A Randomised Controlled Trial to Assess the Efficacy of Dihydroartemisinin-Piperaquine for the Treatment of Uncomplicated Falciparum Malaria in Peru

Background. Multi-drug resistant falciparum malaria is an important health problem in the Peruvian Amazon region. We carried out a randomised open label clinical trial comparing mefloquine-artesunate, the current first line treatment in this region, with dihydroartemisinin-piperaquine. Methods and Findings. Between July 2003 and July 2005, 522 patients with P. falciparum uncomplicated malaria were recruited, randomized (260 with mefloquine-artesunate and 262 with dihydroartemisinin-piperaquine), treated and followed up for 63 days. PCR-adjusted adequate clinical and parasitological response, estimated by Kaplan Meier survival and Per Protocol analysis, was extremely high for both drugs (99.6% for mefloquine-artesunate and 98.4% and for dihydroartemisinin-piperaquine) (RR: 0.99, 95%CI [0.97-1.01], Fisher Exact p=0.21). All recrudescences were late parasitological failures. Overall, gametocytes were cleared faster in the mefloquine-artesunate group (28 vs 35 days) and new gametocytes tended to appear more frequently in patients treated with dihydroartemisinin-piperaquine (day 7: 8 ( 3.6%) vs 2 (0.9%), RR: 3.84, 95%CI [0.82-17.87]). Adverse events such as anxiety and insomnia were significantly more frequent in the mefloquine-artesunate group, both in adults and children. Conclusion. Dihydroartemisinin-piperaquine is as effective as mefloquine-artesunate in treating uncomplicated P. falciparum malaria but it is better tolerated and more affordable than mefloquine-artesunate (US$1.0 versus US$18.65 on the local market). Therefore, it should be considered as a potential candidate for the first line treatment of P. falciparum malaria in Peru. Trial Registration. ClinicalTrials.gov NCT00373607 [http://www.clinicaltrials.gov/ct/show/NCT00373607]

True versus Apparent Malaria Infection Prevalence: The Contribution of a Bayesian Approach

AIMS: To present a new approach for estimating the "true prevalence" of malaria and apply it to datasets from Peru, Vietnam, and Cambodia. METHODS: Bayesian models were developed for estimating both the malaria prevalence using different diagnostic tests (microscopy, PCR & ELISA), without the need of a gold standard, and the tests' characteristics. Several sources of information, i.e. data, expert opinions and other sources of knowledge can be integrated into the model. This approach resulting in an optimal and harmonized estimate of malaria infection prevalence, with no conflict between the different sources of information, was tested on data from Peru, Vietnam and Cambodia. RESULTS: Malaria sero-prevalence was relatively low in all sites, with ELISA showing the highest estimates. The sensitivity of microscopy and ELISA were statistically lower in Vietnam than in the other sites. Similarly, the specificities of microscopy, ELISA and PCR were significantly lower in Vietnam than in the other sites. In Vietnam and Peru, microscopy was closer to the "true" estimate than the other 2 tests while as expected ELISA, with its lower specificity, usually overestimated the prevalence. CONCLUSIONS: Bayesian methods are useful for analyzing prevalence results when no gold standard diagnostic test is available. Though some results are expected, e.g. PCR more sensitive than microscopy, a standardized and context-independent quantification of the diagnostic tests' characteristics (sensitivity and specificity) and the underlying malaria prevalence may be useful for comparing different sites. Indeed, the use of a single diagnostic technique could strongly bias the prevalence estimation. This limitation can be circumvented by using a Bayesian framework taking into account the imperfect characteristics of the currently available diagnostic tests. As discussed in the paper, this approach may further support global malaria burden estimation initiatives

Apparent prevalence figures of <i>P. falciparum</i> (exact binomial 95% CI) for the different diagnostic tests by survey.

<p>ELISA = Enzyme-Linked Immunosorbent Assay; PCR = Polymerase Chain Reaction; S1 = survey 1; S2 = survey 2.</p

Prior information (uniform distributions) based on expert opinion.

<p>ELISA = Enzyme-Linked Immunosorbent Assay; PCR = Polymerase Chain Reaction.</p><p>**for Vietnam. Peru Iquitos and Peru Jaen only.</p

Estimated prevalence and diagnostic tests' sensitivity and specificity by survey.

<p>CI = credibility interval; Se = sensitivity; Sp = specificity; ELISA = Enzyme-Linked Immunosorbent Assay; PCR = Polymerase Chain Reaction; S1 = survey 1; S2 = survey 2.</p

Maximum number of estimable parameters, number of parameters to be estimated in the absence and presence of conditional independence as a function of the number of tests per subject.

<p>Maximum number of estimable parameters, number of parameters to be estimated in the absence and presence of conditional independence as a function of the number of tests per subject.</p

Number of individuals according to the results of 2 or 3 diagnostic tests by survey.

<p>0 = negative test result. 1 = positive test result; number = number of individuals for each result category; S1 = survey 1; S2 = survey 2.</p