Spatial distribution of bednet coverage.

<p>(A) Map of household-level coverage raster. Areas with 0% to 10% community coverage are shown in white; areas with 11%–30% community coverage are shown in brown; areas with 31%–50% community coverage are shown in yellow; and areas with 51% –70% community coverage area shown in green. Major rivers, roads, town centers, and public health facilities are shown. (B) Percent of households within each coverage zones. Colors correspond to map.</p

Factors associated with household possession of at least one bednet in multivariable logistic regression analysis, stratified by broad location.

<p>Data presented here are the odds ratio (OR), p-value, and 95% confidence intervals (CI) for the multivariable logistic regression stratified by urban versus rural households.</p>a<p>Within Webuye town the health centre and hospital are less than 0.5 km apart. The distance and proximity variables were combined to consider these two facilities equal.</p>b<p>Reference variable is nearest facility is dispensary.</p

Exponential of the random effects (with 95% CI) for each sublocation for the mixed effects model.

<p>This plot shows the effect of sublocation of residence on bednet ownership. The random effects plot is the exponent of the random intercept for each sublocation. The exponent of the random effect can be thought of as the quantity that the exponent of the fixed effects intercept would be multiplied by to account for sublocation. So if exp(RE) = 1.5 then the exp(βo) would be multiplied by 1.5 for households in that sublocation. When exp(RE) = 1, that is the zero effect – location has no effect on the outcome. The plot shows that there is considerable heterogeneity between sublocations due to unobserved factors not captured in the model.</p

Numbers of ITNs distributed through public facilities in Bungoma East district, Kenya, 2008 and 2009.

<p>Numbers of ITNs distributed through public facilities in Bungoma East district, Kenya, 2008 and 2009.</p

Cluster analysis of bednet coverage.

<p>The difference between K₁(d), k-function for pattern of households owning at least one bednet, and K₂(d), k-function for pattern of the underlying household distribution (solid line) and the confidence envelope (dashed lines) around the difference of expected distributions (zero line). Positive values indicate greater clustering of households owning at least one bednet in comparison to the underlying clustering of all households. Negative values indicate households owning at least one bednet have a more dispersed pattern than the underlying household distribution.</p

Bednet Ownership and Distribution within Bungoma East District.

<p>Bednet Ownership and Distribution within Bungoma East District.</p

Factors associated with testing uptake among pregnant women.

<p>Factors associated with testing uptake among pregnant women.</p

Socio-demographic and socio-economic characteristic by pregnancy status.

<p>Socio-demographic and socio-economic characteristic by pregnancy status.</p

Factors associated with HIV prevalence among pregnant women.

<p>Factors associated with HIV prevalence among pregnant women.</p

Additional file 1: of Effects of HIV-1 infection on malaria parasitemia in milo sub-location, western Kenya

Table S1. Expressions age, sex, malaria parasitaemia and density against HIV status