Evaluating the Use of Outbreak Detection Algorithms to Detect Tuberculosis Outbreaks in Scotland Ben Tait Dr Janet Stevenson

To use outbreak detection algorithms on Scottish data on Tuberculosis infections reported to the Enhanced Surveillance of Mycobacterial Infections (ESMI) scheme to retrospectively detect outbreaks in order to see if this method could be a useful adjunct to the current methods of contact tracing and tuberculosis genotyping. Aim

Cluster Selection

Between 2007 and 2011 there were 151 clusters of identical genotypes in Scotland, of which 51.6% only involved two individuals After analysing the size of clusters of TB across Scotland a minimum cluster size of four individuals was used. The largest clusters were chosen from each remaining health board, with the next three largest clusters also selected from the Greater Glasgow and Clyde health board. The inclusion of the additional clusters in NHS Greater Glasgow and Clyde was due to the large imbalance in the location of clusters, 10.4cases/100,00 compared to 5.5cases/100,000 in Lothian.

Clusters

Case Count For each cluster, the number of cases of TB with the cluster genotype, that were in the main health board, were calculated for each of a series of 37 overlapping two year time frames which moved forward by one month. Additionally the number of TB cases with the cluster genotype outside of the main health board was also calculated for each time frame. A graph was then conducted showing the number of TB cases in each time frame throughout the five years allowing comparison between cases within the health board and cases outwith the health board.

Case Count

Log Likelihood Ratio The higher the log likelihood ratio, the higher the likelihood that there is an unexpected concentration of the disease or genotyped cluster of TB in question. The ratio compares the rate of the genotype within an area (the number of cases of a specific genotype compared to the total cases), compared to the rate of genotype outside the geographical area. Uses a threshold value to signify an outbreak Despite the drawbacks this method has been shown to be effective, especially on diseases such as TB which is transmitted in small local groups. This method also has additional benefits, as the data is collected via health boards, which simplifies the data analysis and does not rely on historical data.

Log Likelihood Ratio

Case Count

Log Likelihood Ratio Conclusions The Log Likelihood Ratio detected three outbreaks amongst the largest clusters in Scotland. This ratio is based upon the fact that an outbreak will happen within a single health board. As the LLR depends on the number of TB cases of different genotypes, it takes a larger outbreak for the LLR to cross the threshold in Glasgow than it does in Highland or Grampian, as was the case in clusters C and D compared to cluster H. The LLR is useful when a genotype is uncommon outside the health board or the cases occur in a health board with few TB cases. This property is useful to avoid any false alarms, but does mean that LLR is less sensitive in the larger health boards.

CuSum Originally developed for detecting changes in industrial processes and is one of the most commonly used Statistical Process Control (SPC) methods in the surveillance of syndromic data. Primarily used to detect changes in disease trends earlier than they may be detected by simple case count observation. Uses historical data to estimate an average rate and then detects when the current rate rises over what is to be expected by normal fluctuating presence of disease. Concern that CuSums are prone to providing producing false positives especially in the case of rare diseases.

CuSum

Case Count

CuSum Conclusions Ideally the case numbers at the start should be either zero, or at the very least below the threshold level. This was not possible for most of the clusters data that was used. Nevertheless, the CuSum identified six outbreaks amongst the eight clusters; this higher level of sensitivity may be beneficial in the detection of future outbreaks. One of the ways that CuSum differs from Log likelihood ratios is that it does not take into account the cases outwith the health board. The parameters that were used in this study were derived from the Kammerer study Due to the time required for a culture to be confirmed, and then for a MIRU profile to be added this may affect the timeliness of the CuSum and other outbreak detection algorithms

Conclusions Comparing CuSum and Log Likelihood Ratio, we can see that CuSum is the more sensitive of the two outbreak detection algorithms. The Log Likelihood Ratio based upon the health board areas may be suitable in other situations but it does not appear to be able to detect rises in genotype clusters in the larger health boards nor does it detect rises when the genotype is common across multiple health boards. Using the more sensitive CuSum there is a higher risk of false alarms, although it was not possible to test this. Both CuSum and LLR have detected outbreaks amongst the selected clusters, however this can also be done by simply observing a case count graph, comparing numbers with a genotype inside a health board to those outwith a health board. Although neither of the statistical methods were completely successful the method of case counting could be a useful adjunct to monitoring if outbreaks are being brought under control appropriately

Future Work All studies found during the literature search applied outbreak detection algorithms retrospectively. However, a prospective study is currently being carried out by the CDC using these methods on tuberculosis data as cases are reported to the CDC. More research will be needed on this to fully determine whether these outbreak detection algorithms would indeed be useful in real life situations. Further work in evaluating more geo-spatial analysis which does not require the arbitrary boundaries of the health boards.

Acknowledgements This could not have been possible without the help of Alison Smith-Palmer and Dr Ian Laurenson