Suchergebnisse

Background Tuberculosis elimination strategies in Brazil might neglect adolescents and young adults aged 10-24 years, hampering tuberculosis control. However, little is known about factors associated with tuberculosis treatment outcomes in this underserved group. In this study, we aimed to investigate social and health factors associated with unfavourable treatment outcomes in young people with tuberculosis in Brazil. Methods A national retrospective cohort study was done using data from Sistema de Informação de Agravos de Notificação (SINAN), the national tuberculosis registry in Brazil. People aged 10-24 years (young people) with tuberculosis registered in SINAN between Jan 1, 2015, and Dec 31, 2018, were included. Unfavourable outcomes were defined as loss to follow-up, treatment failure, and death. Favourable outcome was defined as treatment success. Multiple logistic regression models estimated the association between social and health factors and tuberculosis treatment outcomes. Findings 67 360 young people with tuberculosis were notified to SINAN, and we included 41 870 young people in our study. 7024 (17%) of the 41 870 included individuals had unfavourable treatment outcomes. Young people who received government cash transfers were less likely to have an unfavourable outcome (adjusted odds ratio 0·83, 95% CI 0·70-0·99). Homelessness (3·03, 2·07-4·42), HIV (2·89, 2·45-3·40), and illicit drug use (2·22, 1·93-2·55) were the main factors associated with unfavourable treatment outcome. Interpretation In this national cohort of young people with tuberculosis in Brazil, tuberculosis treatment success rates were lower than WHO End TB Strategy targets, with almost a fifth of participants experiencing unfavourable treatment outcomes. Homelessness, HIV, and illicit drug use were the main factors associated with unfavourable outcome. In Brazil, strategies are required to support this underserved group to ensure favourable tuberculosis treatment outcomes. Funding Wellcome Trust, UK Medical Research Council, and UK Foreign Commonwealth and Development Office.

ObjectivesMitigating the socioeconomic determinants of Tuberculosis (TB) and systematic screening of contacts and high-risk groups are targets of The World Health Organization (WHO) End TB Strategy by 2035. Our aim was to link socioeconomic information to TB datasets to inform policy makers in Brazil and contribute to addressing current challenges.
ApproachFollowing a signed technical cooperation agreement with the Ministry of Health (MoH), we linked nationwide data on 1.405.682 registries of TB diagnosed between 2004 and 2019 in Brazil to 131.697.800 demographic and socioeconomic registries from the 100 Million Brazilian Cohort (2001-2018) previously linked to nationwide mortality data. We established close links with TB managers to understand the database, clean and deduplicate registries and to analyse the data. We took advantage of the data structure, to set up a cohort of household contacts of TB patients and produce estimates of TB incidence by subgroups of demographic and socioeconomic characteristics.
ResultsThe interaction of the MoH was effective and facilitated by a robust TB Programme in the country. 567.999 (40,41%) TB cases were linked to the 100 Million Brazilian Cohort with high specificity (93.6%) and sensitivity (94.6%). Using family identifiers, we established the first TB case within a family unit (i.e., primary case) and followed their household contacts up to 15 years. We found the TB incidence among household contacts to be 427.8/100.000 person-years (95%CI 419.1-436.8). In the first year following the identification of the primary case, there was higher cumulative incidence among household contacts under 5 years of age, which was followed by a plateau of cases in this age group. Cummulative incidence in the other age groups presented a constant increase over time.
ConclusionThe close collaboration with the MoH, the development of an effective linkage algorithm and the availability of large socioeconomic data allowed for a unique analysis of the high incidence of TB among household contacts. Findings reinforce need for constant dialogue among stakeholders to strengthen case detection by primary health care.

ObjectiveBrazil is one of the most unequal countries in the world. We aimed to create a Social Disparities Index for Covid-19 (SDI-Covid-19) using linked administrative data to understand the role of inequalities in the COVID-19 pandemic to target social and health policies.
ApproachUsing linked administrative data (2010 Census and National Registry of Health Establishments) we selected municipal indicators for three domains: a) Brazilian Deprivation Index; b) % black/brown/indigenous, % people living in housing with density >2 person/room, % poor older people as sociodemographic; c) ICU beds and respirators rates weighted by distance to the health region headquarters as access to health service. A z-score weighted by the population estimated each indicator within the domains. The domains were combined by simple average of the z-scores and classified in quintiles (1-very low to 5-very high). Other methods (TRI, Macharia et al)1 found similar estimative.
ResultsThe SDI-COVID-19 estimated for the three moments of the pandemic (before (T0), first (T1) and second peak (T2)) in Brazil showed that municipalities belonging to the North and Northeast regions had worse SDI-Covid-19 compared to municipalities in the Midwest, Southeast, and South regions of the country. More than 97% of the municipalities in the North and Northeast regions had high and very high SDI in the three estimated periods. There was an increase of 46% in the number of municipalities in the Midwest region with high and very high SDI in T1 and 33% in T2 when compared to T0. Otherwise, we observed an increase of municipalities with very low and low SDI in T1 and T2 in the Southeast and South regions
ConclusionsThe deterministic linkage of administrative data allowed to build the SDI-COVID-19. This index identified the most vulnerable municipalities in the North and Northeast regions in contrast of municipalities in the Southeast and South regions of Brazil with lower inequalities during the pandemic. Findings can target policies to those areas.