variables interacting and confounding on multiple spatial and temporal scales.

Table 1

Summary of the mean daily human-biting rate (MBR), Plasmodium falciparum sporozoite rate, and mean daily entomologic inoculation rate (EIR) by enzyme-linked immunosorbent assay for Anopheles gambiae s.l. and An. funestus collected at 30 sites along the Kenya coast (June 1997 to May 1998)

An. gambaie s.l. An. funestus
District Site MBR Sporozoite rate (n) EIR MBR Sporozoite rate (n) EIR
Malindi Dabaso 0.26 0.61 (163) 0.002 0.00 0.00 (0) 0.000
Garithe 2.45 2.41 (580) 0.059 0.08 0.00 (14) 0.000
Kagombani 1.1 6.19 (517) 0.068 0.03 0.00 (10) 0.000
Majenjeni 1.93 5.78 (501) 0.112 0.03 0.00 (18) 0.000
Masheheni 1.44 7.16 (363) 0.103 0.05 11.11 (9) 0.004
Maziwani 0.81 4.93 (203) 0.040 0.23 0.00 (86) 0.000
Mbarak Chembe 0.25 1.85 (54) 0.005 0.01 0.00 (1) 0.000
Mjanaheri 0.77 5.93 (253) 0.046 0.06 0.00 (8) 0.000
Paziani 0.86 2.39 (373) 0.021 0.12 0.00 (45) 0.000
Mijomboni 1.63 1.32 (378) 0.022 0.10 5.41 (37) 0.007
Total 1.15 3.86 (3385) 0.05 0.07 1.65 (228) 0.001
Kilifi Barani 0.40 4.76 (105) 0.019 0.72 3.57 (196) 0.018
Chasimba 0.00 0.00 (2) 0.000 0.00 0.00 (0) 0.000
Dindiri 0.91 12.77 (235) 0.116 0.38 7.53 (93) 0.029
Jaribuni 1.07 6.87 (233) 0.074 0.44 3.90 (205) 0.023
Kitsoeni 0.38 13.56 (59) 0.052 0.30 12.50 (8) 0.008
Kitengwani 0.11 0.00 (20) 0.000 0.01 0.00 (10) 0.000
Majajani 0.76 10.33 (184) 0.079 0.34 6.59 (91) 0.017
Mtepeni 0.57 15.04 (113) 0.086 0.10 14.81 (27) 0.005
Shariani 0.09 3.45 (58) 0.003 0.00 0.00 (0) 0.000
Takaungu 0.04 0.00 (13) 0.000 0.02 0.00 (6) 0.000
Total 0.43 2.88 (1021) 0.043 0.023 4.89 (636) 0.010
Kwale Amani 2.70 11.92 (386) 0.322 0.59 1.25 (80) 0.006
Dumbule 0.38 2.78 (77) 0.079 0.01 12.50 (8) 0.001
Gazi 0.31 1.67 (60) 0.005 1.34 0.91 (331) 0.014
Magaoni 0.36 6.00 (100) 0.022 4.23 4.32 (1227) 0.151
Moyeni 0.15 16.13 (31) 0.024 0.07 21.43 (14) 0.017
Mwaroni 0.24 5.71 (35) 0.0137 0.01 0.00 (7) 0.000
Tsuini 0.64 9.26 (108) 0.059 1.26 5.02 (319) 0.055
Vinuni 0.44 5.56 (72) 0.025 1.26 3.96 (278) 0.050
Vuga 0.24 0.00 (40) 0.000 0.76 2.70 (148) 0.025
Ziwani 0.14 6.25 (64) 0.009 0.05 4.55 (22) 0.001
Total 0.56 8.33 (973) 0.056 0.96 5.66 (2434) 0.032
Table 2

Relative contribution of the vectorial system at the 30 sites to the transmission intensity along the coast of Kenya*

