Evidence map›Paper›PMID 31622141›Full record

ArticleAmerican journal of public health2019

Injury Burden in the United States: Accurate, Reliable, and Timely Surveillance Using Electronic Health Care Data.

Jae Min, Kelly K Gurka, Bindu Kalesan, Jiang Bian, Mattia Prosperi

Abstract read
In one paragraph

Article in American journal of public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Validation of a Rule-Based ICD-10-CM Algorithm to Detect Fall Injuries in Medicare Data.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024
    Article
  5. Article
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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

5 authors.

Jae MinJae Min, Kelly K. Gurka, and Mattia Prosperi are with the Department of Epidemiology, University of Florida, Gainesville. Bindu Kalesan is with the Department of Medicine, Boston University, Boston, MA. Jiang Bian is with the Department of Health Outcomes and Biomedical Informatics, University of Florida.
Kelly K GurkaJae Min, Kelly K. Gurka, and Mattia Prosperi are with the Department of Epidemiology, University of Florida, Gainesville. Bindu Kalesan is with the Department of Medicine, Boston University, Boston, MA. Jiang Bian is with the Department of Health Outcomes and Biomedical Informatics, University of Florida.
Bindu KalesanJae Min, Kelly K. Gurka, and Mattia Prosperi are with the Department of Epidemiology, University of Florida, Gainesville. Bindu Kalesan is with the Department of Medicine, Boston University, Boston, MA. Jiang Bian is with the Department of Health Outcomes and Biomedical Informatics, University of Florida.
Jiang BianJae Min, Kelly K. Gurka, and Mattia Prosperi are with the Department of Epidemiology, University of Florida, Gainesville. Bindu Kalesan is with the Department of Medicine, Boston University, Boston, MA. Jiang Bian is with the Department of Health Outcomes and Biomedical Informatics, University of Florida.
Mattia ProsperiJae Min, Kelly K. Gurka, and Mattia Prosperi are with the Department of Epidemiology, University of Florida, Gainesville. Bindu Kalesan is with the Department of Medicine, Boston University, Boston, MA. Jiang Bian is with the Department of Health Outcomes and Biomedical Informatics, University of Florida.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current injury surveillance systems in the United States, including the National Electronic Injury Surveillance System (NEISS), are unable to draw reliable subnational and subannual incidence estimates.Compared with the

Indexed as

CausalityClinical CodingData AccuracyElectronic Health RecordsHumansInternational Classification of DiseasesPublic Health SurveillanceTime FactorsUnited StatesWounds and Injuries

Identifiers

PMID31622141
PMCPMC6836806

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.