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Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
Mauricio barreto:Big data: how can it help to expand epidemiological investigation?
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Mauricio barreto:Big data: how can it help to expand epidemiological investigation?

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Talk by Maurício Barreto on the 1st Symposium of Big Data and Public Health, 2013

Talk by Maurício Barreto on the 1st Symposium of Big Data and Public Health, 2013

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  • Get year; pdfs would be nice
  • Stabdardize CIs I all slides
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    • 1. Big data: how can it help to expand epidemiological investigation? Mauricio L. Barreto Rio de Janeiro, Outubro 2013
    • 2. Introduction • The health conditions of a population are defined by a complex set of factors and interventions acting upon this population. • Epidemiology is focused on the study of health determinants at population level using observational, but eventually experimental approaches. • In recent times, it has dedicated part of its effort to study the impact on health of health and non-health related interventions.
    • 3. Introduction • The availability of large data bases, frequently not connected, create new possibilities to conduct large population based studies with a great deal of generalizability. • We are going to use examples from the BCG Revac Trial and evaluations of Family Health Programme and the Bolsa Familia Programme (the Brazilian Conditional Cash Transfer Programme) to present the concept in great detail.
    • 4. Big data: is it something new? Ask Google to translate “Big data” from English to Portuguese: ”Grandes volumes de dados” And from Portuguese to English: ”Large volumes of data”
    • 5. Lessons from the BCG-REVAC: a large randomised trial of the efficacy of BCG revaccination 1
    • 6. Should we revaccinate with BCG? 1995 – Many countries revaccinated but there was no substantive evidence either way WHO recommended NOT to revaccinate! Brazilian TB control program recommended revaccinate! Brazilian Paediatricians and Pneumologists states disagreed!
    • 7. Manaus Salvador BCGREVACC TRIAL A trial of the protection of BCG revaccination against tuberculosis 1
    • 8. Main Objective: To evaluate the impact of a BCG dose at school age on tuberculosis incidence in a population with high coverage of neonatal BCG Secondary objectives: a- to evaluate the impact on leprosy; b- in long term, to compare the BCG effect in places environmentally different (differences in the occurrence of environmental mycobacteria?) 1
    • 9. Study design • Design: Cluster Randomised Controlled Trial • Schools randomized (in pairs) to BCG revaccination/no revaccination • Population: school children (7-14) • Recruitment: school records, visit, confirm ID, examine arm for BCG scar • Cases: from tuberculosis control program, validated & linked to database 1
    • 10. 36.1 (29-44) per 104 1 36.5 (29-44) per 104
    • 11. Protection by BCG revaccination 9% (-16 to 29) Brazilian government suspended recommendation of revaccination 1 Rodrigues et al, Lancet 2005
    • 12. 1
    • 13. 1
    • 14. Findings BCG revaccination does not confer additional protection Neonatal BCG protection can last up to 20 years after vaccination (maybe 40!) First dose at school age have a 25% effectiveness and it is costeffective Methodologically sound & logistically possible to do very large trials of new vaccines against tuberculosis 1
    • 15. More detailed look at methods and process used a lessons learned 1
    • 16. Linkage of cases to database Blind to alocation group and vaccine status Based on child‘s name, birth data and mother‘s name Validation: Home visit of those not linked to confirm not in study population 1
    • 17. Simple + validation: Follow up Ascertainment of cases - cases diagnosed by the Tuberculosis Control Program Weekly contact with TCP officers and visits to key health services; Inlcude cases born a bit outside the range of dates of birth in the study study population. 1
    • 18. Validation Abstracted clinical, radiological and laboratory data from cases Review done by 2 independents chest physicians and blinded to vaccine status and PPD result Classification of cases in confirmed, probable (I would treat based on the information), possible, (not enough information to decide) and not tuberculosis Third chest physician reviewed cases without agreement 1
    • 19. It is possible to do very large simple, relatively inexpensive and scientifically rigorous RCTs in Brazil relying on Cases detected by the Health system
    • 20. Evolução do psf 1998 1999 2005 2002 2000 2006 2007 2003 2008 2004 2009 2010
