Evidence map›Paper›PMID 18826813›Full record

ArticleEmerging infectious diseases2008

Pandemic influenza and excess intensive-care workload.

Raoul E Nap, Maarten P H M Andriessen, Nico E L Meessen, Dinis dos Reis Miranda, Tjip S van der Werf

Open access · diamondAbstract read
In one paragraph

Article in Emerging infectious diseases, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 5 pooled it
2.5field-weighted citation impact, top 10% 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

15 citing papers in PubMed, 5 syntheses or guidelines pooled it, 29 citations in OpenAlex.

  1. Pooled it
  2. Guideline
  3. Pooled it
  4. Guideline
  5. Guideline
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  12. An algorithm to diagnose influenza infection: evaluating the clinical importance and impact on hospital costs of screening with rapid antigen detection tests.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2015
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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 at 1 institution in 1 country.

Raoul E NapUniversity of Groningen, Groningen, The Netherlands. r.e.nap@rvb.umcg.nl
Maarten P H M Andriessen
Nico E L Meessen
Dinis dos Reis Miranda
Tjip S van der Werf
University of Groningen · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In The Netherlands a major part of preparedness planning for an epidemic or pandemic consists of maintaining essential public services, e.g., by the police, fire departments, army personnel, and healthcare workers. We provide estimates for peak demand for healthcare workers, factoring in healthcare worker absenteeism and using estimates from published epidemiologic models on the expected evolution of pandemic influenza in relation to the impact on peak surge capacity of healthcare facilities and intensive care units (ICUs). Using various published scenarios, we estimate their effect in increasing the availability of healthcare workers for duty during a pandemic. We show that even during the peak of the pandemic, all patients requiring hospital and ICU admission can be served, including those who have non-influenza-related conditions. For this rigorous task differentiation, clear hierarchical management, unambiguous communication, and discipline are essential and we recommend informing and training non-ICU healthcare workers for duties in the ICU.

Indexed as

Critical CareDisease OutbreaksComputer SimulationHealth PersonnelHealth PlanningHumansInfluenza, HumanNetherlandsWorkforceWorkload

Identifiers

PMID18826813
PMCPMC2609860
OpenAlexW2127856288

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.