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BACKGROUND: The post-2015 End TB Strategy proposes targets of 50% reduction in tuberculosis incidence and 75% reduction in mortality from tuberculosis by 2025. We aimed to assess whether these targets are feasible in three high-burden countries with contrasting epidemiology and previous programmatic achievements. METHODS: 11 independently developed mathematical models of tuberculosis transmission projected the epidemiological impact of currently available tuberculosis interventions for prevention, diagnosis, and treatment in China, India, and South Africa. Models were calibrated with data on tuberculosis incidence and mortality in 2012. Representatives from national tuberculosis programmes and the advocacy community provided distinct country-specific intervention scenarios, which included screening for symptoms, active case finding, and preventive therapy. FINDINGS: Aggressive scale-up of any single intervention scenario could not achieve the post-2015 End TB Strategy targets in any country. However, the models projected that, in the South Africa national tuberculosis programme scenario, a combination of continuous isoniazid preventive therapy for individuals on antiretroviral therapy, expanded facility-based screening for symptoms of tuberculosis at health centres, and improved tuberculosis care could achieve a 55% reduction in incidence (range 31-62%) and a 72% reduction in mortality (range 64-82%) compared with 2015 levels. For India, and particularly for China, full scale-up of all interventions in tuberculosis-programme performance fell short of the 2025 targets, despite preventing a cumulative 3·4 million cases. The advocacy scenarios illustrated the high impact of detecting and treating latent tuberculosis. INTERPRETATION: Major reductions in tuberculosis burden seem possible with current interventions. However, additional interventions, adapted to country-specific tuberculosis epidemiology and health systems, are needed to reach the post-2015 End TB Strategy

Truncated models are indirect methods to estimate the size of a hidden population which, in contrast to the capture-recapture method. can be used on a single information Source. We estimated the coverage of a tuberculosis screening programme among illicit drug users and homeless persons with a mobile digital X-ray unit between 1 January 2003 and 31 December 2005 in Rotterdam. The Netherlands, using, truncated models. The screening programme reached about two-third of the estimated target Population at least once annually. The intended coverage (at least two chest X-rays per person per year) was about 23%. We conclude that simple truncated models can be used relatively easily oil available single-source routine data to estimate the size of a population of illicit drug users and homeless persons. We assumed that the most likely overall bias in this Study Would be overestimation and therefore the coverage of the targeted mobile tuberculosis screening programme would be higher.

Capture-recapture analysis has been used to evaluate infectious disease surveillance. Violation of the underlying assumptions can jeopardize the validity of the capture-recapture estimates and a tool is needed for cross-validation. We re-examined 19 datasets of log-linear model capture-recapture studies on infectious disease incidence using three truncated models for incomplete count data as alternative population estimators. The truncated models yield comparable estimates to independent log-linear capture-recapture models and to parsimonious log-linear models when the number of patients is limited. or the ratio between patients registered once and twice is between 0.5 and 1.5. Compared to saturated log-linear models the truncated models produce considerably lower and often more plausible estimates. We conclude that for estimating infectious disease incidence independent and parsimonious three-source log-linear capture-recapture models are preferable but truncated models can be used as a heuristic tool to identify possible failure in log-linear models, especially when saturated log-linear models are selected.