Search

Background: The diagnosis of malaria in returning travellers could be a challenge in non-endemic settings. We aimed to assess the performance of LAMP in comparison with standard conventional diagnostic methods using real-time-polymerase chain reaction (PCR) in case of discordant results., Methods: All travellers returning from malaria-endemic areas who presented to our Emergency Department (ED) from January 2017 to December 2020 with signs and symptoms suggestive for malaria were included. Blood microscopy was the reference diagnostic method applied at our laboratory with LAMP implemented as an additional method to aid in malaria diagnosis. PCR was employed only in case of between test's discordant results. Sensitivity and specificity of microscopy compared to LAMP were calculated with the confidence interval of 95%., Results: Four-hundred and eight patients (55.6% male, median age 42 years) were screened for malaria. The diagnosis was confirmed in 49 cases (12%): 44 cases (90%) caused by Plasmodium falciparum . Peripheral blood smear missed to identify three malaria cases, which tested positive with LAMP and PCR. One case of malaria caused by P. malariae in a naive tourist, one case by P. falciparum in a semi-immune pregnant women and one case by P. falciparum in a previously treated semi-immune patient. All the discordant cases were characterized by a very low parasitaemia. Microscopy when compared to LAMP showed a sensitivity of 93.9% (95% confidence interval (CI) 83.1-98.7%) and a specificity of 100% (95% CI 98.9-100%)., Conclusions: In our non-endemic setting LAMP was able to identify malaria cases with low-level parasitaemia otherwise missed by blood microscopy.

Background: Reliable surveillance systems are essential for identifying disease outbreaks and allocating resources to ensure universal access to diagnostics and treatment for endemic diseases. Yet, most countries with high disease burdens rely entirely on facility-based passive surveillance systems, which miss the vast majority of cases in rural settings with low access to health care. This is especially true for malaria, for which the World Health Organization estimates that routine surveillance detects only 14% of global cases. The goal of this study was to develop a novel method to obtain accurate estimates of disease spatio-temporal incidence at very local scales from routine passive surveillance, less biased by populations' financial and geographic access to care., Methods: We use a geographically explicit dataset with residences of the 73,022 malaria cases confirmed at health centers in the Ifanadiana District in Madagascar from 2014 to 2017. Malaria incidence was adjusted to account for underreporting due to stock-outs of rapid diagnostic tests and variable access to healthcare. A benchmark multiplier was combined with a health care utilization index obtained from statistical models of non-malaria patients. Variations to the multiplier and several strategies for pooling neighboring communities together were explored to allow for fine-tuning of the final estimates. Separate analyses were carried out for individuals of all ages and for children under five. Cross-validation criteria were developed based on overall incidence, trends in financial and geographical access to health care, and consistency with geographic distribution in a district-representative cohort. The most plausible sets of estimates were then identified based on these criteria., Results: Passive surveillance was estimated to have missed about 4 in every 5 malaria cases among all individuals and 2 out of every 3 cases among children under five. Adjusted malaria estimates were less biased by differences in populations' financial and geographic access to care. Average adjusted monthly malaria incidence was nearly four times higher during the high transmission season than during the low transmission season. By gathering patient-level data and removing systematic biases in the dataset, the spatial resolution of passive malaria surveillance was improved over ten-fold. Geographic distribution in the adjusted dataset revealed high transmission clusters in low elevation areas in the northeast and southeast of the district that were stable across seasons and transmission years., Conclusions: Understanding local disease dynamics from routine passive surveillance data can be a key step towards achieving universal access to diagnostics and treatment. Methods presented here could be scaled-up thanks to the increasing availability of e-health disease surveillance platforms for malaria and other diseases across the developing world.