Evidence map›Paper›PMID 38395852›Full record

ArticleBMC health services research2024

Simulating the healthcare workforce impact and capacity for pancreatic cancer care in Victoria: a model-based analysis.

Lan Gao, Anna Ugalde, Patricia M Livingston, Victoria White, Jennifer J Watts, Hannah Jongebloed, Nikki McCaffrey, David Menzies, Suzanne Robinson

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.6field-weighted citation impact, top 31% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

9 authors at 1 institution in 1 country.

Lan GaoDeakin Health Economics, Institute of Health Transformation, Faculty of Health, Deakin University, 1 Gheringhap St, 3220, Geelong, Australia. lan.gao@deakin.edu.au.
Anna UgaldeSchool of Nursing & Midwifery, Institute of Health Transformation, Faculty of Health, Deakin University, Melbourne, Australia.
Patricia M LivingstonSchool of Nursing & Midwifery, Institute of Health Transformation, Faculty of Health, Deakin University, Melbourne, Australia.
Victoria WhiteSchool of Nursing & Midwifery, Institute of Health Transformation, Faculty of Health, Deakin University, Melbourne, Australia.
Jennifer J WattsDeakin Health Economics, Institute of Health Transformation, Faculty of Health, Deakin University, 1 Gheringhap St, 3220, Geelong, Australia.
Hannah JongebloedSchool of Nursing & Midwifery, Institute of Health Transformation, Faculty of Health, Deakin University, Melbourne, Australia.
Nikki McCaffreyDeakin Health Economics, Institute of Health Transformation, Faculty of Health, Deakin University, 1 Gheringhap St, 3220, Geelong, Australia.
David MenziesPanSupport Pancare Foundation, Melbourne, Australia.
Suzanne RobinsonDeakin Health Economics, Institute of Health Transformation, Faculty of Health, Deakin University, 1 Gheringhap St, 3220, Geelong, Australia.
Deakin University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe incidence of pancreatic cancer is rising. With improvements in knowledge for screening and early detection, earlier detection of pancreatic cancer will continue to be more common. To support workforce planning, our aim is to perform a model-based analysis that simulates the potential impact on the healthcare workforce, assuming an earlier diagnosis of pancreatic cancer.

methodsWe developed a simulation model to estimate the demand (i.e. new cases of pancreatic cancer) and supply (i.e. the healthcare workforce including general surgeons, medical oncologists, radiation oncologists, pain medicine physicians, and palliative care physicians) between 2023 and 2027 in Victoria, Australia. The model compares the current scenario to one in which pancreatic cancer is diagnosed at an earlier stage. The incidence of pancreatic cancer in Victoria, five-year survival rates, and Victoria's population size were obtained from Victorian Cancer Registry, Cancer Council NSW, and Australian Bureau of Statistics respectively. The healthcare workforce data were sourced from the Australian Government Department of Health and Aged Care's Health Workforce Data. The model was constructed at the remoteness level. We analysed the new cases and the number of healthcare workforce by profession together to assess the impact on the healthcare workforce.

resultsIn the status quo, over the next five years, there will be 198 to 220 stages I-II, 297 to 330 stage III, and 495 to 550 stage IV pancreatic cancer cases diagnosed annually, respectively. Assuming 20-70% of the shift towards pancreatic cancer's earlier diagnosis (shifting from stage IV to stages I-II pancreatic cancer within one year), the stages I-II cases could increase to 351 to 390 or 598 to 665 per year. The shift to early diagnosis led to substantial survival gains, translating into an additional 284 or 795 out of 5246 patients with pancreatic cancer remaining alive up to year 5 post-diagnosis. Workforce supply decreases significantly by the remoteness levels, and remote areas face a shortage of key medical professionals registered in delivering pancreatic cancer care, suggesting travel necessities by patients or clinicians.

conclusionImproving the early detection and diagnosis of pancreatic cancer is expected to bring significant survival benefits, although there are workforce distribution imbalances in Victoria that may affect the ability to achieve the anticipated survival gain.

Indexed as

Pancreatic NeoplasmsPhysiciansAgedHealth WorkforceHumansVictoriaWorkforceDecision-makingModellingPancreatic cancerScreening toolWorkforce planning

Identifiers

PMID38395852
PMCPMC10893744
OpenAlexW4392099004

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.