Evidence map›Paper›PMID 30978264›Full record

ArticlePloS one2019

Improving ambulance coverage in a mixed urban-rural region in Norway using mathematical modeling.

Pieter L van den Berg, Peter Fiskerstrand, Karen Aardal, Jørgen Einerkjær, Trond Thoresen, Jo Røislien

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. The impact of concurrency conflicts on optimal location of air ambulance bases in Norway.Scandinavian journal of trauma, resuscitation and emergency medicine · 2025
    Article
  4. Article
  5. Observational
  6. Reorganization of an Emergency Medical System in a Mixed Urban-Rural Area.International journal of environmental research and public health · 2022
    Article
  7. Article
  8. Review
  9. Defining a mission-based method to determine a HEMS unit's actual service area.Scandinavian journal of trauma, resuscitation and emergency medicine · 2019
    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

6 authors.

Pieter L van den BergDepartment of Technology and Operations Management, Rotterdam School of Management, Erasmus University, Rotterdam, the Netherlands.ORCID 0000-0002-8550-6769
Peter FiskerstrandDepartment of EMS, Vestfold Hospital Trust, Tønsberg, Norway.
Karen AardalDelft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands.
Jørgen EinerkjærDepartment of EMS, Vestfold Hospital Trust, Tønsberg, Norway.
Trond ThoresenDepartment of EMS, Vestfold Hospital Trust, Tønsberg, Norway.
Jo RøislienFaculty of Health Sciences, University of Stavanger, Stavanger, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAmbulance services play a crucial role in providing pre-hospital emergency care. In order to ensure quick responses, the location of the bases, and the distribution of available ambulances among these bases, should be optimized. In mixed urban-rural areas, this optimization typically involves a trade-off between backup coverage in high-demand urban areas and single coverage in rural low-demand areas. The aim of this study was to find the optimal distribution of bases and ambulances in the Vestfold region of Norway in order to optimize ambulance coverage.

methodThe optimal location of bases and distribution of ambulances was estimated using the Maximum Expected Covering Location Model. A wide range of parameter settings were fitted, with the number of ambulances ranging from 1 to 15, and an average ambulance utilization of 0, 15, 35 and 50%, corresponding to the empirical numbers for night, afternoon and day, respectively. We performed the analysis both conditioned on the current base structure, and in a fully greenfield scenario.

resultsFour of the five current bases are located close to the mathematical optimum, with the exception of the northernmost base, in the rural part of the region. Moving this base, along with minor changes to the location of the four other bases, coverage can be increased from 93.46% to 97.51%. While the location of the bases is insensitive to the workload of the system, the distribution of the ambulances is not. The northernmost base should only be used if enough ambulances are available, and this required minimum number increases significantly with increasing system workload.

conclusionAs the load of the system increases, focus of the model shifts from providing single coverage in low-demand areas to backup coverage in high-demand areas. The classification rule for urban and rural areas significantly affects results and must be evaluated accordingly.

Indexed as

AmbulancesEmergency Medical ServicesHumansMathematical ConceptsModels, TheoreticalNorwayRural Health ServicesRural PopulationTime FactorsTravelUrban Health ServicesUrban Population

Identifiers

PMID30978264
PMCPMC6461285

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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.