EDITORIAL

INT J TUBERC LUNG DIS 19(3):255 Q 2015 The Union http://dx.doi.org/10.5588/ijtld.15.0037

To improve our tuberculosis burden estimates we need to learn from each other AS THE GLOBAL tuberculosis (TB) community finalises its assessment of whether we have met the Millennium Development Goals (MDGs) and the 2006–2015 Global Plan to Stop TB targets,1 the crucial role of the methods we use to estimate the global TB burden is again put under the spotlight. One of the challenges we face is that the direct measurement of TB disease incidence, a primary indicator for both the MDGs and post-2015 targets, is unfeasible at country level.2 As a consequence, the World Health Organization (WHO) and others use models to make estimates that rely on a mix of data and assumptions. While there are different modelling approaches, broadly divided into statistical, ecological or dynamic transmission models, they all face similar challenges of bridging the gap between the available data (e.g., TB case notifications) and the required indicator (e.g., TB disease incidence). Each method has its strengths and limitations, and any model is a simplification of reality and will therefore be wrong to some extent. But, to paraphrase George Box, they can be useful,3 and they are very much needed. There is now a growing body of literature on methods to estimate TB burden,2,4–8 and this provides an exciting new opportunity to compare and contrast the results of these different approaches, and to improve our estimates. Such an exercise is currently underway, coordinated by the WHO Global Task Force on TB Impact Measurement, which is holding a global consultation in early 2015 to improve the methods they use to estimate TB disease burden. The paper by Avilov and colleagues in this issue of the Journal9 is therefore a timely addition to the literature. Similar to historical WHO methods,10 the paper applies a dynamic transmission model to estimate TB disease incidence and case detection rate from routine notification data. While the work could be improved, for example by including simultaneous fitting to mortality data and a more comprehensive expression of uncertainty, it is an example of the novel work that is critically needed. With the MDGs and current Stop TB Global Plan cycle ending in 2015 and the ambitious post-2015 targets set,11 now is the ideal time to focus on improving the methods we use to estimate TB burden. By learning from each other, we can improve our estimates and ultimately better inform the global TB community on how far we have to go to end the global TB epidemic. REIN M. G. J. HOUBEN RICHARD G. WHITE

TB Modelling Group, TB Centre and CMMID Faculty of Epidemiology & Population Health London School of Hygiene & Tropical Medicine London, UK e-mail: [email protected] Acknowledgments RGW is funded by the Medical Research Council (UK) (MR/ J005088/1), the Bill and Melinda Gates Foundation (TB Modelling and Analysis Consortium: OPP1084276), the US Centers for Disease Control and Prevention/President’s Emergency Plan For AIDS Relief via the Aurum Institute (U2GPS0008111), and the United States Agency for International Development (USAID)/ IUATLD/The Union North America (TREAT TB: Technology, Research, Education, and Technical Assistance for Tuberculosis; GHN-A-OO-08-00004-00). RMGJH is funded by the Bill and Melinda Gates Foundation (TB Modelling and Analysis Consortium: OPP1084276). Conflicts of interest: none declared.

References 1 World Health Organization/Stop TB. The Global Plan to Stop TB 2006–2015: actions for life: towards a world free of tuberculosis. Geneva, Switzerland: Stop TB Partnership, 2006. 2 World Health Organization. TB Impact measurement: policy and recommendations for how to assess the epidemiological burden of TB and the impact of TB control. Geneva, Switzerland: WHO, 2009. 3 Box G E P, Draper N R. Empirical model-building and response surfaces. New York, NY, USA: John Wiley & Sons, 1987. 4 World Health Organization. Global Tuberculosis Report 2014. WHO/HTM/TB/2014.08. Geneva, Switzerland: WHO, 2014. 5 Pretorius C, Glaziou P, Dodd P J, White R, Houben R. Using the TIME model in Spectrum to estimate tuberculosis-HIV incidence and mortality. AIDS 2014; 28 (Suppl 4): S477–S487. 6 Jenkins H E, Tolman A W, Yuen C M, et al. Incidence of multidrug-resistant tuberculosis disease in children: systematic review and global estimates. Lancet 2014; 383: 1572–1579. 7 Dodd P J, Gardiner E, Coghlan R, Seddon J A. Burden of childhood tuberculosis in 22 high-burden countries: a mathematical modelling study. Lancet Glob Health 2014; 2: e453–e459. 8 Murray C J L, Ortblad K F, Guinovart C, et al. Global, regional, and national incidence and mortality for HIV, tuberculosis, and malaria during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2014; 384: 1005–1070. 9 Avilov K, Romanyukha A A, Borisov S E, Belilovsky E M, Nechaeva O B, Karkach A S. An approach to estimating tuberculosis incidence and case detection rate from routine notification data. Int J Tuberc Lung Dis 2015; 19: 000–000. [in this issue] 10 Dye C, Scheele S, Dolin P, Pathania V, Raviglione M C. Consensus statement. Global burden of tuberculosis: estimated incidence, prevalence, and mortality by country. WHO Global Surveillance and Monitoring Project. JAMA 1999; 282: 677– 686. 11 Sixty-Seventh World Health Assembly. TB resolution - global strategy and targets for tuberculosis prevention, care and control after 2015. (A67.1, A67/VR/6, 21 May 2014.) Geneva, Switzerland: WHO, 2014. http://apps.who.int/gb/ebwha/pdf_ files/WHA67-REC1/A67_2014_REC1-en.pdf Accessed February 2015.

To improve our tuberculosis burden estimates we need to learn from each other.

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