Disease Control Implications of India's Changing Multi-Drug Resistant Tuberculosis Epidemic

<div><p>Background</p><p>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.</p><p>Methods and Findings</p><p>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.</p><p>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.</p><p>Conclusions</p><p>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.</p></div

Projected incidence rate of treatment-generated and transmission-generated MDR TB.

<p>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.</p

Model and treatment schematic.

<p>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 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089822#pone.0089822.s001" target="_blank">File S1</a> for details). Individuals in private treatment are not cured but are exposed to a risk of developing MDR TB (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089822#pone.0089822.s001" target="_blank">File S1</a> for details on modeling exposure to the effects of private-sector treatment on MDR generation). Public sector treatment is modeled according to the DOTS protocols: Patients with prior treatment enter category II treatment, and those who test positive for MDR enter MDR treatment (or Category IV treatment/DOTS-Plus) six months after MDR testing if MDR treatment is available. For patients whose non-MDR TB is not cured by treatment, there is a chance that they develop treatment-generated MDR TB.</p

Analysis Scenarios.

<p>*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 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089822#pone-0089822-t001" target="_blank">Table 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089822#pone.0089822.s001" target="_blank">File S1</a> table S5.8.</p><p>**Treatment categories refer to DOTS treatment category I/III and category II, as explained in the text in section <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089822#s2" target="_blank">Methods</a>: TB Treatment.</p

Reduction in MDR TB prevalence with improvements in treatment of non-MDR TB and diagnosis of MDR TB.

<p>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).</p

Results summary.

<p>Percentage reduction in infectious MDR TB prevalence also shown graphically in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089822#pone-0089822-g005" target="_blank">Figure 5</a>.</p

Selected model parameters for certain ages and model inputs.

<p>Please see full list in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089822#pone.0089822.s001" target="_blank">File S1</a>. Sputum smear positive is abbreviated SS+.</p

Calibration results: comparison of WHO estimates of TB prevalence and incidence in India (blue lines) to modeled outcomes (red lines).

<p>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.</p

Projected prevalence and mortality from non-MDR and MDR TB in India with public DOTS treatment programs and counterfactual private treatment expansion in the absence of public treatment.

<p>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.</p

Cost-effectiveness analysis.

<p>(A) The graph plots the incremental discounted QALYs (<i>y</i>-axis) and incremental discounted lifetime costs (<i>x</i>-axis) for each combined screening and treatment strategy. The solid line represents the cost-effectiveness frontier, those strategies that are potentially economically efficient depending on one’s willingness-to-pay per unit of health benefit gained. (B) The bar graph shows the incremental cost-effectiveness ratios of each combined screening and treatment strategy at different levels of treatment uptake at each opportunity (varied over the range 0–50%). The asterisk denotes that, at 5% uptake, birth-cohort screening followed by universal triple therapy for screen-detected, treatment-eligible individuals is dominated. For both panels, IL-28B = interleukin-28B; QALY = quality-adjusted life-year.</p