Evidence map›Paper›PMID 27718314›Full record

ArticleInfluenza and other respiratory viruses2017

The assessment of data sources for influenza virologic surveillance in New York State.

Kay L Escuyer, Christine L Waters, Donna L Gowie, Angie M Maxted, Gregory M Farrell, Meghan E Fuschino, Kirsten St George

Abstract read
In one paragraph

Article in Influenza and other respiratory viruses, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Article
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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

7 authors.

Kay L EscuyerLaboratory of Viral Diseases, Wadsworth Center, New York State Department of Health, Albany, NY, USA.
Christine L WatersBureau of Communicable Disease Control, New York State Department of Health, Albany, NY, USA.
Donna L GowieBureau of Communicable Disease Control, New York State Department of Health, Albany, NY, USA.
Angie M MaxtedBureau of Communicable Disease Control, New York State Department of Health, Albany, NY, USA.
Gregory M FarrellLaboratory of Viral Diseases, Wadsworth Center, New York State Department of Health, Albany, NY, USA.
Meghan E FuschinoLaboratory of Viral Diseases, Wadsworth Center, New York State Department of Health, Albany, NY, USA.
Kirsten St GeorgeLaboratory of Viral Diseases, Wadsworth Center, New York State Department of Health, Albany, NY, USA.ORCID 0000-0001-5168-763X

Funding

NCHM CDC HHS U60 HM000803
6 · The paper itself

Abstract

backgroundFollowing the 2013 USA release of the Influenza Virologic Surveillance Right Size Roadmap, the New York State Department of Health (NYSDOH) embarked on an evaluation of data sources for influenza virologic surveillance.

objectiveTo assess NYS data sources, additional to data generated by the state public health laboratory (PHL), which could enhance influenza surveillance at the state and national level.

methodsPotential sources of laboratory test data for influenza were analyzed for quantity and quality. Computer models, designed to assess sample sizes and the confidence of data for statistical representation of influenza activity, were used to compare PHL test data to results from clinical and commercial laboratories, reported between June 8, 2013 and May 31, 2014.

resultsSample sizes tested for influenza at the state PHL were sufficient for situational awareness surveillance with optimal confidence levels, only during peak weeks of the influenza season. Influenza data pooled from NYS PHLs and clinical laboratories generated optimal confidence levels for situational awareness throughout the influenza season. For novel influenza virus detection in NYS, combined real-time (rt) RT-PCR data from state and regional PHLs achieved ≥85% confidence during peak influenza activity, and ≥95% confidence for most of low season and all of off-season.

conclusionsIn NYS, combined data from clinical, commercial, and public health laboratories generated optimal influenza surveillance for situational awareness throughout the season. Statistical confidence for novel virus detection, which is reliant on only PHL data, was achieved for most of the year.

Indexed as

Information Storage and RetrievalPopulation SurveillanceComputer SimulationHumansInfluenza, HumanNew YorkPublic HealthSeasonscomputer modelsdata sourcesinfluenzasurveillance

Identifiers

PMID27718314
PMCPMC5304574

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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.