Evidence map›Paper›PMID 26749325›Full record

ArticleBMC public health2016

The usefulness of school-based syndromic surveillance for detecting malaria epidemics: experiences from a pilot project in Ethiopia.

Ruth A Ashton, Takele Kefyalew, Esey Batisso, Tessema Awano, Zelalem Kebede, Gezahegn Tesfaye, Tamiru Mesele, Sheleme Chibsa, Richard Reithinger, Simon J Brooker

Open access · goldAbstract readEvaluation Study
In one paragraph

Article in BMC public health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.7field-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

9 citing papers in PubMed, 18 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

10 authors at 5 institutions in 3 countries.

Ruth A AshtonMalaria Consortium, London, UK. rashton@tulane.edu.
Takele KefyalewMalaria Consortium Ethiopia, Addis Ababa, Ethiopia. t.kefyalew@malariaconsortium.org.
Esey BatissoMalaria Consortium Southern Nations, Nationalities and People's Regional State sub-office, Hawassa, Ethiopia. e.batisso@malariaconsortium.org.
Tessema AwanoMalaria Consortium Southern Nations, Nationalities and People's Regional State sub-office, Hawassa, Ethiopia. t.awano@malariaconsortium.org.
Zelalem KebedeMalaria Consortium Ethiopia, Addis Ababa, Ethiopia. z.kebede@malariaconsortium.org.
Gezahegn TesfayeMalaria Consortium Ethiopia, Addis Ababa, Ethiopia. g_tesfaye@yahoo.com.
Tamiru MeseleSouthern Nations, Nationalities and People's Regional State Health Bureau, Hawassa, Ethiopia. tamex23@yahoo.com.
Sheleme ChibsaPresident's Malaria Initiative, U.S. Agency for International Development, Addis Ababa, Ethiopia. schibsa@usaid.gov.
Richard ReithingerFaculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK. rreithinger@yahoo.co.uk.
Simon J BrookerFaculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK. simon.brooker@lshtm.ac.uk.
Southern Nations, Nationalities, and Peoples' Region · ETInternational Trachoma Initiative · ETLondon School of Hygiene & Tropical Medicine · GBMalaria Consortium · GBRTI International · US

Funding

Wellcome TrustWellcome Trust 098045
6 · The paper itself

Abstract

backgroundSyndromic surveillance is a supplementary approach to routine surveillance, using pre-diagnostic and non-clinical surrogate data to identify possible infectious disease outbreaks. To date, syndromic surveillance has primarily been used in high-income countries for diseases such as influenza--however, the approach may also be relevant to resource-poor settings. This study investigated the potential for monitoring school absenteeism and febrile illness, as part of a school-based surveillance system to identify localised malaria epidemics in Ethiopia.

methodsRepeated cross-sectional school- and community-based surveys were conducted in six epidemic-prone districts in southern Ethiopia during the 2012 minor malaria transmission season to characterise prospective surrogate and syndromic indicators of malaria burden. Changes in these indicators over the transmission season were compared to standard indicators of malaria (clinical and confirmed cases) at proximal health facilities. Subsequently, two pilot surveillance systems were implemented, each at ten sites throughout the peak transmission season. Indicators piloted were school attendance recorded by teachers, or child-reported recent absenteeism from school and reported febrile illness.

resultsLack of seasonal increase in malaria burden limited the ability to evaluate sensitivity of the piloted syndromic surveillance systems compared to existing surveillance at health facilities. Weekly absenteeism was easily calculated by school staff using existing attendance registers, while syndromic indicators were more challenging to collect weekly from schoolchildren. In this setting, enrolment of school-aged children was found to be low, at 54%. Non-enrolment was associated with low household wealth, lack of parental education, household size, and distance from school.

conclusionsSchool absenteeism is a plausible simple indicator of unusual health events within a community, such as malaria epidemics, but the sensitivity of an absenteeism-based surveillance system to detect epidemics could not be rigorously evaluated in this study. Further piloting during a demonstrated increase in malaria transmission within a community is recommended.

Indexed as

AbsenteeismEpidemicsSchoolsAdolescentChildCross-Sectional StudiesDisease OutbreaksEthiopiaFemaleFeverHealth FacilitiesHumansInfluenza, HumanMalariaMalePilot Projects

Identifiers

PMID26749325
PMCPMC4707000
OpenAlexW2225215926

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.