Abstract
BackgroundGlobal progress towards reducing tuberculosis (TB) incidence and mortality has consistently lagged behind the World Health Organization targets leading to a perception that large reductions in TB burden cannot be achieved. However, several recent and historical trials suggest that intervention efforts that are comprehensive and intensive can have a substantial epidemiological impact. We aimed to quantify the potential epidemiological impact of an intensive but realistic, community-wide campaign utilizing existing tools and designed to achieve a “step change” in the TB burden.MethodsWe developed a compartmental model that resembled TB transmission and epidemiology of a mid-sized city in India, the country with the greatest absolute TB burden worldwide. We modeled the impact of a one-time, community-wide screening campaign, with treatment for TB disease and preventive therapy for latent TB infection (LTBI). This one-time intervention was followed by the strengthening of the tuberculosis-related health system, potentially facilitated by leveraging the one-time campaign. We estimated the tuberculosis cases and deaths that could be averted over 10 years using this comprehensive approach and assessed the contributions of individual components of the intervention.ResultsA campaign that successfully screened 70% of the adult population for active and latent tuberculosis and subsequently reduced diagnostic and treatment delays and unsuccessful treatment outcomes by 50% was projected to avert 7800 (95% range 5450–10,200) cases and 1710 (1290–2180) tuberculosis-related deaths per 1 million population over 10 years. Of the total averted deaths, 33.5% (28.2–38.3) were attributable to the inclusion of preventive therapy and 52.9% (48.4–56.9) to health system strengthening.ConclusionsA one-time, community-wide mass campaign, comprehensively designed to detect, treat, and prevent tuberculosis with currently existing tools can have a meaningful and long-lasting epidemiological impact. Successful treatment of LTBI is critical to achieving this result. Health system strengthening is essential to any effort to transform the TB response.
Highlights
Global progress towards reducing tuberculosis (TB) incidence and mortality has consistently lagged behind the World Health Organization targets leading to a perception that large reductions in TB burden cannot be achieved
Health system strengthening is essential to any effort to transform the TB response
After accounting for imperfect test sensitivity, losses to follow-up, and imperfect completion of treatment, one-time screening of 70% of the adult population would result in the successful treatment of 42% of adults with prevalent active TB (Fig. 3, Active case finding (ACF)) and 30% of adults with latent TB infection (LTBI) (Fig. 3, Tuberculosis preventive therapy (TPT))
Summary
Global progress towards reducing tuberculosis (TB) incidence and mortality has consistently lagged behind the World Health Organization targets leading to a perception that large reductions in TB burden cannot be achieved. Community-wide screening for active TB in Cà Mau Province, Vietnam, reduced prevalence by 44% over 3 years, with potential reductions in transmission and TB incidence to be observed over the coming years [4]. These studies, combined with examples of successful TB control programs implemented at the regional and national levels [5, 6], provide evidence that substantial reductions in TB burden can be achieved with focused, intensive effort; scalable and sustainable approaches for achieving such reductions have yet to be developed. Modern tools—including, for example, highly portable digital X-ray devices with emerging artificial intelligence (AI)-based interpretation [7], adoption of novel short-course preventive therapy with drugs whose price is being cut dramatically [8], and rapid high-sensitivity molecular testing for TB (and drug resistance) [9]—could facilitate implementation of intensive, broad-scale efforts to find, treat, and prevent TB that previously might have been deemed infeasible
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