The authors have declared that no competing interests exist.
Conceived and designed the experiments: SS EBD JGF. Performed the experiments: SS. Analyzed the data: SS EBD JGF. Contributed reagents/materials/analysis tools: SS EBD JGF. Wrote the paper: SS EBD JGF.
Multi-drug resistant tuberculosis (MDR TB) is a major health challenge in India that is gaining increasing public attention, but the implications of India's evolving MDR TB epidemic are poorly understood. As India's MDR TB epidemic is transitioning from a treatment-generated to transmission-generated epidemic, we sought to evaluate the potential effectiveness of the following two disease control strategies on reducing the prevalence of MDR TB: a) improving treatment of non-MDR TB; b) shortening the infectious period between the activation of MDR TB and initiation of effective MDR treatment.
We developed a dynamic transmission microsimulation model of TB in India. The model followed individuals by age, sex, TB status, drug resistance status, and treatment status and was calibrated to Indian demographic and epidemiologic TB time trends. The main effectiveness measure was reduction in the average prevalence reduction of MDR TB over the ten years after control strategy implementation.
We find that improving non-MDR cure rates to avoid generating new MDR cases will provide substantial non-MDR TB benefits but will become less effective in reducing MDR TB prevalence over time because more cases will occur from direct transmission – by 2015, the model estimates 42% of new MDR cases are transmission-generated and this proportion continues to rise over time, assuming equal transmissibility of MDR and drug-susceptible TB. Strategies that disrupt MDR transmission by shortening the time between MDR activation and treatment are projected to provide greater reductions in MDR prevalence compared with improving non-MDR treatment quality: implementing MDR diagnostic improvements in 2017 is expected to reduce MDR prevalence by 39%, compared with 11% reduction from improving non-MDR treatment quality.
As transmission-generated MDR TB becomes a larger driver of the MDR TB epidemic in India, rapid and accurate MDR TB diagnosis and treatment will become increasingly effective in reducing MDR TB cases compared to non-MDR TB treatment improvements.
Worldwide, tuberculosis (TB) prevalence has declined by over 30% since 1990
The challenge of addressing TB and MDR TB is critical for India, home to over 25% of the world's TB cases
In order to predict the likely effectiveness of TB control initiatives currently being considered, it is important to understand the relationship between program effectiveness and the transition from treatment-generated to transmission-generated MDR TB. Reducing transmission-generated cases requires rapid identification and treatment of MDR TB cases to prevent further transmission, while eliminating treatment-generated cases requires improving non-MDR TB cure rates. We first examine the Indian transition from a treatment-generated to a transmission-generated MDR TB epidemic over the previous decade as treatment for TB and MDR TB expanded. Then we project how this transition will change the relative effectiveness of improving non-MDR TB treatment versus shortening the infectious period between MDR TB activation and MDR treatment initiation on MDR TB control.
We examine the implications of India's MDR TB epidemic for the effectiveness of public health interventions by using a dynamic transmission model of TB calibrated to Indian demography and TB epidemiology. The simulation model represents India's TB epidemic from 1996–2038. The model tracks TB in individuals from the acquisition of latent infection to active pulmonary disease. It follows individuals from birth to death using sex, age, and detailed representations of their TB and MDR TB infection and disease status as well as their case detection, diagnosis, and treatment status and history. These model stratifications are included to allow the model to capture demographically dependent disease dynamics for a complex disease like TB, since mortality, transmission, activation, as well as treatment uptake and effectiveness vary by age and sex in India.
