Evidence map›Paper›PMID 37956143›Full record

ArticlePloS one2023

Disease mapping: Geographic differences in population rates of interventional treatment for prostate cancer in Australia.

Jessica K Cameron, Upeksha Chandrasiri, Jeremy Millar, Joanne F Aitken, Susanna Cramb, Jeff Dunn, Mark Frydenberg, Prem Rashid, Kerrie Mengersen, Suzanne K Chambers and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.7field-weighted citation impact, top 22% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

12 authors at 7 institutions in 1 country.

Jessica K CameronCentre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia.ORCID 0000-0002-8161-0358
Upeksha ChandrasiriCancer Council Queensland, Spring Hill, Queensland, Australia.
Jeremy MillarCentral Clinical School, Monash University, Clayton, Victoria, Australia.
Joanne F AitkenCancer Council Queensland, Spring Hill, Queensland, Australia.
Susanna CrambCentre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia.ORCID 0000-0001-9041-9531
Jeff DunnProstate Cancer Foundation Australia, St Leonards, New South Wales, Australia.
Mark FrydenbergDepartment of Surgery, Monash University, Clayton, Victoria, Australia.
Prem RashidMedicine & Health, University of New South Wales, Randwick, New South Wales, Australia.
Kerrie MengersenCentre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia.
Suzanne K ChambersHealth Sciences, Australian Catholic University, Banyo, Queensland, Australia.
Peter D BaadeCentre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia.
David P SmithThe Daffodil Centre, The University of Sydney, a Joint Venture with Cancer Council NSW, Sydney, New South Wales (NSW), Australia.
Queensland University of Technology · AUGriffith University · AUMonash University · AUCancer Council Queensland · AUProstate Cancer Foundation of Australia · AUThe University of Sydney · AUUNSW Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTreatment decisions for men diagnosed with prostate cancer depend on a range of clinical and patient characteristics such as disease stage, age, general health, risk of side effects and access. Associations between treatment patterns and area-level factors such as remoteness and socioeconomic disadvantage have been observed in many countries.

objectiveTo model spatial differences in interventional treatment rates for prostate cancer at high spatial resolution to inform policy and decision-making.

methodsHospital separations data for interventional treatments for prostate cancer (radical prostatectomy, low dose rate and high dose rate brachytherapy) for men aged 40 years and over were modelled using spatial models, generalised linear mixed models, maximised excess events tests and k-means statistical clustering.

resultsGeographic differences in population rates of interventional treatments were found (p<0.001). Separation rates for radical prostatectomy were lower in remote areas (12.2 per 10 000 person-years compared with 15.0-15.9 in regional and major city areas). Rates for all treatments decreased with increasing socioeconomic disadvantage (radical prostatectomy 19.1 /10 000 person-years in the most advantaged areas compared with 12.9 in the most disadvantaged areas). Three groups of similar areas were identified: those with higher rates of radical prostatectomy, those with higher rates of low dose brachytherapy, and those with low interventional treatment rates but higher rates of excess deaths. The most disadvantaged areas and remote areas tended to be in the latter group.

conclusionsThe geographic differences in treatment rates may partly reflect differences in patients' physical and financial access to treatments. Treatment rates also depend on diagnosis rates and thus reflect variation in investigation rates for prostate cancer and presentation of disease. Spatial variation in interventional treatments may aid identification of areas of under-treatment or over-treatment.

Indexed as

BrachytherapyProstatic NeoplasmsAdultAustraliaHumansMaleMiddle AgedProstateProstatectomyProstate-Specific AntigenProstate-Specific Antigen

Identifiers

PMID37956143
PMCPMC10642787
OpenAlexW4388626020

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.