Evidence map›Paper›PMID 23501015›Full record

ArticleJournal of biomedical informatics2013

A method for estimating from thermometer sales the incidence of diseases that are symptomatically similar to influenza.

Ricardo Villamarín, Gregory Cooper, Michael Wagner, Fu-Chiang Tsui, Jeremy U Espino

Abstract read
In one paragraph

Article in Journal of biomedical informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

5 authors.

Ricardo VillamarínCenter for Advanced Study of Informatics in Public Health, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. ricardo.villamarin@alumni.pitt.edu
Gregory Cooper
Michael Wagner
Fu-Chiang Tsui
Jeremy U Espino

Funding

Decision Making in BiosurveillanceR01LM009132 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI WAGNER, MICHAEL MATTHEW · 2008 to 2011
$1.9M
NLM NIH HHS R01 LM009132PHITPO CDC HHS 1U38 HK000063-01PHITPO CDC HHS P01 HK000086PHITPO CDC HHS U38 HK000063
6 · The paper itself

Abstract

Early detection and accurate characterization of disease outbreaks are important tasks of public health. Infectious diseases that present symptomatically like influenza (SLI), including influenza itself, constitute an important class of diseases that are monitored by public-health epidemiologists. Monitoring emergency department (ED) visits for presentations of SLI could provide an early indication of the presence, extent, and dynamics of such disease in the population. We investigated the use of daily over-the-counter thermometer-sales data to estimate daily ED SLI counts in Allegheny County (AC), Pennsylvania. We found that a simple linear model fits the data well in predicting daily ED SLI counts from daily counts of thermometer sales in AC. These results raise the possibility that this model could be applied, perhaps with adaptation, in other regions of the country, where commonly thermometer sales data are available, but daily ED SLI counts are not.

Indexed as

CommerceThermometersBayes TheoremDisease OutbreaksHumansIncidenceInfluenza A Virus, H1N1 SubtypeInfluenza, HumanPennsylvania

Identifiers

PMID23501015
PMCPMC4609543

What OpenQuestion holds

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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.