Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-23T14:36:13.691Z Has data issue: false hasContentIssue false

Methods for estimation of associations between multiple species parasite infections

Published online by Cambridge University Press:  21 May 2002

S.C. HOWARD
Affiliation:
Wellcome Trust Centre for the Epidemiology of Infectious Disease, Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3FY
C.A. DONNELLY
Affiliation:
Wellcome Trust Centre for the Epidemiology of Infectious Disease, Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3FY
M.-S. CHAN
Affiliation:
Wellcome Trust Centre for the Epidemiology of Infectious Disease, Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3FY

Abstract

Human populations are often infected with more than one species of parasite, especially in developing countries where overall rates of parasitism are high. Infections with multiple parasite species may not necessarily be independent within an individual as physiological, immunological or ecological factors may result in positive or negative associations between infections with different parasite species. A general framework for estimation of these associations is presented. Data from over 215000 individuals are analysed and the associations between geohelminth (Ascaris lumbricoides, Trichuris trichiura and hookworm) and malaria species are investigated. A method is presented for analysing data from multiple communities and testing whether the associations in different communities are equal. Overall estimates of the associations between species are obtained for each country and continent where data were available. Associations between geohelminth species were, in general, found to be positive whilst both positive and negative associations were found between the different Plasmodium species. There was evidence for significant geographical heterogeneity between the associations. A method for using these parameter estimates to predict the distribution of multiple infections when only marginal prevalence data are available is described and demonstrated.

Type
Research Article
Copyright
© 2001 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)