Evidence map›Paper›PMID 34530780›Full record

ArticleBMC public health2021

The analysis of the geographical distribution of emergency departments' frequent users: a tool to prioritize public health policies?

Romain Hellmann, Anne-Laure Feral-Pierssens, Alain Michault, Enrique Casalino, Agnès Ricard-Hibon, Frederic Adnet, Dominique Brun-Ney, Donia Bouzid, Axelle Menu, Mathias Wargon

Abstract read
In one paragraph

Article in BMC public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Frequent Users in Psychiatric Consultations: A 6-Year Retrospective Study in the Emergency Department.International journal of environmental research and public health · 2025
    Observational
  6. Article
  7. Observational
  8. Observational
  9. Observational
  10. Article
  11. 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

10 authors.

Romain HellmannHealth Regional Agency of Ile de France, Paris, France.
Anne-Laure Feral-PierssensSAMU 93 - Emergency Department, Avicenne hospital, Assistance Publique-Hôpitaux de Paris, Bobigny, France. feralal@gmail.com.
Alain MichaultHealth Regional Agency of Ile de France, Paris, France.
Enrique CasalinoEmergency Department, Bichat hospital, Assistance Publique-Hôpitaux de Paris, Paris, France.
Agnès Ricard-HibonEmergency Department, Centre hospitalier René Dubos, Pontoise, France.
Frederic AdnetSAMU 93 - Emergency Department, Avicenne hospital, Assistance Publique-Hôpitaux de Paris, Bobigny, France.
Dominique Brun-NeyDirection de l'organisation médicale et des relations avec l'université, Assistance Publique-Hôpitaux de Paris, Paris, France.
Donia BouzidEmergency Department, Bichat hospital, Assistance Publique-Hôpitaux de Paris, Paris, France.
Axelle MenuHealth Regional Agency of Ile de France, Paris, France.
Mathias WargonEmergency Department, Centre Hospitalier de Saint-Denis, Saint-Denis, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe individual factors associated to Frequent Users (FUs) in Emergency Departments are well known. However, the characteristics of their geographical distribution and how territorial specificities are associated and intertwined with ED use are limited. Investigating healthcare use and territorial factors would help targeting local health policies. We aim at describing the geographical distribution of ED's FUs within the Paris region.

methodsWe performed a retrospective analysis of all ED visits in the Paris region in 2015. Data were collected from the universal health insurance's claims database. Frequent Users (FUs) were defined as having visited ≥3 times any ED of the region over the period. We assessed the FUs rate in each geographical unit (GU) and assessed correlations between FUs rate and socio-demographics and economic characteristics of GUs. We also performed a multidimensional analysis and a principal component analysis to identify a typology of territories to describe and target the FUs phenomenon.

resultsFUs accounted for 278,687 (11.7%) of the 2,382,802 patients who visited the ED, living in 232 GUs. In the region, median FUs rate in each GU was 11.0% [interquartile range: 9.5-12.5]. High FUs rate was correlated to the territorial markers of social deprivation. Three different categories of GU were identified with different profiles of healthcare providers densities.

conclusionFUs rate varies between territories and is correlated to territorial markers of social deprivation. Targeted public policies should focus on disadvantaged territories.

Indexed as

Emergency Service, HospitalVulnerable PopulationsHealth PolicyHumansPublic PolicyRetrospective StudiesAccess to careEmergency departmentFrequent usersHealthcare useHealth geography

Identifiers

PMID34530780
PMCPMC8447576

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.