Design and Analysis of Prevalence Surveys for Neglected Tropical Diseases
In low-resource settings, disease registries do not exist, and prevalence mapping relies on data collected form surveys of disease prevalence taken in a sample of the communities at risk within the region of interest, possibly supplemented by remotely sensed images that can act as proxies for environmental risk factors. A standard geostatistical model for data of this kind is a generalized linear mixed model, Yᵢ ~ Binomial(mᵢ; P(xᵢ)) log [P(x)/{(1- P(xᵢ)}] = d(x)β + S(x), where Yᵢ is the number of positives in a sample of mi individuals at location xᵢ, d(x) is a vector of spatially referenced explanatory variables available at any location x within the region of interest, and S(x) is a Gaussian process. In this talk, I will first review statistical methods and software associated with this standard model, then consider several methodological extensions and their applications to some Africa-wide control programmes for Neglected Tropical Diseases to demonstrate the very substantial gains in efficiency that can be obtained by comparison with currently used methods.
Overview
Abstract
In low-resource settings, disease registries do not exist, and prevalence mapping relies on data collected form surveys of disease prevalence taken in a sample of the communities at risk within the region of interest, possibly supplemented by remotely sensed images that can act as proxies for environmental risk factors. A standard geostatistical model for data of this kind is a generalized linear mixed model, Yi ~ Binomial(mi; P(xi)) log [P(xi)/{(1- P(xi)}] = d(xi)β + S(xi), where Yi is the number of positives in a sample of mi individuals at location xi, d(xi) is a vector of spatially referenced explanatory variables available at any location x within the region of interest, and S(xi) is a Gaussian process. In this talk, I will first review statistical methods and software associated with this standard model, then consider several methodological extensions and their applications to some Africa-wide control programmes for Neglected Tropical Diseases to demonstrate the very substantial gains in efficiency that can be obtained by comparison with currently used methods.
This is joint work with Benjamin Amoah, Claudio Fronterre, Emanuele Giorgi, Olatunji Johnson.
Brief Biography
Peter Diggle is Distinguished University Professor of Statistics in the Faculty of Health and Medicine, Lancaster University. He also holds Adjunct positions at Johns Hopkins, Yale and Columbia Universities, and was president of the Royal Statistical Society between July 2014 and December 2016. Peter began his academic career at the University of Newcastle upon Tyne in 1974, moved to Australia in 1984 as a research scientist with the Commonwealth Scientific and Industrial Research organization and returned to the UK in 1988 to take up his current post in Lancaster. His research involves the development of statistical methods for spatial and longitudinal data analysis, and their application to substantive research in the biomedical and health sciences.
