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BMC Public Health. 2018 Jun 26;18(1):786. doi: 10.1186/s12889-018-5539-x.

Modelling the impact of social protection on tuberculosis: the S-PROTECT project.

Author information

1
Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK. Delia.Boccia@lshtm.ac.uk.
2
Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
3
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.
4
Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.
5
Institute for Disease Modeling, Bellevue, USA.
6
Department of Medicine, Epidemiology & International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.
7
Federal University of Brasilia, Brasilia, Brazil.
8
Federal University of Espírito Santo, Maruipe, Vitória, Brazil.
9
Oswaldo Cruz Foundation (FIOCRUZ), Brasília, DF, Brazil.
10
Global Tuberculosis Programme, World Health Organization, Geneva, Switzerland.
11
Division of Pulmonary and Critical Care Medicine, University of California San Francisco, San Francisco, USA.
12
TB Modelling Group, TB Centre and CMMID, London School of Hygiene and Tropical Medicine, Faculty of Epidemiology and Population Health, London, UK.

Abstract

BACKGROUND:

Tackling the social determinants of Tuberculosis (TB) through social protection is a key element of the post-2015 End TB Strategy. However, evidence informing policies are still scarce. Mathematical modelling has the potential to contribute to fill this knowledge gap, but existing models are inadequate. The S-PROTECT consortium aimed to develop an innovative mathematical modelling approach to better understand the role of social protection to improve TB care, prevention and control.

METHODS:

S-PROTECT used a three-steps approach: 1) the development of a conceptual framework; 2) the extraction from this framework of three high-priority mechanistic pathways amenable for modelling; 3) the development of a revised version of a standard TB transmission model able to capture the structure of these pathways. As a test case we used the Bolsa Familia Programme (BFP), the Brazilian conditional cash transfer scheme.

RESULTS:

Assessing one of these pathways, we estimated that BFP can reduce TB prevalence by 4% by improving households income and thus their nutritional status. When looking at the direct impact via malnutrition (not income mediated) the impact was 33%. This variation was due to limited data availability, uncertainties on data transformation and the pathway approach taken. These results are preliminary and only aim to serve as illustrative example of the methodological challenges encountered in this first modelling attempt, nonetheless they suggest the potential added value of integrating TB standard of care with social protection strategies.

CONCLUSIONS:

Results are to be confirmed with further analysis. However, by developing a generalizable modelling framework, S-PROTECT proved that the modelling of social protection is complex, but doable and allowed to draw the research road map for the future in this field.

KEYWORDS:

Brazil; Cash transfers; Control; Evaluation; Tuberculosis

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