Evidence map›Paper›PMID 32948155›Full record

ArticleBMC public health2020

Assessment of electronic disease early warning system for improved disease surveillance and outbreak response in Yemen.

Fekri Dureab, Kamran Ahmed, Claudia Beiersmann, Claire J Standley, Ali Alwaleedi, Albrecht Jahn

Abstract read
In one paragraph

Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Fekri DureabHeidelberg Institute of Global Health, Medical School, Ruprecht-Karls-University, Heidelberg, Germany. fekridureab@yahoo.com.ORCID http://orcid.org/0000-0002-8414-4129
Kamran AhmedWorld Health Organization, WHO Health Emergencies, Regional Office for Africa (AFRO), Brazzaville, Republic of Congo, Republic of the Congo.
Claudia BeiersmannHeidelberg Institute of Global Health, Medical School, Ruprecht-Karls-University, Heidelberg, Germany.
Claire J StandleyCenter for Global Health Science and Security, Georgetown University, Washington, D.C., USA.
Ali AlwaleediFaculty of Medicine and Health Sciences,, University of Aden, Aden, Yemen.
Albrecht JahnHeidelberg Institute of Global Health, Medical School, Ruprecht-Karls-University, Heidelberg, Germany.

Funding

World Health Organization 001
6 · The paper itself

Abstract

backgroundDiseases Surveillance is a continuous process of data collection, analysis interpretation and dissemination of information for swift public health action. Recent advances in health informatics have led to the implementation of electronic tools to facilitate such critical disease surveillance processes. This study aimed to assess the performance of the national electronic Disease Early Warning System in Yemen (eDEWS) using system attributes: data quality, timeliness, stability, simplicity, predictive value positive, sensitivity, acceptability, flexibility, and representativeness, based on the Centres for Disease Control & Prevention (US CDC) standard indicators.

methodsWe performed a mixed methods study that occurred in two stages: first, the quantitative data was collected from weekly epidemiological bulletins from 2013 to 2017, all alerts of 2016, and annual eDEWS reports, and then the qualitative method using in-depth interviews was carried out in a convergent strategy. The CDC guideline used to describe the following system attributes: data quality (reporting, and completeness), timeliness, stability, simplicity, predictive value positive, sensitivity, acceptability, flexibility and representativeness.

resultsThe finding of this assessment showed that eDEWS is a resilient and reliable system, and despite the conflict in Yemen, the system is still functioning and expanding. The response timeliness remains a challenge, since only 21% of all eDEWS alerts were verified within the first 24 h of detection in 2016. However, identified gaps did not affect the system's ability to identify outbreaks in the current fragile situation. Findings show that eDEWS data is representative, since it covers the entire country. Although, eDEWS covers only 37% of all health facilities, this represents 83% of all functional health facilities in all 23 governorates and all 333 districts.

conclusionThe quality and timeliness of responses are major challenges to eDEWS' functionality, the eDEWS remains the only system that provides regular data on communicable diseases in Yemen. In particular, public health response timeliness needs improvement.

Indexed as

Communicable DiseasesDisease OutbreaksElectronicsHumansPopulation SurveillancePublic HealthYemenAssessmentDisease surveillanceEarly warning systemeDEWSOutbreak responsePerformance indicatorsPublic health emergenciesYemen

Identifiers

PMID32948155
PMCPMC7501711

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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.