Spatially variable risk factors for malaria in a geographically heterogeneous landscape, western Kenya: an explorative study

Abstract

Background: Large reductions in malaria transmission and mortality have been achieved over the last decade, and this has mainly been attributed to the scale-up of long-lasting insecticidal bed nets and indoor residual spraying with insecticides. Despite these gains considerable residual, spatially heterogeneous, transmission remains. To reduce transmission in these foci, researchers need to consider the local demographical, environmental and social context, and design an appropriate set of interventions. Exploring spatially variable risk factors for malaria can give insight into which human and environmental characteristics play important roles in sustaining malaria transmission.

Methods: On Rusinga Island, western Kenya, malaria infection was tested by rapid diagnostic tests during two cross-sectional surveys conducted 3 months apart in 3632 individuals from 790 households. For all households demographic data were collected by means of questionnaires. Environmental variables were derived using Quickbird satellite images. Analyses were performed on 81 project clusters constructed by a traveling salesman algorithm, each containing 50-51 households. A standard linear regression model was fitted containing multiple variables to determine how much of the spatial variation in malaria prevalence could be explained by the demographic and environmental data. Subsequently, a geographically-weighted regression (GWR) was performed assuming non-stationarity of risk factors. Special attention was taken to investigate the effect of residual spatial autocorrelation and local multicollinearity.

Results: Combining the data from both surveys, overall malaria prevalence was 24%. Scan statistics revealed two clusters which had significantly elevated numbers of malaria cases compared to the background prevalence across the rest of the study area. A multivariable linear model including environmental and household factors revealed that higher socioeconomic status, outdoor occupation and population density were associated with increased malaria risk. The local GWR model improved the model fit considerably and the relationship of malaria with risk factors was found to vary spatially over the island; in different areas of the island socio-economic status, outdoor occupation and population density were found to be positively or negatively associated with malaria prevalence.

Discussion: Identification of risk factors for malaria that vary geographically can provide insight into the local epidemiology of malaria. Examining spatially variable relationships can be a helpful tool in exploring which set of targeted interventions could locally be implemented. Supplementary malaria control may be directed at areas, which are identified as at risk. For instance, areas with many people that work outdoors at night may need more focus in terms of vector control.

Trial registration: Trialregister.nl NTR3496-SolarMal, registered on 20 June 2012.

Fig. 1

Kenya with the Homa Bay County highlighted where the study site is located. Rusinga Island is mapped showing population density per 250 m² with the boundaries of 81 clusters with equal numbers of households. The blank space in the centre of the map is an uninhabited hill and the densely populated south-east is magnified—depicted in the bottom right of the figure

Fig. 2

a Mean malaria prevalence per cluster on the basis of sampled individuals across Rusinga Island using Aerial interpolation. b Map of Rusinga Island showing two clusters of households (orange dots) with significantly elevated levels of malaria prevalence. The primary cluster is located at the central north of the island; a secondary cluster is covering an area to the west. Figure 2 a would suggest another cluster of malaria in the south-east, however prevalence in this area is not significantly greater than in neighbouring areas. The grey dots b with black outlines are the sampled houses in the prevalence surveys; the paler grey dots indicate all houses on the island

Fig. 3

Semivariogram of the residuals of the final GWR model, with the dotted line showing the fitted value. The semivariance is shown on the y-axis. The semivariance of the residuals between households starts at 0.61 (nugget) demonstrating some spatial autocorrelation on distances up to 2.7 km (range). Beyond this threshold the semivariance is high and stabilizes at 0.825 (sill) indicating minimal RSA

Fig. 4

a Goodness-of-fit statistics indicate how well the GWR model fits per cluster, expressed by R² and b Multicollinearity per cluster, expressed by the condition number. A higher condition number indicates an increased degree of multicollinearity

Fig. 5

Geographically varying coefficients expressed as the relative risk per cluster for predictor variables of malaria prevalence in the final GWR model. a Outdoor occupation, b highest SES, c population density

Fig. 6

Geographically varying values of significance per cluster for predictor variables of malaria prevalence in the final GWR model. a Outdoor occupation, b highest SES, c population density