Evidence map›Paper›PMID 22952719›Full record

ArticlePloS one2012

Comparison: Flu prescription sales data from a retail pharmacy in the US with Google Flu trends and US ILINet (CDC) data as flu activity indicator.

Avinash Patwardhan, Robert Bilkovski

Abstract readComparative Study
In one paragraph

Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

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

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  20. Surveillance for emerging respiratory viruses.The Lancet. Infectious diseases · 2014
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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

2 authors.

Avinash PatwardhanWalgreen Co., Deerfield, Illinois, United States of America. Avinash.Patwardhan@Walgreens.com
Robert Bilkovski

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The potential threat of bioterrorism along with the emergence of new or existing drug resistant strains of influenza virus, added to expanded global travel, have increased vulnerability to epidemics or pandemics and their aftermath. The same factors have also precipitated urgency for having better, faster, sensitive, and reliable syndromic surveillance systems. Prescription sales data can provide surrogate information about the development of infectious diseases and therefore serve as a useful tool in syndromic surveillance. This study compared prescription sales data from a large drug retailing pharmacy chain in the United States with Google Flu trends surveillance system data as a flu activity indicator. It was found that the two were highly correlated. The correlation coefficient (Pearson 'r') for five years' aggregate data (2007-2011) was 0.92 (95% CI, 0.90-0.94). The correlation coefficients for each of the five years between 2007 and 2011 were 0.85, 0.92, 0.91, 0.88, and 0.87 respectively. Additionally, prescription sales data from the same large drug retailing pharmacy chain in the United States were also compared with US Outpatient Influenza-like Illness Surveillance Network (ILINet) data for 2007 by Centers for Disease Control and Prevention (CDC). The correlation coefficient (Pearson 'r') was 0.97 (95% CI, 0.95-0.98).

Indexed as

Databases, FactualEpidemiological MonitoringPharmacyAntiviral AgentsCenters for Disease Control and Prevention, U.S.Drug PrescriptionsHumansInfluenza, HumanUnited StatesAntiviral Agents

Identifiers

PMID22952719
PMCPMC3431370

What OpenQuestion holds

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LicenceCC BY
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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.