No. identified by PCR (% of species total) % of annual EIR
District Site An. gambiae s.s. An. arabiensis An. merus Annual EIR An. gambiae s.s. An. arabiensis An. merus An. funestus
*PCR = polymerase chain reaction; EIR = entomologic inoculation rate; An. =Anopheles.
Malindi Dabaso 6 (35.3) 11 (64.7) 0 (0.0) 0.7 35.3 64.7 0.0 0.0
Garithe 91 (40.4) 63 (28.6) 66 (30.0) 21.5 40.4 28.0 29.3 2.4
Kagombani 48 (92.3) 4 (7.7) 0 (0.0) 24.8 90.6 7.5 0.0 1.9
Majenjeni 179 (86.9) 23 (11.2) 4 (1.9) 40.9 84.0 10.8 1.9 3.3
Masheheni 127 (75.1) 37 (21.9) 5 (3.0) 39.1 73.3 21.4 2.9 2.4
Maziwani 43 (89.6) 5 (10.4) 0 (0.0) 14.6 62.9 7.3 0.0 29.8
M/chembe 7 (58.3) 5 (41.7) 0 (0.0) 1.8 57.3 40.9 0.0 1.8
Mjanaheri 22 (51.2) 18 (41.9) 3 (7.0) 16.8 49.6 40.6 6.8 3.1
Paziani 96 (80.7) 23 (19.3) 0 (0.0) 7.7 72.0 17.2 0.0 10.8
Mijomboni 21 (95.5) 1 (4.5) 0 (0.0) 10.6 86.9 4.1 0.0 8.9
Total 640 (70.5) 190 (20.9) 78 (8.6) 17.9 65.2 24.3 4.1 6.4
Kilifi Barani 50 (96.2) 2 (3.8) 0 (0.0) 13.5 33.5 1.3 0.0 65.1
Chasimba 2 (100.0) 0 (0.0) 0 (0.0) 0.0 0.0 0.0 0.0 0.0
Dindiri 110 (95.7) 2 (1.7) 3 (2.6) 52.9 68.5 1.2 1.9 28.4
Jaribuni 66 (83.5) 9 (11.4) 4 (5.1) 35.4 44.4 6.1 2.7 46.8
Kitsoeni 52 (100.0) 0 (0.0) 0 (0.0) 21.9 88.1 0.0 0.0 11.9
Kitengwani 5 (100.0) 0 (0.0) 0 (0.0) 0.0 0.0 0.0 0.0 0.0
Majajani 48 (96.0) 0 (0.0) 2 (4.0) 35.0 64.2 0.0 2.7 33.1
Mtepeni 98 (98.0) 2 (2.0) 0 (0.0) 33.2 79.1 1.6 0.0 19.3
Shariani 14 (100.0) 0 (0.0) 0 (0.0) 1.1 100.0 0.0 0.0 0.0
Takaungu 10 (90.9) 1 (9.1) 0 (0.0) 0.0 0.0 0.0 0.0 0.0
Total 455 (94.8) 16 (3.3) 9 (1.9) 19.3 68.3 1.46 1.04 29.2
Kwale Amani 196 (94.7) 10 (4.8) 1 (0.5) 119.7 78.4 4.0 0.4 17.2
Dumbule 74 (98.7) 1 (1.3) 0 (0.0) 29.2 89.4 1.2 0.0 9.4
Gazi 17 (73.9) 1 (4.3) 5 (21.8) 6.9 11.3 0.7 3.3 84.7
Magaoni 81 (98.8) 1 (1.2) 0 (0.0) 63.2 7.4 0.1 0.0 92.5
Moyeni 23 (95.8) 1 (4.2) 0 (0.0) 15.0 66.0 2.9 0.0 31.1
Mwaroni 8 (72.7) 3 (27.3) 0 (0.0) 5.1 60.6 22.7 0.0 16.7
Tsuini 30 (68.2) 11 (25.0) 3 (6.8) 41.6 17.2 6.3 1.7 74.7
Vinuni 30 (100.0) 0 (0.0) 0 (0.0) 27.4 20.6 0.0 0.0 79.4
Vuga 13 (100.0) 0 (0.0) 0 (0.0) 9.1 21.3 0.0 0.0 78.7
Ziwani 21 (100.0) 0 (0.0) 0 (0.0) 3.7 74.4 0.0 0.0 25.6
Total 493 (93.0) 28 (5.3) 9 (1.7) 32.1 44.7 3.8 0.5 51.0
Table 3

Parasite prevalence and densities for Plasmodium falciparum and P. malariae at 30 sites along the coast of Kenya, May 1998

P. falciparum asexual P. falciparum gametocytes P. malariae
District Site No.examined Prevalence Geometric mean/μL Prevalence Geometric mean/μL Prevalence Geometric mean/μL
Malindi Dabaso 99 38 512.9 0.0 0.0 2 778.8
Garithe 100 79 807.2 2.0 1.1 3 628.3
Kagombani 100 72 1195.6 1.0 0.0 1 161.0
Majenjeni 100 58 501.8 2.0 1.2 2 99.4
Masheheni 101 66 699.5 3.0 1.1 2 249.7
Maziwani 100 48 348.5 3.0 1.2 1 281.0
Mbarak chembe 100 59 944.5 1.0 1.1 0 0.0
Mjanaheri 100 49 394.7 2.0 1.2 2 221.9
Paziani 100 72 455.8 6.0 1.4 0 0.0
Mijomboni 100 63 516.5 10.0 2.2 4 402.1
Mean 1000 60.4 637.7 3.0 1.1 1.7 282.2
Kilifi Barani 100 65 620.3 1.0 1.1 0 0.0
Chasimba 100 39 1288 2.0 1.2 1 641.1
Dindiri 101 61 637.7 3.0 1.1 0 0.0
Jaribuni 98 54 404.6 7.0 1.5 0 0.0
Kitsoeni 100 69 1044 5.0 1.2 1 841.0
Kitengwani 99 70 1067.3 8.1 1.7 0 0.0
Majajani 101 75 579.4 6.0 1.3 0 0.0
Mtepeni 99 83 773.4 6.1 1.3 1 121.0
Shariani 100 54 593.5 7.0 1.7 3 1059.3
Takaungu 100 52 533.3 8.0 2.1 5 235.4
Mean 998 62.2 754.2 5.3 1.4 1.1 289.8
Kwale Amani 100 73 614.6 10.0 1.9 0 0.0
Dumbule 100 82 761.1 1.0 1.1 2 283.9
Gazi 100 60 763.3 2.0 1.2 0 0.0
Magaoni 78 64 422.3 2.6 1.1 0 0.0
Moyeni 100 78 603.1 6.0 1.3 0 0.0
Mwaroni 100 50 333.4 5.0 1.5 1 441.0
Tsuini 100 67 607.6 5.0 1.3 0 0.0
Vinuni 100 66 418.1 2.0 1.1 0 0.0
Vuga 100 44 345.5 3.0 1.2 0 0.0
Ziwani 100 59 663.4 3.0 1.1 2 311.1
Mean 978 64.3 553.2 4.0 1.3 0.5 103.6
Figure 1.
Figure 1.

