title: Counties eliminating racial disparities in colorectal cancer mortality.
abstract: Although colorectal cancer (CRC) mortality rates are declining, racial-ethnic disparities in CRC mortality nationally are widening. Herein, the authors attempted to identify county-level variations in this pattern, and to characterize counties with improving disparity trends.
  
  
  
  The authors examined 20-year trends in US county-level black-white disparities in CRC age-adjusted mortality rates during the study period between 1989 and 2010. Using a mixed linear model, counties were grouped into mutually exclusive patterns of black-white racial disparity trends in age-adjusted CRC mortality across 20 three-year rolling average data points. County-level characteristics from census data and from the Area Health Resources File were normalized and entered into a principal component analysis. Multinomial logistic regression models were used to test the relation between these factors (clusters of related contextual variables) and the disparity trend pattern group for each county.
  
  
  
  Counties were grouped into 4 disparity trend pattern groups: 1) persistent disparity (parallel black and white trend lines); 2) diverging (widening disparity); 3) sustained equality; and 4) converging (moving from disparate outcomes toward equality). The initial principal component analysis clustered the 82 independent variables into a smaller number of components, 6 of which explained 47% of the county-level variation in disparity trend patterns.
  
  
  
  County-level variation in social determinants, health care workforce, and health systems all were found to contribute to variations in cancer mortality disparity trend patterns from 1990 through 2010. Counties sustaining equality over time or moving from disparities to equality in cancer mortality suggest that disparities are not inevitable, and provide hope that more communities can achieve optimal and equitable cancer outcomes for all. Cancer 2016;122:1735-48. © 2016 American Cancer Society.
authors.affiliation: Department of Behavioral Sciences and Social Medicine, Florida State University College of Medicine, Tallahassee, FL.|Department of Community Health And Preventive Medicine, Morehouse School of Medicine, Atlanta, Georgia.
authors.forename: George
authors.initials: G
authors.lastname: Rust
authors.affiliation: Statistics and Methodology Department, NORC at the University of Chicago, Chicago, Illinois.
authors.forename: Shun
authors.initials: S
authors.lastname: Zhang
authors.affiliation: School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey.
authors.forename: Zhongyuan
authors.initials: Z
authors.lastname: Yu
authors.affiliation: Deparment of Community Health and Preventive Medicine, Morehouse School of Medicine, Atlanta, Georgia.
authors.forename: Lee
authors.initials: L
authors.lastname: Caplan
authors.affiliation: Department of Medicine, Morehouse School of Medicine, Atlanta, Georgia.
authors.forename: Sanjay
authors.initials: S
authors.lastname: Jain
authors.affiliation: Department of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia.
authors.forename: Turgay
authors.initials: T
authors.lastname: Ayer
authors.affiliation: School of Business and Management, Southern Adventist University, Collegedale, Tennessee.
authors.forename: Luceta
authors.initials: L
authors.lastname: McRoy
authors.affiliation: Department of Family and Community Medicine, Baylor College of Medicine, Houston, Texas.
authors.forename: Robert S
authors.initials: RS
authors.lastname: Levine
mesh_terms: D001741:Black or African American / Q000706:statistics & numerical data*; D000367:Age Factors; D044383:Black People / Q000706:statistics & numerical data*; D015179:Colorectal Neoplasms / Q000208:ethnology* / Q000401:mortality*; D062306:Geography, Medical / Q000706:statistics & numerical data; D006297:Health Services Accessibility / Q000706:statistics & numerical data; D006280:Health Workforce / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D009026:Mortality / Q000639:trends; D025341:Principal Component Analysis; D012044:Regression Analysis; D012959:Socioeconomic Factors; D013997:Time Factors; D014481:United States / Q000453:epidemiology; D044465:White People / Q000706:statistics & numerical data*
keywords: African American; black; cancer mortality; colorectal cancer; disparities; geographic variation; health equity; local-area variation; race; trends