ASPO Abstracts
Development of a novel community connectedness classification and association with modifiable cancer risk factors in Southern Arizona: A spatial analysis.
Category: Lifestyles Behavior, Energy Balance & Chemoprevention
Conference Year: 2023
Abstract Body:
Purpose of the study: Develop and validate community connectedness classification (C3) to represent positive factors representing social determinants of health (SDOH) and evaluate the relationship between C3 and modifiable population level cancer risk factors in Southern Arizona (SAZ). Methods: Publicly available 2020 Census and 2018 Behavioral Risk Factor Surveillance System (BRFSS) data were normalized and merged by zip code. To represent positive SDOH factors, C3 was developed using principal components analysis and evaluated for reliability and validity. Spatial autoregressive (SAR) modeling with generalized spatial two-stage least-squares estimation was used to quantify the direct effects of C3 and modifiable cancer risk factors in SAZ (obesity, low fruit and vegetable intake, physical inactivity, smoking, and alcohol use) adjusting for demographics, technology access, and urban/rural designation. Geographic information systems (GIS) analysis using geographically weighted regressions with density modeling visualized relationships. Results: C3 demonstrated good reliability and validity. Items with high factor loadings included greater population percent that: 1) have higher household income, 2) are above the federal poverty line, 3) are considered food secure, 4) have internet access, 5) attained higher education, and 6) have a primary care provider. Values were classified into deciles for final C3 scores. A C3 score of 10 indicates communities with greater prosperity (high) while a 1 indicates communities with greater privation (low). SAR models indicated that C3 was significantly inversely associated with SAZ population level obesity (ß=-0.20; 95%CI: -0.35, -0.06), low fruit and vegetable intake (ß=-0.35; 95%CI: -0.51, -0.19), physical inactivity (ß=-0.32; 95%CI: -0.48, -0.16), and smoking (ß =-0.34; 95%CI: -0.62, -0.07). GIS revealed patterns of C3 clustering and modifiable cancer risk factors by zip code. Conclusions: C3 is directly inversely associated with population level modifiable cancer risk factors. Spatial analysis is a valuable tool for researchers in other US regions to inform policy, healthcare delivery, and intervention design to improve cancer outcomes locally.
Keywords: social determinants of health, cancer prevention, health behaviors, spatial analysis