Thirty study sites along the coast of Kenya. 1 = Dabaso; 2 = Garithe; 3 = Kagombani; 4 = Majenjeni; 5 = Masheheni; 6 = Maziwani; 7 = Mbarak Chembe; 8 = Mjanaheri; 9 = Paziani; 10 = Mijomboni; 11 = Barani; 12 = Chasimba; 13 = Dindiri; 14 = Jaribuni; 15 = Kitsoeni; 16 = Kitengwani; 17 = Majajani; 18 = Mtepeni; 19 = Shariani; 20 = Takaungu; 21 = Amani; 22 = Dumbule; 23 = Gazi; 24 = Magaoni; 25 = Moyeni; 26 = Mwaroni; 27 = Tsuini; 28 = Vinuni; 29 = Vuga; 30 = Ziwani.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 68, 6; 10.4269/ajtmh.2003.68.734

Figure 2.
Figure 2.

Spatial distribution of Anopheles gambiae, An. arabiensis, An. merus, and An. funestus at 30 sites along the coast of Kenya. For identification of sites, see Figure 1.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 68, 6; 10.4269/ajtmh.2003.68.734

Figure 3.
Figure 3.

Temporal distribution dynamics for Anopheles gambiae s.l. and An. funestus relative to monthly rainfall in the Malindi, Kilifi, and Kwale Districts of Kenya.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 68, 6; 10.4269/ajtmh.2003.68.734

Figure 4.
Figure 4.

Cross-correlation between mean density of Anopheles gambiae, An. funestus, and rainfall in the Malindi, Kilifi, and Kwale Districts of Kenya. The x-axis denotes lags in months between mosquito samples and rainfall.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 68, 6; 10.4269/ajtmh.2003.68.734

Figure 5.
Figure 5.

Relationship between annual entomological inoculation rates and the prevalence of Plasmodium falciparum malaria at 30 sites along the coast of Kenya.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 68, 6; 10.4269/ajtmh.2003.68.734

Authors’ addresses: Charles M. Mbogo, Joseph M. Mwangangi, and Joseph G. Nzovu, Centre for Geographic Medicine Research-Coast, Kenya Medical Research Institute, PO Box 428, Kilifi, Kenya, Telephone: 254-125-220-63, Fax: 254-125-223-80, E-mail: cmbogo@kilifi.mimcom.net. Weidong Gu, Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907. Guiyun Yan, Department of Biologic Sciences, State University of New York at Buffalo, Buffalo, NY 14260. James T. Gunter and James L. Regens, Institute of Science and Public Policy, University of Oklahoma, 100 E. Boyd Room 510, Norman, OK 73019. Chris Swalm, Department of Pharmacology, Tulane University, 1440 Canal Street, New Orleans, LA 70112. Joseph Keating, Department of International Health and Development, School of Public Health and Tropical Medicine, TB46, Tulane University, 1440 Canal Street, New Orleans, LA 70112. Josephat I. Shililu and John I. Githure, International Centre of Insect Physiology and Ecology, PO Box 30772, Nairobi, Kenya. John C. Beier, Department of Tropical Medicine, School of Public Health and Tropical Medicine, SL17, Tulane University, 1501 Tulane Avenue, Room 505, New Orleans, LA 70112.

Acknowledgments: We are grateful for the assistance of all scientific staff at the Center for Geographic Medicine Research-Coast, particularly Dr. N.M. Peshu and Professor Kevin Marsh. We thank the technical and field staff for the collection and processing of the mosquitoes, particularly Pamela Seda, Festus Yaah, Shida David, and Gabriel Nzai. This paper is published with the permission of the Director of the Kenya Medical Research Institute.

Financial support: This study was supported by core funds from the International Centre of Insect Physiology and Ecology, and National Institutes of Health grants U19 AI-45511 and D43 TWO1142.

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