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Due to the restricted cluster investigation activity and outcome data (described above), alternative approaches to measure the impact of the TB-STS on TB control are presented in the following section. These alternative methods and data sources are explored based on the assumption that strain typing will enable targeted cluster investigations and as a result one would expect to identify a greater number of LTBI and cases of TB disease earlier. It was hypothesised that this would lead to three indirect outcomes (see the diagram of the aims of the TB-STS, Figure 11 on page 31):

1. An increased yield of LTBI and TB disease identified through contact tracing when the index case is in a cluster that is investigated (compared to when the index case is in a cluster that is not investigated);

2. A reduction in the time between symptom onset and diagnosis, termed diagnostic delay; and

3. A change in the rate of cluster growth following a cluster investigation (compared to no investigation).

All data were cleaned in Excel (2010) and Access (2010).

3.7.1 Contact tracing yield

Contact tracing yield is the proportion of contacts who have LTBI or active TB disease. It was hypothesised that the TB-STS would increase contact tracing yield. If a case is in a cluster and the cluster is investigated then one might expect more TB

74 disease and LTBI to be identified, as a result of the additional information about the transmission occurring within that cluster. Figure 22 shows how more targeted contact tracing (because of the additional information collected in a cluster investigation) could produce a higher yield of active TB and LTBI, rather than following the traditional stone in the pond principle (Figure 8, page 8).

Figure 22 – More targeted contact tracing (shown in orange) as a result of the TB-STS might lead to a greater contact tracing yield (as compared with approach illustrated with the blue/green concentric circles)

To explore this, the outcomes of contact tracing were investigated using two different datasets. The first used data collected from nurses in the initial and follow-up survey to summarise the number of contacts screened per index case and the outcomes of the screening (see page 58 for a description of the surveys, and see below for a description of the data collected, and Appendix 3 for the survey questions). The second data source combined data from North Central London Sector and Leicester TB clinics and was used to analyse the impact of cases being part of a cluster and/or part of a cluster that was being investigated on the contact tracing yield (see below). Means, medians and proportions are all presented, as contact tracing yield is a key parameter for the cost-effectiveness model. Figure 17 (page 57) shows the data collection time periods in the wider context of the TB-STS implementation and evaluation.

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Overall estimates of contact tracing activity and yield: survey data

Using the initial and follow-up survey (page 53), the aim was to estimate the yield of contacts with TB disease or LTBI identified through contact tracing, and capture any changes over time.

Nurses were asked to detail up to five recent TB index cases whose contacts had been screened. They were asked for the following information for each case: date of birth, site of disease (pulmonary or extra-pulmonary), smear test result, culture result, when the strain type was available to the nurse, the number of contacts identified, and the number of contacts screened. They were also asked to report the number of contacts that had active TB disease and the number of contacts that had LTBI.

Means, medians, standard deviations, inter-quartile ranges or ranges were presented and results were compared across the initial and follow-up surveys using the Wilcoxon rank sum test. The proportion of contacts screened, with active disease and LTBI and the exact confidence intervals based on the Binomial distribution were calculated and compared across surveys using the chi2 test of significance and the Fisher’s exact test of significance where one or more cells has an expected frequency of less than five.

The impact of cluster status and cluster investigations on contact tracing yield: clinic data

Data collected from North Central London (NCL) Sector and Leicester TB Services that contained information on the number of contacts screened per patient and the outcome of that screening in July 2012 for pulmonary TB cases diagnosed in 2011, were used in this analysis. These data were linked to the ETS and the cluster database. For each case reported in these areas the number of their contacts that were screened was calculated and the outcomes of the screening were summarised (e.g. three contacts with LTBI and zero with active disease). The number of contacts screened, the number with active disease and the number with LTBI were calculated

76 and compared between cases that were unique (not clustered), clustered and part of a cluster investigation, and clustered but not part of a cluster investigation.

Medians and inter-quartile ranges (IQR) for the contact tracing yield for pulmonary index cases are presented by clustering and whether the cluster was investigated. The Wilcoxon-rank sum test was used to compare differences between the groups. A sensitivity analysis was conducted assuming that index cases with missing contact tracing information yielded no cases of active disease or LTBI. This was based on the assumption that those with positive results would be more likely to be recorded. The proportions of contacts screened, with active disease and LTBI and the exact confidence intervals based on the Binomial distribution were calculated and compared across unique/clustered and investigated/not investigated clusters using the chi2 test of significance and the Fisher’s exact test of significance where one or more cells had an expected frequency of less than five.

3.7.2 Diagnostic delay

Diagnostic delay is the time between symptom onset and case notification. It was hypothesised that the diagnostic delay would be reduced following the introduction of the TB-STS because cluster investigations would lead to undiagnosed TB cases being actively identified earlier. A case-control design was used to compare the diagnostic delay observed with cluster investigations and without. Diagnostic delay was defined as the number of days between onset of symptoms and the date of notification. Date of notification was used instead of date of treatment as it was a more complete field.

Cases were defined as pulmonary TB cases diagnosed in 2011 that were part of a cluster that was investigated and were diagnosed after the cluster investigation was initiated. There were two comparison (control) groups: a) pulmonary cases diagnosed in 2011 that were part of a cluster that was investigated and were diagnosed before the cluster investigation was initiated; b) pulmonary TB cases diagnosed in 2011 that were part of a cluster that was not investigated. The first two cases from each cluster

77 were excluded to take into account possible household transmission (as the diagnostic delay for these two cases will not be affected by the presence or absence of a cluster investigation).

The analysis included 121 pulmonary cases diagnosed after the cluster was investigated, 117 diagnosed before the cluster was investigated and 139 cases that were part of clusters that were not investigated at all. The analysis was stratified by cases that were UK born and non UK born, and was re-run excluding children under the age of 16. Medians and inter-quartile ranges (IQR) were presented and compared using the Wilcoxon rank sum test.

3.7.3 Rate of cluster growth

The rate at which new cases of TB are added to a cluster indicates the rate of ongoing transmission in the community. The relative changes in the size of clusters were explored. It was hypothesised that the rate of cluster growth would differ before and after a cluster investigation was initiated (Figure 23). Cluster investigation may increase apparent rate of cluster growth (as a result of additional cases being identified more quickly) or may decrease the rate of cluster growth (if the earlier identification of cases limits transmission). In ideal circumstances a cluster investigation may transiently increase the rate of cluster growth, later decrease the rate and then plateau as transmission is interrupted (Figure 23).

78 It was hypothesised that the rate at which new cases were added to a cluster would change following the initiation of a cluster investigation. To visualise the data, using a selection of the national TB notification data from 2010 and 2011, the number of cases in a cluster was plotted by the number of days before or after the cluster investigation was started.

2010 and 2011 data from the ETS were merged with the cluster monitoring database. After cleaning the data and excluding the first case in each of the 113 clusters, there remained a total of 949 cases. A univariate linear regression was initially conducted and the final set of variables used in the multivariate regression were: sex, UK born, age group (treated as an ordinal variable), site of disease, case order (the order in which cases were added to the cluster), cases discovered after a cluster investigation was started, and cluster level (local, regional and national) and lineage, the latter being factor variables. See Appendix 4 for the model equations.