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Use of prospective hospital surveillance data to define spatiotemporal heterogeneity of malaria risk in coastal Kenya

Overview of attention for article published in Malaria Journal, December 2015
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Title
Use of prospective hospital surveillance data to define spatiotemporal heterogeneity of malaria risk in coastal Kenya
Published in
Malaria Journal, December 2015
DOI 10.1186/s12936-015-1006-7
Pubmed ID
Authors

Donal Bisanzio, Francis Mutuku, Angelle D. LaBeaud, Peter L. Mungai, Jackson Muinde, Hajara Busaidy, Dunstan Mukoko, Charles H. King, Uriel Kitron

Abstract

Malaria in coastal Kenya shows spatial heterogeneity and seasonality, which are important factors to account for when planning an effective control system. Routinely collected data at health facilities can be used as a cost-effective method to acquire information on malaria risk for large areas. Here, data collected at one specific hospital in coastal Kenya were used to assess the ability of such passive surveillance to capture spatiotemporal heterogeneity of malaria and effectiveness of an augmented control system. Fever cases were tested for malaria at Msambweni sub-County Referral Hospital, Kwale County, Kenya, from October 2012 to March 2015. Remote sensing data were used to classify the development level of each monitored community and to identify the presence of rice fields nearby. An entomological study was performed to acquire data on the seasonality of malaria vectors in the study area. Rainfall data were obtained from a weather station located in proximity of the study area. Spatial analysis was applied to investigate spatial patterns of malarial and non-malarial fever cases. A space-time Bayesian model was performed to evaluate risk factors and identify locations at high malaria risk. Vector seasonality was analysed using a generalized additive mixed model (GAMM). Among the 25,779 tested febrile cases, 28.7 % were positive for Plasmodium infection. Malarial and non-malarial fever cases showed a marked spatial heterogeneity. High risk of malaria was linked to patient age, community development level and presence of rice fields. The peak of malaria prevalence was recorded close to rainy seasons, which correspond to periods of high vector abundance. Results from the Bayesian model identified areas with significantly high malaria risk. The model also showed that the low prevalence of malaria recorded during late 2012 and early 2013 was associated with a large-scale bed net distribution initiative in the study area during mid-2012. The results indicate that the use of passive surveillance was an effective method to detect spatiotemporal patterns of malaria risk in coastal Kenya. Furthermore, it was possible to estimate the impact of extensive bed net distribution on malaria prevalence among local fever cases over time. Passive surveillance based on georeferenced malaria testing is an important tool that control agencies can use to improve the effectiveness of interventions targeting malaria (and other causes of fever) in such high-risk locations.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Netherlands 1 <1%
United States 1 <1%
Unknown 104 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 13%
Researcher 13 12%
Student > Master 13 12%
Student > Doctoral Student 9 8%
Student > Bachelor 9 8%
Other 19 18%
Unknown 30 28%
Readers by discipline Count As %
Medicine and Dentistry 24 22%
Agricultural and Biological Sciences 13 12%
Social Sciences 8 7%
Nursing and Health Professions 7 7%
Environmental Science 5 5%
Other 17 16%
Unknown 33 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 May 2022.
All research outputs
#14,328,639
of 24,457,696 outputs
Outputs from Malaria Journal
#3,394
of 5,764 outputs
Outputs of similar age
#192,232
of 397,332 outputs
Outputs of similar age from Malaria Journal
#74
of 147 outputs
Altmetric has tracked 24,457,696 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,764 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 397,332 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 147 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.