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The effect of case rate and coinfection rate on the positive predictive value of a registry data-matching algorithm

Qiang Xia1, Sarah L Braunstein1, Laura E Stadelmann1

  • 1New York City Department of Health and Mental Hygiene, Bureau of HIV/AIDS Prevention and Control, Long Island City, NY.

Public Health Reports (Washington, D.C. : 1974)
|January 4, 2014
PubMed
Abstract

Insights

Data matching for HIV/AIDS and STD surveillance is affected by case and coinfection rates. Higher case rates and lower coinfection rates reduce the positive predictive value (PPV) of matching algorithms.

Area of Science:

  • Public Health
  • Epidemiology
  • Biostatistics

Background:

  • Statistical modeling suggests false match prevalence decreases with rarer events or more matches.
  • Understanding factors influencing data matching accuracy is crucial for public health surveillance.

Purpose of the Study:

  • To examine the impact of population case and coinfection rates on the positive predictive value (PPV) of a data-matching algorithm for HIV/AIDS and sexually transmitted disease (STD) surveillance registries.

Main Methods:

  • Utilized LinkPlus™, a probabilistic data-matching program, to link HIV/AIDS cases with syphilis and chlamydia cases in New York City.
  • Manually reviewed match results to determine true matches and assess PPV across different subpopulations.

Main Results:

  • PPV varied significantly based on case and coinfection rates. Male syphilis cases (low case rate, high HIV coinfection) had the highest PPV (91.6%), while female chlamydia cases (high case rate, low HIV coinfection) had the lowest (18.0%) at a cutoff of 10.0.
  • Increasing the agreement/disagreement score cutoff to 15.0 substantially improved PPV in both groups, reaching 98.3% for male syphilis and 90.5% for female chlamydia cases.

Conclusions:

  • Case and coinfection rates significantly influence the PPV of registry data-matching algorithms.
  • PPV decreases with increasing case rates and decreasing coinfection rates.
  • Public health program staff should evaluate these rates and select appropriate matching algorithms before data matching.

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