EDITORIAL
INT J TUBERC LUNG DIS 18(5):507–507 Q 2014 The Union http://dx.doi.org/10.5588/ijtld.14.0235
The benefits and risks of mathematical modelling in tuberculosis IN THIS ISSUE of the Journal, Houben et al. describe how mathematical models can be used to improve tuberculosis (TB) control in settings of high human immunodeficiency virus (HIV) prevalence.1 Based on a literature review and an expert meeting, five key areas for modelling are suggested and examples of research questions given for each. As the authors suggest, current efforts to control TB in high HIV-burden settings have been ineffective, particularly in southern Africa, where not only has TB not been declining, it has continued to increase in many settings.2 With a number of promising new tools now available or on the horizon, particularly in the area of new TB diagnostics,3 and improved treatment,4 models that provide evidence as to which interventions are the most cost-effective to implement, in which settings, and in what combinations, have the potential to be useful to policy makers. In real terms, this means that models can be used to direct scarce resources towards the optimal mix of interventions that potentially have the greatest impact, and away from those that have minimal impact. However, mathematical models in public health are becoming increasingly complex, and as a result some of the subtleties of interpretation are in danger of being overlooked. Policy makers will rarely be equipped to fully understand the complex equations used, the embedded assumptions in models and the potential limitations these assumptions may impose in complex models. It is inherent in modelling that there is often more than one way to represent a disease process or how TB is transmitted in a community, for example, which may result in different, but still valid, model outcomes. The move towards models that attempt to capture the complexities of health systems (one of the key areas outlined by Houben et al.) will only increase the complexity of the model.5 Hence, while models can provide evidence to support decision making, their relative place in the evidence hierarchy perhaps needs to be clarified. A recent example is a model where the authors arrive at the strong conclusion that a rapid isoniazid resistance test will have minimal impact on the transmission of TB and drug-resistant TB.6 Such model conclusions can be used to make decisions with
far-reaching consequences, in this case reducing the relative priority of isoniazid resistance detection in the design of next-generation rapid TB susceptibility tests. In the absence of controlled trials or direct programmatic data that provide a higher level of evidence, policy makers should be wary of basing such decisions on model outcomes alone. On the other hand, TB models can and do highlight major gaps in data and areas where greater empiric evidence is needed. Making these assumptions and therefore their uncertainties explicit provides benefit in itself. In addition to highlighting a research agenda for TB-HIV modelling, Houben et al. suggest that greater coordination between modellers and key stakeholders, including policy makers, is required. One of the aims of greater coordination should be improved understanding of the limitations of models and how they should be interpreted, with a view to providing the best possible evidence base for decision making. HELEN S COX, PHD Wellcome Trust Research Fellow Division of Medical Microbiology and Institute for Infectious Disease and Molecular Medicine University of Cape Town Cape Town South Africa e-mail: [email protected] References 1 Houben R M G J, Dowdy D W, Vassall A, et al. How can mathematical models advance TB control in high HIV prevalence settings? Int J Tuberc Lung Dis 2012; 18: 509-514. 2 World Health Organization. Global Tuberculosis Report 2013. WHO/HTM/TB/2013.11. Geneva, Switzerland: WHO, 2013. 3 Drobniewski F, Nikolayevskyy V, Balabanova Y, Bang D, Papaventsis D. Diagnosis of tuberculosis and drug resistance: what can new tools bring us? Int J Tuberc Lung Dis 2012; 16: 860–870. 4 Grosset J H, Singer T G, Bishai W R. New drugs for the treatment of tuberculosis: hope and reality. Int J Tuberc Lung Dis 2012; 16: 1005–1014. 5 Basu S, Andrews J. Complexity in mathematical models of public health policies: a guide for consumers of models. PLOS MED 2013; 10(10): e1001540. 6 Denkinger C M, Pai M, Dowdy D. Do we need to detect isoniazid resistance in addition to rifampicin resistace in diagnostic tests for tuberculosis? PLOS ONE 2014; 9(1): e84197.
