Using Community-Level Prevalence of Loa loa Infection to Predict the Proportion of Highly-Infected Individuals: Statistical Modelling to Support Lymphatic Filariasis and Onchocerciasis Elimination Programs

Abstract

Lymphatic Filariasis and Onchocerciasis (river blindness) constitute pressing public health issues in tropical regions. Global elimination programs, involving mass drug administration (MDA), have been launched by the World Health Organisation. Although the drugs used are generally well tolerated, individuals who are highly co-infected with Loa loa are at risk of experiencing serious adverse events. Highly infected individuals are more likely to be found in communities with high prevalence. An understanding of the relationship between individual infection and population-level prevalence can therefore inform decisions on whether MDA can be safely administered in an endemic community. Based on Loa loa infection intensity data from individuals in Cameroon, the Republic of the Congo and the Democratic Republic of the Congo we develop a statistical model for the distribution of infection levels in communities. We then use this model to make predictive inferences regarding the proportion of individuals whose parasite count exceeds policy-relevant levels. In particular we show how to exploit the positive correlation between community-level prevalence and intensity of infection in order to predict the proportion of highly infected individuals in a community given only prevalence data from the community in question. The resulting prediction intervals are not substantially wider, and in some cases narrower, than the corresponding binomial confidence intervals obtained from data that include measurements of individual infection levels. Therefore the model developed here facilitates the estimation of the proportion of individuals highly infected with Loa loa using only estimated community level prevalence. It can be used to assess the risk of rolling out MDA in a specific community, or to guide policy decisions.

Bivariate geostatistical modelling of the relationship between Loa loa prevalence and intensity of infection

“…The estimates of μ 1 , μ 2 , , , and κ are comparable with those reported by Schlüter et al (). Additionally, the processes and account for spatial variation in MF density up to about 35 km, beyond which their correlation function falls below 0.05, whereas the corresponding spatial range of the process is about 300 km.…”

“…In Schlüter et al (), the outcome of interest Y ( x ) represents the MF density of an individual living at a village location x . They assume that Y ( x ) is dependent on a latent bivariate zero‐mean Gaussian random variable Z ( x )=( Z 1 ( x ), Z 2 ( x )) and that the Z ( x ) at different locations are stochastically independent.…”

“…Empirical standardised variograms based on the predictive mean of the random effects associated with (a) prevalence and (b) intensity, and (c) their standardised cross‐variogram, from the nonspatial model of Schlüter et al (). The dashed lines represent the theoretical standardised variograms and cross‐variogram from the fitted model of Section 3, given by Equations and , respectively…”

“…Scatter plots of (a) the point estimates and (b) length of the 95% predictive intervals for the prediction target, defined in Equation with c =8,000, from the nonspatial model of Schlüter et al () and the spatial model of Section 3…”

“…Thus, efforts to date have focussed on mapping Loa loa prevalence to distinguish areas in which LF and onchocerciasis can be safely treated from those in which caution is needed (Diggle et al, ; Thomson et al, ; WHO, ). Work has also been done to understand the distribution of Loa loa microfilarial loads in communities (Pion et al, ; Schlüter et al, ).…”