There was no correlation between the change in smear-positive TB numbers and any index of the TB services.
As an example (Fig. 3), we anticipate that if antiretrovirals are newly introduced into a population, HIV numbers will decrease and in consequence, with a certain time lag, TB numbers will also decrease.
Ideally the dynamic indicator will compare time trends for the two diseases that account for the time lag expected to occur between changes in HIV numbers and changes in TB numbers in a population.
Conversely, a disproportionate relative increase in HIV (indicated by low RTB/HIV values) may indicate that HIV prevention programs are not effective, and that we can expect to see rising TB numbers in the coming years.
The latter appears to have been the case in Kenya, because following the completion of our study the official trends for both diseases have been revised: TB numbers have been revised downwards [42] and HIV numbers are in the process of being revised upwards [43].
PCT data for TB numbers showed a similar trend for the highest three cities, although suggested an increase in cases for Leicester.
The rate of change in TB numbers since the point of inflexion was highest for Manchester, Leeds and Sheffield, but lowest for Leicester and the ranking did not change substantially by using the 10 year average for the clinic.
There was a correlation between the trend in TB numbers available to PCTs and the number of items successfully completed in the audit (Spearman's rank correlation R2 = 0.64, P < 0.05), whereas correlations with actual trends within the clinic or since the point of inflexion showed less if any correlation.
The last 3 proteins (numbers 12 to 14) were obtained as a result of comparisons between Mtb-specific antigen-stimulated and unstimulated samples collected from patients with active TB; numbers 12 to 14 (T4203, T3107, and T4208) were all identified as clusterin.
TB is number one cause of death, in HIV infected patients specially children.
