Biology Reference
In-Depth Information
Modeling Treatment
The effects of anthelmintic treatment on A. lumbricoides, and on STHs in
general, have been modeled in two ways (Chapter 9). The first assumes
that treatments are administered to the population at random for a certain
time period and that during this period adult worms incur an increased
mortality rate which is a function of the coverage, the drug efficacy and
the frequency of treatment.
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This approach has yielded: (1) analytical
results pertaining to a critical proportion of the population that it is
necessary to treat to achieve specific reductions in mean worm burden,
including reductions which reduce burdens under breakpoint densities,
for different intensities of transmission (encapsulated in the basic repro-
duction ratio or R
0
), and (2) an empirical understanding of how selectively
treating specific age groups leads to collateral reductions in infection
levels in untreated age groups due to the overall effect on environmental
transmission.
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The alternative approach is to model treatment rounds explicitly by
assuming the instantaneous death of a fixed
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or random
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fraction of
worms following each treatment which is determined by the drug efficacy
and the level of coverage.
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The framework has also been elaborated to
take into account the dynamic effects of treatment on the distribution of
worms among hosts.
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Together these developments permit: (1) an
accurate reflection of the effects of treatment on the net severity of
density-dependent processes; (2) the estimation of community morbidity
using a threshold worm burden to define disease; (3) the quantification of
community benefit as the area between the infection (prevalence or
intensity) time curve and the equilibrium level of infection (i.e. the level
of infection in the absence of intervention),
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and (4) by linking rounds of
treatments to monetary costs, cost-effectiveness analysis.
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Although the effect of mass treatments has largely been assessed in
terms of population-level effects, such as bounce-back (reinfection) times
following treatment
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or the effectiveness of targeting specific age groups,
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models have also beenused to explore individual-based treatment effects. In
particular, the so-called selective strategy, which involves selectively treat-
ingheavily infectedor high risk individuals, has been shown tobe extremely
effective in reducing average worm burdens, so long as the degree of
overdispersion is high.
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This result is in parallel with the effectiveness of
targeting “superspreaders” of directly-transmitted microparasites.
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Further, individual-based stochastic simulations have indicated that the
population-level (mean) rate of reinfection following treatment is strongly
dependent on the predominant aggregation-generating mechanism;
aggregation arising from differences in host susceptibilities (analogous to
predisposition) causes more rapid reinfection than when aggregation is
generated solely by a clumped infection process.
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