Environmental, socio-demographic and behavioural determinants of malaria risk in the western Kenyan highlands: a case–control study

Authors


Corresponding Author Kacey C. Ernst, Division of Epidemiology and Biostatistics, University of Arizona, 1295 N Martin Ave., Tucson, AZ 85724-5183, USA. E-mail: kernst@email.arizona.edu

Summary

Objective  To identify risk factors for uncomplicated malaria in highland areas of East Africa at higher risk of malaria epidemics, in order to design appropriate interventions.

Methods  Prospective, population-based, case–control study in the Nandi Hills, a highland area of western Kenya, to identify environmental, sociodemographic and behavioural factors associated with clinical malaria. Data were collected using field observation, a structured questionnaire, and a global positioning system device.

Results  We interviewed 488 cases of slide-confirmed malaria and 980 age-matched controls. Multivariate analyses associated higher malaria risk with living <250 m of a forest [OR = 3.3 (95% CI 1.5, 7.1)], <250 m of a swamp [2.8 (1.3, 5.9)], <200 m of maize fields [2.0 (1.2, 3.4)], in the absence of trees <200 m [1.6 (1.2, 2.2)], on flat land [1.6 (1.2, 2.2)], in houses without ceilings [1.5 (1.1, 2.2)], in houses with a separate kitchen building [1.8 (1.4, 2.3)] and in households where the female household head had no education [1.9 (1.1, 3.1)]. Travelling out of the study site [2.2 (1.2, 4.1)] was also associated with increased risk.

Conclusions  In this East African highland area, risk of developing uncomplicated malaria was multifactorial with a risk factor profile similar to that in endemic regions. Households within close proximity to forest and swamp borders are at higher risk of malaria and should be included in indoor residual spraying campaigns.

Abstract

Objectif:  Identifier les facteurs de risque de la malaria non compliquée dans les régions montagneuses de l’Afrique de l’Est à risque plus élevé d’épidémies de malaria, dans le but de concevoir des interventions appropriées.

Méthodes:  Etude prospective, cas-témoins, basée sur la population dans les collines de Nandi, une région montagneuse de l’ouest du Kenya, afin d’identifier les facteurs environnementaux, sociodémographiques et comportementaux associés à la malaria clinique. Les données ont été recueillies au moyen de l’observation sur le terrain, d’un questionnaire structuré et d’un système de positionnement spatial.

Résultats:  Nous avons interviewé 488 cas de malaria confirmés par des frottis et 980 cas témoins appariés par l’âge. Les analyses multivariées ont associé un risque plus élevé de la malaria avec la résidence: à < 250m de la forêt [OR = 3,3 (IC95%: 1,5 - 7,1)], à < 250 m d’un marécage [2,8 (1,3 - 5,9)], à < 200 m de champs de maïs [2,0 (1,2 - 3,4)], à l’absence d’arbres à < 200 m [1,6 (1,2 - 2,2)], sur un terrain plat [1,6 (1,2 - 2,2)], dans des maisons sans plafond [1,5 (1,1- 2,2)], dans des maisons avec une cuisine séparée [1,8 (1,4 - 2,3)] et dans les ménages où le chef de ménage féminin n’a pas d’éducation [1,9 (1,1 - 3,1)]. Voyager en dehors du site de l’étude a également été associée à un risque accru [2,2 (1,2 - 4,1)].

Conclusions:  Dans cette zone montagneuse d’Afrique de l’Est, le risque de la malaria non compliquée est multifactoriel avec un profil des facteurs de risque semblable à celui dans les régions d’endémie. Les ménages à proximité de la forêt et des marais sont plus à risque de la malaria et doivent être inclus dans les campagnes de pulvérisation de résidus d’intérieur.

Abstract

Objetivo:  Identificar los factores de riesgo de malaria no complicada en áreas del altiplano de África del Este con mayor riesgo de epidemias de malaria, con el fin de diseñar intervenciones apropiadas.

