Evidence map›Paper›PMID 38115064›Full record

ArticleInternational journal of health geographics2023

Mapping the prevalence of cancer risk factors at the small area level in Australia.

James Hogg, Jessica Cameron, Susanna Cramb, Peter Baade, Kerrie Mengersen

Abstract read
In one paragraph

Article in International journal of health geographics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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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.

James HoggCentre for Data Science, Queensland University of Technology (QUT), 2 George St, Brisbane City, Queensland, 4000, Australia. james.hogg@hdr.qut.edu.au.
Jessica CameronCentre for Data Science, Queensland University of Technology (QUT), 2 George St, Brisbane City, Queensland, 4000, Australia.
Susanna CrambCentre for Data Science, Queensland University of Technology (QUT), 2 George St, Brisbane City, Queensland, 4000, Australia.
Peter BaadeCentre for Data Science, Queensland University of Technology (QUT), 2 George St, Brisbane City, Queensland, 4000, Australia.
Kerrie MengersenCentre for Data Science, Queensland University of Technology (QUT), 2 George St, Brisbane City, Queensland, 4000, Australia.

Funding

National Health and Medical Research Council Investigator Grant 2008313
6 · The paper itself

Abstract

backgroundCancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area-level data on cancer risk factors with high resolution and complete reach, which hinders the development of targeted prevention strategies.

methodsUsing Australia as a case study, the 2017-2018 National Health Survey was used to generate prevalence estimates for 2221 small areas across Australia for eight cancer risk factor measures covering smoking, alcohol, physical activity, diet and weight. Utilising a recently developed Bayesian two-stage small area estimation methodology, the model incorporated survey-only covariates, spatial smoothing and hierarchical modelling techniques, along with a vast array of small area-level auxiliary data, including census, remoteness, and socioeconomic data. The models borrowed strength from previously published cancer risk estimates provided by the Social Health Atlases of Australia. Estimates were internally and externally validated.

resultsWe illustrated that in 2017-2018 health behaviours across Australia exhibited more spatial disparities than previously realised by improving the reach and resolution of formerly published cancer risk factors. The derived estimates revealed higher prevalence of unhealthy behaviours in more remote areas, and areas of lower socioeconomic status; a trend that aligned well with previous work.

conclusionsOur study addresses the gaps in small area level cancer risk factor estimates in Australia. The new estimates provide improved spatial resolution and reach and will enable more targeted cancer prevention strategies at the small area level. Furthermore, by including the results in the next release of the Australian Cancer Atlas, which currently provides small area level estimates of cancer incidence and relative survival, this work will help to provide a more comprehensive picture of cancer in Australia by supporting policy makers, researchers, and the general public in understanding the spatial distribution of cancer risk factors. The methodology applied in this work is generalisable to other small area estimation applications and has been shown to perform well when the survey data are sparse.

Indexed as

NeoplasmsAustraliaBayes TheoremHumansPrevalenceRisk FactorsBayesian statisticsCancer preventionDisease mappingSmall area analysis

Identifiers

PMID38115064
PMCPMC10729400

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Registered trials

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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.