MDR-TB treatment as prevention: The projected population-level impact of expanded treatment for multidrug-resistant tuberculosis

<div><p>Background</p><p>In 2013, approximately 480,000 people developed active multidrug-resistant tuberculosis (MDR-TB), while only 97,000 started MDR-TB treatment. We sought to estimate the impact of improving access to MDR-TB diagnosis and treatment, under multiple diagnostic algorithm and treatment regimen scenarios, on ten-year projections of MDR-TB incidence and mortality.</p><p>Methods</p><p>We constructed a dynamic transmission model of an MDR-TB epidemic in an illustrative East/Southeast Asian setting. Using approximate Bayesian computation, we investigated a wide array of potential epidemic trajectories consistent with current notification data and known TB epidemiology.</p><p>Results</p><p>Despite an overall projected decline in TB incidence, data-consistent simulations suggested that MDR-TB incidence is likely to rise between 2015 and 2025 under continued 2013 treatment practices, although with considerable uncertainty (median 17% increase, 95% Uncertainty Range [UR] -38% to +137%). But if, by 2017, all identified active TB patients with previously-treated TB could be tested for drug susceptibility, and 85% of those with MDR-TB could initiate MDR-appropriate treatment, then MDR-TB incidence in 2025 could be reduced by 26% (95% UR 4–52%) relative to projections under continued current practice. Also expanding this drug-susceptibility testing and appropriate MDR-TB treatment to treatment-naïve as well as previously-treated TB cases, by 2020, could reduce MDR-TB incidence in 2025 by 29% (95% UR 6–55%) compared to continued current practice. If this diagnosis and treatment of all MDR-TB in known active TB cases by 2020 could be implemented via a novel second-line regimen with similar effectiveness and tolerability as current first-line therapy, a 54% (95% UR 20–74%) reduction in MDR-TB incidence compared to current-practice projections could be achieved by 2025.</p><p>Conclusions</p><p>Expansion of diagnosis and treatment of MDR-TB, even using current sub-optimal second-line regimens, is expected to significantly decrease MDR-TB incidence at the population level. Focusing MDR diagnostic efforts on previously-treated cases is an efficient first-step approach.</p></div

Impact of expanded drug-resistance diagnosis and second-line treatment availability.

<p>Under the intervention, use of drug susceptibility testing for previously-treated patients increases linearly from current levels in 2015 to 100% in 2017, and individuals found to have MDR-TB start second-line treatment, with allowance for 15% initial loss to follow up. Median and 95% uncertainty range values of MDR-TB incidence are shown, with continued current practice (gray) and under the intervention of expanded MDR-TB diagnosis and treatment (black with dotted 95% uncertainty range); their values in 2025 indicated numerically on the right. The outcome of this intervention in year 2025 is compared in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172748#pone.0172748.g004" target="_blank">Fig 4</a> with outcomes of other modeled interventions.</p

Dynamics of population susceptibility and herd immunity.

<p>Dynamics following mass vaccination (100% coverage) with kOCV (left column) or a hypothetical vaccine with VE = 1 indefinitely (right column). (<b>A-B</b>) Population susceptibility increases over time in the presence of migration rates of (solid line), (dashed line), and zero (dotted). (<b>C-D</b>) The effective reproductive number changes over time with X(t) differently for settings with basic reproductive numbers of 2 (red), 1.5 (green), and 1 (blue). (<b>E-F</b>) The probability that a single case sparks an outbreak of more than 10 cases. Birth and death rates are set to zero in each simulation.</p

Revaccination strategies to maximize duration of herd immunity (DHI).

<p>(<b>A</b>) Recurring mass vaccination events (arrows) with 100% coverage of susceptible people every year (dashed line) or two years (dotted line) is shown to periodically achieve then lose herd immunity, designated by the horizontal line at <i>R</i>_<i>e</i> = 1. Faded horizontal bars show times with herd immunity under each strategy and the total DHI is annotated to the right of each. (<b>B</b>) Routine vaccination of 2.4% (green), 3.6% (teal), or 4.8% (purple) of the population per month achieve herd immunity for 0, 4.4, and 4.3 years, respectively. (<b>C</b>) A “Mass and Maintain” strategy with one-time vaccination at 75% coverage followed by routine vaccination of 2.4% (green), 3.6% (teal), or 4.8% (purple) of the population per month can render herd immunity for 1.6, 5.2, and 4.3 years, respectively. The following are held constant for all simulations: population size = 10,000; maximum vaccine courses = 30,000; <i>R</i>₀ = 1.5; migration rate = ; and birth and death rates = .</p

Model structure.

<p>Simplified diagram of modeled compartments. Separate compartments for never-treated and previously-treated individuals and for DS and MDR-TB at each stage are included in the model but not shown here. Also not shown: mortality (occurs at an increased rate during active disease) and spontaneous self-cure (can occur from any active disease or treatment compartment).</p

Model projections for East/Southeast Asian TB epidemic assuming continuation of current practice.

<p>Simulations are fitted to notification data for Vietnam through year 2013. Median and uncertainty ranges among the data-consistent projections are shown through year 2025, assuming unchanged diagnostic and treatment practices. The model assumes decline over time in the number of transmissions per infectious person-year, and therefore total TB incidence falls (panel A), but the fraction of both new and previously-treated patients who present to care with MDR-TB continues to rise (panel B), and MDR-TB incidence (panel C) and mortality (panel D) also rise until at least 2025 in the majority of data-consistent simulations.</p

Bentiu PoC Camp case study.

<p>(<b>A</b>) Reported population size of the Bentiu PoC Camp (blue line), approximate number of people vaccinated assuming two-dose coverage (green bars), and monthly case counts from October to January (inset grey bars). IOM began reporting entries and exits in December 2015, which are represented by the faint green and red ribbons around the blue line. (<b>B</b>) The proportion susceptible over time (green line) decreases due to mass vaccination events and increases over time since vaccination. (<b>C</b>) The probability that a single case sparks an outbreak of more than 10 cases increases with <i>X</i>(<i>t</i>) and R₀, as represented by line color: R₀ = 1 (blue); 1.5 (green); 1.8 (black); and 2 (red). For reference, R_e = 0 yields an outbreak probability of 0; R_e = 1.01 yields a probability of 0.25; R_e = 1.35 yields a probability of 0.50; R_e = 1.84 yields a probability of 0.75; and R_e>4.66 yields an outbreak probability over 99%.</p

Model parameters.

<p>Model parameters.</p

Vaccine targeting optimized in settings with intermediate rates of migration.

<p>Vaccine impact, as measured by the difference in the cumulative probability of an outbreak comparing a mass kOCV campaign (coverage 100%) versus no vaccination, is shown to reach maxima (triangles) at intermediate levels of mobility (x axis). The time since vaccination (colored lines) modifies these maxima. Grey dashed lines denote the estimated migration rates for Calcutta, Bentiu PoC Camp, and Dhaka. In this example, <i>R</i>₀ = 1.5 and the average probability that a migrant is infected is 1/<i>N</i>, where <i>N</i> is the population size.</p

Relative contribution of four potential drivers of waning herd immunity in Bentiu PoC Camp.

<p>Relative contribution of four potential drivers of waning herd immunity in Bentiu PoC Camp.</p