Evidence map›Paper›PMID 36519949›Full record

ArticleProceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data2019

Using hospital administrative data to infer patient-patient contact via the consistent co-presence algorithm.

Jeffrey Lienert, Felix Reed-Tsochas, Laura Koehly, Christopher Steven Marcum

Abstract read
In one paragraph

Article in Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Jeffrey LienertDepartment of General Medicine, Perelman School of Medicine, Philadelphia, USA.
Felix Reed-TsochasSaïd Business School, University of Oxford, Oxford, UK.
Laura KoehlySocial and Behavioral Research Branch, National Human Genome Research Institute, Bethesda, USA.
Christopher Steven MarcumSocial and Behavioral Research Branch, National Human Genome Research Institute, Bethesda, USA.

Funding

Methodological DevelopmentZIAHG200397 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI KOEHLY, LAURA M · 2016 to 2025
$1.7M
Intramural NIH HHS ZIA HG200397
6 · The paper itself

Abstract

In health care settings, patients who are physically proximate to other patients (co-presence) for a meaningful amount of time may have differential health outcomes depending on who they are in contact with. How to best measure this co-presence, however is an open question and previous approaches have limitations that may make them inappropriate for complex health care settings. Here, we introduce a novel method which we term "consistent co-presence", that

Indexed as

affiliation networkshospital administrative dataspatial networkstemporal networks

Identifiers

PMID36519949
PMCPMC9745640

What OpenQuestion holds

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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.