Evidence map›Paper›PMID 31639037›Full record

ArticleBMC research notes2019

Cholera hotspots and surveillance constraints contributing to recurrent epidemics in Tanzania.

Yaovi M G Hounmanou, Kåre Mølbak, Jonas Kähler, Robinson H Mdegela, John E Olsen, Anders Dalsgaard

Open access · goldAbstract read
In one paragraph

Article in BMC research notes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.0field-weighted citation impact, top 26% 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

10 citing papers in PubMed, 21 citations in OpenAlex.

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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

6 authors at 3 institutions in 3 countries.

Yaovi M G HounmanouDepartment of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, 1870, Frederiksberg C, Copenhagen, Denmark. gil@sund.ku.dk.ORCID http://orcid.org/0000-0003-3991-0864
Kåre MølbakDepartment of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, 1870, Frederiksberg C, Copenhagen, Denmark.
Jonas KählerDivision of Infectious Disease Preparedness, Statens Serum Institut, Artillerivej 5, 2300, Copenhagen, Denmark.
Robinson H MdegelaDepartment of Veterinary Medicine and Public Health, College of Veterinary and Biomedical Sciences, Sokoine University of Agriculture, PO Box: 3021, Morogoro, Tanzania.
John E OlsenDepartment of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, 1870, Frederiksberg C, Copenhagen, Denmark.
Anders DalsgaardDepartment of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, 1870, Frederiksberg C, Copenhagen, Denmark.
University of Copenhagen · DKStatens Serum Institut · DKSokoine University of Agriculture · TZ

Funding

Danida Fellowship Centre DFC File No. 14-P01-TANInternational Foundation for Science 1-2-A-6100-1
6 · The paper itself

Abstract

objectiveWe described the dynamics of cholera in Tanzania between 2007 and 2017 and assessed the weaknesses of the current surveillance system in providing necessary data in achieving the global roadmap to 2030 for cholera control.

resultsThe Poisson-based spatial scan identified cholera hotspots in mainland Tanzania. A zero-inflated Poisson regression investigated the relationship between the incidence of cholera and available demographic, socio-economic and climatic exposure variables. Four cholera hotspots were detected covering 17 regions, home to 28 million people, including the central regions and those surrounding the Lakes Victoria, Tanganyika and Nyaza. The risk of experiencing cholera in these regions was up to 2.9 times higher than elsewhere in the country. Regression analyses revealed that every 100 km of water perimeter in a region increased the cholera incidence by 1.5%. Due to the compilation of surveillance data at regional level rather than at district, we were unable to reliably identify any other significant risk factors and specific hotspots. Cholera high-risk populations in Tanzania include those living near lakes and central regions. Successful surveillance require disaggregated data available weekly and at district levels in order to serve as data for action to support the roadmap for cholera control.

Indexed as

Disease OutbreaksAdolescentAdultAgedChildChild, PreschoolCholeraDemographyDiarrheaEpidemiological MonitoringFemaleHumansIncidenceLakesMaleMiddle AgedCholeraCholera dynamicsGreat LakesSpatial–temporal analysisTanzania

Identifiers

PMID31639037
PMCPMC6805412
OpenAlexW2981322965

What OpenQuestion holds

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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.