Chapter 5 - The Applications of Model-Based Geostatistics in Helminth Epidemiology and Control

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Abstract

Funding agencies are dedicating substantial resources to tackle helminth infections. Reliable maps of the distribution of helminth infection can assist these efforts by targeting control resources to areas of greatest need. The ability to define the distribution of infection at regional, national and subnational levels has been enhanced greatly by the increased availability of good quality survey data and the use of model-based geostatistics (MBG), enabling spatial prediction in unsampled locations. A major advantage of MBG risk mapping approaches is that they provide a flexible statistical platform for handling and representing different sources of uncertainty, providing plausible and robust information on the spatial distribution of infections to inform the design and implementation of control programmes. Focussing on schistosomiasis and soil-transmitted helminthiasis, with additional examples for lymphatic filariasis and onchocerciasis, we review the progress made to date with the application of MBG tools in large-scale, real-world control programmes and propose a general framework for their application to inform integrative spatial planning of helminth disease control programmes.

Introduction

Effective control of human helminth infections requires reliable estimates of the geographical distribution of infection and the size of populations requiring intervention (Boatin and Richards, 2006, Brooker and Michael, 2000, Brooker et al., 2006b, Brooker et al., 2006c, Molyneux, 2009). For the purposes of control planning, nationwide surveillance data are desirable, but few endemic countries have suitably detailed data (Brooker et al., 2000b). To address this paucity of data, research over the past decade has explored ways to maximise the usefulness of available data based on disease mapping and prediction (Brooker, 2002, Brooker, 2007, Brooker and Michael, 2000, Brooker et al., 2006b, Brooker et al., 2006c, Simoonga et al., 2009). Most recently, these predictive approaches have employed Bayesian model-based geostatistics (MBG) which embeds classical geostatistics in a generalised linear modelling framework. Using this approach, relationships and associated uncertainty between infection outcomes and covariates are estimated and the resultant model is used to predict the outcome at unsampled locations (Diggle, Tawn et al., 1998). This approach has the advantage over traditional spatial prediction methods of providing a robust and comprehensive handling of spatial structure and the uncertainty associated with predicted infection patterns.

This review focuses on human helminth infections: schistosomiasis, intestinal nematodes (or soil-transmitted helminths, STH), lymphatic filariasis (LF) and onchocerciasis; but it is important to recognise the increasing number of applications of MBG to spatial modelling of malaria infection (Craig et al., 2007, Diggle et al., 2002, Gosoniu et al., 2006, Gosoniu et al., 2009, Hay et al., 2009, Kazembe et al., 2006, Noor et al., 2008, Noor et al., 2009, Raso et al., 2009b, Silue et al., 2008), malaria-related mortality (Gemperli et al., 2004) and malaria entomological inoculation rates (Gemperli et al., 2006a, Gemperli et al., 2006b).

The primary aim of this review is to demonstrate the applications of MBG to helminth epidemiology and encourage its wider application in helminth disease control programmes. The first section highlights the disease burden of helminth infections in SSA and identifies the main treatment strategies. The second section examines the importance of mapping in guiding helminth control. The third section introduces the principal concepts that underpin MBG. This is followed by a description of the survey data requirements for MBG, before showing how survey and satellite-derived environmental data have been integrated into an MBG platform to establish and predict species-specific prevalence and intensity distributions, and describes how these tools could be extended to accommodate sampling uncertainty and greater biological realism. Finally, we review how these tools have already helped inform large-scale control programmes and look forward to their future potential application. The search strategy and selection criteria of the review are shown in Box 5.1.

Section snippets

Disease Burden and Intervention Strategies

Helminths are some of the most common infections of humans. In sub-Saharan Africa (SSA), 740 million individuals are estimated to be infected with soil-transmitted helminths (Ascaris lumbricoides, Trichuris trichiura, and the hookworms Necator Americanus and Ancylostoma duodenale) (de Silva et al., 2003), 207 million with schistosomiasis (Schistosoma haematobium and S. mansoni) (Steinmann et al., 2006), 50 million with LF due to Wuchereria bancrofti (Michael and Bundy, 1997), and 18–37 million

The Role of Mapping in Helminthology

The inherent spatial heterogeneity of infection varies between individual helminth species. Generally, the more complex the life cycle, the more spatially heterogeneous infection patterns appear. For example, in East Africa, schistosomiasis, LF or onchocerciasis, for which transmission involves either an intermediate host or vector, typically have a focal distribution, whereas STH are more widely distributed in space owing to their direct transmission life cycle (Brooker, 2007, Brooker et al.,

Principles of Model-Based Geostatistics

A central feature of MBG is that it can take into account spatial dependence, also known as spatial autocorrelation (Box 5.2). This is the phenomenon that values at nearby locations tend to be more similar than those further apart (Tobler, 1970). Standard regression techniques rely on an assumption of conditional independence in model residuals. When handling spatially autocorrelated data, this assumption is often violated, with model residuals likely to display some degree of spatial

Data Requirements for MBG

Any model is only as reliable as the data on which it is based. In turn, the most appropriate sampling design for data collection will depend on the intended purpose of the mapping exercise, which is linked to the objectives of the control programme. However, risk mapping is rarely based on data explicitly collected for the purpose of spatial prediction. Instead, data have often been collected for purposes other than spatial analysis, potentially limiting their usefulness for spatial

Mapping prevalence of infection

MBG has been applied to the mapping of helminth infection at various spatial scales. Applications at national and subnational levels include: S. mansoni in western Cote d'Ivoire (Beck-Worner et al., 2007, Raso et al., 2005), Mali (Clements et al., 2009) and Tanzania (Clements et al., 2006a); S. haematobium in Mali (Clements et al., 2009) and Tanzania (Clements et al., 2006a); STHs in western Cote d'Ivoire (Raso et al., 2006a, Raso et al., 2006b); and Loa loa infection in Cameroon (Diggle et

Methodological Refinements in Model-Based Geostatistics

Whilst the past decade has seen a dramatic expansion in the number of helminthological studies employing MBG, each incorporating iterative improvements in modelling approach, there remain a number of areas requiring further investigation. Below we highlight three main areas that deserve attention.

Applications to Planning and Evaluating Helminth Control

The flexibility afforded by MBG provides a powerful planning tool for the design and implementation of intervention strategies. For schistosomiasis, applications have primarily focused on predicting the prevalence of infection, enabling areas to be stratified according to intervention strategy: for example, identifying areas where the posterior mean predicted prevalence exceeds 50% in Tanzania (Clements et al., 2006a). Possibly more useful for the control programme manager is an estimate of the

Conclusion

MBG represents a key advance in the spatial prediction of helminth disease at different spatial scales. There are an increasing number of examples in the published literature where maps produced using these methods have been used in the planning and implementation of disease control programmes. Methods for representing uncertainty constitute a major advantage of MBG compared to classical geostatistics and other spatial prediction methods. However, there is a need to translate the benefits of

Acknowledgements

A. C. A. C. is funded by an Australian National Health and Medical Research Council Career Development Award (#631619), A. P. P. is funded under a Wellcome Trust Principal Research Fellowship held by Professor Bob Snow (#079080), P. W. G. is funded under the Wellcome Trust Senior Research Fellowship held by Dr. Simon Hay (#079091) and S. B. is supported by a Research Career Development Fellowship from the Wellcome Trust (#081673). Finally, we are most grateful to the SCI-supported national

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