Evidence map›Paper›PMID 19754978›Full record

ArticleBMC public health2009

A profile of the online dissemination of national influenza surveillance data.

Calvin Ky Cheng, Eric Hy Lau, Dennis Km Ip, Alfred Sy Yeung, Lai Ming Ho, Benjamin J Cowling

Abstract read
In one paragraph

Article in BMC public health, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 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

19 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  6. Predicting Infectious Disease Using Deep Learning and Big Data.International journal of environmental research and public health · 2018
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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.

Calvin Ky ChengDepartment of Community Medicine and School of Public Health, the University of Hong Kong, Hong Kong. chengkyc@hkucc.hku.hk
Eric Hy Lau
Dennis Km Ip
Alfred Sy Yeung
Lai Ming Ho
Benjamin J Cowling

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInfluenza surveillance systems provide important and timely information to health service providers on trends in the circulation of influenza virus and other upper respiratory tract infections. Online dissemination of surveillance data is useful for risk communication to health care professionals, the media and the general public. We reviewed national influenza surveillance websites from around the world to describe the main features of surveillance data dissemination.

methodsWe searched for national influenza surveillance websites for every country and reviewed the resulting sites where available during the period from November 2008 through February 2009. Literature about influenza surveillance was searched at MEDLINE for relevant hyperlinks to related websites. Non-English websites were translated into English using human translators or Google language tools.

resultsA total of 70 national influenza surveillance websites were identified. The percentage of developing countries with surveillance websites was lower than that of developed countries (22% versus 57% respectively). Most of the websites (74%) were in English or provided an English version. The most common surveillance methods included influenza-like illness consultation rates in primary care settings (89%) and laboratory surveillance (44%). Most websites (70%) provided data within a static report format and 66% of the websites provided data with at least weekly resolution.

conclusionAppropriate dissemination of surveillance data is important to maximize the utility of collected data. There may be room for improvement in the style and content of the dissemination of influenza data to health care professionals and the general public.

Indexed as

Influenza, HumanInformation DisseminationInternetOnline SystemsHumansPopulation SurveillanceRespiratory Tract Infections

Identifiers

PMID19754978
PMCPMC2754460

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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.