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

Abstract

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

Situation analysis of healthcare service delivery using geographically weighted regression: (A case study of Sironko District, Eastern Uganda)

“…Two regression models were developed for comparison; one using adaptive method of kernel where the computer was allowed to choose the optimal bandwidth for the distance or number of neighbors. An adaptive kernel function looks at an adaptive number neighbours and the influence of these neighbours decays a Gaussian distribution so that closer observations have more weight (Homan et al, 2016). The other model employed a fixed kernel distance of 5km to determine the neighbors to be used in the regression computation.…”

Combining malaria control with rural electrification : social and behavioural factors that influenced the design, use and sustainability of solar-powered mosquito trapping systems (SMoTS) for malaria elimination on Rusinga Island, western Kenya

“…The island is located between 0°20′51.53″-0°26′33.73″ South, and 34°13′43.19″-34°07′23.78″ East) (Homan, Maire et al 2016). Administratively, Rusinga Island is part of Homabay County in western Kenya (Figure 4: courtesy of Homan et.…”

“…Due to the island's close proximity to the mainland, the waterway separating Rusinga Island from the mainland was filled in 1985 and a road to Rusinga Island was constructed to facilitate the transportation of people, goods and services Nagi, Chadeka et al 2014). A project-initiated census carried out in 2012 revealed the community consisted roughly of 4,063 homesteads and 23,337 inhabitants (Homan, Maire et al 2016). Most of the residential areas were situated between 1100 and 1200 metres above sea level around the lakeshore of the island (Homan, Maire et al 2016).…”

“…In addition to the social mobilisation activities, home visits by project staff for entomological, parasitological, demographic, and sociological data collection Homan, Di Pasquale et al 2015;Homan, Maire et al 2016), also provided an opportunity for the multi-disciplinary teams within SolarMal to talk with participants about malaria control. A community advisory board (CAB) that evolved through the feedback sessions and activities ) provided on-going opportunities to ensure the involvement of the research community and key stakeholders in guiding the implementation of SolarMal.…”

“…A homestead is a single fenced-in house or group of houses occupied by one nuclear or extended family respectively. The homesteads were allocated to a cluster according to a travelling salesman algorithm by which the shortest imaginary route connect every homestead on the island was identified (Homan, Maire et al 2016). Nine clusters formed a metacluster.…”