Comparison of microscopy and PCR for the detection of human Plasmodium species and Plasmodium knowlesi in southern Myanmar

To determine the distribution of Plasmodium (P) species including Plasmodium knowlesi and to compare the specificity and sensitivity of microscopy with nested PCR in malaria diagnosis.MethodsThe study was conducted in Kawthaung, southern Myanmar. Ninety clinically suspected malaria patients were screened for malaria by Giemsa stained microscopy and confirmed by nested PCR.ResultsAmong the participants, 57 (63.3%) were positive and 33 (36.7%) were negative by microscopy. Of positive samples, 39 (68.4%) were Plasmodium falciparum, 17 (29.8%) Plasmodium vivax and 1 (1.8%) Plasmodium malariae, whereas 59-amplified by PCR were 40 (67.8%), 18 (30.5%) and 1 (1.7%) respectively. PCR amplified 2 microscopy negative samples. Two samples of P. falciparum detected by microscopy were amplified as P. vivax and vice versa. All samples were negative for Plasmodium ovale, P. knowlesi and mixed infections. Microscopy had a very good measure of agreement (κ = 0.95) compared to nested PCR. Sensitivity and specificity of microscopy for diagnosis of P. falciparum were 92.5% (95% CI: 79.6-98.4) and 96.0% (95% CI: 86.3-99.5) respectively, whereas for P. vivax were 83.3% (95% CI: 58.6-96.4) and 97.2% (95% CI: 90.3-99.7).ConclusionsP. knowlesi was not detected by both microscopy and PCR. Giemsa stained microscopy can still be applied as primary method for malaria diagnosis and is considered as gold standard. As to the lower sensitivity of microscopy for vivax malaria, those with previous history of malaria and relapse cases should be diagnosed by RDT or PCR combined with microscopy. Inaccuracy of species diagnosis highlighted the requirement of training and refresher courses for microscopists.

Spatio-Temporal Distribution of Malaria in Perak, Malaysia

Malaria incidence in Perak, Malaysia has generally declined, but there remain regions of high incidence. The spatio-temporal distribution pattern of malaria in Perak was studied using Geographical Information Systems (GIS) and spatial statistical tools. Malaria data cases at the subdistrict level in Perak from 2007 to 2011 were analysed to determine the spatial and temporal distribution patterns of malaria incidence. Geographical Information Systems (GIS) and spatial statistical tools were used to identify spatial correlation in the data and malaria hot-spots. Spatial correlation was tested by using an autocorrelation method called Moran’s I. Hot spot analysis was done using Getis-Ord G*?statistic technique. Malaria incidence rates were categorized into 3 classes to map the spatial distribution. Malaria cases in Perak were geo-spatially clustered. Most of the hot spots locations were in Kenering, Ulu Kinta, Gerik and Kampar sub-districts. The prevalence of malaria among foreigners was noticeably higher than Malaysians. Improved surveillance of foreign workers can prevent outbreaks and identify high risk areas. This study implies that geographic-based mapping and information system are needed for an effective malaria control.