    • 21. Am J Public Health. 2009;99(1):87-93 Fixed-Effects Models for the Bivariate Association Between Infant Mortality Rate and Family Health Program Coverage: Brazil, 1996–2004
    • 22. Impacto do PSF na Saúde: resumo Redução da mortalidade infantil, principalmente nos municípios com menor índice de desenvolvimento humano (Macinko et al.,2007; Aquino et al.,2008). Redução da mortalidade em menores de 5 anos, por diarreia e IRAs (Rasella et al., 2010a).  Redução dos óbitos sem assistência e melhoria das informações vitais (Rasella et al., 2010b).  Redução das internações hospitalares para causas sensíveis a atenção primaria (Macinko et al., 2010). Redução da mortalidade por doenças cardiovasculares e cerebrovasculares (Rasella et al, não publicado) 30
    • 23. Programas de transferência condicional de renda /Programa bolsa família
    • 24. Programas de transferência condicional de renda (PTCC) – PTCR fornecem uma renda para famílias pobres com a condição que cumpram algumas condicionalidades, geralmente relacionadas com a saúde e a educação dos filhos. Implantação dos PTCR no mundo Fonte: World Bank 2009 32
    • 25. 33
    • 26. Sinergia entre o PBF e PSF O PSF e o PBF são programas federais com ampla cobertura nacional Evolução temporal da cobertura populacional do PBF e PSF no Brasil Fonte: MDS, 2011 MS, 2011 34
    • 27. Efeitos do PBF na saúde – Estudos Prévios Poucos estudos avaliaram o impacto do PBF sobre a saúde: 1) Número de crianças nascidas a termo 14% maior nas famílias beneficiarias (SAGI, 2010). 2) Percentual de crianças consideradas nutridas, segundo o Índice de Massa Corporal (IMC), 39,4% maior entre os beneficiários (SAGI, 2010). 3) Aumento de crianças vacinadas no período apropriado: na vacinação contra a Poliomielite e o Tétano, Difteria e Coqueluche (DPT) de 15 a 25% maiores nos beneficiários (SAGI, 2010). 4) Freqüência de desnutrição crônica (altura para idade) 29% menor em criança que as famílias não são beneficiarias. Desnutrição aguda (peso para altura) menor de 58% nas crianças de famílias beneficiarias (Santos et al., 2007). 5) Estudo publicado recentemente mostrou o impacto do PBF na desnutrição crônica, tendo as crianças de famílias beneficiárias 26% mais chances de ter altura para idade normal (Paes-Sousa et al., 2011). 35
    • 28. Discussão  Os resultados mostram que o Programa Bolsa Família contribui de maneira significativa para a redução da mortalidade de menores de cinco anos (TMM5), em particular por desnutrição e doenças diarreicas.  Este efeito foi mantido mesmo após o ajuste por diversas covariáveis socio-econômicas e o PSF e aumenta com o nível de cobertura, sendo maior quando altas coberturas são mantidas durante 4 anos ou mais.  O PBF teve um efeito de redução das internações hospitalares (em geral e por causas especificas), similar ao observado na mortalidade. Indicando um efeito do PBF na redução da ocorrência de desnutrição, diarreias e infecções respiratórias 41
    • 29. Conclusões Os resultados de nosso estudo fornecem evidências de que programas de transferência condicional de renda como o PBF, juntamente com uma estratégia de atenção básica eficaz, pode contribuir fortemente para a redução da mortalidade na infância, em particular por causas relacionadas à pobreza. Uma pequena quantia de dinheiro (do bolsa família) pode modificar significativamente as chances de sobrevivência das crianças. 42
    • 30. Development of a tool for assessment of the effects of Bolsa Família and other social programs on health, education, labor and gender relations based on population cohorts referenced in the Cadastro Único. Proponentes : Mauricio L. Barreto, Rômulo Paes-Sousa, Gerson Penna Davide Rasella, Susan M. Pereira, Rosana Aquino Instituições proponentes: 1INCT-CITECS-Ciência, Tecnologia e Inovação em Saúde, 2Instituto de Saúde Coletiva/UFBa, 3Centro Rio+/PNUD, 4Escola de Governo da FIOCRUZ, 5Núcleo de Medicina Tropical/UNB e 6COPPE/UFRJ (a ser confirmada) Julho 2013
    • 31. Big data: can it help epidemiological investigation and in particular evaluative studies?
    • 32. Yes • Extraordinary opportunities for research • Extraordinary opportunities to improve health  Can help us move towards a “learning health care” system where evaluation is integrated into practice and policy whenever clear evidence is lacking
    • 33. What is needed? • Expertise • Novel methods and approaches • Ensuring high data quality • Confidentiality and security of data  An expectation by patients/citizens, clinicians and policy makers that research and evaluation is a normal - in fact a necessary part of health care and policy
    • 34. That’s all for now!
    • 35. Thank you! 1

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