Trends in the Utilization of Breast, Cervical, and Colorectal Cancer Screening from 2010 to 2019 Among a Commercially Insured Population Using the MarketScan Commercial Claims Database
Sun, J.; Wat, R.; Frick, K. D.; Kong, X.; Liang, H.; Chow, C.; Shi, L.
Show abstract
Introduction: Breast, cervical, and colorectal cancer screening guidelines changed substantially between 2010 and 2019. We examined trends in the annual utilization of these screenings among commercially insured enrollees in the United States from 2010 to 2019 by age group, geographic region, and screening modality. Methods: We conducted a retrospective, serial cross-sectional analysis of the MarketScan Commercial Claims Database from 2010 through 2019, comprising approximately 141.2 million privately insured enrollees. Annual screening rates, defined as the proportion of eligible enrollees receiving a given test within each calendar year, were estimated for cervical, breast, and colorectal cancer using procedure codes, stratified by age group, screening modality, and geographic residence. These reflect annual utilization rather than up-to-date (guideline-concordant) screening. Temporal trends were evaluated using two-sided Poisson regression, and urban-rural disparities in 2019 were assessed using multivariate generalized estimating equations. Results: Cancer screening utilization remained stagnant or declined across all three cancer types over the study period. Among women aged 30-64 years, cervical cytology alone declined substantially from 28.2% in 2010 to 8.8% in 2019, while co-testing increased from 11.4% to 20.3%. Screening mammography among women aged 50-64 showed minimal change, remaining stable at 45.7% in 2010 and 45.8% in 2019. Colorectal cancer screening across enrollees aged <64 decreased modestly from 7.7% in 2010 to 6.5% in 2019, with a more pronounced decline among adults aged 45-49 years. Across all three cancer types, screening utilization was higher among urban residents than rural residents, with incidence rate ratios ranging from 1.02 to 1.05 in 2019. Conclusions: Utilization of cervical, breast, and colorectal cancer screening among commercially insured adults did not improve between 2010 and 2019. Persistent urban-rural disparities highlight ongoing gaps in preventive care delivery. Targeted interventions may help improve screening utilization, particularly in rural and underserved populations.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Use of Skin Cancer Procedures, Medicare Reimbursement, and Overall Expenditures, 2012-2017 92%
- COVID-19 outcomes, risk factors and associations by race: a comprehensive analysis using electronic health records data in Michigan Medicine 91%
- Disparities in COVID-19 Reported Incidence, Knowledge, and Behavior 91%
Similar papers in this journal
- Understanding motivations of older women to continue or discontinue breast cancer screening 94%
- Uptake of Cervical Cancer Screening and Its Associated Factors Among Women of Reproductive Age in Kericho County 92%
- Decreasing median age of COVID-19 cases in the United States: changing epidemiology or changing surveillance? 91%
Similar papers in this journal
Similar papers in this journal
- Missing data and missed infections: Investigating racial and ethnic disparities in SARS-CoV-2 testing and infection rates in Holyoke, Massachusetts 90%
- Obtaining prevalence estimates of COVID-19: A model to inform decision-making 90%
- Risk of COVID-19 Reinfection and Vaccine Breakthrough Infection, Madera County, California, 2021 90%
Similar papers in this journal
- Deprivation and Segregation in Ovarian cancer survival among African American Women: a mediated analysis 92%
- Racial/Ethnic Disparities in the Observed COVID-19 Case Fatality Rate Among the U.S. Population 91%
- COVID-19 Mortality in California Based on Death Certificates: Disproportionate Impacts Across Racial/Ethnic Groups and Nativity 91%
"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.