BACKGROUND: One measure of the accuracy of cancer mortality statistics is the concordance between cancer defined as the underlying cause of death from death certificates and cancer diagnoses recorded in central, population-based cancer registries. Previous studies of such concordance are outdated. OBJECTIVE: To characterize the accuracy of cancer mortality statistics from the concordance between cancer cause of death and primary cancer site at diagnosis. DESIGN: Central cancer registry records from California, Colorado, and Idaho in the U.S. were linked with state vital statistics data and evaluated by demographic and tumor information across 79 site categories. A retrospective arm (confirmation rate per 100 deaths) compared death certificate data from 2002 to 2004 with cancer registry diagnoses from 1993 to 2004, while a prospective arm (detection rate per 100 deaths) compared cancer registry diagnoses from 1993 to 1995 with death certificate data from 1993 to 2004 by International Statistical Classification of Diseases and Related Health Problems (ICD) version used to code deaths. RESULTS: With n=265,863 deaths where cancer was recorded as the underlying cause based on the death certificate, the overall confirmation rate for ICD-10 was 82.8% (95% confidence interval [CI], 82.6-83.0%), the overall detection rate for ICD-10 was 81.0% (95% CI, 80.4-81.6%), and the overall detection rate for ICD-9 was 85.0% (95% CI, 84.8-85.2%). These rates varied across primary sites, where some rates were <50%, some were 95% or greater, and notable differences between confirmation and detection rates were observed. CONCLUSIONS: Important unique information on the quality of cancer mortality data obtained from death certificates is provided. In addition, information is provided for future studies of the concordance of primary cancer site between population-based cancer registry data and data from death certificates, particularly underlying causes of death coded in ICD-10.
BACKGROUND: One measure of the accuracy of cancer mortality statistics is the concordance between cancer defined as the underlying cause of death from death certificates and cancer diagnoses recorded in central, population-based cancer registries. Previous studies of such concordance are outdated. OBJECTIVE: To characterize the accuracy of cancer mortality statistics from the concordance between cancer cause of death and primary cancer site at diagnosis. DESIGN: Central cancer registry records from California, Colorado, and Idaho in the U.S. were linked with state vital statistics data and evaluated by demographic and tumor information across 79 site categories. A retrospective arm (confirmation rate per 100 deaths) compared death certificate data from 2002 to 2004 with cancer registry diagnoses from 1993 to 2004, while a prospective arm (detection rate per 100 deaths) compared cancer registry diagnoses from 1993 to 1995 with death certificate data from 1993 to 2004 by International Statistical Classification of Diseases and Related Health Problems (ICD) version used to code deaths. RESULTS: With n=265,863 deaths where cancer was recorded as the underlying cause based on the death certificate, the overall confirmation rate for ICD-10 was 82.8% (95% confidence interval [CI], 82.6-83.0%), the overall detection rate for ICD-10 was 81.0% (95% CI, 80.4-81.6%), and the overall detection rate for ICD-9 was 85.0% (95% CI, 84.8-85.2%). These rates varied across primary sites, where some rates were <50%, some were 95% or greater, and notable differences between confirmation and detection rates were observed. CONCLUSIONS: Important unique information on the quality of cancer mortality data obtained from death certificates is provided. In addition, information is provided for future studies of the concordance of primary cancer site between population-based cancer registry data and data from death certificates, particularly underlying causes of death coded in ICD-10.
Authors: Nadia A Nabulsi; Ali Alobaidi; Brian Talon; Alemseged A Asfaw; Jifang Zhou; Lisa K Sharp; Karen Sweiss; Pritesh R Patel; Naomi Y Ko; Brian C-H Chiu; Gregory S Calip Journal: Cancer Causes Control Date: 2020-04-30 Impact factor: 2.506
Authors: Christopher H Gu; David E Lucero; Chaorui C Huang; Demetre Daskalakis; Jay K Varma; Neil M Vora Journal: Public Health Rep Date: 2018 Sep/Oct Impact factor: 2.792
Authors: Christopher J Johnson; Hannah K Weir; Aliza K Fink; Robert R German; Jack L Finch; Randi K Rycroft; Daixin Yin Journal: Cancer Epidemiol Date: 2012-09-07 Impact factor: 2.984
Authors: Michael Wirth; John E Vena; Emily K Smith; Sarah E Bauer; John Violanti; James Burch Journal: Am J Ind Med Date: 2012-12-19 Impact factor: 2.214
Authors: Travis P Baggett; Stephen W Hwang; James J O'Connell; Bianca C Porneala; Erin J Stringfellow; E John Orav; Daniel E Singer; Nancy A Rigotti Journal: JAMA Intern Med Date: 2013-02-11 Impact factor: 21.873