Mortality inequalities measured by socioeconomic indicators in Brazil: a scoping review

OBJECTIVE Summarize the literature on the relationship between composite socioeconomic indicators and mortality in different geographical areas of Brazil. METHODS This scoping review included articles published between January 1, 2000, and August 31, 2020, retrieved by means of a bibliographic search carried out in the Medline, Scopus, Web of Science, and Lilacs databases. Studies reporting on the association between composite socioeconomic indicators and all-cause, or specific cause of death in any age group in different geographical areas were selected. The review summarized the measures constructed, their associations with the outcomes, and potential study limitations. RESULTS Of the 77 full texts that met the inclusion criteria, the study reviewed 24. The area level of composite socioeconomic indicators analyzed comprised municipalities (n = 6), districts (n = 5), census tracts (n = 4), state (n = 2), country (n = 2), and other areas (n = 5). Six studies used composite socioeconomic indicators such as the Human Development Index, Gross Domestic Product, and the Gini Index; the remaining 18 papers created their own socioeconomic measures based on sociodemographic and health indicators. Socioeconomic status was inversely associated with higher rates of all-cause mortality, external cause mortality, suicide, homicide, fetal and infant mortality, respiratory and circulatory diseases, stroke, infectious and parasitic diseases, malnutrition, gastroenteritis, and oropharyngeal cancer. Higher mortality rates due to colorectal cancer, leukemia, a general group of neoplasms, traffic accident, and suicide, in turn, were observed in less deprived areas and/or those with more significant socioeconomic development. Underreporting of death and differences in mortality coverage in Brazilian areas were cited as the main limitation. CONCLUSIONS Studies analyzed mortality inequalities in different geographical areas by means of composite socioeconomic indicators, showing that the association directions vary according to the mortality outcome. But studies on all-cause mortality and at the census tract level remain scarce. The results may guide the development of new composite socioeconomic indicators for use in mortality inequality analysis

Mortality inequalities measured by socioeconomic indicators in Brazil: a scoping review

Objective: Summarize the literature on the relationship between composite socioeconomic indicators and mortality in different geographical areas of Brazil. Methods: This scoping review included articles published between January 1, 2000, and August 31, 2020, retrieved by means of a bibliographic search carried out in the Medline, Scopus, Web of Science, and Lilacs databases. Studies reporting on the association between composite socioeconomic indicators and all-cause, or specific cause of death in any age group in different geographical areas were selected. The review summarized the measures constructed, their associations with the outcomes, and potential study limitations. Results: Of the 77 full texts that met the inclusion criteria, the study reviewed 24. The area level of composite socioeconomic indicators analyzed comprised municipalities (n = 6), districts (n = 5), census tracts (n = 4), state (n = 2), country (n = 2), and other areas (n = 5). Six studies used composite socioeconomic indicators such as the Human Development Index, Gross Domestic Product, and the Gini Index; the remaining 18 papers created their own socioeconomic measures based on sociodemographic and health indicators. Socioeconomic status was inversely associated with higher rates of all-cause mortality, external cause mortality, suicide, homicide, fetal and infant mortality, respiratory and circulatory diseases, stroke, infectious and parasitic diseases, malnutrition, gastroenteritis, and oropharyngeal cancer. Higher mortality rates due to colorectal cancer, leukemia, a general group of neoplasms, traffic accident, and suicide, in turn, were observed in less deprived areas and/or those with more significant socioeconomic development. Underreporting of death and differences in mortality coverage in Brazilian areas were cited as the main limitation. Conclusions: Studies analyzed mortality inequalities in different geographical areas by means of composite socioeconomic indicators, showing that the association directions vary according to the mortality outcome. But studies on all-cause mortality and at the census tract level remain scarce. The results may guide the development of new composite socioeconomic indicators for use in mortality inequality analysis

Large scale genome-centric metagenomic data from the gut microbiome of food-producing animals and humans