Métodos:  Estudio caso-control prospectivo, basado en la población, en las Colinas Nandi, un área del altiplano de Kenia occidental, con el fin de identificar los factores ambientales, socio-demográficos y de comportamiento asociados con la malaria clínica. Los datos fueron recolectados mediante observación en el terreno, un cuestionario estructurado y un dispositivo de sistema de posicionamiento global (GPS).

Resultados:  Se entrevistaron 488 casos de malaria confirmada mediante lámina y 980 controles apareados por edad. Un análisis multivariado asoció un mayor riesgo de malaria con vivir a < 250m de un bosque [OR = 3.3 (95% IC 1.5, 7.1)], <250m de un pantano [2.8 (1.3, 5.9)], <200m de campos sembrados con maíz [2.0 (1.2, 3.4)], en la ausencia de árboles a < 200m [1.6 (1.2, 2.2)], sobre tierra plana [1.6 (1.2, 2.2)], en casas sin techos [1.5 (1.1 2.2)], en casas con una cocina en un edificio separado [1.8 (1.4, 2.3)] y en hogares en los cuales la ama de casa no tenía educación [1.9 (1.1, 3.1)]. El viajar fuera del lugar del estudio [2.2 (1.2, 4.1)] estaba también asociado con un aumento del riesgo.

Conclusiones:  En esta área de altiplano de África del Este, el riesgo de desarrollar una malaria no complicada era multifactorial, con un perfil de factores de riesgo similar al de regiones endémicas. Los hogares con más proximidad al bosque o a áreas pantanosas tenían un mayor riesgo de malaria y deberían incluirse en las campañas de rociamiento intradomiciliario.

Introduction

Historically, malaria transmission in the East African highlands (>1500 m) has been low and unstable but notable increases have occurred in the past few decades. Transmission is spatially focal in the highlands. Cases and anopheline mosquitoes cluster in low-lying valley bottoms, often in close proximity to swamps or rivers (Brooker 2004; Minakawa 2004; Zhou 2004, 2007; Ernst 2006; Githeko 2006). Although overall risk may be higher in these areas, behavioural, micro-environmental and socioeconomic factors likely modify the relationship between malaria risk and environmental suitability for transmission. Little research has been conducted to examine household-level environmental and behavioural factors that mediate risk in the East African highlands. Studies have been restricted to children despite significant incidence of clinical malaria in adults in these areas (Ghebreyesus 2000; Brooker 2004), and were conducted during an epidemic when risk factors may have differed from those during normal seasonal transmission (Brooker 2004). The clinical malaria age distribution, climate, agricultural activities, cultural practices and biophysical environments in highland regions differ from the endemic lowlands, potentially resulting in a different risk profiles.

To identify risk factors for malaria in the highlands that differ from the lowlands, a prospective, population-based, case–control study was conducted in a highland area of western Kenya. Living close to swamps and forests and at lower elevations was associated with greater malaria risk (Ernst 2006) in this area but did not fully explain malaria risk. Accordingly, additional environmental factors that may influence vector abundance, density or activity, as well as sociodemographic and behavioural factors potentially related to developing clinical malaria were investigated.

Methods

Study sites

The study was conducted in Kipsamoite and Kapsisiywa (sub)locations (pop. ∼7000) in Nandi District, western Kenya (Figure 1), at elevations of 1887–2100 m. North Nandi forest borders Kipsamoite’s western side. Kimondi swamp borders much of the remaining study area (Ernst 2006 for details). Primary occupations are subsistence farming and animal husbandry.

Figure 1.

 Nandi District in western Kenya with map of study site (Kipsamoite Sublocation and Kapsisiywa Location).

Study design

A prospective, population-based, case–control study design was used. Cases were identified as individuals with Plasmodium species identified in thick or thin blood smears who had presented to either of the two local health centres with symptoms consistent with malaria (fever, chills, headache or severe malaise).

