Evidence map›Paper›PMID 38156849›Full record

ArticleAsian Pacific journal of cancer prevention : APJCP2023

Exploring Disparities in Breast Cancer Screening: An Ecological Analysis of Australian Data.

Scott Baum

Abstract read
In one paragraph

Article in Asian Pacific journal of cancer prevention : APJCP, 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
–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

1 citing paper in PubMed.

  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

1 author.

Scott BaumSchool of Engineering and Built Environment, Griffith University, Australia.ORCID 0000-0003-1711-2087

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe objective of this study is to analyse the socio-spatial patterns of breast cancer screening across Australian regions.

methodsThe research is an ecological study. Data for breast screening participation and associated social and demographic factors are obtained from the Australian Institute of Health and Welfare and the Australian Bureau of Statistics. The unit of measurement for the analysis is spatially aggregated regions (Statistical Area 3). Geographically weighted regression is used to analyse the relationship between a dependent variable and one or more independent variables while considering the spatial or geographic relationships among the data points.

resultsGlobally, there was a significant (p<0.05) association between screening participation and income, English ability, education level, Indigenous background, and transport availability. The geographically weighted regression model represented an improved fit with a higher R2 (R2=0.89) and the Akaike information criterion (AIC) has improved, (AIC= 391.92). The Monte-Carlo tests for spatial variability were significant for all independent variables (p<0.05). Visually, there was marked spatial variation in the association between breast cancer screening rates and the significant independent variables from the global model.

conclusionThe identification of significant spatial variability in the association between breast cancer screening participation and important social and demographic factors provides important input into the design of programs aimed at increasing participation in screening regimes.

Indexed as

Breast NeoplasmsAustraliaBreastEarly Detection of CancerFemaleHumansIncomeBreast Cancer ScreeningEarly Detection of CancerSpatial Regression, Australia

Identifiers

PMID38156849
PMCPMC10909116

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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.