Evidence map›Paper›PMID 24597553›Full record

SynthesisThe Milbank quarterly2014

Social media and internet-based data in global systems for public health surveillance: a systematic review.

Edward Velasco, Tumacha Agheneza, Kerstin Denecke, Göran Kirchner, Tim Eckmanns

Abstract readSystematic Review
In one paragraph

Synthesis in The Milbank quarterly, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers, 15 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
111citing papers in PubMed, 15 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

111 citing papers in PubMed, 15 syntheses or guidelines pooled it.

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  9. A systematic literature review of machine learning in online personal health data.Journal of the American Medical Informatics Association : JAMIA · 2019
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  14. Twitter as a Tool for Health Research: A Systematic Review.American journal of public health · 2017
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  16. Utilizing Machine Learning for Predicting PrEP Use Status Among Sexual and Gender Minority Young Adults.Prevention science : the official journal of the Society for Prevention Research · 2026
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51 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Edward VelascoRobert Koch Institute.
Tumacha Agheneza
Kerstin Denecke
Göran Kirchner
Tim Eckmanns

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

contextThe exchange of health information on the Internet has been heralded as an opportunity to improve public health surveillance. In a field that has traditionally relied on an established system of mandatory and voluntary reporting of known infectious diseases by doctors and laboratories to governmental agencies, innovations in social media and so-called user-generated information could lead to faster recognition of cases of infectious disease. More direct access to such data could enable surveillance epidemiologists to detect potential public health threats such as rare, new diseases or early-level warnings for epidemics. But how useful are data from social media and the Internet, and what is the potential to enhance surveillance? The challenges of using these emerging surveillance systems for infectious disease epidemiology, including the specific resources needed, technical requirements, and acceptability to public health practitioners and policymakers, have wide-reaching implications for public health surveillance in the 21st century.

methodsThis article divides public health surveillance into indicator-based surveillance and event-based surveillance and provides an overview of each. We did an exhaustive review of published articles indexed in the databases PubMed, Scopus, and Scirus between 1990 and 2011 covering contemporary event-based systems for infectious disease surveillance.

findingsOur literature review uncovered no event-based surveillance systems currently used in national surveillance programs. While much has been done to develop event-based surveillance, the existing systems have limitations. Accordingly, there is a need for further development of automated technologies that monitor health-related information on the Internet, especially to handle large amounts of data and to prevent information overload. The dissemination to health authorities of new information about health events is not always efficient and could be improved. No comprehensive evaluations show whether event-based surveillance systems have been integrated into actual epidemiological work during real-time health events.

conclusionsThe acceptability of data from the Internet and social media as a regular part of public health surveillance programs varies and is related to a circular challenge: the willingness to integrate is rooted in a lack of effectiveness studies, yet such effectiveness can be proved only through a structured evaluation of integrated systems. Issues related to changing technical and social paradigms in both individual perceptions of and interactions with personal health data, as well as social media and other data from the Internet, must be further addressed before such information can be integrated into official surveillance systems.

Indexed as

InternetSocial MediaCommunicable DiseasesDatabases, FactualHumansInformation DisseminationPublic HealthPublic Health Surveillancehealth informationInternetsocial mediasurveillance

Identifiers

PMID24597553
PMCPMC3955375

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.