Quarterly demographic surveillance was conducted in a concurrent study during which residents were assigned permanent unique study identification numbers. Using the enumerated population, malaria cases were frequency-matched to two controls by site (Kispsamoite or Kapsisiywa) and age category (<5, 5–14, 15–44, or >45 years old). Three visits on three separate days, at different times of day, were made to homes of potential controls to determine eligibility and request participation. Exclusion criteria for selected controls were (1) prior malaria diagnosis during the study period, (2) symptoms of malaria in the preceding month, or (3) moved or primary residence out of study area. Controls deemed ineligible or refusing to participate were replaced. Controls that later became cases were reinterviewed and replaced.

Exposure measurements

Environmental data were collected for cases and controls from direct household observations, Global Positioning System (GPS) measurements (Trimble Navigation, Sunnyvale, CA, USA), and a questionnaire. The dwelling in which the participant normally slept was assessed and distances (GPS corrected 1 m resolution) to mapped forest edge, swamps, rivers, roads, and health centres were calculated using ordinary Euclidean distance in ArcGIS V 9.0 (Redlands, CA, USA). Distances from the perimeter of the sleeping place to agricultural fields and penned livestock within a radius of 200 m were measured using steel tape. Type of vegetation within 200 m of the house was recorded (Earth Institute 2005).

A questionnaire was orally administered to cases and controls in the local language, Kalenjin, to ascertain sociodemographic and behavioural characteristics such as number of occupants, travel history, malaria prevention practices, and presence of livestock and other assets.

Variables

Bednet use was defined as sleeping under a net each day of the previous week. Individuals who reported using mosquito coils (at least three times a week), taking malaria medication as prophylaxis (at least once a month) and clearing brush (at least once a month) were classified as exposed to those factors. Distances from household to swamp, forest, river and roadsides were divided into four categories <250, 250–500, 500–1000, >1000 m [average flight range of Anopheles gambiae (Costantini 1996)]. Elevation and distance from the household to the nearest clinic were divided into equal quartiles. Tea, maize, trees and bushes were classified as present if located within 200 m of the structure where the participant slept. Exposure to channelled swamp water was defined as participants who lived within 250 m of a swamp and reported channelling swamp water. Geographic slope was categorized by the field assistants into four classes (flat land, gentle slope, medium slope, and steep slope). The final variable for slope was dichotomous; sloped, including any grade of slope, or flat. Similarly, field assistants used a model illustration to determine if there were no trees, few trees, some trees or many trees within 200 m of the house. The final variable was also dichotomous; no trees vs. trees. Overnight travel was restricted to people sleeping outside the home 7–21 days prior to interview, adjusting for the average incubation period of malaria. Travel to any area outside the study sites was included.

Household Wealth Index

Principal component analysis (PCA) was conducted using sas V 9.1 (Cary, NC, USA) to estimate a relative household wealth index from a combination of household and asset variables (Filmer & Pritchett 2001). Household characteristics included building materials used for wall, floor, separate kitchen and roof. Assets included television, radio, bicycle, pressure lamp, car, sofa set, and charcoal cooking stove. The first component accounted for 30% of the total variance. A linear combination of the original asset variables (standardized to mean = 0 and variance = 1) multiplied by their respective standardized scoring coefficients (Table 1) was used to create the household wealth variable which was then divided into quartiles.

Table 1.   Standardized scoring coefficients
VariablePressure lampStoveSofaLanternBikeRadioTVCement FloorMud WallsMetal RoofNo Separate Kitchen
Standardized Scoring Coefficient0.170.230.090.260.360.370.170.17−0.170.02−0.10

Statistical analyses

Bivariate analyses, logistic regression, and then backwards stepwise regression with generalized estimating equations (GEE) were used to create multivariable models using sas V 9.1. All explanatory variables were initially included in the model and variables making the smallest contribution were sequentially dropped until all variables included in the model were P < 0.10. Matching variables, site and age were kept in the model with week of interview which was used to control for the average 2-week time difference in interviews for controls vs. cases. An exchangeable correlation matrix was used to adjust for correlation among members of the same household. People from different households were considered independent of each other. Model residuals were assessed for spatial autocorrelation using Moran’s I statistics (ArcGIS ver. 9.1, ESRI, Redlands, CA, USA).

