Methods for estimation of associations between multiple species parasite infections

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...

Full description

Saved in:
Bibliographic Details
Published in:Parasitology 2001-02, Vol.122 (2), p.233-251
Main Authors: HOWARD, S.C., DONNELLY, C.A., CHAN, M.-S.
Format: Article
Language:English
Subjects:
Adult
Animals
Ascariasis - complications
Ascariasis - epidemiology
Ascaris
Biological and medical sciences
Child
Developing countries
Female
General aspects
geohelminths
Heterogeneity
Hookworm Infections - complications
Hookworm Infections - epidemiology
Human populations
Humans
Infectious diseases
LDCs
Linear Models
log-linear
Malaria
Malaria - complications
Malaria - epidemiology
Male
Medical sciences
Models, Biological
multiple species
Parasites
Parasitic diseases
Parasitic Diseases - complications
Parasitic Diseases - epidemiology
Parasitism
Plasmodium
Plasmodium spp
Prevalence
Statistics as Topic - methods
Trichuriasis - complications
Trichuriasis - epidemiology
Trichuris
Vector-borne diseases
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0031-1820
1469-8161
DOI:10.1017/S0031182001007272