BACKGROUND AND AIMS: The goals of this study are to evaluate determinants of the time in the medical system until a colorectal cancer diagnosis and to explore characteristics associated with stage at diagnosis. METHODS: We examined medical records and survey data for 468 patients with colorectal cancer at 15 Veterans Affairs medical centers. Patients were classified as screen-detected, bleeding-detected, or other (resulting from the evaluation of another medical concern). Patients who presented emergently with obstruction or perforation were excluded. We used Cox proportional hazards models to determine predictors of time in the medical system until diagnosis. Logistic regression models were used to determine predictors of stage at diagnosis. RESULTS: We excluded 21 subjects who presented emergently, leaving 447 subjects; the mean age was 67 years and 98% were male, 66% Caucasian, and 43% stage I or II. Diagnosis was by screening for 39%, bleeding symptoms for 27%, and other for 34%. The median times to diagnosis were 73-91 days and were not significantly different by diagnostic category. In the multivariable model for time to diagnosis, older age, having comorbidities, and Atlantic region were associated with a longer time to diagnosis. In the multivariable model for stage-at-diagnosis, only the diagnostic category was associated with stage; the screen-detected category was associated with decreased risk of late-stage cancer. CONCLUSIONS: Our results point to several factors associated with a longer time from the initial clinical event until diagnosis. This increased time in the health care system did not clearly translate into more advanced disease at diagnosis.
BACKGROUND AND AIMS: The goals of this study are to evaluate determinants of the time in the medical system until a colorectal cancer diagnosis and to explore characteristics associated with stage at diagnosis. METHODS: We examined medical records and survey data for 468 patients with colorectal cancer at 15 Veterans Affairs medical centers. Patients were classified as screen-detected, bleeding-detected, or other (resulting from the evaluation of another medical concern). Patients who presented emergently with obstruction or perforation were excluded. We used Cox proportional hazards models to determine predictors of time in the medical system until diagnosis. Logistic regression models were used to determine predictors of stage at diagnosis. RESULTS: We excluded 21 subjects who presented emergently, leaving 447 subjects; the mean age was 67 years and 98% were male, 66% Caucasian, and 43% stage I or II. Diagnosis was by screening for 39%, bleeding symptoms for 27%, and other for 34%. The median times to diagnosis were 73-91 days and were not significantly different by diagnostic category. In the multivariable model for time to diagnosis, older age, having comorbidities, and Atlantic region were associated with a longer time to diagnosis. In the multivariable model for stage-at-diagnosis, only the diagnostic category was associated with stage; the screen-detected category was associated with decreased risk of late-stage cancer. CONCLUSIONS: Our results point to several factors associated with a longer time from the initial clinical event until diagnosis. This increased time in the health care system did not clearly translate into more advanced disease at diagnosis.
Authors: E S Fisher; J E Wennberg; T A Stukel; J S Skinner; S M Sharp; J L Freeman; A M Gittelsohn Journal: Health Serv Res Date: 2000-02 Impact factor: 3.402
Authors: J D Hardcastle; J O Chamberlain; M H Robinson; S M Moss; S S Amar; T W Balfour; P D James; C M Mangham Journal: Lancet Date: 1996-11-30 Impact factor: 79.321
Authors: J Wattacheril; J R Kramer; P Richardson; B D Havemann; L K Green; A Le; H B El-Serag Journal: Aliment Pharmacol Ther Date: 2008-08-08 Impact factor: 8.171
Authors: Mark Corkum; Robin Urquhart; Cynthia Kendell; Fred Burge; Geoffrey Porter; Grace Johnston Journal: Cancer Causes Control Date: 2011-11-20 Impact factor: 2.506
Authors: Sanja Percac-Lima; Lydia E Pace; Kevin H Nguyen; Charis N Crofton; Katharine A Normandin; Sara J Singer; Meredith B Rosenthal; Alyna T Chien Journal: J Gen Intern Med Date: 2018-01-04 Impact factor: 5.128
Authors: Melissa R Partin; Diana J Burgess; James F Burgess; Amy Gravely; David Haggstrom; Sarah E Lillie; Sean Nugent; Adam A Powell; Aasma Shaukat; Louise C Walter; David B Nelson Journal: Cancer Epidemiol Biomarkers Prev Date: 2014-12-03 Impact factor: 4.254
Authors: Jochim S Terhaar sive Droste; Frank A Oort; René W M van der Hulst; Veerle M H Coupé; Mike E Craanen; Gerrit A Meijer; Linde M Morsink; Otto Visser; Roy L J van Wanrooij; Chris J J Mulder Journal: BMC Cancer Date: 2010-06-28 Impact factor: 4.430
Authors: Leah L Zullig; George L Jackson; Morris Weinberger; Dawn Provenzale; Bryce B Reeve; William R Carpenter Journal: Clin Colorectal Cancer Date: 2013-08-27 Impact factor: 4.481