INT J TUBERC LUNG DIS 18(5):507–507 Q 2014 The Union http://dx.doi.org/10.5588/ijtld.14.0235
The benefits and risks of mathematical modelling in tuberculosis IN THIS ISSUE of the Journal, Houben et al. describe how mathematical models can be used to improve tuberculosis (TB) control in settings of high human immunodeficiency virus (HIV) prevalence.1 Based on a literature review and an expert meeting, five key areas for modelling are suggested and examples of research questions given for each. As the authors suggest, current efforts to control TB in high HIV-burden settings have been ineffective, particularly in southern Africa, where not only has TB not been declining, it has continued to increase in many settings.2 With a number of promising new tools now available or on the horizon, particularly in the area of new TB diagnostics,3 and improved treatment,4 models that provide evidence as to which interventions are the most cost-effective to implement, in which settings, and in what combinations, have the potential to be useful to policy makers. In real terms, this means that models can be used to direct scarce resources towards the optimal mix of interventions that potentially have the greatest impact, and away from those that have minimal impact. However, mathematical models in public health are becoming increasingly complex, and as a result some of the subtleties of interpretation are in danger of being overlooked. Policy makers will rarely be equipped to fully understand the complex equations used, the embedded assumptions in models and the potential limitations these assumptions may impose in complex models. It is inherent in modelling that there is often more than one way to represent a disease process or how TB is transmitted in a community, for example, which may result in different, but still valid, model outcomes. The move towards models that attempt to capture the complexities of health systems (one of the key areas outlined by Houben et al.) will only increase the complexity of the model.5 Hence, while models can provide evidence to support decision making, their relative place in the evidence hierarchy perhaps needs to be clarified. A recent example is a model where the authors arrive at the strong conclusion that a rapid isoniazid resistance test will have minimal impact on the transmission of TB and drug-resistant TB.6 Such model conclusions can be used to make decisions with
far-reaching consequences, in this case reducing the relative priority of isoniazid resistance detection in the design of next-generation rapid TB susceptibility tests. In the absence of controlled trials or direct programmatic data that provide a higher level of evidence, policy makers should be wary of basing such decisions on model outcomes alone. On the other hand, TB models can and do highlight major gaps in data and areas where greater empiric evidence is needed. Making these assumptions and therefore their uncertainties explicit provides benefit in itself. In addition to highlighting a research agenda for TB-HIV modelling, Houben et al. suggest that greater coordination between modellers and key stakeholders, including policy makers, is required. One of the aims of greater coordination should be improved understanding of the limitations of models and how they should be interpreted, with a view to providing the best possible evidence base for decision making. HELEN S COX, PHD Wellcome Trust Research Fellow Division of Medical Microbiology and Institute for Infectious Disease and Molecular Medicine University of Cape Town Cape Town South Africa e-mail: [email protected] References 1 Houben R M G J, Dowdy D W, Vassall A, et al. How can mathematical models advance TB control in high HIV prevalence settings? Int J Tuberc Lung Dis 2012; 18: 509-514. 2 World Health Organization. Global Tuberculosis Report 2013. WHO/HTM/TB/2013.11. Geneva, Switzerland: WHO, 2013. 3 Drobniewski F, Nikolayevskyy V, Balabanova Y, Bang D, Papaventsis D. Diagnosis of tuberculosis and drug resistance: what can new tools bring us? Int J Tuberc Lung Dis 2012; 16: 860–870. 4 Grosset J H, Singer T G, Bishai W R. New drugs for the treatment of tuberculosis: hope and reality. Int J Tuberc Lung Dis 2012; 16: 1005–1014. 5 Basu S, Andrews J. Complexity in mathematical models of public health policies: a guide for consumers of models. PLOS MED 2013; 10(10): e1001540. 6 Denkinger C M, Pai M, Dowdy D. Do we need to detect isoniazid resistance in addition to rifampicin resistace in diagnostic tests for tuberculosis? PLOS ONE 2014; 9(1): e84197.