The model was calibrated to match India's TB and MDR TB epidemics between 1996 and 2010, when private-sector treatment continued even as DOTS was scaled up and DOTS-Plus initiated, and it predictions were then compared to multiple epidemiologic and care outcomes to assess simultaneous consistency (see
Model Inputs | Value | Source | |
Monthly mortality for 2000–2009 | Male | Female | |
Age 5 | 0.0002 | 0.0002 | |
Age 25 | 0.0003 | 0.0003 | |
Age 45 | 0.0008 | 0.0005 | |
Age 75 | 0.0085 | 0.0071 | |
Monthly untreated TB mortality for 2000–2009 | |||
Age 5 | 0.0248 | 0.0249 | |
Age 25 | 0.0275 | 0.0251 | |
Age 45 | 0.0298 | 0.0257 | |
Age 75 | 0.0364 | 0.0328 | |
Monthly activation probability for latent TB | <2 years ago | >2 years ago | |
Age 5 | 0.0010 | 0.000432 | |
Age 25 | 0.0012 | 0.000395 | |
Age 45 | 0.0010 | 0.000275 | |
Age 75 | 0.0004 | 0.000267 | |
Probability of self-cure (all ages) | 0 | Assumed | |
Relative infectivity of MDR TB strains (compared to non-MDR TB strains) | 1 | Assumed | |
Sensitivity of 3 sputum smear tests for active pulmonary TB | 0.60 | ||
Specificity of 3 sputum smear tests for active pulmonary TB | 1.00 | ||
Overall probability of receiving RNTCP treatment if treatment-naive, given that treatment is available | Male | Female | |
Age 20 | 0.1254 | 0.0477 | |
Age 40 | 0.3743 | 0.1033 | |
Age 60 | 0.6000 | 0.1158 | |
Overall probability of receiving RNTCP treatment conditional on prior treatment and current treatment availability | |||
Age 20 | 0.2821 | 0.1073 | |
Age 40 | 0.6000 | 0.2325 | |
Age 60 | 0.6000 | 0.2606 | |
Treatment-naive patients | |||
Probability of death | 0.010 | ||
Default probability | Male | Female | |
Age 20 | 0.023 | 0.018 | |
Age 40 | 0.017 | 0.015 | |
Age 60 | 0.022 | 0.012 | |
Probability of successful treatment if non-MDR patient completes treatment regimen | 0.980 | ||
Previously treated patients | |||
Probability of death | 0.026 | ||
Default probability | Male | Female | |
Age 20 | 0.057 | 0.043 | |
Age 40 | 0.041 | 0.037 | |
Age 60 | 0.054 | 0.028 | |
Probability of successful treatment if non-MDR patient completes treatment regimen | 0.940 | ||
Probability of testing SS+ at month 4 for non-MDR previously treated patients | 0.570 | ||
Probability of developing MDR TB | |||
If default from treatment | 0.242 | ||
If fail treatment | 0.187 | ||
Relative infectivity of MDR patient in non-MDR treatment (compared to no treatment) | 1 | Assumed | |
Category IV (DOTS-Plus) treatment | |||
Probability of death | 0.017 | ||
Default probability | 0.017 | ||
Probability of successful treatment if patient completes treatment regimen | 0.738 | ||
Probability treatment suppresses TB to latent infection | 0.197 |
Please see full list in
The model follows individuals through health and treatment states (see
Model schematic: individuals are born healthy and may subsequently acquire latent TB (non-MDR or MDR) infections through transmission. Individuals who develop active TB disease may subsequently seek treatment. Treatment schematic: individuals with active TB may enter public- or private-sector treatment (see
Individuals with active disease require diagnosis to begin treatment. Those diagnosed with active disease may undergo treatment under RNTCP protocols or in private-sector clinics (see
Individuals with TB symptoms may seek care based on age, sex, and previous treatment status
Private clinics may be an important contributor to India's MDR epidemic and are included in the model (see
In order to explore how the changing MDR epidemic could alter the effectiveness of control efforts, we examine the effect of two policies that target treatment-generated and transmission-generated MDR. One approach is to further improve non-MDR treatment. Improving non-MDR treatment directly reduces the number of treatment-generated MDR cases by reducing cases of incomplete or ineffective treatment that may lead to development of MDR TB strains. We examine the effect of improving non-MDR default, mortality, and success rates to match those of the best-performing Indian state RNTCP program in 2010 (see
Base Case | Intervention: Improving non-MDR treatment quality | Intervention: Improving rapidity of MDR diagnosis | |
CAT I/III |
|||
Death (monthly) | 0.010 | 0.003 | Same as base case |
Default (monthly), conditional on alive | 0.021 | 0.005 | Same as base case |
Failure, conditional on completion | 0.980 | 0.990 | Same as base case |
CAT II | |||
Death (monthly) | 0.026 | 0.015 | Same as base case |
Default (monthly), conditional on alive | 0.052 | 0.007 | Same as base case |
Failure, conditional on completion | 0.940 | 0.972 | Same as base case |
CAT I/III | Month 4 | Month 4 | At initial patient assessment |
CAT II | Month 8 | Month 8 | At initial patient assessment |
6 months | 6 months | 1 month |
*Treatment death, default, and failure rates vary by age and sex. Probabilities for 30 year old males are used here as an example. Default probabilities are conditional on being alive, and failure probabilities are conditional on being alive and completing treatment. Probabilities for other ages and sexes for base case and improving non-MDR treatment quality (best state outcomes) are given in