Bill & Melinda Gates Foundation [INV-00764] and CNPq/DECIT [443805/2018-0]; Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ) E-26/201.046/2022; Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) 307145/2021-2; 312066/2019-8 Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)National Laboratory of Scientific Computing. Bioinformatics Laboratory. Rio de Janeiro, RJ, BrazilNational Laboratory of Scientific Computing. Bioinformatics Laboratory. Rio de Janeiro, RJ, BrazilUniversidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Alerta. São Paulo, SP, BrazilUniversidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Alerta. São Paulo, SP, BrazilUniversidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Alerta. São Paulo, SP, BrazilRegional University of Blumenau. Blumenau, SC, BrazilNational Laboratory of Scientific Computing. Bioinformatics Laboratory. Rio de Janeiro, RJ, BrazilNational Laboratory of Scientific Computing. Bioinformatics Laboratory. Rio de Janeiro, RJ, BrazilMinistério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, BrasilMinistério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, BrasilFederal University of Ceará. Postgraduate Program in Medical Microbiology. Group of Applied Medical Microbiology. Fortaleza, CE, Brazil.Regional University of Blumenau. Blumenau, SC, Brazil.Universidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Alerta. São Paulo, SP, Brazil / Universidade Federal de São Paulo. Instituto de Ciências Ambientais, Químicas e Farmacêuticas. Departamento de Ciências Biológicas. Laboratório de Imunologia e Bacteriologia. Setor de Biologia Molecular, Microbiologia e Imunologia. Diadema, SP, BrazilFederal University of Ceará. Postgraduate Program in Medical Microbiology. Group of Applied Medical Microbiology. Fortaleza, CE, Brazil.Universidade Federal da Grande Dourados. Laboratório de Pesquisa em Ciências da Saúde. Dourados, MS, BrazilUniversity São Francisco. Laboratory of Molecular Biology of Microorganisms. Bragança Paulista, SP, BrazilMinistério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, BrasilUniversidade Federal da Grande Dourados. Laboratório de Pesquisa em Ciências da Saúde. Dourados, MS, BrazilUniversidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Alerta. São Paulo, SP, BrazilUniversidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Alerta. São Paulo, SP, BrazilUniversidade Federal da Grande Dourados. Laboratório de Pesquisa em Ciências da Saúde. Dourados, MS, BrazilUniversity São Francisco. Laboratory of Molecular Biology of Microorganisms. Bragança Paulista, SP, BrazilMinistério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, BrasilUniversidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Especial de Microbiologia Clínica. São Paulo, SP, BrazilUniversidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Alerta. São Paulo, SP, Brazil / Universidade Federal de São Paulo. Instituto de Ciências Ambientais, Químicas e Farmacêuticas. Departamento de Ciências Biológicas. Laboratório de Imunologia e Bacteriologia. Setor de Biologia Molecular, Microbiologia e Imunologia. Diadema, SP, Brazil.Universidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Alerta. São Paulo, SP, Brazil / Universidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Especial de Microbiologia Clínica. São Paulo, SP, BrazilNational Laboratory of Scientific Computing. Bioinformatics Laboratory. Rio de Janeiro, RJ, BrazilThe One Health concept is a global strategy to study the relationship between human and animal health and the transfer of pathogenic and non-pathogenic species between these systems. However, to the best of our knowledge, no data based on One Health genome-centric metagenomics are available in public repositories. Here, we present a dataset based on a pilot-study of 2,915 metagenome-assembled genomes (MAGs) of 107 samples from the human (N = 34), cattle (N = 28), swine (N = 15) and poultry (N = 30) gut microbiomes. Samples were collected from the five Brazilian geographical regions. Of the draft genomes, 1,273 were high-quality drafts (>= 90% of completeness and = 50% of completeness and <= 10% of contamination). Taxonomic predictions were based on the alignment and concatenation of single-marker genes, and the most representative phyla were Bacteroidota, Firmicutes, and Proteobacteria. Many of these species represent potential pathogens that have already been described or potential new families, genera, and species with potential biotechnological applications. Analyses of this dataset will highlight discoveries about the ecology and functional role of pathogens and uncultivated Archaea and Bacteria from food-producing animals and humans. Furthermore, it also represents an opportunity to describe new species from underrepresented taxonomic groups

Exploring the bacteriome and resistome of humans and food-producing animals in Brazil

