Cancer causes & control : CCC · 2025
Determinants of breast screening participation using small-area data in South Australia: gaining past and future insights from geospatial evidence.
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Li Ming, van Gaans Deborah, Ahmed Muktar, Nguyen Anh-Minh, Reintals Michelle, Holmes Andrew, Roder David
Аннотация
PURPOSE: To 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.
METHODS: SA2 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.
RESULTS: BreastScreen 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).
CONCLUSION: Further 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.
METHODS: SA2 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.
RESULTS: BreastScreen 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).
CONCLUSION: Further 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.