Evidence map›Paper›PMID 28645285›Full record

ArticleBMC medical informatics and decision making2017

Completeness and timeliness of notifiable disease reporting: a comparison of laboratory and provider reports submitted to a large county health department.

Brian E Dixon, Zuoyi Zhang, Patrick T S Lai, Uzay Kirbiyik, Jennifer Williams, Rebecca Hills, Debra Revere, P Joseph Gibson, Shaun J Grannis

Abstract readComparative Study
In one paragraph

Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 2 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

26 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. The benefits of health information exchange: an updated systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018
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  20. Clinical data sharing improves quality measurement and patient safety.Journal of the American Medical Informatics Association : JAMIA · 2021
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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

9 authors.

Brian E DixonIndiana University, Richard M. Fairbanks School of Public Health, 1050 Wishard Blvd, RG 5000, Indianapolis, IN, 46202, USA. bedixon@regenstrief.org.
Zuoyi ZhangRegenstrief Institute, Center for Biomedical Informatics, 1101 W 10th St, Indianapolis, IN, USA.
Patrick T S LaiRegenstrief Institute, Center for Biomedical Informatics, 1101 W 10th St, Indianapolis, IN, USA.
Uzay KirbiyikIndiana University, Richard M. Fairbanks School of Public Health, 1050 Wishard Blvd, RG 5000, Indianapolis, IN, 46202, USA.
Jennifer WilliamsRegenstrief Institute, Center for Biomedical Informatics, 1101 W 10th St, Indianapolis, IN, USA.
Rebecca HillsUniversity of Washington, School of Public Health, 1107 NE 45th St, Suite 400, Box 354809, Seattle, WA, 98195-4809, USA.
Debra RevereUniversity of Washington, School of Public Health, 1107 NE 45th St, Suite 400, Box 354809, Seattle, WA, 98195-4809, USA.
P Joseph GibsonMarion County Public Health Department, 3838 N Rural St, Indianapolis, IN, 46205, USA.
Shaun J GrannisRegenstrief Institute, Center for Biomedical Informatics, 1101 W 10th St, Indianapolis, IN, USA.

Funding

Improving Population Health Through Enhanced Targeted Regional Decision SupportR01HS020909 · AHRQ · INDIANA UNIVERSITY INDIANAPOLIS · PI DIXON, BRIAN E. · 2011 to 2015
$2.0M
AHRQ HHS R01 HS020909
6 · The paper itself

Abstract

backgroundMost public health agencies expect reporting of diseases to be initiated by hospital, laboratory or clinic staff even though so-called passive approaches are known to be burdensome for reporters and produce incomplete as well as delayed reports, which can hinder assessment of disease and delay recognition of outbreaks. In this study, we analyze patterns of reporting as well as data completeness and timeliness for traditional, passive reporting of notifiable disease by two distinct sources of information: hospital and clinic staff versus clinical laboratory staff. Reports were submitted via fax machine as well as electronic health information exchange interfaces.

methodsData were extracted from all submitted notifiable disease reports for seven representative diseases. Reporting rates are the proportion of known cases having a corresponding case report from a provider, a faxed laboratory report or an electronic laboratory report. Reporting rates were stratified by disease and compared using McNemar's test. For key data fields on the reports, completeness was calculated as the proportion of non-blank fields. Timeliness was measured as the difference between date of laboratory confirmed diagnosis and the date the report was received by the health department. Differences in completeness and timeliness by data source were evaluated using a generalized linear model with Pearson's goodness of fit statistic.

resultsWe assessed 13,269 reports representing 9034 unique cases. Reporting rates varied by disease with overall rates of 19.1% for providers and 84.4% for laboratories (p < 0.001). All but three of 15 data fields in provider reports were more often complete than those fields within laboratory reports (p <0.001). Laboratory reports, whether faxed or electronically sent, were received, on average, 2.2 days after diagnosis versus a week for provider reports (p <0.001).

conclusionsDespite growth in the use of electronic methods to enhance notifiable disease reporting, there still exists much room for improvement.

Indexed as

Local GovernmentPopulation SurveillanceDisease NotificationHealth Information ExchangeHealth PersonnelHumansIndianaPublic Health AdministrationCompletenessDisease notificationElectronic laboratory reportingHealth information exchangePublic health surveillanceTimeliness

Identifiers

PMID28645285
PMCPMC5481902

What OpenQuestion holds

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LicenceCC BY
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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.