Evidence map›Paper›PMID 31570101›Full record

ArticleInternational journal of health geographics2019

Development of the Australian Cancer Atlas: spatial modelling, visualisation, and reporting of estimates.

Earl W Duncan, Susanna M Cramb, Joanne F Aitken, Kerrie L Mengersen, Peter D Baade

Abstract read
In one paragraph

Article in International journal of health geographics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  11. Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023
    Article
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  16. A Review of Models Used for Investigating Barriers to Healthcare Access in Australia.International journal of environmental research and public health · 2020
    Review
  17. Geographical Disparities in Screening and Cancer-Related Health Behaviour.International journal of environmental research and public health · 2020
    Article
  18. 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

5 authors.

Earl W DuncanARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology (QUT), Brisbane, Australia.
Susanna M CrambARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology (QUT), Brisbane, Australia.
Joanne F AitkenCancer Council Queensland, PO Box 201, Spring Hill, Brisbane, QLD, 4004, Australia.
Kerrie L MengersenARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology (QUT), Brisbane, Australia.
Peter D BaadeCancer Council Queensland, PO Box 201, Spring Hill, Brisbane, QLD, 4004, Australia. PeterBaade@cancerqld.org.au.ORCID 0000-0001-8576-8868

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt is well known that the burden caused by cancer can vary geographically, which may relate to differences in health, economics or lifestyle. However, to date, there was no comprehensive picture of how the cancer burden, measured by cancer incidence and survival, varied by small geographical area across Australia.

methodsThe Atlas consists of 2148 Statistical Areas level 2 across Australia defined by the Australian Statistical Geography Standard which provide the best compromise between small population and small area. Cancer burden was estimated for males, females, and persons separately, with 50 unique sex-specific (males, females, all persons) cancer types analysed. Incidence and relative survival were modelled with Bayesian spatial models using the Leroux prior which was carefully selected to provide adequate spatial smoothing while reflecting genuine geographic variation. Markov Chain Monte Carlo estimation was used because it facilitates quantifying the uncertainty of the posterior estimates numerically and visually.

resultsThe results of the statistical model and visualisation development were published through the release of the Australian Cancer Atlas ( https://atlas.cancer.org.au ) in September, 2018. The Australian Cancer Atlas provides the first freely available, digital, interactive picture of cancer incidence and survival at the small geographical level across Australia with a focus on incorporating uncertainty, while also providing the tools necessary for accurate estimation and appropriate interpretation and decision making.

conclusionsThe success of the Atlas will be measured by how widely it is used by key stakeholders to guide research and inform decision making. It is hoped that the Atlas and the methodology behind it motivates new research opportunities that lead to improvements in our understanding of the geographical patterns of cancer burden, possible causes or risk factors, and the reasons for differences in variation between cancer types, both within Australia and globally. Future versions of the Atlas are planned to include new data sources to include indicators such as cancer screening and treatment, and extensions to the statistical methods to incorporate changes in geographical patterns over time.

Indexed as

Atlases as TopicGeographic Information SystemsModels, StatisticalAustraliaFemaleGeographic MappingHumansMaleMonte Carlo MethodNeoplasmsAustralian Cancer AtlasCancer incidenceRelative survivalSpatial smoothing

Identifiers

PMID31570101
PMCPMC6771109

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.