Ethical considerations

All cases and controls, or the guardians of subjects <18 years old, provided written, informed consent before participation. Approval to undertake this study was obtained from the Institutional Review Board (IRB) committees at the University of Michigan (Ann Arbor, MI, USA), Kenya Medical Research Institute (KEMRI, Nairobi, Kenya), and U.S. Centers for Disease Control and Prevention (CDC, Atlanta, GA, USA). Approval for the demographic and malaria surveillance in this study was obtained from the IRB committees at Case Western Reserve University (Cleveland, OH, USA), CDC, University of Michigan and KEMRI.

Results

Study population characteristics

From March through September 2004, 492 malaria cases were identified, of whom 99.1% (n = 488) agreed to be in the study. Participation was similarly high (98.9%, n = 980) for the 990 eligible controls. Malaria cases were equally distributed between the sexes (M:F = 251:237) and were identified in all age groups [<5 years of age, n = 100 (20.5%); 5–14 years, n = 163 (33.4%); 15–44 years, n = 182 (37.5%); >45 years, n = 43 (8.8%)].

Associations with sociodemographic and environmental variables

In bivariate analyses, increased malaria risk was associated with various sociodemographic and behavioural factors (Table 2) including low education levels of female household heads, overnight travel, living near channelled swamp water and keeping livestock near the house at night. Living in a house with a metal roof, no ceiling or separate kitchen also were related to higher risk, while open eaves and uncovered windows did not appear to have an effect. Environmental risks included living on flat land, living close to forests, and having bushes but not trees <200 m from the house (Table 2).

