Bayesian geostatistical modelling of soil-transmitted helminth survey data in the People’s Republic of China

Abstract

BackgroundSoil-transmitted helminth infections affect tens of millions of individuals in the People’s Republic of China (P.R. China). There is a need for high-resolution estimates of at-risk areas and number of people infected to enhance spatial targeting of control interventions. However, such information is not yet available for P.R. China.MethodsA geo-referenced database compiling surveys pertaining to soil-transmitted helminthiasis, carried out from 2000 onwards in P.R. China, was established. Bayesian geostatistical models relating the observed survey data with potential climatic, environmental and socioeconomic predictors were developed and used to predict at-risk areas at high spatial resolution. Predictors were extracted from remote sensing and other readily accessible open-source databases. Advanced Bayesian variable selection methods were employed to develop a parsimonious model.ResultsOur results indicate that the prevalence of soil-transmitted helminth infections in P.R. China considerably decreased from 2005 onwards. Yet, some 144 million people were estimated to be infected in 2010. High prevalence (>20%) of the roundworm Ascaris lumbricoides infection was predicted for large areas of Guizhou province, the southern part of Hubei and Sichuan provinces, while the northern part and the south-eastern coastal-line areas of P.R. China had low prevalence (<5%). High infection prevalence (>20%) with hookworm was found in Hainan, the eastern part of Sichuan and the southern part of Yunnan provinces. High infection prevalence (>20%) with the whipworm Trichuris trichiura was found in a few small areas of south P.R. China. Very low prevalence (<0.1%) of hookworm and whipworm infections were predicted for the northern parts of P.R. China.ConclusionsWe present the first model-based estimates for soil-transmitted helminth infections throughout P.R. China at high spatial resolution. Our prediction maps provide useful information for the spatial targeting of soil-transmitted helminthiasis control interventions and for long-term monitoring and surveillance in the frame of enhanced efforts to control and eliminate the public health burden of these parasitic worm infections.

An environmental assessment and risk map of Ascaris lumbricoides and Necator americanus distributions in Manufahi District, Timor-Leste

“…americanus infection, supporting studies that find hookworm requires a minimum level of soil moisture or humidity throughout the year to survive and develop. [9, 15, 57] The positive association between EVI and N . americanus infection is consistent with Saathoff et al [44] As EVI is a measure of vegetation and thus a proxy for environmental moisture and shade, this finding further supports that hookworm require sufficient soil moisture for survival and transmission.…”

“…[8] Since the transmission of infective STH relies upon favourable environmental conditions, environmental factors may be used to identify high risk areas. [9] Over the past two decades, geographic information systems (GIS) coupled with remotely sensed environmental data have been used to identify areas of high STH infection risk in several countries, [10–15] allowing governments to develop cost-effective targeted STH control strategies. [16].…”

The spatial distribution of Schistosoma mansoni infection in four regions of western Côte d’Ivoire

“…a posterior probability higher than 50%) into our final geostatistical model. The details of the method have been described elsewhere (Lai et al, 2013).…”

Strongyloides stercoralis: spatial distribution of a highly prevalent and ubiquitous soil-transmitted helminth in Cambodia

“…While collecting urine samples is much easier than fecal 102 samples, this test has a high sensitivity for S. stercoralis detection and does not cross-react 103 with other soil-transmitted helminth (STH) species [20,21]. 104 Geostatistical models have been increasingly used in the past decade to delineate risk 105 zones for helminthic infections at small and large scale and help targeting control efforts in 106 areas of highest need [22][23][24][25][26][27][28][29]. Based on the association between environmental variables and 107 infection levels at survey locations, such models can be used to predict infection levels 108 through entire geographical zones.…”