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Effect of climatic variability on malaria trends in Baringo County, Kenya

Overview of attention for article published in Malaria Journal, May 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)

Mentioned by

1 policy source
6 tweeters


23 Dimensions

Readers on

103 Mendeley
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Effect of climatic variability on malaria trends in Baringo County, Kenya
Published in
Malaria Journal, May 2017
DOI 10.1186/s12936-017-1848-2
Pubmed ID

Edwin K. Kipruto, Alfred O. Ochieng, Douglas N. Anyona, Macrae Mbalanya, Edna N. Mutua, Daniel Onguru, Isaac K. Nyamongo, Benson B. A. Estambale


Malaria transmission in arid and semi-arid regions of Kenya such as Baringo County, is seasonal and often influenced by climatic factors. Unravelling the relationship between climate variables and malaria transmission dynamics is therefore instrumental in developing effective malaria control strategies. The main aim of this study was to describe the effects of variability of rainfall, maximum temperature and vegetation indices on seasonal trends of malaria in selected health facilities within Baringo County, Kenya. Climate variables sourced from the International Research Institute (IRI)/Lamont-Doherty Earth Observatory (LDEO) climate database and malaria cases reported in 10 health facilities spread across four ecological zones (riverine, lowland, mid-altitude and highland) between 2004 and 2014 were subjected to a time series analysis. A negative binomial regression model with lagged climate variables was used to model long-term monthly malaria cases. The seasonal Mann-Kendall trend test was then used to detect overall monotonic trends in malaria cases. Malaria cases increased significantly in the highland and midland zones over the study period. Changes in malaria prevalence corresponded to variations in rainfall and maximum temperature. Rainfall at a time lag of 2 months resulted in an increase in malaria transmission across the four zones while an increase in temperature at time lags of 0 and 1 month resulted in an increase in malaria cases in the riverine and highland zones, respectively. Given the existence of a time lag between climatic variables more so rainfall and peak malaria transmission, appropriate control measures can be initiated at the onset of short and after long rains seasons.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 1 <1%
Unknown 102 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 25 24%
Researcher 15 15%
Student > Ph. D. Student 13 13%
Student > Bachelor 11 11%
Student > Doctoral Student 7 7%
Other 8 8%
Unknown 24 23%
Readers by discipline Count As %
Medicine and Dentistry 15 15%
Biochemistry, Genetics and Molecular Biology 12 12%
Environmental Science 11 11%
Nursing and Health Professions 8 8%
Agricultural and Biological Sciences 8 8%
Other 21 20%
Unknown 28 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 05 March 2018.
All research outputs
of 18,661,136 outputs
Outputs from Malaria Journal
of 4,990 outputs
Outputs of similar age
of 279,668 outputs
Outputs of similar age from Malaria Journal
of 1 outputs
Altmetric has tracked 18,661,136 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,990 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has done well, scoring higher than 76% of its peers.
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 279,668 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 72% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them