**Treatment categories refer to DOTS treatment category I/III and category II, as explained in the text in section
A second approach is to target transmission-generated MDR by decreasing the time between activation of MDR TB and effective treatment by improving the rapidity of MDR diagnosis. Differentiating MDR from non-MDR TB and placing MDR patients on appropriate treatment in the first month after they begin directly reduces transmission-generated MDR by shortening their infectious period prior to receiving appropriate treatment. Currently it can take a patient almost 12 months from entry into non-MDR treatment to be identified with MDR TB and placed on MDR TB treatment, as patients are not tested for MDR immediately after entering treatment and long test turn-around times delay appropriate treatment after diagnosis
We examine the effectiveness of these two control strategies and explore how a transition from a treatment- to transmission-generated MDR TB epidemic alters their effectiveness in reducing the prevalence of infectious MDR TB (i.e., the prevalence of MDR TB cases not on effective treatment). We consider hypothetical scenarios in which these approaches are implemented in 1997, 2007, 2017, and 2027. The 1997 and 2007 scenarios benchmark what might have happened if these approaches had been implemented when MDR TB levels in India were relatively low. The 2017 and 2027 scenarios illustrate the effect of prompt versus delayed implementation of such measures. We compare outcomes under these scenarios to the base case of continuing at current quality levels with MDR treatment programs scaling up as scheduled. We also consider combinations of the two strategies to assess their interaction in the presence of MDR TB epidemic transitions.
Given current interest in public-private TB treatment efforts to improve outcomes in private-sector clinics
Because no studies existed to provide direct measures for several model inputs in the Indian context, we calibrated the effective transmission risk (the probability of TB transmission given contact between a susceptible and infectious individual), the average rate of TB activation (the average rate at which latent infection transitions to active disease), and the average treatment take-up rate to match model outputs to empirical data on TB prevalence, incidence, and RNTCP patient demographics (see
Effective MDR TB control depends in part on MDR treatment availability, but MDR treatment has not yet reached nationwide coverage
Calibrated model outputs for India's annual overall TB incidence, MDR TB incidence, and TB prevalence between 1996 and 2012 simultaneously matched empirical estimates (additional calibration results in
The simulation model's output matches WHO reports on Indian TB prevalence and incidence, fitting time trends for non-MDR TB in 1996–2012 and WHO estimates of MDR TB in 2008.
Continuing non-MDR treatment at current coverage and treatment success rates is projected to provide substantial health benefits for individuals with non-MDR TB in the future relative to a scenario with no effective TB treatment (see
Figure shows model estimations and projections of disease prevalence and deaths after 1996, when public nationwide TB treatment in India began. Private treatment curves (dashed lines) represent outcomes in a scenario where DOTS was never implemented and private clinic population coverage increased to half of the level that DOTS currently covers. Solid lines represent disease prevalence and deaths given observed public treatment levels in India and assume public TB treatment will continue at current levels.
Current levels of TB treatment and control in the public sector have led to lower levels of MDR TB prevalence than if TB treatment had been provided exclusively by private-sector clinics that do not follow effective TB treatment protocols. If DOTS had never been implemented and instead the private sector had expanded to cover half of the population, MDR TB prevalence would be approximately 33% larger in 2038 – rising from 32 per 100,000 in 2013 to 56 per 100,000 in 2038 (see
However, even with DOTS and the planned scale-up of DOTS-Plus in the public sector, MDR TB is projected to grow through 2038 if additional measures are not taken (see
The source of incident MDR TB cases is changing. The model estimates of transmission- and treatment-generated MDR TB levels generally fell within confidence intervals for the WHO estimates of the number of transmission-generated MDR cases in 2008 – 52,000 (95% CI 47,000–56,000); WHO: 55,000 (95% CI 40,000–74,000) – and the number of incident treatment-acquired MDR cases: 73,000 (95% CI 52,000–94,000); WHO 43,000 (95% CI 33,000–56,000)
The fraction of the Indian population with incident MDR TB disease is shown over time. The blue region represents the fraction of the population with incident transmission-generated MDR TB, while the yellow denotes the fraction with treatment-generated MDR TB.
