Averting epidemics of extensively drug-resistant tuberculosis

Abstract

Extensively drug-resistant tuberculosis (XDR TB) has been detected in most provinces of South Africa, particularly in the KwaZulu-Natal province where several hundred cases have been reported since 2004. We analyzed the transmission dynamics of XDR TB in the region using mathematical models, and observed that nosocomial transmission clusters of XDR TB may emerge into community-based epidemics under the public health conditions of many South African communities. The effective reproductive number of XDR TB in KwaZulu-Natal may be around 2. Intensified community-based case finding and therapy appears critical to curtailing transmission. In the setting of delayed disease presentation and high system demand, improved diagnostic approaches may need to be employed in community-based programs rather than exclusively at tertiary hospitals. Using branching process mathematics, we observed that early, community-based drug-susceptibility testing and effective XDR therapy could help curtail ongoing transmission and reduce the probability of XDR TB epidemics in neighboring territories.

Fig. 1.

Burden and dynamics of XDR TB. (A) Reported XDR TB cases in South Africa, by province, from January 2004 to March 2007. The KwaZulu-Natal province has reported the highest number of cases to date. (B) Flow diagram of the model. Subscripts “c” and “h” refer to the community and hospital environments, respectively (separated by a dotted vertical line). Non-XDR patients can be admitted to the ward for other TB disease, and risk superinfection/nosocomial infection. Admission and discharge are symbolized by gray arrows. Health states are further stratified by HIV and antiretroviral therapy status, and mortality is subtracted from all compartments. The model equations are detailed in SI Appendix. Persons can be uninfected with XDR TB (S), latently-infected (L; long latency; E, brief latency), actively infected and not yet detected (U, undetected for TB generally; I, detected for TB but placed on empirical first-line therapy before drug-susceptibility testing; X, queueing for drug-susceptibility testing as a result of having suspected XDR; D, defaulted from the testing and treatment system), identified as XDR and initiated on XDR therapy (T_h reflecting inpatient XDR therapy; T_c reflecting potential community-based XDR therapy programs), or recovered after therapy (R, among a proportion for whom XDR therapy is potentially effective). Proportion z of the population is also infected with non-XDR TB strains. Dashed lines indicate a strategy of early outpatient screening among detected TB cases for XDR TB and potential community-based XDR therapy.

Fig. 2.

Epidemic dynamics. (A) Epidemic trajectory. Results of 10,000 stochastic simulations displaying the cumulative nosocomial (red) and community-based (blue) XDR TB infection rates in a population of 100,000 served by a 60 bed TB hospital facility. Note epidemic die-out occurs in some simulations (bottom of error bars). (B) Queuing for care. Waiting times for therapy under the current system (blue), with rapid drug-susceptibility testing of 1 week turnaround (green), or with community-based XDR TB screening and therapy with capacity of 20 treatment slots at a time per 100,000 population (red; 20 slots per 100,000 have been provided in the Tugela Ferry pilot program). Cumulative XDR TB mortality among those patients waiting for therapy in each system (dashed lines) is also displayed. The y axis scale is both the waiting time to obtain therapy (in days) and deaths among those waiting (per 100,000 population).

Fig. 3.

Sensitivity analysis of the effective reproductive number. Contour plots of R (right-side color bar) are displayed under different transmission conditions and public health measures. See Table 1 for parameter definitions. (A) Varying the efficacy of infection control on the TB ward (y axis) and the speed of drug-susceptibility test results (x axis). (B) Varying the case detection rate of incident TB cases (y axis) and the proportion of cases lost to follow-up between drug-susceptibility testing and the initiation of XDR therapy (x axis). (C) Varying the efficacy of XDR therapy (y axis) and access to outpatient community-based drug-susceptibility testing (DST) and XDR therapy (x axis).

Fig. 4.

Distributions of R and the probability of an XDR TB epidemic. Baseline, existing health system; default, reduce the rate of default from therapy to 5%; detection, increase case detection to 70%; DST loss, prevent loss to follow-up during the drug-susceptibility testing process and accelerate turnaround of results to 1 week; nosocomial, institute fully effective infection control program to avert nosocomial transmission; CBST, institute community-based outpatient DST screening and XDR therapy; combined, combine all of the preceding measures. (A) Box plots of R displaying 10,000 iterations of Latin Hypercube Sampling from the probability distributions of the input parameters. The horizontal mark is the median, the box designates the interquartile range, whiskers extend to 1.5 times the interquartile range, and outliers are plotted individually. (B) Probabilities of XDR TB epidemics under each of the simulated public health control measures. (C) Variation in the probability of XDR TB epidemics in communities with different adult HIV prevalences and congregate TB hospital ward occupancy (per 100,000 persons).