Completeness of reporting of race and ethnicity data in the nationally notifiable diseases surveillance system, United States, 2006-2010.

Abstract

During 1994-1997, approximately 70% and 60% of the cases of conditions reported to the National Notifiable Diseases Surveillance System included persons of known race and ethnicity, respectively. A major goal of the Healthy People 2020 initiative is to eliminate health disparities. To describe trends in the completeness of race and ethnicity in case reports of the National Notifiable Diseases Surveillance System during 2006-2010. The National Notifiable Diseases Surveillance System is a public health surveillance system that aggregates case reports of infectious diseases and conditions that are designated nationally notifiable and are collected by US states and territories. The Centers for Disease Control and Prevention (Atlanta, Georgia) maintains this surveillance system in collaboration with the Council of State and Territorial Epidemiologists. We used Cochran-Armitage Trend Test (SAS, version 9.2) to test the hypothesis that the percentage of case reports with the completeness of race and ethnicity data increased or decreased linearly during 2006-2010. Completeness of race and ethnicity variables. The 32 conditions reviewed included 1 030 804 case records. Seventy percent of records included a known value for race, and 49% of records included ethnicity during 2006-2010. During 2006-2010, race was known in 70% or more of records in 24 of 32 conditions and in 23 of 51 jurisdictions. During 2006-2010, the systemwide reporting of race remained at the same level of completeness (70%) but the reporting of ethnicity increased slightly from 48% in 2006 to 53% in 2010. In comparison with race, the proportions of records coded to ethnicity were less among all conditions. Significant change has occurred in the completeness of reporting of ethnicity but not race during 2006-2010. However, the reporting of ethnicity still lags substantially behind the reporting of race. Jurisdictions that identify conditions with lower rates of completeness of race and ethnicity can assess the net benefits of efforts to improve the completeness of race and ethnicity data.