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Open Access Modelling the social and structural determinants of tuberculosis: opportunities and challenges

INTRODUCTION: Despite the close link between tuberculosis (TB) and poverty, most mathematical models of TB have not addressed underlying social and structural determinants.

OBJECTIVE: To review studies employing mathematical modelling to evaluate the epidemiological impact of the structural determinants of TB.

METHODS: We systematically searched PubMed and personal libraries to identify eligible articles. We extracted data on the modelling techniques employed, research question, types of structural determinants modelled and setting.

RESULTS: From 232 records identified, we included eight articles published between 2008 and 2015; six employed population-based dynamic TB transmission models and two non-dynamic analytic models. Seven studies focused on proximal TB determinants (four on nutritional status, one on wealth, one on indoor air pollution, and one examined overcrowding, socio-economic and nutritional status), and one focused on macro-economic influences.

CONCLUSIONS: Few modelling studies have attempted to evaluate structural determinants of TB, resulting in key knowledge gaps. Despite the challenges of modelling such a complex system, models must broaden their scope to remain useful for policy making. Given the intersectoral nature of the interrelations between structural determinants and TB outcomes, this work will require multidisciplinary collaborations. A useful starting point would be to focus on developing relatively simple models that can strengthen our knowledge regarding the potential effect of the structural determinants on TB outcomes.

Keywords: mathematical modelling; social determinants; tuberculosis

Document Type: Research Article

Affiliations: 1: TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London 2: Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London 3: Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, UK 4: World Health Organization, Global Tuberculosis Programme, Geneva, Switzerland, Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden 5: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA 6: World Health Organization, Global Tuberculosis Programme, Geneva, Switzerland 7: KNCV, Tuberculosis Foundation, The Hague, The Netherlands

Publication date: September 1, 2017

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