Geostatistical analysis of active human cysticercosis: Results of a large-scale study in 60 villages in Burkina Faso

Abstract

Cysticercosis is a neglected tropical disease caused by the larval stage of the zoonotic tapeworm (Taenia solium). While there is a clear spatial component in the occurrence of the parasite, no geostatistical analysis of active human cysticercosis has been conducted yet, nor has such an analysis been conducted for Sub-Saharan Africa, albeit relevant for guiding prevention and control strategies. The goal of this study was to conduct a geostatistical analysis of active human cysticercosis, using data from the baseline cross-sectional component of a large-scale study in 60 villages in Burkina Faso. The outcome was the prevalence of active human cysticercosis (hCC), determined using the B158/B60 Ag-ELISA, while various environmental variables linked with the transmission and spread of the disease were explored as potential explanatory variables for the spatial distribution of T. solium. A generalized linear geostatistical model (GLGM) was run, and prediction maps were generated. Analyses were conducted using data generated at two levels: individual participant data and grouped village data. The best model was selected using a backward variable selection procedure and models were compared using likelihood ratio testing. The best individual-level GLGM included precipitation (increasing values were associated with an increased odds of positive test result), distance to the nearest river (decreased odds) and night land temperature (decreased odds) as predictors for active hCC, whereas the village-level GLGM only retained precipitation and distance to the nearest river. The range of spatial correlation was estimated at 45.0 [95%CI: 34.3; 57.8] meters and 28.2 [95%CI: 14.0; 56.2] km for the individual- and village-level datasets, respectively. Individual- and village-level GLGM unravelled large areas with active hCC predicted prevalence estimates of at least 4% in the south-east, the extreme south, and north-west of the study area, while patches of prevalence estimates below 2% were seen in the north and west. More research designed to analyse the spatial characteristics of hCC is needed with sampling strategies ensuring appropriate characterisation of spatial variability, and incorporating the uncertainty linked to the measurement of outcome and environmental variables in the geostatistical analysis. Trial registration: ClinicalTrials.gov; NCT0309339.

Fig 1. Location of Burkina Faso in Africa, and of the study area in Burkina Faso.

(Study provinces: N = Nayala, S = Sanguié, B = Boulkiemde) (https://www.diva-gis.org/gdata).

Fig 2. Study area with village-level data points and prevalence (derived from the individual-level dataset), and histogram of the prevalence (https://www.diva-gis.org/gdata).

Fig 3

Correlation plot for individual-level (A) and village-level (B) environmental data in the study area. Evap: potential evapotranspiration, tempday: land surface temperature, day; tempnight: land surface temperature, night; ndvi: normalized difference vegetation index; rain: precipitation; soilpH: soil pH (0–5 cm); soilsilt: soil silt (0–5 cm); soilsand: soil sand (0-5cm); soilclay: soil clay (0–5 cm); waterdist: distance to the nearest river.

Fig 4

Final empirical variogram with fitted variogram model and number of pairs added for individual-level (A) and village-level (B) outcome data (distance in km).

Fig 5

Predicted prevalence (A), standard errors (B) and probability to exceed 5% prevalence (C) based on the geostatistical model ℳ₁ for the participant-level data (https://www.diva-gis.org/gdata).

Fig 6

Predicted prevalence (A), standard errors (B) and probability to exceed 5% prevalence (C) based on the geostatistical model ℳ₂ for the village-level data (https://www.diva-gis.org/gdata).

Fig 7

Predicted prevalence (A), standard errors (B) and probability to exceed 5% prevalence (C) based on the geostatistical model ℳ₃ for the participant-level data (https://www.diva-gis.org/gdata).