Dataset Information

Confirmation of the absence of local transmission and geographic assignment of imported falciparum malaria cases to China using microsatellite panel.

ABSTRACT:

Background

Current methods to classify local and imported malaria infections depend primarily on patient travel history, which can have limited accuracy. Genotyping has been investigated as a complementary approach to track the spread of malaria and identify the origin of imported infections.

Methods

An extended panel of 26 microsatellites (16 new microsatellites) for Plasmodium falciparum was evaluated in 602 imported infections from 26 sub-Saharan African countries to the Jiangsu Province of People's Republic of China. The potential of the 26 microsatellite markers to assign imported parasites to their geographic origin was assessed using a Bayesian method with Markov Chain Monte Carlo (MCMC) as implemented in the program Smoothed and Continuous Assignments (SCAT) with a modification to incorporate haploid genotype data.

Results

The newly designed microsatellites were polymorphic and are not in linkage disequilibrium with the existing microsatellites, supporting previous findings of high rate of recombination in sub-Saharan Africa. Consistent with epidemiology inferred from patients' travel history, no evidence for local transmission was found; nearly all genetically related infections were identified in people who travelled to the same country near the same time. The smoothing assignment method assigned imported cases to their likely geographic origin with an accuracy (Angola: 59%; Nigeria: 51%; Equatorial Guinea: 40%) higher than would be achieved at random, reaching statistical significance for Angola and Equatorial Guinea.

Conclusions

Genotyping using an extended microsatellite panel is valuable for malaria case classification and programme evaluation in an elimination setting. A Bayesian method for assigning geographic origin of mammals based on genetic data was adapted for malaria and showed potential for identification of the origin of imported infections.

SUBMITTER: Liu Y

PROVIDER: S-EPMC7359230 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

Confirmation of the absence of local transmission and geographic assignment of imported falciparum malaria cases to China using microsatellite panel.

Liu Yaobao Y Tessema Sofonias K SK Murphy Maxwell M Xu Sui S Schwartz Alanna A Wang Weiming W Cao Yuanyuan Y Lu Feng F Tang Jianxia J Gu Yaping Y Zhu Guoding G Zhou Huayun H Gao Qi Q Huang Rui R Cao Jun J Greenhouse Bryan B

Malaria journal 20200713 1

<h4>Background</h4>Current methods to classify local and imported malaria infections depend primarily on patient travel history, which can have limited accuracy. Genotyping has been investigated as a complementary approach to track the spread of malaria and identify the origin of imported infections.<h4>Methods</h4>An extended panel of 26 microsatellites (16 new microsatellites) for Plasmodium falciparum was evaluated in 602 imported infections from 26 sub-Saharan African countries to the Jiangs ...[more]

PMID: 32660491

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Project description:Malaria still continues to be the most important parasitic disease worldwide, affecting 228 million people and causing 405,000 deaths each year. In this retrospective study during 2013 to 2018, we documented the incidence of imported malaria infection and evaluated the impact of malaria preventive measures in Kuwait, a non-endemic country. The epidemiologic and demographic data of all malaria cases was collected from the Infectious Diseases Hospital, Kuwait where all suspected cases of malaria are referred for confirmation and therapeutic intervention. The diagnosis of malaria infection was done by microscopy of Giemsa stained blood films. Selected samples were retested with BinaxNOW® Malaria rapid test and molecular assay to reconfirm the Plasmodium spp. or mixed infection. Overall, 1913 (25.9%) malaria cases were detected, 81.5% of which were among male subjects. Male subjects had higher incidence of P. vivax malaria (113; 91.1%) and mixed infection with P. falciparum and P. vivax (1245; 90.0%) compared to females who had higher rate of P. falciparum infection (52.4%). An overwhelming majority of malaria cases (1895; 99.1%) were detected among expatriates from malaria-endemic countries; India (1012; 52.9%), Pakistan (390; 20.4%), Afghanistan (94; 4.9%) and African countries (313; 16.3%). Only 18 cases involved Kuwaiti nationals, all with a history of travel to African countries. The majority of malaria cases were detected during the summer and fall months (May-October). Our data showed that the incidence rate of imported malaria cases was stable during 2013 to 2018, however, the incidence of total malaria cases showed a declining trend over the years. This study confirms that the preventive program has been successful in reducing the incidence of imported malaria infections in Kuwait. The most striking finding of this study was high incidence of mixed infection with P. falciparum and P. vivax, with almost all (97%) cases among workers from India.

Project description:BackgroundA key issue in achieving and sustaining malaria elimination is the need to prevent local transmission arising from imported cases of malaria. The likelihood of this occurring depends on a range of local factors, and these can be used to allocate resources to contain transmission. Therefore, a risk assessment and management strategy is required to identify risk indexes for malaria transmission when imported cases occur. These risks also need to be quantified and combined to give a weighted risk index score. This can then be used to allocate the resources to each administrative region to prevent transmission according to the degree of risk.MethodsA list of potential risk indexes were generated from a literature review, expert consultation and panel discussion. These were initially classified into 4 first-level indexes including infection source, transmitting conditions, population vulnerability and control capacity. Each of these was then expanded into more detailed second-level indexes. The Delphi method was then used to obtain expert opinion to review and revise these risk indexes over two consecutive rounds to quantify agreement among experts as to their level of importance. Risk indexes were included in the final Transmission Risk Framework if they achieved a weighted importance score ≥ 4. The Analytic Hierarchy Process was then used to calculate the weight allocated to each of the final risk indexes. This was then used to create an assessment framework that can be used to evaluate local transmission risk in different areas.ResultsTwo rounds of Delphi consultation were conducted. Twenty-three experts were used at each round with 100% recovery rate of participant questionnaires. The coordination coefficients (W) for the two rounds of Delphi consultation were 0.341 and 0.423, respectively (P < 0.05). Three first-level indexes and 13 second-level indexes were identified. The Analytic Hierarchy Process was performed to calculate the weight of the indexes. For the first-level indexes, infection source, transmitting conditions, and control capacity, the index weight was 0.5396, 0.2970 and 0.1634 respectively. For the three top second-level indexes, number of imported malaria cases, Anopheles species, and awareness of timely medical visit of patient, the index weight was 0.3382, 0.2475, and 0.1509 respectively.ConclusionsAn indexed system of transmission risk assessment for imported malaria was established using the Delphi method and the Analytic Hierarchy Process. This was assessed to be an objective and practical tool for assessing transmission risk from imported cases of malaria into China.

Dataset Information

Confirmation of the absence of local transmission and geographic assignment of imported falciparum malaria cases to China using microsatellite panel.

Background

Methods

Results

Conclusions

Publications

Confirmation of the absence of local transmission and geographic assignment of imported falciparum malaria cases to China using microsatellite panel.

Similar Datasets

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