Improving Surveillance of Sexually Transmitted Diseases through Geocoded Morbidity Assignment

Objectives. We assessed the added value of using a geocoder to improve sexually transmitted disease (STD) surveillance data and decision support through redistribution of inaccurately assigned morbidity in Richmond, Virginia. Methods. Virginia initiated geocoding of STD data as a data quality tool in 2002. Geocoded output files were assessed and discordant proportions of reported gonorrhea and chlamydia morbidity were reassigned appropriately for the city of Richmond, Chesterfield County, and Henrico County (2002 to 2006). We used Chi-square analysis to compare assignment proportions and calculated crude odds ratios for 2006 data to estimate increased case reassignment likelihood. Results. From 2002 to 2006, 149,229 cases of gonorrhea and chlamydia were reported within the Commonwealth of Virginia. Of the reported morbidity, 81% of cases (n=120,875) were successfully geocoded; 7% (n=8,461) of geocoded addresses were reassigned. Approximately 76% (n=6,412) of all reassigned cases occurred within Richmond and Chesterfield and Henrico counties. In 2006, 84% (n=654) of reassigned cases in this tri-city/county area were initially reported as Richmond morbidity. Data quality improvements reduced Richmond's artificially inflated morbidity by 18% and increased Chesterfield and Henrico morbidity by 17% and 55%, respectively. Richmond morbidity was three times more likely to be reassigned than Chesterfield cases (odds ratio [OR] = 2.93, 95% confidence interval [CI] 2.21, 3.90), and two times more likely than Henrico cases (OR=2.12, 95% CI 1.63, 2.76). Richmond's number one national rank for STD rates was reduced beginning in 2002. Conclusions. Declining rates of STDs were statistically associated with geocoded morbidity reassignments. Implementation of this data quality business process has improved epidemiologic analyses, prevention planning, and assessment of resource allocations. The reduction in Richmond's national STD rankings is indicative of the effect geocoding can have on surveillance data.