Title |
Malaria hotspots defined by clinical malaria, asymptomatic carriage, PCR and vector numbers in a low transmission area on the Kenyan Coast
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Published in |
Malaria Journal, April 2016
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DOI | 10.1186/s12936-016-1260-3 |
Pubmed ID | |
Authors |
David Tiga Kangoye, Abdisalan Noor, Janet Midega, Joyce Mwongeli, Dora Mkabili, Polycarp Mogeni, Christine Kerubo, Pauline Akoo, Joseph Mwangangi, Chris Drakeley, Kevin Marsh, Philip Bejon, Patricia Njuguna |
Abstract |
Targeted malaria control interventions are expected to be cost-effective. Clinical, parasitological and serological markers of malaria transmission have been used to detect malaria transmission hotspots, but few studies have examined the relationship between the different potential markers in low transmission areas. The present study reports on the relationships between clinical, parasitological, serological and entomological markers of malaria transmission in an area of low transmission intensity in Coastal Kenya. Longitudinal data collected from 831 children aged 5-17 months, cross-sectional survey data from 800 older children and adults, and entomological survey data collected in Ganze on the Kenyan Coast were used in the present study. The spatial scan statistic test used to detect malaria transmission hotspots was based on incidence of clinical malaria episodes, prevalence of asymptomatic asexual parasites carriage detected by microscopy and polymerase chain reaction (PCR), seroprevalence of antibodies to two Plasmodium falciparum merozoite antigens (AMA1 and MSP1-19) and densities of Anopheles mosquitoes in CDC light-trap catches. There was considerable overlapping of hotspots by these different markers, but only weak to moderate correlation between parasitological and serological markers. PCR prevalence and seroprevalence of antibodies to AMA1 or MSP1-19 appeared to be more sensitive markers of hotspots at very low transmission intensity. These findings may support the choice of either serology or PCR as markers in the detection of malaria transmission hotspots for targeted interventions. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 25% |
Australia | 1 | 25% |
Unknown | 2 | 50% |
Demographic breakdown
Type | Count | As % |
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Scientists | 2 | 50% |
Members of the public | 1 | 25% |
Science communicators (journalists, bloggers, editors) | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | <1% |
Kenya | 1 | <1% |
Unknown | 110 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 21 | 19% |
Student > Master | 19 | 17% |
Student > Ph. D. Student | 16 | 14% |
Student > Bachelor | 8 | 7% |
Student > Doctoral Student | 5 | 4% |
Other | 11 | 10% |
Unknown | 32 | 29% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 21 | 19% |
Agricultural and Biological Sciences | 18 | 16% |
Biochemistry, Genetics and Molecular Biology | 6 | 5% |
Immunology and Microbiology | 4 | 4% |
Social Sciences | 4 | 4% |
Other | 19 | 17% |
Unknown | 40 | 36% |