Malaria Clinical Trial
Official title:
Rapid Malaria Diagnostic Tests in Fever Patients Attending Primary Health Care Facilities in Zanzibar - Effectiveness as Diagnostic and Surveillance Tool in the New Context of Low Malaria Endemicity
The purpose of this study is to study the effectiveness of wide scale RDT use at the primary health care level in previously high malaria endemic area during malaria pre-elimination phase for improved targeting of anti-malarial drugs, malaria surveillance and epidemic alertness.
During the last 6 years Zanzibar has undergone a dramatic change in malaria epidemiology and
burden of disease, with a marked decline of Plasmodium falciparum malaria among febrile
children from approximately 30% to 1% or below and a reduction of crude child mortality of
50% Overuse of the expensive ACTs will not only be a substantial financial burden on the
health care system in Zanzibar, but will also spur anti-malarial drug resistance with
devastating effect on global malaria control efforts and prevent other causes of fever from
being appropriately treated, e.g. pneumonias which require antibiotics. Rapid Diagnostic
Tests (RDTs), based on antigen detection of P. falciparum, are proposed as a future
cornerstone to improve diagnostic efficiency also at the peripheral health care level beyond
the reach of microscopy services
IMCI algorithms based on clinical symptoms could potentially be made more efficient and cost
effective if simple parasitological diagnostic methodologies were incorporated. Zanzibar is
among the first regions to incorporate RDT in the IMCI guidelines in Africa, which provides
a unique research opportunity to scientifically evaluate the effectiveness of incorporating
RDT in the existing IMCI algorithm.
Another key challenge for Zanzibar is to monitor potential development of parasite
resistance to ACT when the number of malaria positive patients is insufficient to conduct
standard in vivo efficacy trials. We propose that RDT could play a critical new role also in
this regard.
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Observational Model: Cohort
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