A likelihood-based method of identifying contaminated lots of blood product

In 1994 a small cluster of hepatitis-C cases in Rhesus-negative women in Ireland prompted a nationwide screening programme for hepatitis-C antibodies in all anti-D recipients. A total of 55 386 women presented for screening and a history of exposure to anti-D was sought from all those testing positive and a sample of those testing negative. The resulting data comprised 620 antibody-positive and 1708 antibody-negative women with known exposure history, and interest was focused on using these data to estimate the infectivity of anti-D in the period 1970-1993.

Any exposure to anti-D provides an opportunity for infection, but the infection status at each exposure time is not observed. Instead, the available data from antibody testing only indicate whether at least one of the exposures resulted in infection. Using a simple Bernoulli model to describe the risk of infection in each year, the absence of information regarding which exposure(s) led to infection fits neatly into the framework of 'incomplete data'. Hence the expectation-maximization (EM) algorithm provides estimates of the infectiousness of anti-D in each of the 24 years studied.

The analysis highlighted the 1977 anti-D as a source of infection, a fact which was confirmed by laboratory investigation. Other suspect batches were also identified, helping to direct the efforts of laboratory investigators.

We have presented a method to estimate the risk of infection at each exposure time from multiple exposure data. The method can also be used to estimate transmission rates and the risk associated with different sources of infection in a range of infectious disease applications.