Table 2.   Bivariate analysis of association between malaria risk (cases/controls) and various demographic, social and environmental variables in Kipsamoite and Kapsisiywa, Kenya, March–September 2004
Potential risk factorsNo. cases (= 488)No. controls (= 980)Crude odds ratio (95% CI) P
Socio-demographics
Female Household Head Education
 None 78 (16)155 (16)1.85 (1.09, 3.13)0.02
 Primary/some secondary 386 (79)737 (75)1.92 (1.20, 3.07)0.006
 Completed Secondary + 22 (5)84 (9)1 
Household Wealth Index
 First quartile (poorest)123 (25)244(25)0.85 (0.62, 1.16)0.31
 Second quartile 146 (26)253 (30)0.95 (0.70, 1.30)0.75
 Third quartile115 (24)240 (24)1.14 (0.85, 1.54)0.37
 Fourth quartile104 (21)243 (25)1 
People sleeping in room
 <3 168(35)309 (31)1 
 3–5 271 (45)571 (48)0.87 (0.69, 1.11)0.26
 >5 49 (20)100 (20)0.90 (0.61, 1.31)0.60
Prevention activities
Malaria prophylaxis 4 (1)10 (1)0.82 (0.25, 2.56)0.71
Mosquito coils 10 (2)28 (3)0.71 (0.34, 1.48)0.36
Bednet use 15 (3)37 (4)0.81 (0.44, 1.49)0.49
Clearing of bushes 167 (34)398 (38)0.84 (0.64, 1.09)0.17
Burning herbs at night 55 (11)95 (9)1.12 (0.63, 1.99)0.69
Travel/residential history
Overnight travel 7–21 days prior 21 (4)20 (2)2.16 (1.16, 4.02)0.02
Immigrated from outside study 22 (5)36 (4)1.24 (0.72, 2.13)0.44
Agricultural and animal husbandry activities
Channelled swamp water 50 (10)44 (4)2.43 (1.59, 3.70)<0.001
Animals sleeping in compound
 Insecticide treated cattle254 (52)509 (52)1.00 (0.81, 1.25)0.97
 Insecticide free cattle84 (17)131 (13)1.35 (1.00, 1.82)0.05
 Goats43 (9)48 (5)1.88 (1.23, 2.88)0.003
 Dogs252 (52)435 (44)1.34 (1.08, 1.66)0.01
House construction
Metal roof 284 (58)483 (49)1.43 (1.15, 1.78)0.001
Mud walls 430 (88)867 (88)0.97 (0.69, 1.35)0.84
Absence of ceiling 409 (84)771 (79)1.40 (1.05, 1.87)0.02
Open eaves 387 (79)795 (81)0.89 (0.68, 1.17)0.41
Uncovered windows 52 (11)119 (12)1.06 (0.74, 1.51)0.76
Separate kitchen 129 (74)356 (64)1.59 (1.25, 2.02)0.001
Surrounding environment
Livestock holding area <100 m 206 (42)374 (38)1.18 (0.95,1.48)0.14
House on flat land 121 (25)179 (18)1.47 (1.13, 1.91)0.004
Location at bottom of hill 52 (11)85 (9)1.26 (0.87, 1.81)0.22
Elevation (quartiles)
 < 1932 m 110(23)260 (27)0.95 (0.69, 1.30)0.74
 1932–1947 m114 (23)247 (25)1.03 (0.76, 1.41)0.84
 1947–1983 m151 (31)220 (22)1.53 (1.13, 2.08)0.06
 > 1983 m113 (23)253 (26)1 
Distance from forest
 < 250 m33 (7)40 (4)1.77 (1.10, 2.85)0.02
 250–500 m37 (8)50 (5)1.58 (1.02, 2.47)0.04
 500–1000 m 55 (11)113 (12)1.02 (0.71, 1.45)0.82
 > 1000 m 363 (74)777 (79)1 
Distance from swamp
 < 250 m166 (34)263 (27)1.36 (0.94, 1.98)0.10
 250–500 m198 (41)421 (43)1.02 (0.71, 1.45)0.93
 500–1000 m 68 (14)175 (18)0.84 (0.55, 1.28)0.42
 > 1000 m 56 (11)121 (12)1 
Distance from clinics (quartiles)
 1190 m115 (23)253 (26)0.78 (0.57, 1.06)0.11
 1190–2081 m125 (26)241 (24)0.89 (0.66, 1.20)0.44
 2081–2711 m 112 (23)253 (26)0.76 (0.56, 1.03)0.08
 >2711 m136 (28)233 (24)1 
Distance from Stream
 < 250 m97 (19)186 (20)1.19 (0.92, 1.53)0.19
 250–500 m83 (17)113 (12)1.79 (1.23, 2.59)0.002
 500–1000 m 6 (1)12 (1)1.11 (0.41, 2.97)0.84
 > 1000 m 302 (62)669 (68)1 
Absence of trees within 200 m 394 (81)703 (72)1.65 (1.27, 2.15)0.0002
Presence of bushes within 200 m 124 (25)194 (20)1.38 (1.07, 1.79)0.01
Presence of tea within 200 m 243 (50)522 (53)0.87 (0.70, 1.08)0.21
Presence of maize within 200 m 445 (91)887 (90)1.09 (0.74, 1.59)0.67

The final multivariable model included many of the same variables, indicating that greater malaria risk was associated with lower levels of education of female household heads, recent overnight travel, living near channelled swamp water and near forests, lack of ceiling in the house, a separate kitchen and having goats in the compound. External environmental influences such as living on flat land, in close proximity to maize fields, and on land lacking nearby trees also increased malaria risk (Table 3).

Table 3.   Multivariate model of associations between measured variables and malaria risk in Kipsamoite and Kapsisiywa, western Kenyan highlands, March–September 2004
Potential risk factorsAdjusted* odds ratio
(95% CI)
P
  1. *Model adjusted for age, site and week of interview.

