Effect of household and community interventions on the burden of tuberculosis in southern Africa: the ZAMSTAR community-randomised trial.

BACKGROUND: Southern Africa has had an unprecedented increase in the burden of tuberculosis, driven by the HIV epidemic. The Zambia, South Africa Tuberculosis and AIDS Reduction (ZAMSTAR) trial examined two public health interventions that aimed to reduce the burden of tuberculosis by facilitating either rapid sputum diagnosis or integrating tuberculosis and HIV services within the community. METHODS: ZAMSTAR was a community-randomised trial done in Zambia and the Western Cape province of South Africa. Two interventions, community-level enhanced tuberculosis case-finding (ECF) and household level tuberculosis-HIV care, were implemented between Aug 1, 2006, and July 31, 2009, and assessed in a 2×2 factorial design between Jan 9, 2010, and Dec 6, 2010. All communities had a strengthened tuberculosis-HIV programme implemented in participating health-care centres. 24 communities, selected according to population size and tuberculosis notification rate, were randomly allocated to one of four study groups using a randomisation schedule stratified by country and baseline prevalence of tuberculous infection: group 1 strengthened tuberculosis-HIV programme at the clinic alone; group 2, clinic plus ECF; group 3, clinic plus household intervention; and group 4, clinic plus ECF and household interventions. The primary outcome was the prevalence of culture-confirmed pulmonary tuberculosis in adults (≥18 years), defined as Mycobacterium tuberculosis isolated from one respiratory sample, measured 4 years after the start of interventions in a survey of 4000 randomly selected adults in each community in 2010. The secondary outcome was the incidence of tuberculous infection, measured using tuberculin skin testing in a cohort of schoolchildren, a median of 4 years after a baseline survey done before the start of interventions. This trial is registered, number ISRCTN36729271. FINDINGS: Prevalence of tuberculosis was evaluated in 64,463 individuals randomly selected from the 24 communities; 894 individuals had active tuberculosis. Averaging over the 24 communities, the geometric mean of tuberculosis prevalence was 832 per 100,000 population. The adjusted prevalence ratio for the comparison of ECF versus non-ECF intervention groups was 1·09 (95% CI 0·86-1·40) and of household versus non-household intervention groups was 0·82 (0·64-1·04). The incidence of tuberculous infection was measured in a cohort of 8809 children, followed up for a median of 4 years; the adjusted rate ratio for ECF versus non-ECF groups was 1·36 (95% CI 0·59-3·14) and for household versus non-household groups was 0·45 (0·20-1·05). INTERPRETATION: Although neither intervention led to a statistically significant reduction in tuberculosis, two independent indicators of burden provide some evidence of a reduction in tuberculosis among communities receiving the household intervention. By contrast the ECF intervention had no effect on either outcome. FUNDING: Bill & Melinda Gates Foundation.

Development and validation of a predictive ecological model for TB prevalence.

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.