BackgroundAn unprecedented number of nationwide tuberculosis (TB) prevalence surveys will be implemented between 2010 and 2015, to better estimate the burden of disease caused by TB and assess whether global targets for TB control set for 2015 are achieved. It is crucial that results are analysed using best-practice methods.ObjectiveTo provide new theoretical and practical guidance on best-practice methods for the analysis of TB prevalence surveys, including analyses at the individual as well as cluster level and correction for biases arising from missing data.Analytic methodsTB prevalence surveys have a cluster sample survey design; typically 50-100 clusters are selected, with 400-1000 eligible individuals in each cluster. The strategy recommended by the World Health Organization (WHO) for diagnosing pulmonary TB in a nationwide survey is symptom and chest X-ray screening, followed by smear microscopy and culture examinations for those with an abnormal X-ray and/or TB symptoms. Three possible methods of analysis are described and explained. Method 1 is restricted to participants, and individuals with missing data on smear and/or culture results are excluded. Method 2 includes all eligible individuals irrespective of participation, through multiple missing value imputation. Method 3 is restricted to participants, with multiple missing value imputation for individuals with missing smear and/or culture results, and inverse probability weighting to represent all eligible individuals. The results for each method are then compared and illustrated using data from the 2007 national TB prevalence survey in the Philippines. Simulation studies are used to investigate the performance of each method.Key findingsA cluster-level analysis, and Methods 1 and 2, gave similar prevalence estimates (660 per 100,000 aged ≥ 10 years old), with a higher estimate using Method 3 (680 per 100,000). Simulation studies for each of 4 plausible scenarios show that Method 3 performs best, with Method 1 systematically underestimating TB prevalence by around 10%.ConclusionBoth cluster-level and individual-level analyses should be conducted, and individual-level analyses should be conducted both with and without multiple missing value imputation. Method 3 is the safest approach to correct the bias introduced by missing data and provides the single best estimate of TB prevalence at the population level.

In this paper, a class of resampling techniques for finite populations under complex sampling design is introduced. The basic idea on which they rest is a two-step procedure consisting in:(i) constructing a "pseudo-population" on the basis of sample data; (ii) drawing a sample from the predicted population according to an appropriate resampling design. From a logical point of view, this approach is essentially based on the plug-in principle by Efron, at the "sampling design level". Theoretical justifications based on large sample theory are provided. New approaches to construct pseudo populations based on various forms of calibrations are proposed. Finally, a simulation study is performed.

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Pakistan’s national tuberculosis control programme (NTP) is among the many programmes worldwide that value the importance of subnational tuberculosis (TB) burden estimates to support disease control efforts, but do not have reliable estimates. A hackathon was thus organised to solicit the development and comparison of several models for small area estimation of TB. The TB hackathon was launched in April 2019. Participating teams were requested to produce district-level estimates of bacteriologically positive TB prevalence among adults (over 15 years of age) for 2018. The NTP provided case-based data from their 2010–2011 TB prevalence survey, along with data relating to TB screening, testing and treatment for the period between 2010–2011 and 2018. Five teams submitted district-level TB prevalence estimates, methodological details and programming code. Although the geographical distribution of TB prevalence varied considerably across models, we identified several districts with consistently low notification-to-prevalence ratios. The hackathon highlighted the challenges of generating granular spatiotemporal TB prevalence forecasts based on a cross-sectional prevalence survey data and other data sources. Nevertheless, it provided a range of approaches to subnational disease modelling. The NTP’s use and plans for these outputs shows that, limitations notwithstanding, they can be valuable for programme planning.

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The number of samples is a major issue when estimating the mean herbage mass of grazed paddocks. The aim of this study was to assess the number of samples required for accurate visual estimation of mean herbage mass in relation to the herbage mass heterogeneity and size of paddocks. Data were collected across scales of space and time (273 sampling events) from paddocks on Campos grasslands in Uruguay, using the visual estimation technique. The mean herbage mass of the paddocks ranged from 270 to 6350 kg of dry matter (DM) per hectare with coefficient of variation (CV) of 0.13 to 1.26. Twenty‐four events representing four levels of herbage mass heterogeneity (CV = 0.3, 0.5, 0.7 and 1.0) × three levels of paddock size (small, 5–13 ha; medium, 41–67 ha; large, 100–140 ha) were chosen (two replicates per group), and analyzed for the probability that the estimation error exceeded 10% of the mean (10% error probability) using the bootstrap technique. The number of samples required for controlling the 10% error probability below 0.1 increased gradually from 50 to 150 per paddock as the CV increased from 0.3 to 0.7, then sharply to 350 until the CV increased to 1.0, with no effect of paddock size. Taking account of the distribution of CV (< 0.7 in nearly 80% of the events), we propose a general recommendation to take a minimum of 150 samples per paddock for accurate estimation of mean herbage mass in Campos grasslands irrespective of the size of paddocks.