Sorting it Out: Pile Sorting as a Mixed Methodology for Exploring Barriers to Cancer Screening

Area of Science:

• Public Health
• Health Services Research
• Behavioral Science

Background:

• Colon cancer screening is crucial for early detection and improved outcomes.
• Understanding barriers to screening is essential for developing effective interventions, particularly in diverse populations.
• American Indian (AI) communities face unique challenges that may impact cancer screening rates.

Purpose of the Study:

• To present a novel mixed methodology for analyzing pile sorting data.
• To identify and categorize barriers to colon cancer screening within three AI communities.
• To compare the identified barrier clusters across different AI community sites.

Main Methods:

• A mixed-methods approach combining quantitative pile sorting with qualitative data analysis.
• Quantitative analysis involved cluster analysis and multidimensional scaling of 14 identified barriers.
• Qualitative data, collected by AI staff, informed the naming of quantitative clusters, with site differences assessed via permutation bootstrapping.

Main Results:

• Five distinct clusters of colon cancer screening barriers were identified consistently across all three AI communities.
• While the number of clusters was consistent, the specific barriers within each cluster showed minor variations between sites.
• Simulation analyses confirmed appropriate type I error rates and provided insights into statistical power based on cluster characteristics.

Conclusions:

• The developed mixed methodology is effective for analyzing pile sorting data in diverse community settings.
• The identification of consistent barrier clusters provides a foundation for culturally tailored colon cancer screening programs in AI communities.
• Further research can refine this methodology to enhance statistical power and address inter-site variability in barrier perceptions.