Evidence map›Paper›PMID 18172157›Full record

ArticleAmerican journal of public health2008

A comparison of the completeness and timeliness of automated electronic laboratory reporting and spontaneous reporting of notifiable conditions.

J Marc Overhage, Shaun Grannis, Clement J McDonald

Abstract readComparative StudyEvaluation Study
In one paragraph

Article in American journal of public health, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
92citing papers in PubMed, 3 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

92 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Towards public health decision support: a systematic review of bidirectional communication approaches.Journal of the American Medical Informatics Association : JAMIA · 2013
    Pooled it
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  14. Estimating Neonatal Herpes Simplex Virus Incidence and Mortality Using Capture-recapture, Florida.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021
    Article
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  17. Using informatics to improve cancer surveillance.Journal of the American Medical Informatics Association : JAMIA · 2020
    Review
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32 more citing papers are in PubMed but not listed here.

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

3 authors.

J Marc OverhageRegenstrief Institute, 410 W 10th St, Suite 2000, Indianapolis, IN 46202-3012, USA. moverhage@regenstrief.org
Shaun Grannis
Clement J McDonald

Funding

NLM's Personal Health Record and Consumer Service R&DZIALM008925 · NLM · NATIONAL LIBRARY OF MEDICINE · PI MCDONALD, CLEMENT · 2009 to 2020
$11.8M
Using large Clinical Data bases to assess the association between patient factors, medication usage and patient outcomesZIALM008927 · NLM · NATIONAL LIBRARY OF MEDICINE · PI MCDONALD, CLEMENT · 2009 to 2015
$1.5M
13: The Indiana IAIMS InitiativeG08LM008232 · NLM · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI OVERHAGE, JOSEPH MARCUS · 2004 to 2007
$1.3M
HEALTH APPLICATIONS FOR THE NATIONAL INFROMATIONN01LM063546 · NLM · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI MCDONALD, CLEMENT J · 1996 to 2000
$0k
NLM NIH HHS G08 LM008232NLM NIH HHS N01-LM-4-3410NLM NIH HHS N01-LM-6-3546
6 · The paper itself

Abstract

objectivesWe examined whether automated electronic laboratory reporting of notifiable-diseases results in information being delivered to public health departments more completely and quickly than is the case with spontaneous, paper-based reporting.

methodsWe used data from a local public health department, hospital infection control departments, and a community-wide health information exchange to identify all potential cases of notifiable conditions that occurred in Marion County, Ind, during the first quarter of 2001. We compared traditional spontaneous reporting to the health department with automated electronic laboratory reporting through the health information exchange.

resultsAfter reports obtained using the 2 methods had been matched, there were 4785 unique reports for 53 different conditions during the study period. Chlamydia was the most common condition, followed by hepatitis B, hepatitis C, and gonorrhea. Automated electronic laboratory reporting identified 4.4 times as many cases as traditional spontaneous, paper-based methods and identified those cases 7.9 days earlier than spontaneous reporting.

conclusionsAutomated electronic laboratory reporting improves the completeness and timeliness of disease surveillance, which will enhance public health awareness and reporting efficiency.

Indexed as

Chlamydia InfectionsDisease NotificationElectronic Data ProcessingGonorrheaHepatitis BHepatitis CHumansIndianaLaboratoriesMedical Records Systems, ComputerizedPopulation SurveillancePublic Health Administration

Identifiers

PMID18172157
PMCPMC2376898

What OpenQuestion holds

Textmetadata
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.