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The Brazil Bolsa Familia (BF) program is a conditional cash transfer program aimed to reduce short-term poverty by direct cash transfers and to fight long-term poverty by increasing human capital among poor Brazilian people. Eligibility for Bolsa Familia benefits depends on a cutoff rule, which classifies the BF study as a regression discontinuity (RD) design. Extracting causal information from RD studies is challenging. Following Li et al (2015) and Branson and Mealli (2019), we formally describe the BF RD design as a local randomized experiment within the potential outcome approach. Under this framework, causal effects can be identified and estimated on a subpopulation where a local overlap assumption, a local SUTVA and a local ignorability assumption hold. Potential advantages of this framework over local regression methods based on continuity assumptions concern the causal estimands, the analysis, and the interpretation of the results. A critical issue of this local randomization approach is how to choose subpopulations for which we can draw valid causal inference. We propose a Bayesian model-based finite mixture approach to clustering to classify observations into subpopulations where the RD assumptions hold and do not hold. This approach has important advantages: a) it allows to account for the uncertainty in the subpopulation membership, which is typically neglected; b) it does not impose any constraint on the shape of the subpopulation; c) it is scalable to high-dimensional settings; e) it allows to target alternative causal estimands than average effects; and f) it is robust to a certain degree of manipulation/selection of the running variable. We apply our proposed approach to assess causal effects of the BF program on leprosy incidence in 2009, for Brazilian households who registered in the Brazilian National Registry for Social Programs in 2007-2008 for the first time

Additional file 1: Table S1. Univariate analyses of factors associated with incident and recurrent preterm birth in the second pregnancy, 2001-2015 (n = 1,764,025). Table S2. Frequency and factors associated with incident and recurrent preterm birth in the second pregnancy, 2012-2015 (n = 189,227). 1Analysis adjusted for all model variables. 2 Pdifference represents the interaction between incident and recurrent preterm birth for the respective characteristic. The equation for this model is as follows: Logit [ Pr (PTB2=1/Risk factors)] = β0 + β1 (PTB1) + β2 (risk factor X2) + β3 (PTB1) × (risk factor X2), where PTB1 and PTB2 are indicators of a preterm birth in the first and second pregnancy, respectively. Table S3. Incident and recurrent preterm birth in the second pregnancy according to change in the number of prenatal visits between the first and the second pregnancies, 2012-2015 (n = 189,482). 1Adjusted analysis by mother’s residence area, family density, mother’s self-declared race/skin color, mother’s level of education, mother’s marital status, maternal age, type of delivery in the second birth. 2Pdifference represents the interaction between incident and recurrent preterm delivery for the respective characteristic. The equation for this model is as follows: Logit [ Pr (PTB2=1/Risk factors)] = β0 + β1 (PTB1) + β2 (risk factor X2) + β3 (PTB1) × (risk factor X2), where PTB1 and PTB2 are indicators of a preterm birth in the first and second pregnancy, respectively. Table S4. Factors associated with incident and recurrent preterm birth in the second pregnancy, using cross-validation technique, 2001-2015. 1Analysis adjusted for all model variables. Figure S1. ROC curve for logistic regression model of incident preterm birth in cross-validation analysis. (A) Empirical ROC curve; (B) Binomial ROC curve. Non-parametric ROC curve not generated due to database size. Figure S2. ROC curve for logistic regression model of recurrent preterm birth in cross-validation analysis. (A) Empirical ROC curve; (B) Binomial ROC curve; (C) Non-parametric ROC curve.

Additional file 1. The RECORD statement.

Additional file 1. Table S1. Vaccination plan in Brazil.

Additional file 1: Table S1. Adjusted models† used to assess factors associated with term low birth weight by year of birth. Table S2. Adjusted models† used to assess factors associated with term low birth weight by exposure time at birth categorized into quartiles intervals.

Additional file 2: Figure S2. Performance on the health-related SDG index and individual health-related indicators in Brazilian States in 2016. The numbers represent the rescaled indicators and combined indexes on a scale of 0 to 100, with 0 representing the worst value for each indicator or index among all 195 countries covered by GBD from 1990 to 2016, and 100 representing the best value among them within the same period. The colours represent a scale from the best (dark green) to the worse (red) indicator or index values (rows) within all Brazilian States in 2016.

Additional file 1: Figure S1. Unscaled individual health-related indicators in Brazilian States in 2017. The numbers represent the unscaled (or true values) of the health-related indicators for Brazil and each Brazilian State from 1990 to 2017, We omitted the colours for better visualization and added decimals for the indicators when relevant.

Additional file 1: Description of: (1) the different linkage tools; (2) the datasets and the creation of the gold standard dataset; (3) the comparison metrics; (4) the descriptive analysis of the linkage between CadUnico and SINAN-TB; (5) the determination of Threshold in Weight Vector Classification.

