In 2010, the Centers for Disease Control and Prevention (CDC) implemented a national data quality assessment and feedback system for CDC-funded HIV testing program data.
Our objective was to analyze data quality before and after feedback.
Coinciding with required quarterly data submissions to CDC, each health department received data quality feedback reports and a call with CDC to discuss the reports. Data from 2008 to 2011 were analyzed.
Fifty-nine state and local health departments that were funded for comprehensive HIV prevention services.
Data collected by a service provider in conjunction with a client receiving HIV testing.
National data quality assessment and feedback system.
MAIN OUTCOME MEASURES
Before and after intervention implementation, quality was assessed through the number of new test records reported and the percentage of data values that were neither missing nor invalid. Generalized estimating equations were used to assess the effect of feedback in improving the completeness of variables.
Data were included from 44 health departments. The average number of new records per submission period increased from 197 907 before feedback implementation to 497 753 afterward. Completeness was high before and after feedback for race/ethnicity (99.3% vs 99.3%), current test results (99.1% vs 99.7%), prior testing and results (97.4% vs 97.7%), and receipt of results (91.4% vs 91.2%). Completeness improved for HIV risk (83.6% vs 89.5%), linkage to HIV care (56.0% vs 64.0%), referral to HIV partner services (58.9% vs 62.8%), and referral to HIV prevention services (55.3% vs 63.9%). Calls as part of feedback were associated with improved completeness for HIV risk (adjusted odds ratio [AOR] = 2.28; 95% confidence interval [CI], 1.75-2.96), linkage to HIV care (AOR = 1.60; 95% CI, 1.31-1.96), referral to HIV partner services (AOR = 1.73; 95% CI, 1.43-2.09), and referral to HIV prevention services (AOR = 1.74; 95% CI, 1.43-2.10).
Feedback contributed to increased data quality. CDC and health departments should continue monitoring the data and implement measures to improve variables of low completeness.