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

Malaria Journal

Research

Nicolas Maire¹  Aurelio Di Pasquale¹  Thomas A. Smith¹  Amanda Ross¹  Ibrahim Kiche²  Collins Mweresa²  Kelvin Onoka²  Tobias Homan³  Willem Takken³  Alexandra Hiscox³  Wolfgang R. Mukabana⁴ 

[1] Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland;University of Basel, Basel, Switzerland;Department of Medical Entomology, International Centre of Insect Physiology and Ecology, Nairobi, Kenya;Laboratory of Entomology, Wageningen University and Research Centre, Wageningen, The Netherlands;School of Biological Sciences, University of Nairobi, Nairobi, Kenya;

关键词: Malaria;    Spatial heterogeneity;    Geographically weighted regression;    Spatially variable risk factors;    Kenya;    Socio-economic status;    Occupation;    Population density;   

DOI  :  10.1186/s12936-015-1044-1

 received in 2015-07-17, accepted in 2015-12-09,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundLarge 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.MethodsOn 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.ResultsCombining 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.DiscussionIdentification 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

【 授权许可】

CC BY   
© Homan et al. 2015

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