National Council for Science and Technological Development (CNPq) and the Bill & Melinda Gates Foundation (process numbers 402659/2018-0, 443805/2018-0, and OPP1193112); Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES); CNPq (process number 312066/2019-8), CNPq (307145/2021-2); FAPERJ (E-26/201.046/2022)National Laboratory of Scientific Computing. Bioinformatics Laboratory. Rio de Janeiro, RJ, Brazil.Universidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Alerta. São Paulo, SP, Brazil.Universidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Alerta. São Paulo, SP, Brazil.Regional University of Blumenau. Blumenau, SC, Brazil.National Laboratory of Scientific Computing. Bioinformatics Laboratory. Rio de Janeiro, RJ, Brazil.National Laboratory of Scientific Computing. Bioinformatics Laboratory. Rio de Janeiro, RJ, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Universidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Alerta. São Paulo, SP, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Federal University of Ceará. Postgraduate Program in Medical Microbiology. Group of Applied Medical Microbiology. Fortaleza, CE, Brazil.Regional University of Blumenau. Blumenau, SC, Brazil.Universidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Alerta. São Paulo, SP, Brazil / Universidade Federal de São Paulo. Instituto de Ciências Ambientais, Químicas e Farmacêuticas. Departamento de Ciências Biológicas. Setor de Biologia Molecular, Microbiologia e Imunologia. Laboratório de Imunologia e Bacteriologia. Diadema, SP, Brazil.Federal University of Ceará. Postgraduate Program in Medical Microbiology. Group of Applied Medical Microbiology. Fortaleza, CE, Brazil.Universidade Federal da Grande Dourados. Laboratório de Pesquisa em Ciências da Saúde. Dourados, MS, Brazil.National Laboratory of Scientific Computing. Bioinformatics Laboratory. Rio de Janeiro, RJ, Brazil.University São Francisco. Laboratory of Molecular Biology of Microorganisms. Bragança Paulista, SP, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Universidade Federal da Grande Dourados. Laboratório de Pesquisa em Ciências da Saúde. Dourados, MS, Brazil.Universidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Alerta. São Paulo, SP, Brazil / Universidade Federal de São Paulo. Instituto de Ciências Ambientais, Químicas e Farmacêuticas. Departamento de Ciências Biológicas. Setor de Biologia Molecular, Microbiologia e Imunologia. Laboratório de Imunologia e Bacteriologia. Diadema, SP, Brazil.Universidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Alerta. São Paulo, SP, Brazil.Universidade Federal da Grande Dourados. Laboratório de Pesquisa em Ciências da Saúde. Dourados, MS, Brazil.University São Francisco. Laboratory of Molecular Biology of Microorganisms. Bragança Paulista, SP, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Universidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Especial de Microbiologia Clínica. São Paulo, SP, Brazil.Universidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Especial de Microbiologia Clínica. São Paulo, SP, Brazil.Universidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Alerta. São Paulo, SP, Brazil / Universidade Federal de São Paulo. Instituto de Ciências Ambientais, Químicas e Farmacêuticas. Departamento de Ciências Biológicas. Setor de Biologia Molecular, Microbiologia e Imunologia. Laboratório de Imunologia e Bacteriologia. Diadema, SP, Brazil.National Laboratory of Scientific Computing. Bioinformatics Laboratory. Rio de Janeiro, RJ, Brazil.Universidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Alerta. São Paulo, SP, Brazil / Universidade Federal de São Paulo. Escola Paulista de Medicina. Department of Internal Medicine. Division of Infectious Diseases. Laboratório Especial de Microbiologia Clínica. São Paulo, SP, Brazil.The epidemiology of antimicrobial resistance (AMR) is complex, with multiple interfaces (human-animal-environment). In this context, One Health surveillance is essential for understanding the distribution of microorganisms and antimicrobial resistance genes (ARGs). This report describes a multicentric study undertaken to evaluate the bacterial communities and resistomes of food-producing animals (cattle, poultry, and swine) and healthy humans sampled simultaneously from five Brazilian regions. Metagenomic analysis showed that a total of 21,029 unique species were identified in 107 rectal swabs collected from distinct hosts, the highest numbers of which belonged to the domain Bacteria, mainly Ruminiclostridium spp. and Bacteroides spp., and the order Enterobacterales. We detected 405 ARGs for 12 distinct antimicrobial classes. Genes encoding antibiotic-modifying enzymes were the most frequent, followed by genes related to target alteration and efflux systems. Interestingly, carbapenemase-encoding genes such as blaAIM-1, blaCAM-1, blaGIM-2, and blaHMB-1 were identified in distinct hosts. Our results revealed that, in general, the bacterial communities from humans were present in isolated clusters, except for the Northeastern region, where an overlap of the bacterial species from humans and food-producing animals was observed. Additionally, a large resistome was observed among all analyzed hosts, with emphasis on the presence of carbapenemase-encoding genes not previously reported in Latin America. IMPORTANCE Humans and food production animals have been reported to be important reservoirs of antimicrobial resistance (AMR) genes (ARGs). The frequency of these multidrug-resistant (MDR) bacteria tends to be higher in low- and middle-income countries (LMICs), due mainly to a lack of public health policies. Although studies on AMR in humans or animals have been carried out in Brazil, this is the first multicenter study that simultaneously collected rectal swabs from humans and food-producing animals for metagenomics. Our results indicate high microbial diversity among all analyzed hosts, and several ARGs for different antimicrobial classes were also found. As far as we know, we have detected for the first time ARGs encoding carbapenemases, such as blaAIM-1, blaCAM-1, blaGIM-2, and blaHMB-1, in Latin America. Thus, our results support the importance of metagenomics as a tool to track the colonization of food-producing animals and humans by antimicrobial-resistant bacteria. In addition, a network surveillance system called GUARANI, created for this study, is ready to be expanded and to collect additional data