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Published on: September 27, 2024
Socioeconomic determinants of cancer screening adherence among cancer survivors: analysis from the 2020 Behavioral
Edoardo Beatrici1,2, Zhiyu Qian1, Dejan K Filipas1,3
1Department of Urology and Center for Surgery and Public Health, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, United States.
Background:
Factors associated with cancer survivors' preventive health behaviors are understudied. We hypothesized that socioeconomic and health-care access factors may be associated with adherence to recommended cancer screenings.
Methods:
We conducted a cross-sectional analysis using the 2020 Behavioral Risk Factor Surveillance System. Cancer survivors eligible for United States Preventive Services Task Force-recommended breast, cervical, prostate, and colorectal screenings were included. Multivariable logistic regression models were used to identify socioeconomic factors significantly associated with screening adherence.
Results:
Overall, 64 958 (weighted national estimate = 29 066 143) cancer survivors were included. Adherence rates varied across cancer types: 80.9% for breast, 88.9% for cervical, 54.1% for prostate, and 84.7% for colorectal cancer. Key predictors of low adherence included lower income (breast: adjusted odds ratio [aOR] = 0.56, 95% confidence interval [CI] = 0.43 to 0.74; cervical: aOR = 0.38, 95% CI = 0.24 to 0.59; prostate: aOR = 0.36, 95% CI = 0.24 to 0.52; colorectal: aOR = 0.74, 95% CI = 0.57 to 0.96), lack of health-care coverage for colorectal cancer (aOR = 0.51, 95% CI = 0.36 to 0.73), time since last checkup between 1 and 2 years prior for breast (aOR = 0.58, 95% CI = 0.45 to 0.75), prostate (aOR = 0.66, 95% CI = 0.47 to 0.91), and colorectal (aOR = 0.69, 95% CI = 0.56 to 0.86) cancer, and no health-care provider for breast (aOR = 0.68, 95% CI = 0.47 to 0.98), prostate (aOR = 0.45, 95% CI = 0.31 to 0.65), and colorectal (aOR = 0.51, 95% CI = 0.40 to 0.66) cancer.
Conclusion:
Cancer survivors' adherence to screening is associated with factors including lack of health-care coverage, lower income, time since the last exam, and having a personal provider. Targeted interventions accounting for such factors may help mitigate these disparities.
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