Evidence map›Paper›PMID 24386335›Full record

ArticlePloS one2013

Combining surveillance systems: effective merging of U.S. Veteran and military health data.

Julie A Pavlin, Howard S Burkom, Yevgeniy Elbert, Cynthia Lucero-Obusan, Carla A Winston, Kenneth L Cox, Gina Oda, Joseph S Lombardo, Mark Holodniy

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2013. 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
1.2field-weighted citation impact, top 21% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Article
  3. Prediction of Peaks of Seasonal Influenza in Military Health-Care Data.Biomedical engineering and computational biology · 2016
    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

9 authors at 4 institutions in 1 country.

Julie A PavlinArmed Forces Health Surveillance Center, US Department of Defense, Silver Spring, Maryland, United States of America.
Howard S BurkomApplied Physics Laboratory, Johns Hopkins University, Laurel, Maryland, United States of America.
Yevgeniy ElbertApplied Physics Laboratory, Johns Hopkins University, Laurel, Maryland, United States of America.
Cynthia Lucero-ObusanOffice of Public Health, US Department of Veterans Affairs, Washington, District of Columbia, United States of America.
Carla A WinstonOffice of Public Health, US Department of Veterans Affairs, Washington, District of Columbia, United States of America.
Kenneth L CoxUS Army Institute of Public Health, US Department of Defense, Aberdeen Proving Ground, Maryland, United States of America.
Gina OdaOffice of Public Health, US Department of Veterans Affairs, Washington, District of Columbia, United States of America.
Joseph S LombardoApplied Physics Laboratory, Johns Hopkins University, Laurel, Maryland, United States of America.
Mark HolodniyOffice of Public Health, US Department of Veterans Affairs, Washington, District of Columbia, United States of America.
United States Department of Veterans Affairs · USJohns Hopkins University Applied Physics Laboratory · USArmed Forces Health Surveillance Center · USU.S. Army Center for Health Promotion and Preventive Medicine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe U.S. Department of Veterans Affairs (VA) and Department of Defense (DoD) had more than 18 million healthcare beneficiaries in 2011. Both Departments conduct individual surveillance for disease events and health threats.

methodsWe performed joint and separate analyses of VA and DoD outpatient visit data from October 2006 through September 2010 to demonstrate geographic and demographic coverage, timeliness of influenza epidemic awareness, and impact on spatial cluster detection achieved from a joint VA and DoD biosurveillance platform.

resultsAlthough VA coverage is greater, DoD visit volume is comparable or greater. Detection of outbreaks was better in DoD data for 58% and 75% of geographic areas surveyed for seasonal and pandemic influenza, respectively, and better in VA data for 34% and 15%. The VA system tended to alert earlier with a typical H3N2 seasonal influenza affecting older patients, and the DoD performed better during the H1N1 pandemic which affected younger patients more than normal influenza seasons. Retrospective analysis of known outbreaks demonstrated clustering evidence found in separate DoD and VA runs, which persisted with combined data sets.

conclusionThe analyses demonstrate two complementary surveillance systems with evident benefits for the national health picture. Relative timeliness of reporting could be improved in 92% of geographic areas with access to both systems, and more information provided in areas where only one type of facility exists. Combining DoD and VA data enhances geographic cluster detection capability without loss of sensitivity to events isolated in either population and has a manageable effect on customary alert rates.

Indexed as

Public Health SurveillanceAdolescentAdultAgedBiosurveillanceChildChild, PreschoolDatabases, FactualData MiningHumansInfantInfant, NewbornInfluenza A Virus, H1N1 SubtypeInfluenza A Virus, H3N2 SubtypeInfluenza, HumanMiddle Aged

Identifiers

PMID24386335
PMCPMC3873400
OpenAlexW2019672061

What OpenQuestion holds

Textmetadata
LicenceCC0
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.