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Objective To estimate of the number of children younger than 5 years who were household contacts of people with tuberculosis and were eligible for tuberculosis preventive treatment in 2017. Methods To estimate the number of eligible children, we obtained national values for the number of notified cases of bacteriologically confirmed pulmonary tuberculosis in 2017, the proportion of the population younger than 5 years in 2017 and average household size from published sources. We obtained global values for the number of active tuberculosis cases per household with an index case and for the prevalence of latent tuberculosis infection among children younger than 5 years who were household contacts of a tuberculosis case through systematic reviews, meta-analysis and Poisson regression models. Findings The estimated number of children younger than 5 years eligible for tuberculosis preventive treatment in 2017 globally was 1.27 million (95% uncertainty interval, UI: 1.24-1.31), which corresponded to an estimated global coverage of preventive treatment in children of 23% at best. By country, the estimated number ranged from less than one in the Bahamas, Iceland, Luxembourg and Malta to 350 000 (95% UI: 320 000-380 000) in India. Regionally, the highest estimates were for the World Health Organization (WHO) South-East Asia Region (510 000; 95% UI: 450 000-580 000) and the WHO African Region (470 000; 95% UI: 440 000-490 000). Conclusion Tuberculosis preventive treatment in children was underutilized globally in 2017. Treatment should be scaled up to help eliminate the pool of tuberculosis infection and achieve the End TB Strategy targets. [ABSTRACT FROM AUTHOR]

Background: Nationally representative tuberculosis (TB) prevalence surveys provide invaluable empirical measurements of TB burden but are a massive and complex undertaking. Therefore, methods that capitalize on data from these surveys are both attractive and imperative. The aim of this study was to use existing TB prevalence estimates to develop and validate an ecological predictive statistical model to indirectly estimate TB prevalence in low- and middle-income countries without survey data., Methods: We included national and subnational estimates from 30 nationally representative surveys and 2 district-level surveys in India, resulting in 50 data points for model development (training set). Ecological predictors included TB notification and programmatic data, co-morbidities and socio-environmental factors extracted from online data repositories. A random-effects multivariable binomial regression model was developed using the training set and was used to predict bacteriologically confirmed TB prevalence in 63 low- and middle-income countries across Africa and Asia in 2015., Results: Out of the 111 ecological predictors considered, 14 were retained for model building (due to incompleteness or collinearity). The final model retained for predictions included five predictors: continent, percentage retreated cases out of all notified, all forms TB notification rates per 100 000 population, population density and proportion of the population under the age of 15. Cross-fold validations in the training set showed very good average fit (R-sq = 0.92)., Conclusion: Predictive ecological modelling is a useful complementary approach to indirectly estimating TB burden and can be considered alongside other methods in countries with limited robust empirical measurements of TB among the general population.