Qiang Xia1, Sarah L Braunstein1, Laura E Stadelmann1, Preeti Pathela2, Lucia V Torian1. 1. New York City Department of Health and Mental Hygiene, Bureau of HIV/AIDS Prevention and Control, Long Island City, NY. 2. New York City Department of Health and Mental Hygiene, Bureau of STD Control, Long Island City, NY.
Abstract
OBJECTIVE: Statistical modeling has suggested that the prevalence of false matches in data matching declines as the events become rarer or the number of matches increases. We examined the effect of case rate and coinfection rate in the population on the positive predictive value (PPV) of a matching algorithm for HIV/AIDS and sexually transmitted disease (STD) surveillance registry data. METHODS: We used LinkPlus™, a probabilistic data-matching program, to match HIV/AIDS cases diagnosed in New York City (NYC) from 1981 to March 31, 2012, and reported to the NYC HIV/AIDS surveillance registry against syphilis and chlamydia cases diagnosed in NYC from January 1 to June 30, 2010, and reported to the NYC STD registry. Match results were manually reviewed to determine true matches. RESULTS: With an agreement/disagreement comparison score cutoff value of 10.0, LinkPlus identified 3,013 matches, of which 1,582 were determined to be true by manual review. PPV varied greatly in subpopulations with different case rates and coinfection rates. PPV was the highest (91.6%) in male syphilis cases, who had a relatively low case rate but a high HIV coinfection rate, and lowest (18.0%) in female chlamydia cases, who had a high case rate but a low HIV coinfection rate. When the cutoff value was increased to 15.0, PPVs in male syphilis and female chlamydia cases increased to 98.3% and 90.5%, respectively. CONCLUSIONS: Case rates and coinfection rates have a significant effect on the PPV of a registry data-matching algorithm: PPV decreases as the case rate increases and coinfection rate decreases. Before conducting registry data matching, program staff should assess the case rate and coinfection rate of the population included in the data matching and select an appropriate matching algorithm.
OBJECTIVE: Statistical modeling has suggested that the prevalence of false matches in data matching declines as the events become rarer or the number of matches increases. We examined the effect of case rate and coinfection rate in the population on the positive predictive value (PPV) of a matching algorithm for HIV/AIDS and sexually transmitted disease (STD) surveillance registry data. METHODS: We used LinkPlus™, a probabilistic data-matching program, to match HIV/AIDS cases diagnosed in New York City (NYC) from 1981 to March 31, 2012, and reported to the NYC HIV/AIDS surveillance registry against syphilis and chlamydia cases diagnosed in NYC from January 1 to June 30, 2010, and reported to the NYC STD registry. Match results were manually reviewed to determine true matches. RESULTS: With an agreement/disagreement comparison score cutoff value of 10.0, LinkPlus identified 3,013 matches, of which 1,582 were determined to be true by manual review. PPV varied greatly in subpopulations with different case rates and coinfection rates. PPV was the highest (91.6%) in male syphilis cases, who had a relatively low case rate but a high HIV coinfection rate, and lowest (18.0%) in female chlamydia cases, who had a high case rate but a low HIV coinfection rate. When the cutoff value was increased to 15.0, PPVs in male syphilis and female chlamydia cases increased to 98.3% and 90.5%, respectively. CONCLUSIONS: Case rates and coinfection rates have a significant effect on the PPV of a registry data-matching algorithm: PPV decreases as the case rate increases and coinfection rate decreases. Before conducting registry data matching, program staff should assess the case rate and coinfection rate of the population included in the data matching and select an appropriate matching algorithm.
Authors: Qiang Xia; Janice L Westenhouse; Alan F Schultz; Atsuko Nonoyama; William Elms; Nancy Wu; Lisette Tabshouri; Juan D Ruiz; Jennifer M Flood Journal: Health Informatics J Date: 2011-03 Impact factor: 2.681
Authors: Lucia V Torian; Hadi A Makki; Isaura B Menzies; Christopher S Murrill; Isaac B Weisfuse Journal: Sex Transm Dis Date: 2002-02 Impact factor: 2.830
Authors: Susan E Manning; Melissa R Pfeiffer; Denis Nash; Susan Blank; Judith Sackoff; Julia Schillinger Journal: Sex Transm Dis Date: 2007-12 Impact factor: 2.830
Authors: Lucia V Torian; Uriel R Felsen; Qiang Xia; Fabienne Laraque; Eric J Rude; Herbert Rose; Adam Cole; Angelica Bocour; Gary J Williams; Robert F Bridgforth; Lisa A Forgione; Howard Doo; Sarah L Braunstein; Demetre C Daskalakis; Barry S Zingman Journal: Am J Public Health Date: 2018-03-22 Impact factor: 9.308