Evidence map›Paper›PMID 24381360›Full record

ArticlePublic health reports (Washington, D.C. : 1974)

Automated influenza-like illness reporting--an efficient adjunct to traditional sentinel surveillance.

W Katherine Yih, Noelle M Cocoros, Molly Crockett, Michael Klompas, Benjamin A Kruskal, Martin Kulldorff, Ross Lazarus, Lawrence C Madoff, Monica J Morrison, Sandra Smole and 1 more

Abstract readComparative Study
In one paragraph

Article in Public health reports (Washington, D.C. : 1974). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.7field-weighted citation impact, top 15% of its field
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

8 citing papers in PubMed, 16 citations in OpenAlex.

  1. How to ensure effective and sustainable ILI surveillance in Belgian general practices? : protocol.Archives of public health = Archives belges de sante publique · 2026
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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

11 authors at 5 institutions in 1 country.

W Katherine YihHarvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA.
Noelle M CocorosMassachusetts Department of Public Health, Bureau of Infectious Disease, Boston, MA.
Molly CrockettMassachusetts Department of Public Health, Bureau of Infectious Disease, Boston, MA.
Michael KlompasHarvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA.
Benjamin A KruskalHarvard Vanguard Medical Associates, Boston, MA ; Atrius Health, Boston, MA ; Harvard Medical School, Boston, MA.
Martin KulldorffHarvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA.
Ross LazarusHarvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA.
Lawrence C MadoffMassachusetts Department of Public Health, Bureau of Infectious Disease, Boston, MA ; University of Massachusetts Medical School, Worcester, MA.
Monica J MorrisonMassachusetts Department of Public Health, Bureau of Infectious Disease, Boston, MA.
Sandra SmoleMassachusetts Department of Public Health, William A. Hinton State Laboratory Institute, Bureau of Laboratory Sciences, Molecular Diagnostics and Virology, Boston, MA.
Richard PlattHarvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA.
Harvard Pilgrim Health Care · USMassachusetts Department of Public Health · USHarvard University · USHarvard Vanguard Medical Associates · USUniversity of Massachusetts Chan Medical School · US

Funding

Modeling Health System Infectious Disease DataU01GM076672 · NIGMS · UNIVERSITY OF CALIFORNIA-IRVINE · PI HUANG, SUSAN S. · 2006 to 2010
$3.0M
NIGMS NIH HHS U01 GM076672NIGMS NIH HHS U01GM076672PHITPO CDC HHS 1P01HK00088PHITPO CDC HHS P01 HK000088
6 · The paper itself

Abstract

objectivesWe compared an electronic health record-based influenza-like illness (ILI) surveillance system with manual sentinel surveillance and virologic data to evaluate the utility of the automated system for routine ILI surveillance.

methodsWe obtained weekly aggregate ILI reports from the Electronic medical record Support for Public Health (ESP) disease-detection and reporting system, which used an automated algorithm to identify ILI visits among a patient population of about 700,000 in Eastern Massachusetts. The percentage of total visits for ILI ("percent ILI") in ESP, percent ILI in the Massachusetts Department of Public Health's sentinel surveillance system, and percentage of laboratory specimens submitted to participating Massachusetts laboratories that tested positive for influenza were compared for the period October 2007-September 2011. We calculated Spearman's correlation coefficients and compared ESP and sentinel surveillance systems qualitatively, in terms of simplicity, flexibility, data quality, acceptability, timeliness, and usefulness.

resultsESP and sentinel surveillance percent ILI always peaked within one week of each other. There was 80% correlation between the two and 71%-73% correlation with laboratory data. Sentinel surveillance percent ILI was higher than ESP percent ILI during influenza seasons. The amplitude of variation in ESP percent ILI was greatest for 5- to 49-year-olds and typically peaked for the 5- to 24-year-old age group before the others.

conclusionsThe ESP system produces percent ILI data of similar quality to sentinel surveillance and offers the advantages of shifting disease reporting burden from clinicians to information systems, allowing tracking of disease by age group, facilitating efficient surveillance for very large populations, and producing consistent and timely reports.

Indexed as

Electronic Health RecordsAdolescentAdultAgedAlgorithmsHumansInfantInfluenza, HumanMassachusettsMiddle AgedPopulation SurveillanceSentinel Surveillance

Identifiers

PMID24381360
PMCPMC3863004
OpenAlexW2203857001

What OpenQuestion holds

Textmetadata
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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.