D Boccia1, W Rudgard2, S Shrestha3, K Lönnroth4, P Eckhoff5, J Golub6, M Sanchez7, E Maciel8, D Rasella9, P Shete10,11, D Pedrazzoli2, R Houben12, S Chang5, D Dowdy3. 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.
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.
Authors: David W Dowdy; Jonathan E Golub; Richard E Chaisson; Valeria Saraceni Journal: Proc Natl Acad Sci U S A Date: 2012-05-29 Impact factor: 11.205
Authors: E L N Maciel; J E Golub; R L Peres; D J Hadad; J L Fávero; L P Molino; J W Bae; C M Moreira; V do V Detoni; S A Vinhas; M Palaci; R Dietze Journal: Int J Tuberc Lung Dis Date: 2010-11 Impact factor: 2.373
Authors: Ana W Torrens; Davide Rasella; Delia Boccia; Ethel L N Maciel; Joilda S Nery; Zachary D Olson; Draurio C N Barreira; Mauro N Sanchez Journal: Trans R Soc Trop Med Hyg Date: 2016-03 Impact factor: 2.184
Authors: Tom Wingfield; Marco A Tovar; Doug Huff; Delia Boccia; Rosario Montoya; Eric Ramos; James J Lewis; Robert H Gilman; Carlton A Evans Journal: Eur Respir J Date: 2016-09-22 Impact factor: 16.671
Authors: Daniel J Carter; Rhian Daniel; Ana W Torrens; Mauro N Sanchez; Ethel Leonor N Maciel; Patricia Bartholomay; Draurio C Barreira; Davide Rasella; Mauricio L Barreto; Laura C Rodrigues; Delia Boccia Journal: BMJ Glob Health Date: 2019-01-24
Authors: M Gabriela M Gomes; Juliane F Oliveira; Adelmo Bertolde; Diepreye Ayabina; Tuan Anh Nguyen; Ethel L Maciel; Raquel Duarte; Binh Hoa Nguyen; Priya B Shete; Christian Lienhardt Journal: Nat Commun Date: 2019-06-06 Impact factor: 14.919
Authors: William E Rudgard; Daniel J Carter; James Scuffell; Lucie D Cluver; Nicole Fraser-Hurt; Delia Boccia Journal: BMC Public Health Date: 2018-08-22 Impact factor: 3.295