Colorectal Cancer Screening in Adults 45-49: Provider Availability, CT Colonography Access, and Screening Rates
Liu-Galvin, R.; Xie, Z.; Hong, Y.-R.
Show abstract
BackgroundThe US Preventive Services Task Force updated colorectal cancer (CRC) screening guidelines in 2021, recommending screening for adults aged 45-49. This study aimed to evaluate CRC screening prevalence among this newly eligible population and examine associations with healthcare provider supply and CT colonography facility availability in 2022. MethodsUsing 2022 Behavioral Risk Factor Surveillance System data (n=25,592), we estimated CRC screening prevalence among adults aged 45-49. We examined associations between screening rates and state-level healthcare provider supply using 2021-2022 Area Health Resources File data. Spearman rank-order correlations assessed relationships between provider supply, CT colonography facility availability, and screening prevalence. ResultsOverall CRC screening prevalence was 34.5% (95% CI: 33.4%-35.8%). Endoscopic tests were most common (74.9%), followed by stool-based tests (9.3%) and CT colonography (0.5%). Significant variations in screening modalities were observed across sociodemographic factors. Gastroenterology physician supply positively correlated with overall CRC screening prevalence ({rho}=0.42, P=.002) and endoscopy screening prevalence ({rho}=0.39, P=.005). CT colonography facility availability weakly correlated with CT colonography screening prevalence ({rho}=0.18, P=.22). ConclusionsCRC screening rates among newly eligible adults aged 45-49 appear to be suboptimal in 2022. Disparities in screening methods across sociodemographic factors highlight potential access barriers. The association between gastroenterology physician supply and screening rates emphasizes the importance of addressing projected workforce shortages. Targeted efforts are needed to increase CRC screening uptake in this age group and ensure equitable access to screening services.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Deprivation and Segregation in Ovarian cancer survival among African American Women: a mediated analysis 91%
- Racial/Ethnic Disparities in the Observed COVID-19 Case Fatality Rate Among the U.S. Population 90%
- Epidemiology of urinary tract infection among community-living seniors aged 50 plus: population estimates and risk factors 90%
Similar papers in this journal
- Missing data in the medical record for oncology patients: prevalence and association with outcomes 91%
- The Impact of the “Muslim Ban” Executive Order on Healthcare Utilization in Minneapolis-St. Paul, Minnesota 90%
- COVID-19 outcomes, risk factors and associations by race: a comprehensive analysis using electronic health records data in Michigan Medicine 90%
Similar papers in this journal
- Non-endoscopic screening for Barrett’s esophagus and Esophageal Adenocarcinoma in at risk Veterans 91%
- Colorectal cancer screening based on predicted risk: a pilot randomized controlled trial 91%
- Incidence, prevalence, and survival of colorectal cancer in the United Kingdom from 2000-2021: a population-based cohort study 89%
Similar papers in this journal
- Factors Influencing Precision Medicine Knowledge and Attitudes 92%
- Relative contribution of COVID-19 vaccination and SARS-CoV-2 infection to population-level seroprevalence of SARS-CoV-2 spike antibodies in a large integrated health system 91%
- Unplanned Hospital Visits after Ambulatory Surgical Care 91%
Similar papers in this journal
- Impact of Screening and Follow-up Colonoscopy Adenoma Sensitivity on Colorectal Cancer Screening Outcomes in the CRC-AIM Microsimulation Model 92%
- Detecting PI3K and TP53 Pathway Disruptions in Early-Onset Colorectal Cancer Among Hispanic/Latino Patients 91%
- Associations of the 2018 World Cancer Research Fund/American Institute of Cancer Research (WCRF/AICR) Cancer Prevention Recommendations with Stages of Colorectal Carcinogenesis 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.