Evidence map›Paper›PMID 37144009›Full record

ArticleBMJ neurology open2023

Modelling accessibility of adult neurology care in Australia, 2020-2034.

Steve Simpson-Yap, Federico Frascoli, Lucinda Harrison, Charles Malpas, James Burrell, Nicholas Child, Lauren P Giles, Christian Lueck, Merrilee Needham, Benjamin Tsang and 1 more

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ neurology open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Gaps in biomedical research in frontotemporal dementia: A call for diversity and disparities focused research.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Review
  5. Review
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Steve Simpson-YapCORe, Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia.ORCID 0000-0001-6521-3056
Federico FrascoliDepartment of Mathematics Computing, and Engineering Technologies, Swinburne University of Technology, Hawthorn, Victoria, Australia.
Lucinda HarrisonCORe, Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia.
Charles MalpasCORe, Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia.
James BurrellConcord Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
Nicholas ChildNeurology North Shore, Auckland, New Zealand.
Lauren P GilesDepartment of Neurology, Launceston General Hospital, Launceston, Tasmania, Australia.
Christian LueckSchool of Medicine and Psychology, Australian National University, Canberra, Australian Capital Territory, Australia.
Merrilee NeedhamCentre for Neuromuscular and Neurological Disorders, Perron Institute for Neurological and Translational Science, University of Western Australia, Nedlands, Western Australia, Australia.
Benjamin TsangNeurology, Sunshine Coast University Hospital, Sunshine Coast, Queensland, Australia.
Tomas KalincikCORe, Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: In 2015/2016, annual national expenditure on neurological conditions exceeded $A3 billion. However, a comprehensive study of the Australian neurological workforce and supply/demand dynamics has not previously been undertaken. Methods: Current neurological workforce was defined using neurologist survey and other sources. Workforce supply modelling used ordinary differential equations to simulate neurologist influx and attrition. Demand for neurology care was estimated by reference to literature regarding incidence and prevalence of selected conditions. Differences in supply versus demand for neurological workforce were calculated. Potential interventions to increase workforce were simulated and effects on supply versus demand estimated. Results: Modelling of the workforce from 2020 to 2034 predicted an increase in neurologist number from 620 to 89. We estimated a 2034 capacity of 638 024 Initial and 1 269 112 Review encounters annually, and deficits against demand estimated as 197 137 and 881 755, respectively. These deficits were proportionately greater in regional Australia, which has 31% of Australia's population (Australian Bureau of Statistics) but is served by only 4.1% of its neurologists as determined by our 2020 survey of Australia and New Zealand Association of Neurologists members. Nationally, simulated additions to the neurology workforce had some effect on the review encounter supply deficit (37.4%), but in Regional Australia, this impact was only 17.2%. Interpretation: Modelling of the neurologist workforce in Australia for 2020-2034 demonstrates a significant shortfall of supply relative to current and projected demand. Interventions to increase neurologist workforce may attenuate this shortfall but will not eliminate it. Thus, additional interventions are needed, including improved efficiency and additional use of support staff.

Indexed as

epidemiologyneuroepidemiologystatistics

Identifiers

PMID37144009
PMCPMC10151858

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.