The MDR control benefits to India from improving non-MDR TB treatment are shrinking, though there are still important direct benefits for reducing non-MDR TB prevalence and incidence. Because India's MDR TB epidemic is expected to continue transitioning from a treatment-generated towards a transmission-generated epidemic, the impact on MDR TB of improving non-MDR TB treatment declines over time. In contrast, the impact of improving the rapidity of MDR TB diagnosis remains constant (see
The figure shows the average percentage reduction in infectious MDR prevalence over ten years after the improvements begin (either in 1997, 2007, 2017, or 2027).
Base Case | Intervention: Improving non-MDR treatment quality | Intervention: Improving rapidity of MDR diagnosis | |
1997 | 22.4 | 16.0 | 22.5 |
2007 | 22.5 | 18.5 | 14.0 |
2017 | 25.4 | 22.7 | 15.4 |
2027 | 30.2 | 27.1 | 18.6 |
1997 | NA | 28.9% | NA (No DOTS-Plus) |
2007 | NA | 17.6% | 38.0% |
2017 | NA | 10.8% | 39.2% |
2027 | NA | 10.3% | 38.5% |
Percentage reduction in infectious MDR TB prevalence also shown graphically in
Failure to increase population coverage of MDR treatment programs beyond the current level of 26% strongly influences the MDR control benefits achieved through improved MDR diagnosis. We assessed the benefits from improved MDR diagnosis if MDR treatment program coverage did not expand as currently planned. If MDR treatment coverage remained at 2011 levels, the benefits from improving MDR testing would decline by 7% relative to the scenario where MDR treatment fully scaled up by 2015 (from 39% to 32% reduction in MDR TB). Longer term effects on various MDR control strategies remain similar to those in the main analyses even if MDR treatment scale-up is slower than expected (see sensitivity analyses in
Because of uncertainties about patterns of TB care and TB disease natural history that have potentially important implications for MDR TB control, we examined how alternative assumptions impact the effectiveness of the policies we consider. Specifically, we examined assumptions about delaying MDR TB treatment initiation even after rapid MDR diagnosis (after two months vs. within the first month in the base case); non-MDR TB cure rates in private clinics (22% vs. 0% in the base case); rates of MDR TB generation from treatment in private clinics (0.03x–1.70x of the base case rate); the rate and heterogeneity of latent non-MDR and MDR TB activation (see sensitivity analyses in
The rise of MDR TB presents serious challenges to TB control. We provide results from a detailed TB epidemic model that illustrate the implications of India's transition from a treatment-generated MDR TB epidemic towards one that is dominated by transmission-generated disease. This shift has important implications for disease control policies, as programs that target treatment-generated MDR TB are predicted to become less effective. As transmission-generated MDR TB becomes a larger driver of the MDR TB epidemic in India, rapid and accurate MDR TB diagnosis and treatment will become increasingly important for reducing MDR TB cases compared to non-MDR TB treatment improvement.
We show that transmission is likely to play an increasingly important, direct role in driving India's MDR TB epidemic. A reservoir of prevalent latent MDR TB infections has been accumulating, originally infected from incident treatment-generated active MDR TB cases. The activation of these latent infections contributes to the growth of incident active MDR TB and, without rapid identification and treatment, can generate a self-sustaining MDR TB epidemic. As a result, rapid identification and treatment of incident MDR TB is increasingly effective as the transmission-generated epidemic grows in importance, though the magnitude of this increase depends on making effective MDR treatment widely available. Correspondingly, our findings show that the window of time available for controlling the growth of MDR TB by improving non-MDR treatment is closing. This has public health implications given the duration, toxicities, costs, and complexities associated with MDR TB treatment. Our findings suggest that successful efforts to address MDR TB in India will require understanding the source of new infections and tailoring disease control measures based on the relative contribution of treatment-generated versus transmission-generated infections to the MDR epidemic.
