Evidence map›Paper›PMID 37980384›Full record

ArticleScientific reports2023

Mapping brucellosis risk in Kenya and its implications for control strategies in sub-Saharan Africa.

James M Akoko, Athman Mwatondo, Mathew Muturi, Lillian Wambua, Hussein M Abkallo, Richard Nyamota, Caroline Bosire, Stephen Oloo, Konongoi S Limbaso, Francis Gakuya and 8 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
4.9field-weighted citation impact, top 4% of its field
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

14 citing papers in PubMed, 16 citations in OpenAlex.

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  12. Pathogens (Basel, Switzerland) · 2024
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  13. Review
  14. Seroprevalence ofFrontiers in veterinary science · 2024
    Article
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

18 authors at 9 institutions in 4 countries.

James M AkokoInternational Livestock Research Institute, Nairobi, Kenya. jamesakoko@yahoo.com.
Athman MwatondoInternational Livestock Research Institute, Nairobi, Kenya.
Mathew MuturiInternational Livestock Research Institute, Nairobi, Kenya.
Lillian WambuaInternational Livestock Research Institute, Nairobi, Kenya.
Hussein M AbkalloInternational Livestock Research Institute, Nairobi, Kenya.
Richard NyamotaInternational Livestock Research Institute, Nairobi, Kenya.
Caroline BosireInternational Livestock Research Institute, Nairobi, Kenya.
Stephen OlooInternational Livestock Research Institute, Nairobi, Kenya.
Konongoi S LimbasoKenya Medical Research Institute, Nairobi, Kenya.
Francis GakuyaWildlife Research and Training Institute, Naivasha, Kenya.
Daniel NthiwaDepartment of Biological Sciences, University of Embu, Embu, Kenya.
Andrew BartlowLos Alamos National Laboratory, Los Alamos, NM, USA.
Earl MiddlebrookLos Alamos National Laboratory, Los Alamos, NM, USA.
Jeanne FairLos Alamos National Laboratory, Los Alamos, NM, USA.
Joseph O OgutuBiostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany.
John GachohiGlobal Health Programme, Washington State University, Nairobi, Kenya.
Kariuki NjengaGlobal Health Programme, Washington State University, Nairobi, Kenya.
Bernard BettInternational Livestock Research Institute, Nairobi, Kenya.
International Livestock Research Institute · KELos Alamos National Laboratory · USKenya Wildlife Service · KEWashington State University · USFreie Universität Berlin · DEKenya Medical Research Institute · KEOrganisation Mondiale de la Santé Animale · FRUniversity of Embu · KEUniversity of Hohenheim · DE

Funding

Defense Threat Reduction Agency Grant No. HDTRA11910031
6 · The paper itself

Abstract

In Sub-Saharan Africa (SSA), effective brucellosis control is limited, in part, by the lack of long-term commitments by governments to control the disease and the absence of reliable national human and livestock population-based data to inform policies. Therefore, we conducted a study to establish the national prevalence and develop a risk map for Brucella spp. in cattle to contribute to plans to eliminate the disease in Kenya by the year 2040. We randomly generated 268 geolocations and distributed them across Kenya, proportionate to the area of each of the five agroecological zones and the associated cattle population. Cattle herds closest to each selected geolocation were identified for sampling. Up to 25 cattle were sampled per geolocation and a semi-structured questionnaire was administered to their owners. We tested 6,593 cattle samples for Brucella immunoglobulin G (IgG) antibodies using an Enzyme-linked immunosorbent assay (ELISA). We assessed potential risk factors and performed spatial analyses and prevalence mapping using approximate Bayesian inference implemented via the integrated nested Laplace approximation (INLA) method. The national Brucella spp. prevalence was 6.8% (95% CI: 6.2-7.4%). Exposure levels varied significantly between agro-ecological zones, with a high of 8.5% in the very arid zone with the lowest agricultural potential relative to a low of 0.0% in the agro-alpine zone with the highest agricultural potential. Additionally, seroprevalence increased with herd size, and the odds of seropositivity were significantly higher for females and adult animals than for males or calves. Similarly, animals with a history of abortion, or with multiple reproductive syndromes had higher seropositivity than those without. At the herd level, the risk of Brucella spp. transmission was higher in larger herds, and herds with a history of reproductive problems such as abortion, giving birth to weak calves, or having swollen testes. Geographic localities with high Brucella seroprevalence occurred in northern, eastern, and southern regions of Kenya all primarily characterized by semi-arid or arid agro-ecological zones dominated by livestock pastoralism interspersed with vast areas with mixed livestock-wildlife systems. The large spatial extent of our survey provides compelling evidence for the widespread geographical distribution of brucellosis risk across Kenya in a manner easily understandable for policymakers. Our findings can provide a basis for risk-stratified pilot studies aiming to investigate the cost-effectiveness and efficacy of singular and combined preventive intervention strategies that seek to inform Kenya's Brucellosis Control Policy.

Indexed as

BrucellaBrucellosisAnimal HusbandryAnimalsAntibodies, BacterialBayes TheoremCattleCross-Sectional StudiesFemaleKenyaLivestockMalePregnancyRisk FactorsSeroepidemiologic StudiesAntibodies, Bacterial

Identifiers

PMID37980384
PMCPMC10657468
OpenAlexW4388796547

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.