Socio-demographic factors drive regional differences in participation in the National Bowel Cancer Screening Program - An ecological analysis

Aust N Z J Public Health. 2018 Feb;42(1):92-97. doi: 10.1111/1753-6405.12722. Epub 2017 Oct 18.

Abstract

Objective: To examine if geographic variations in the participation rates in the National Bowel Cancer Screening Program (NBCSP) are related to population-level socio-demographic characteristics.

Methods: Data reflecting participation in the NBCSP for 504 Local Government Areas (LGAs) between July 2011 and June 2013 were extracted from the Social Health Atlas of Australia. Logistic regression models were used to examine independent associations (odds ratios [ORs]) between participation, Remoteness Area (RA) and selected socio-demographic variables.

Results: Compared to the participation rate for major cities (33.4%), participation was significantly higher in inner regional areas (36.5%, OR=1.15), but was much lower in remote (27.9%, OR=0.77) or very remote areas (25.0%, OR=0.65). When controlling for study period, gender, proportion of persons aged 65 years and older, Indigenous status, cultural background and socioeconomic status, significantly higher rates were observed in all non-metropolitan areas than in major cities. Indigenous status was strongly related to the poorer participation in remote areas.

Conclusions: Socio-demographic characteristics, particularly Indigenous status, cultural background and population ageing, seem to be more important drivers of regional disparities in NBCSP participation than geographic remoteness. Implications for public health: This study provides important evidence to understand the regional disparities in participating in the national screening program.

Keywords: NBCSP; bowel cancer; colorectal cancer; remoteness; screening.

MeSH terms

  • Aged
  • Australia
  • Colorectal Neoplasms / prevention & control*
  • Early Detection of Cancer / statistics & numerical data*
  • Female
  • Healthcare Disparities / statistics & numerical data*
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • National Health Programs*
  • Residence Characteristics / statistics & numerical data*
  • Socioeconomic Factors