The simulation model excludes extra-pulmonary TB because its mode of transmission differs significantly from pulmonary disease, and pulmonary TB contributes over 80% of the TB cases in India. The model does not explicitly account for the effect of co-infections such as HIV or for risk factors such as malnutrition, which are implicitly incorporated through the use of India-specific data sources and calibration. The prevalence of HIV among individuals with active TB in India is relatively low compared to other high-prevalence TB countries, such as South Africa, and may not have substantial influence on India's overall TB epidemic dynamics. We assume the probability of self-cure for both non-MDR and MDR TB to zero and note that there are few data with which to inform self-cure rates in settings like India. To the extent that self-cure does occur in this context, the model may still implicitly incorporate this effect via its activation and transmission rates as both are calibrated to match WHO reports on TB incidence rates through the 1990s. Explicit estimation and incorporation of self-cure rates is left to future work.
The model also does not explicitly account for mixed-strain infections, where individuals may be simultaneously infected with both non-MDR and MDR TB; we assume non-MDR TB infection is protective and individuals cannot be additionally infected with MDR TB. As latent non-MDR TB infection prevalence in India is high, if the likelihood mixed-strain infection were sufficiently large, our model would underestimate the rate of MDR TB growth, though it would also likely underestimate the effectiveness of the interventions we considered. The model similarly omits explicit consideration of XDR TB from the analysis as little is known about XDR selection and transmission dynamics in the context of India. The incorporation of extra-pulmonary TB, HIV, and other risk factors is left to future work.
Much uncertainty remains around MDR measures (such as average patient response rates in non-MDR treatment, self-cure rates, relative infectivity when on non-MDR treatment and without, etc.). These can substantially change the growth rate of MDR TB prevalence and incidence, though we note that sensitivity analyses around these parameters show that the model results remain robust regarding decreasing effectiveness of improved treatment quality and constant effectiveness of improved MDR diagnosis policies. We therefore leave the detailed exploration of these MDR measures to future work.
The model does incorporate age, sex, and behavioral risk factors, such as differences in treatment-seeking behavior. We include age- and sex-stratifications in the model to more accurately capture mortality, disease transmission and activation, and treatment uptake and outcomes, which differ by age and sex and generate dynamics important for predicting disease outcomes. However, these stratifications may also introduce additional uncertainties as the number of model inputs increase, and simpler models may also be more transparent. We believe that inclusion of these stratifications is justified because the model must match empirically measured outcomes that depend on age and sex, which is much harder to do without age- and sex-stratification.
The purpose of the study was to characterize how shifts in India's MDR TB epidemic over time from treatment-generated cases to transmission-generated cases impact the effectiveness of general classes of control measures aimed at MDR TB. In general, these policies may target MDR generation in treatment (such as increasing non-MDR treatment quality in the RNTCP, as discussed in this paper, or other methods, such as reducing private clinic use through referral programs, etc.) or may try to limit direct MDR transmission (by reducing time to effective MDR treatment, as discussed in this paper, or potentially through other measures such as limiting transmissible contacts, etc.). While we illustrate the implications of this transition on the effectiveness of two example policies, we have not specified the means by which DOTS treatment is improved and cannot make specific recommendations about which policies should be implemented as our study did not include all potential policies, assess feasibility, or consider costs. Demonstration studies considering rapid MDR diagnostics in India are underway
Our analysis focuses on MDR TB in the context of India's general TB epidemic, and identifies how epidemiological trends may alter the effectiveness of control of non-MDR TB. We illustrate and quantify the reductions in non-MDR TB burden with the growth in India's treatment programs. We estimate that by 2038, TB treatment programs in India will contribute to a substantial decline in TB incidence, averting 48 million TB-related deaths through effective TB case management. However, the incidence of MDR TB is growing. Taken together, the projected declines in non-MDR TB and increases in MDR TB further emphasize the growing role of drug resistant disease and the need to critically consider MDR control measures.
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The authors wish to thank Dr. Ted Cohen for his feedback on early versions of the manuscript and the reviewers whose comments and suggestions strengthened this work.