title: Classification tree analysis to examine influences on colorectal cancer screening.
abstract: OBJECTIVE
  Identifying correlates of colorectal cancer screening (CRCS) is critical for cancer control and prevention. Classification tree analysis (CTA) is a potentially powerful analytic tool that can identify distinct population subgroups for which CRCS is influenced by any number of multivariable interactions. This study used CTA to identify correlates of CRCS for exclusive population subgroups.
  
  
  METHODS
  Data were obtained from the 2007 Health Information National Trends Survey (HINTS) and analyzed in 2014. CTA was employed to determine the association between demographic (n = 11), psychosocial (n = 6), and numeracy (n = 3) variables and CRCS status of adults ≥50 years (n = 3,769).
  
  
  RESULTS
  Overall CRCS rate was 66.9 %. Level of doctor avoidance (three categories) was the initial splitting variable, leading to a total of 21 terminal node subgroups of CRCS utilization: (1) avoid doctor, not for fear of illness/death [n = 625 (16.5 %), four subgroups]; (2) avoid doctor, fear illness/death [n = 366 (9.7 %), two subgroups]; (3) do not avoid doctor [n = 2,778 (73.7 %), 15 subgroups].
  
  
  CONCLUSIONS
  Doctor avoidance was an important behavioral influence on CRCS adherence. Use of CTA to identify unique characteristics within population subgroups has merit for tailoring future intervention strategies. Community-based approaches may be effective for reaching individuals who avoid routine doctor visits.
authors.affiliation: Department of Behavioral Health and Nutrition, University of Delaware, 26 North College Avenue, Newark, DE, 19716, USA, gdominic@udel.edu.
authors.forename: Gregory M
authors.initials: GM
authors.lastname: Dominick
authors.forename: Mia A
authors.initials: MA
authors.lastname: Papas
authors.forename: Michelle L
authors.initials: ML
authors.lastname: Rogers
authors.forename: William
authors.initials: W
authors.lastname: Rakowski
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D015179:Colorectal Neoplasms / Q000175:diagnosis* / Q000453:epidemiology / Q000523:psychology; D003430:Cross-Sectional Studies; D003625:Data Collection; D016208:Databases, Factual; D055088:Early Detection of Cancer / Q000379:methods* / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000379:methods* / Q000706:statistics & numerical data; D008875:Middle Aged; D015233:Models, Statistical; D013223:Statistics as Topic; D014481:United States