ArticleCancer causes & control : CCC2025
Determinants of breast screening participation using small-area data in South Australia: gaining past and future insights from geospatial evidence.
Article in Cancer causes & control : CCC, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
purposeTo profile breast screening participation at small-area (SA2) level in South Australia (SA) and capture local variations in socio-economic factors, access to healthcare, and cultural influences screening behaviors in ways that larger administrative units might overlook.
methodsSA2 demographic (2016 Census) and breast screening data in SA (2014-2015) were linked and analyzed. The dependent variable, biennial screening participation (ages 50-74 years), was classified as "low" if below the SA-wide biennial participation rate of 58%. Independent variables included SA2-level sociodemographic factors (e.g., socio-economic status, residential remoteness, country of birth) derived from Census data. Stepwise multivariable logistic regression was used to estimate the adjusted odds ratios (aORs) for low screening participation associated with SA2 demographic characteristics.
resultsBreastScreen SA participation for the 164 SA2 areas was 50.6%, ranging from 41.1% for ages 55-59 to 67.8% for ages 60-64. Indicators of low participation included disadvantaged socio-economic quintile (aOR increasing to 17.00, 95% CI 9.84-29.36 for quintiles 3-5 compared with the least disadvantaged quintile 1), non-metropolitan residence (aOR 4.94, 95% CI 2.30-10.60), and mortgage/rental stress in low-income households (aOR increasing to 6.59, 95% CI 3.34-13.00 for the third compared with first stress tertile). Areas providing more unpaid care support for disabled/aged people had reduced odds of low screening participation (aOR 0.41, 95% CI 0.24-0.70). Characteristics indicating low odds of low screening included a higher proportion of Australian born (tertile 2, aOR 0.52, 95% CI 0.30-0.88, and tertile 3, aOR 0.27, 95% CI 0.11-0.67).
conclusionFurther model that aims to improve breast screening participation need to be explored at both individual and SA2 levels. Potential cultural and linguistically diverse (CALD), Indigenous, and socio-economic indicators could be drawn from the newly available ABS-managed PLIDA platform. More contemporary SA2 and screening data should also be used for prospective evaluation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.