Prevalence of adult smokers in Brazilian capitals according to socioeconomic deprivation.

OBJECTIVE: To estimate the prevalence of adult smokers in the 26 capitals and the Federal District according to the Brazilian Deprivation Index (Índice Brasileiro de Privação - IBP). METHODS: Dataset on smoking were obtained from the Surveillance of Risk and Protective Factors for Noncommunicable Diseases by Survey (Vigitel) system for the 26 capitals and the Federal District, in the period from 2010 to 2013. The IBP classifies the census sectors according to indicators such as: income less than ½ minimum wage, illiterate population and without sanitary sewage. In the North and Northeast regions, the census sectors were grouped into four categories (low, medium, high and very high deprivation) and in the South, Southeast and Midwest regions into three (low, medium and high deprivation). Prevalence estimates of adult smokers were obtained using the indirect estimation method in small areas. To calculate the prevalence ratios, Poisson models are used. RESULTS: The positive association between prevalence and deprivation of census sector categories was found in 16 (59.3%) of the 27 cities. In nine (33.3%) cities, the sectors with the greatest deprivation had a higher prevalence of smokers when compared to those with the least deprivation, and in two (7.4%) there were no differences. In Aracaju, Belém, Fortaleza, João Pessoa, Macapá and Salvador, the prevalence of adult smokers was three times higher in the group of sectors with greater deprivation compared to those with less deprivation. CONCLUSION: Sectors with greater social deprivation had a higher prevalence of smoking, compared with less deprivation, pointing to social inequalities.

Prevalence of adult smokers in Brazilian capitals according to socioeconomic deprivation / Prevalência de fumantes adultos nas capitais brasileiras, segundo privação socioeconômica

ABSTRACT Objective: To estimate the prevalence of adult smokers in the 26 capitals and the Federal District according to the Brazilian Deprivation Index (Índice Brasileiro de Privação - IBP). Methods: Dataset on smoking were obtained from the Surveillance of Risk and Protective Factors for Noncommunicable Diseases by Survey (Vigitel) system for the 26 capitals and the Federal District, in the period from 2010 to 2013. The IBP classifies the census sectors according to indicators such as: income less than ½ minimum wage, illiterate population and without sanitary sewage. In the North and Northeast regions, the census sectors were grouped into four categories (low, medium, high and very high deprivation) and in the South, Southeast and Midwest regions into three (low, medium and high deprivation). Prevalence estimates of adult smokers were obtained using the indirect estimation method in small areas. To calculate the prevalence ratios, Poisson models are used. Results: The positive association between prevalence and deprivation of census sector categories was found in 16 (59.3%) of the 27 cities. In nine (33.3%) cities, the sectors with the greatest deprivation had a higher prevalence of smokers when compared to those with the least deprivation, and in two (7.4%) there were no differences. In Aracaju, Belém, Fortaleza, João Pessoa, Macapá and Salvador, the prevalence of adult smokers was three times higher in the group of sectors with greater deprivation compared to those with less deprivation. Conclusion: Sectors with greater social deprivation had a higher prevalence of smoking, compared with less deprivation, pointing to social inequalities.