Female Household Head Education
 None 2.02 (1.08, 3.75)0.03
 Primary/some secondary 2.01 (1.16, 3.05)0.01
 Completed Secondary + 1 
Mosquito coils 0.48 (0.21, 1.10)0.08
Overnight travel 7–21 days prior 2.17 (1.15, 4.11)0.02
Channelled swamp water 1.75 (0.96, 3.20)0.07
Goats sleeping in compound 1.61 (0.95, 2.74)0.07
Absence of ceiling 1.53 (1.08, 2.18)0.02
Separate kitchen 1.77 (1.35, 2.32)<0.0001
House on flat land 1.61 (1.19, 2.18)0.002
Elevation (quartiles)
 < 1932 m 1.35 (0.60, 3.04)0.47
 1932–1947 m1.68 (0.76,3.71)0.20
 1947–1983 m2.02 (1.08, 3.79)0.03
 > 1983 m1 
Distance from forest
 < 250 m3.26 (1.49, 7.12)0.003
 250–500 m2.23 (1.02, 4.85)0.04
 500–1000 m 1.26 (0.72, 2.20)0.41
 > 1000 m 1 
Distance from swamp
 < 250 m2.81 (1.34, 5.89)0.006
 250–500 m2.31 (1.15, 4.65)0.02
 500–1000 m 1.52 (0.80,2.89)0.20
 > 1000 m 1 
Absence of trees within 200 m 1.59 (1.15, 2.19)0.005
Presence of maize within 200 m 2.02 (1.21, 3.37)0.007

Discussion

Previous analyses of passive malaria surveillance data from Kipsamoite suggested ‘hotspots’ of transmission associated with distance from swamps and forests (Ernst 2006). Even in these apparent hotspots, there were malaria-free households. Although shared bio-physical environments can produce clusters of higher transmission, other factors at the individual and household levels can mediate this risk. Accordingly, associations between malaria risk and environmental, socio-demographic, and behavioural variables were explored.

Environment around the home was important. Closer proximity to swamps and forest border was associated with increased malaria risk and is consistent with findings in other highland areas (Lindblade 2000; Staedke 2003; Brooker 2004; Minakawa 2004; Zhou 2004, 2007). These habitats may represent enhanced Anopheles breeding sites or microclimates that prolong adult vector survival. Our analyses also demonstrated a 500-m threshold for these relationships. Absence of trees and presence of maize near dwellings also were associated with increased risk. Anopheles gambiae s.s. prefer breeding in open areas (Minakawa 2004) and exposure to sunlight increases water temperatures potentially speeding up the development rate of the aquatic stages of An. gambiae s.s. (Bayoh & Lindsay 2003). Maize pollen is a good nutritional source for An. gambiae larvae (Ye-Ebiyo 2003) and has been related to higher malaria incidence (Kebede 2005). The presence of many mixed vegetation classes within 200 m of the house may have obscured further relationships.

Living on flat ground, where water is most likely to accumulate, was associated with increased risk corroborating results found by Cohen (2008) that demonstrated topography, when used as a predictor of wetness, is highly correlated with malaria risk. Absolute elevation, however, was not associated with risk. This is counter to results from a study by Brooker (2004), who found a significant relationship between malaria risk and elevation. The narrower range of elevation across this study area (213 m) as compared to theirs (400 m) may have prevented a similar association.

Similarly to the study by Brooker (2004), we found no association between household wealth and malaria risk. Our index may not have accurately differentiated levels of household wealth or variation may have been too limited to impact risk. Having a kitchen separate from the sleeping area was strongly associated with increased malaria odds, perhaps because smoke repels vector mosquitoes, although sleeping in a smoky room was not associated with risk in another study in the Kenyan highlands (Brooker 2004). Many relatives and older children reported sleeping in the kitchen area and there was a tendency to keep fires burning at night for warmth in this study area which may differ from other highland communities. House construction also influences mosquito access to people sleeping inside and we found that having a ceiling in the home was associated with decreased risk of malaria. Open eaves have been associated with increased risk in some past studies (e.g. Ghebreysus et al. 2000) but not by Brooker (2004) or in our study.

