Quality Improvements in a State HIV Testing Program

BACKGROUND: Publicly funded HIV testing provides an avenue for people to learn their HIV status enabling them to protect their own health and to reduce the risk of further transmission if they learn they are HIV-positive. The effectiveness of testing programs is evaluated using the following metrics: number of total and new positives identified, and linkage to prevention services for negatives and to partner services and HIV care for positives. Accurate data are critical for distinguishing new positives from those previously identified but who test again. Ways to improve data and program quality include increasing the percent of clients using name-associated versus anonymous testing, comparing names of confirmed positives from laboratory data to those in surveillance data, and adding a variable to capture when clients acknowledge a previous HIV diagnosis.  

METHODS: First, we assessed the percent of name-associated versus anonymous tests over time. Second, we assessed how accurately the Unique Code provided by clients captures new as opposed to previously-identified positives. Third, we compared names of laboratory-confirmed positives to those previously entered into the HIV surveillance system. Fourth, we assessed the impact of a variable (“Acknowledges previously positive”) added in Wisconsin in 2013 that accounts for testers who do not disclose their positive status until after receiving a rapid reactive result and who would therefore not receive a confirmatory test. 

RESULTS: 1) The percent of tests that were name-associated versus anonymous increased from 61% to 90% between 2010 and 2014 while the total number of publicly funded tests remained level. 2) Using the Unique Code alone to determine positives indicated that 10 of 200 (5%) positives were previously diagnosed in 2013-2014. 3) However, comparison of names between laboratory and surveillance data showed that 65 (33%) confirmed positives in those years had been diagnosed previously. 4) Wisconsin’s new variable indicated an additional 7 (3.5%) positives in 2013-2014 had previous HIV diagnoses.

CONCLUSIONS: Wisconsin’s efforts to increase name-associated testing have markedly improved data quality. Receiving names of confirmed positives from laboratory data allow us to match to the surveillance database. Matching establishes positives as new or previously diagnosed and enables assessment of linkage to care. Finding that the majority of previously diagnosed individuals use different Unique Codes at subsequent testing visits underscores the importance of name-associated testing.  Wisconsin’s “Acknowledges previously positive” variable provides additional precision to positivity measurements and could prove valuable if adopted by other states.