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Open Access Improving the quality of modelling evidence used for tuberculosis policy evaluation

Mathematical modelling is commonly used to evaluate policy options for tuberculosis (TB) control in high-burden countries. Although major policy and funding decisions are made based on these analyses, there is concern about the variability of results produced using modelled policy analyses. We discuss new guidance for country-level TB policy modelling. The guidance was developed by the TB Modelling and Analysis Consortium in collaboration with the World Health Organization Global TB Programme, with input from a range of TB stakeholders (funders, modelling groups, country TB programme staff and subject matter experts). The guidance describes principles for country-level TB modelling, as well as good practices for operationalising the principles. The principles cover technical concerns such as model design, parameterisation and validation, as well as approaches for incorporating modelling into country-led policy making and budgeting. For modellers, this guidance suggests approaches to improve the quality and relevance of modelling undertaken to support country-level planning. For non-modellers, this guidance describes considerations for engaging modelling technical assistance, contributing to a modelling exercise and reviewing the results of modelled analyses. If routinely adopted, this guidance should improve the reliability, transparency and usefulness of modelling for country-level TB policy making. However, this guidance will not address all challenges facing modelling, and ongoing work is needed to improve the empirical evidence base for TB policy evaluation and develop stronger mechanisms for validating models. Increasing country ownership of the modelling process remains a challenge, requiring sustained engagement and capacity building.

Keywords: country-level; guidance; mathematical modeling; resource allocation

Document Type: Research Article

Affiliations: 1: Department of Global Health and Population , Center for Health Decision Science, Harvard T H Chan School of Public Health, Boston, Massachusetts, USA 2: Department of Infectious Disease Epidemiology, TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases 3: Department of Global Health and Development, London School of Hygiene & Tropical Medicine, London, UK 4: Global TB Programme, World Health Organization, Geneva, Switzerland

Publication date: 01 April 2019

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