OBJECTIVES: Dengue is the most common viral mosquito-borne disease, and women of reproductive age who live in or travel to endemic areas are at risk. Little is known about the effects of dengue during pregnancy on birth outcomes. The objective of this study is to examine the effect of maternal dengue severity on live birth outcomes. DESIGN AND SETTING: We conducted a population-based cohort study using routinely collected Brazilian data from 2006 to 2012. PARTICIPATING: We linked birth registration records and dengue registration records to identify women with and without dengue during pregnancy. Using multinomial logistic regression and Firth method, we estimated risk and ORs for preterm birth (

Summary Background Government policies can strongly influence migrants' health. Using a Health in All Policies approach, we systematically reviewed evidence on the impact of public policies outside of the health-care system on migrant health. Methods We searched the PubMed, Embase, and Web of Science databases from Jan 1, 2000, to Sept 1, 2017, for quantitative studies comparing the health effects of non-health-targeted public policies on migrants with those on a relevant comparison population. We searched for articles written in English, Swedish, Danish, Norwegian, Finnish, French, Spanish, or Portuguese. Qualitative studies and grey literature were excluded. We evaluated policy effects by migration stage (entry, integration, and exit) and by health outcome using narrative synthesis (all included studies) and random-effects meta-analysis (all studies whose results were amenable to statistical pooling). We summarised meta-analysis outcomes as standardised mean difference (SMD, 95% CI) or odds ratio (OR, 95% CI). To assess certainty, we created tables containing a summary of the findings according to the Grading of Recommendations Assessment, Development, and Evaluation. Our study was registered with PROSPERO, number CRD42017076104. Findings We identified 43 243 potentially eligible records. 46 articles were narratively synthesised and 19 contributed to the meta-analysis. All studies were published in high-income countries and examined policies of entry (nine articles) and integration (37 articles). Restrictive entry policies (eg, temporary visa status, detention) were associated with poor mental health (SMD 0·44, 95% CI 0·13–0·75; I2=92·1%). In the integration phase, restrictive policies in general, and specifically regarding welfare eligibility and documentation requirements, were found to increase odds of poor self-rated health (OR 1·67, 95% CI 1·35–1·98; I2=82·0%) and mortality (1·38, 1·10–1·65; I2=98·9%). Restricted eligibility for welfare support decreased the odds of general health-care service use (0·92, 0·85–0·98; I2=0·0%), but did not reduce public health insurance coverage (0·89, 0·71–1·07; I2=99·4%), nor markedly affect proportions of people without health insurance (1·06, 0·90–1·21; I2=54·9%). Interpretation Restrictive entry and integration policies are linked to poor migrant health outcomes in high-income countries. Efforts to improve the health of migrants would benefit from adopting a Health in All Policies perspective. Funding Swedish Council for Health, Working Life, and Social Research; UK Medical Research Council; Scottish Government Chief Scientist Office.

The steadily growing epidemic of diabetes mellitus poses a threat for global tuberculosis (TB) control. Previous studies have identified an important association between diabetes mellitus and TB. However, these studies have limitations: very few were carried out in low-income countries, with none in Africa, raising uncertainty about the strength of the diabetes mellitus-TB association in these settings, and many critical questions remain unanswered. An expert meeting was held in November 2009 to discuss where there was sufficient evidence to make firm recommendations about joint management of both diseases, to address research gaps and to develop a research agenda. Ten key research questions were identified, of which 4 were selected as high priority: (i) whether, when and how to screen for TB in patients with diabetes mellitus and vice versa; (ii) the impact of diabetes mellitus and non-diabetes mellitus hyperglycaemia on TB treatment outcomes and deaths, and the development of strategies to improve outcomes; (iii) implementation and evaluation of the tuberculosis 'DOTS' model for diabetes mellitus management; and (iv) the development and evaluation of better point-of-care diagnostic and monitoring tests, including measurements of blood glucose and glycated haemoglobin A(1c) (HbA(1c)) for patients with diabetes mellitus. Implementation of this research agenda will benefit the control of both diseases.

BACKGROUND: Although leprosy is recognised as a disease of poverty, there is little evidence on the specific socioeconomic factors associated with disease risk. To inform targeted strategies for disease elimination, we investigated socioeconomic markers of leprosy risk in Brazil. METHODS: Socioeconomic data from the 100 Million Brazilian Cohort were linked to the Brazilian national disease registry (Sistema de Informação de Agravos de Notificação) for leprosy from Jan 1, 2007, to Dec 31, 2014. Using Poisson regression, we assessed the association of socioeconomic factors with risk of incident leprosy in the full cohort and in children (aged 0-15 years), by leprosy subtype and region of residence. FINDINGS: In an analysis of 23 899 942 individuals including 18 518 patients with leprosy, increased levels of deprivation were associated with an increased risk of leprosy in Brazil. Directions of effect were consistent in children younger than 15 years and across disease subtypes. Individuals residing in regions with the highest poverty in the country (central-west, north, and northeast regions) had a risk of leprosy incidence five-to-eight times greater than did other individuals. Decreased levels of income and education and factors reflecting unfavourable living conditions were associated with an up to two-times increase in leprosy incidence (incidence rate ratio 1·46, 95% CI 1·32-1·62, for lowest vs highest quartile of income per capita; 2·09, 95% CI 1·62-2·72, for lowest vs highest level of education). INTERPRETATION: Within the poorest half of the Brazilian population, the most deprived individuals have the greatest risk of leprosy. Strategies focusing on early detection and treatment in the poorest populations could contribute substantially to global disease control. FUNDING: Medical Research Council, Wellcome Trust, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazil), the Conselho Nacional das Fundações Estaduais de Amparo à Pesquisa, Economic and Social Research Council, Biotechnology and Biological Sciences Research Council, Conselho Nacional de Desenvolvimento Científico e Tecnológico, and Fundação de Apoio à Pesquisa do Distrito Federal.