RESUMO Objetivo: Estimar as prevalências de adultos fumante nas 26 capitais e no Distrito Federal segundo o Índice Brasileiro de Privação. Métodos: Os dados sobre tabagismo foram obtidos junto ao sistema de Vigilância de Fatores de Risco e Proteção para Doenças Crônicas por Inquérito (Vigitel) para as 26 capitais e o Distrito Federal, no período de 2010 a 2013. O Índice Brasileiro de Privação classifica os setores censitários segundo indicadores como: renda menor que meio salário mínimo, população não alfabetizada e sem esgotamento sanitário. Nas regiões Norte e Nordeste, os setores censitários foram agrupados em quatro categorias (baixa, média, alta e muito alta privação) e, nas regiões Sul, Sudeste e Centro-Oeste, em três (baixa, média e alta privação). As estimativas de prevalências de adultos fumantes foram obtidas pelo método indireto de estimação em pequenas áreas. Para o cálculo das razões de prevalências, empregram-se modelos de Poisson. Resultados: A associação positiva entre a prevalência e a privação das categorias de setores censitários foi encontrada em 16 (59,3%) das 27 cidades. Em nove (33,3%) cidades, os setores de maior privação apresentaram maior prevalência de fumantes quando comparados aos de menor privação e, em duas (7,4%), não apresentaram diferenças. Em Aracaju, Belém, Fortaleza, João Pessoa, Macapá e Salvador, as prevalências de adultos fumantes foram três vezes maiores no grupo de setores com maior privação em relação aos de menor privação. Conclusão: Setores de maior privação social apresentaram maiores prevalências de tabagismo, comparados com menor privação, apontando desigualdades sociais.

GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence-An overview in the context of health decision-making.

OBJECTIVES: The objective of the study is to present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modeling studies (i.e., certainty associated with model outputs). STUDY DESIGN AND SETTING: Expert consultations and an international multidisciplinary workshop informed development of a conceptual approach to assessing the certainty of evidence from models within the context of systematic reviews, health technology assessments, and health care decisions. The discussions also clarified selected concepts and terminology used in the GRADE approach and by the modeling community. Feedback from experts in a broad range of modeling and health care disciplines addressed the content validity of the approach. RESULTS: Workshop participants agreed that the domains determining the certainty of evidence previously identified in the GRADE approach (risk of bias, indirectness, inconsistency, imprecision, reporting bias, magnitude of an effect, dose-response relation, and the direction of residual confounding) also apply when assessing the certainty of evidence from models. The assessment depends on the nature of model inputs and the model itself and on whether one is evaluating evidence from a single model or multiple models. We propose a framework for selecting the best available evidence from models: 1) developing de novo, a model specific to the situation of interest, 2) identifying an existing model, the outputs of which provide the highest certainty evidence for the situation of interest, either "off-the-shelf" or after adaptation, and 3) using outputs from multiple models. We also present a summary of preferred terminology to facilitate communication among modeling and health care disciplines. CONCLUSION: This conceptual GRADE approach provides a framework for using evidence from models in health decision-making and the assessment of certainty of evidence from a model or models. The GRADE Working Group and the modeling community are currently developing the detailed methods and related guidance for assessing specific domains determining the certainty of evidence from models across health care-related disciplines (e.g., therapeutic decision-making, toxicology, environmental health, and health economics).

Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond.

The last 6 years have seen sustained investment in health data science in the United Kingdom and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and well-being. However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper, we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency. We believe a step change can be achieved through meaningful stakeholder involvement at every stage of research planning, design, and execution and team-based data science, as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social licence for health data research and ensure transparent and secure data usage for public benefit.

GRADE equity guidelines 4: considering health equity in GRADE guideline development: evidence to decision process.

OBJECTIVES: The aim of this paper is to provide detailed guidance on how to incorporate health equity within the GRADE (Grading Recommendations Assessment and Development Evidence) evidence to decision process. STUDY DESIGN AND SETTING: We developed this guidance based on the GRADE evidence to decision framework, iteratively reviewing and modifying draft documents, in person discussion of project group members and input from other GRADE members. RESULTS: Considering the impact on health equity may be required, both in general guidelines and guidelines that focus on disadvantaged populations. We suggest two approaches to incorporate equity considerations: (1) assessing the potential impact of interventions on equity and (2) incorporating equity considerations when judging or weighing each of the evidence to decision criteria. We provide guidance and include illustrative examples. CONCLUSION: Guideline panels should consider the impact of recommendations on health equity with attention to remote and underserviced settings and disadvantaged populations. Guideline panels may wish to incorporate equity judgments across the evidence to decision framework. This is the fourth and final paper in a series about considering equity in the GRADE guideline development process. This series is coming from the GRADE equity subgroup.