Biology Reference
In-Depth Information
In contrast to worm counts, fecal egg counts vary considerably from
day to day and among samples taken from the same stool.
47,48
Moreover,
because the distribution of worms among individuals within a commu-
nity is ubiquitously overdispersed relative to the Poisson or random
distribution,
20
egg counts are highly variable (and zero-inflated, see
“Statistical models,” below), providing a very inaccurate measure of
infection intensity (
Figure 7.1
). That said, epidemiological surveys using
egg counts are fast, affordable and relatively easy to undertake, making it
the most widely abundant available data for estimation of infection
intensity.
60
Heterogeneities in Infection
The distribution of worms among hosts adheres to the Pareto
principle, also known as the 80/20 rule; approximately 80% of the
worm population tends to be harbored by about 20% of hosts. This
reflects a high degree of heterogeneity in infection rates among indi-
viduals.
20,61
Such heterogeneity may be caused by variability in
exposure; innate (genetic) susceptibility; acquired immunity or by a so-
called “clumped” infection process whereby multiple adult worms
establish simultaneously, presumably because multiple infectious
larvae are acquired per infection event.
62
e
64
In a highly endemic urban
community in Bangladesh, correlation observed among the weights of
individual adult A. lumbricoides within infra-populations (the pop-
ulation of worms within a host) has been presented as evidence for
a clumped infection process.
49,65
Similar conclusions have been drawn
from a study conducted in Guatemala, where worm mitochondrial
DNA sequences were shown to be clustered (genetically similar)
within individuals.
66
Measuring exposure to infectious larvae directly is notoriously diffi-
cult.
67
Consequently, the estimation of exposure has been restricted to the
measurement of concentrations of fecal silica as a proxy for soil contam-
ination of food and geophagic activity.
68
e
70
An alternative approach has
been to infer heterogeneities in exposure from statistical identification of
risk factors associated with infection intensity; factors that explain some
of the observed variability in intensity among individuals. Numerous
studies have identified a diverse and often inconsistent range of factors
associated with A. lumbricoides egg output,
71
although a recent meta-
analysis of prevalence-based epidemiological surveys has demonstrated
the protective effect of access to sanitation facilities.
72
Just four studies
have used gold-standard worm count data to study risk factors,
73
e
76
identifying household-, agricultural-, host sex- and poverty-related
factors associated with worm burdens.
