Evidence map›Paper›PMID 33602160›Full record

ArticleBMC infectious diseases2021

Spatial clustering of livestock Anthrax events associated with agro-ecological zones in Kenya, 1957-2017.

Leonard M Nderitu, John Gachohi, Frederick Otieno, Eddy G Mogoa, Mathew Muturi, Athman Mwatondo, Eric M Osoro, Isaac Ngere, Peninah M Munyua, Harry Oyas and 6 more

Abstract read
In one paragraph

Article in BMC infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Leonard M NderituPaul G Allen School for Global Health, Washington State University, Pullman, Washington, USA.
John GachohiWashington State University Global `Health Program-Kenya, WSU, Nairobi, Kenya. john.gachohi@wsu.edu.ORCID http://orcid.org/0000-0001-9854-7490
Frederick OtienoInternational Livestock Research Institute, Nairobi, Kenya.
Eddy G MogoaUniversity of Nairobi, College of Agriculture and Veterinary Sciences, Nairobi, Kenya, University of Nairobi, Nairobi, Kenya.
Mathew MuturiInternational Livestock Research Institute, Nairobi, Kenya.
Athman MwatondoInternational Livestock Research Institute, Nairobi, Kenya.
Eric M OsoroWashington State University Global `Health Program-Kenya, WSU, Nairobi, Kenya.
Isaac NgereWashington State University Global `Health Program-Kenya, WSU, Nairobi, Kenya.
Peninah M MunyuaDivision of Global Health Protection, United States Centers for Disease Control and Prevention, Nairobi, Kenya.
Harry OyasKenya Ministry of Agriculture, Livestock and Fisheries, Nairobi, Kenya.
Obadiah NjagiKenya Ministry of Agriculture, Livestock and Fisheries, Nairobi, Kenya.
Eric LofgrenPaul G Allen School for Global Health, Washington State University, Pullman, Washington, USA.
Thomas MarshPaul G Allen School for Global Health, Washington State University, Pullman, Washington, USA.
Marc-Alain WiddowsonDivision of Global Health Protection, United States Centers for Disease Control and Prevention, Nairobi, Kenya.
Bernard BettInternational Livestock Research Institute, Nairobi, Kenya.
M Kariuki NjengaPaul G Allen School for Global Health, Washington State University, Pullman, Washington, USA.

Funding

Defense Threat Reduction Agency HDTRA11710043.
6 · The paper itself

Abstract

backgroundDeveloping disease risk maps for priority endemic and episodic diseases is becoming increasingly important for more effective disease management, particularly in resource limited countries. For endemic and easily diagnosed diseases such as anthrax, using historical data to identify hotspots and start to define ecological risk factors of its occurrence is a plausible approach. Using 666 livestock anthrax events reported in Kenya over 60 years (1957-2017), we determined the temporal and spatial patterns of the disease as a step towards identifying and characterizing anthrax hotspots in the region.

methodsData were initially aggregated by administrative unit and later analyzed by agro-ecological zones (AEZ) to reveal anthrax spatio-temporal trends and patterns. Variations in the occurrence of anthrax events were estimated by fitting Poisson generalized linear mixed-effects models to the data with AEZs and calendar months as fixed effects and sub-counties as random effects.

resultsThe country reported approximately 10 anthrax events annually, with the number increasing to as many as 50 annually by the year 2005. Spatial classification of the events in eight counties that reported the highest numbers revealed spatial clustering in certain administrative sub-counties, with 12% of the sub-counties responsible for over 30% of anthrax events, whereas 36% did not report any anthrax disease over the 60-year period. When segregated by AEZs, there was significantly greater risk of anthrax disease occurring in agro-alpine, high, and medium potential AEZs when compared to the agriculturally low potential arid and semi-arid AEZs of the country (p < 0.05). Interestingly, cattle were > 10 times more likely to be infected by B. anthracis than sheep, goats, or camels. There was lower risk of anthrax events in August (P = 0.034) and December (P = 0.061), months that follow long and short rain periods, respectively.

conclusionTaken together, these findings suggest existence of certain geographic, ecological, and demographic risk factors that promote B. anthracis persistence and trasmission in the disease hotspots.

Indexed as

LivestockAgricultureAnimalsAnthraxBacillus anthracisCluster AnalysisKenyaRainRisk FactorsSpatial AnalysisAgro-ecological zonesAnthraxClusteringKenyaLivestock

Identifiers

PMID33602160
PMCPMC7890876

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.