The education level of female heads of household was inversely associated with malaria risk. Other studies have shown that use of malaria prevention measures has been consistently related to higher maternal education (e.g. Keating 2005; Noor 2006), yet we saw no notable associations between use of preventive measures, including bednets, and malaria risk except for a modest decreased risk among those using mosquito coils. In our study, as in that by Brooker (2004), practice of preventive measures was quite low. Environmental control (removal of tins and brush) was the most commonly reported activity, indicating effective environmental management techniques could be well-received. Prevalence of bednet use was low and we did not test the nets for insecticide or examine them for holes, potentially obscuring their true association with malaria risk. Although people expressed interest in bednets, anecdotal evidence suggests that even those who owned bednets did not use them, primarily because vector density, hence perceived risk, was low. Some believed that only those living near the swamps were at risk of malaria. Education regarding the importance of net use, even when vector density is low, could improve effectiveness of bednets in this area.

In our study, like others (e.g. Shanks 2005), overnight travel increased malaria risk, even though such movement was primarily to neighbouring highland communities. Increased malaria risk may be related to increased vector-human contact in such settings. Indeed, some interviewees informally reported that when attending funerals it was common to sleep in the open air. The relationship between malaria risk and travel should be interpreted with caution, however, as frequency of travel was low and positive bias could have arisen if residents temporarily living outside the study area were included as cases (perhaps at home on holiday and seen at the clinic) versus controls (replaced if residing out of area for work or school).

This study had both strengths and potential entries of bias. High participation rates and a fully enumerated population permitting strata-specific random selection of controls created a highly representative sample. Recall bias, a concern in case–control studies, was minimized by interviewing shortly after case and control identification. The need to replace ineligible and unavailable controls put their interview times an average of 2 weeks later than cases; however, week of interview was adjusted for in the multivariable model. Although all cases were slide-confirmed, logistical constraints precluded the testing of controls and asymptomatic cases may have been misclassified as controls, thus biasing associations. Past studies, however, demonstrated that the level of asymptomatic malaria in this region is low (John 2005). The large sample size required multiple field assistants, potentially resulting in differential information bias particularly for subjectively measured variables such as slope and tree cover. To minimize such bias, an intensive training program was combined with spot checks of 10% of all questionnaires by the study coordinator and by dichotomizing highly subjective variables (tree cover and slope).

Results of our study underscore the complexity of highland malaria transmission. Prior studies have assessed some of the factors that we evaluated, but our investigation adds to these earlier reports by assessing multiple environmental, social and behavioural factors in a single multivariate model. The report by Brooker (2004) is the only other study of this nature from the east African highlands, but their study focused on children during an epidemic and a different set of variables. Although our results indicate that malaria risk may cluster near specific land covers, considerable time and resources are needed to identify such high risk areas, and only some of the malaria cases are included. Moreover, predictors of high risk may vary among regions. The resources needed to map high risk areas may be better allocated, for example, toward achieving universal coverage of indoor residual spraying. Workers administering the spraying should keep in mind that not all members of the household sleep in the main house and spraying of outbuildings, particularly kitchen buildings, may be necessary to ensure complete coverage.

Acknowledgements

We thank the communities of Kipsamoite and Kapsisiywa for their incredible support of this research. The work would not have been possible without the contribution of the field assistants (Rosebella Chepchumba, Paul Lelei, Peter Cheboiywo, Usillah Biwott, Haron Rugut, Moses Sawe, Gideon Kurgat, Josphat Koech, Raymond Bungei, Steven Koros, Japheth Koech, Jeruto Ogla, Jepn’getich Melly, Celestine Rotich, Japhet Kipleting, Simeon Kipleting, Dorothy Kirwa), the microscopists (John Oluoch, Joseph Otieno), the study coordinators (Lillian Kipkagat, Peter Siwat), and the clinical officer (Willy Rotich). Financial support was provided by USPHS grants (AI-056184 and AI-01572) and the Global Health Program at the University of Michigan. The findings and conclusions in this [presentation/report] are those of the author(s) and do not necessarily represent the views of the Centers for Disease Control and Prevention.

Ancillary