Abstract
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.
Highlights
There is increasing demand for tuberculosis (TB) estimates at subnational level to inform TB programme planning in low and middle-income countries [1] there is substantial geographical heterogeneity in TB prevalence in high TB burden countries
We presented anonymised maps of each model’s 2018 district estimates to four Pakistan TB experts and asked them to grade the estimates on a scale from 1 to 10 based on how credible they deemed the estimates, based on their knowledge of the TB epidemic in their country
The hackathon models included a Bayesian binomial logistic regression with Markov Chain Monte Carlo (MCMC) inference (Model 1), an approximate Bayesian binomial logistic regression model with integrated nested Laplace approximations (INLA) inference (Model 2), an approximate Bayesian binomial-logistic model fit using the Broyden–Fletcher–Goldfarb– Shanno (BFGS) algorithm (Model 3), a Small Area Estimation and Latent Markov model with MCMC inference (Model 4) and artificial neural network followed by an Bayesian network (Model 5)
Summary
There is increasing demand for tuberculosis (TB) estimates at subnational level to inform TB programme planning in low and middle-income countries [1] there is substantial geographical heterogeneity in TB prevalence in high TB burden countries. Issues with subnational TB burden based on notifications are further exacerbated in contexts where access to care and reporting vary geographically. National population-based prevalence surveys provide a direct measurement of the burden of disease. They are considered the gold standard in estimating the prevalence of TB, but they are typically not powered to provide subnational estimates of TB. Most TB prevalence surveys only allow the generation of reasonably precise estimates of TB prevalence at the national level in a small number of strata (e.g., two to three geographical regions) [1]
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