Phylogenomic Perspective on a Unique Mycobacterium bovis Clade Dominating Bovine Tuberculosis Infections among Cattle and Buffalos in Northern Brazil.

Lack of routine surveillance in countries endemic for bovine tuberculosis (TB) and limited laboratory support contributes to the inability to differentiate the Mycobacterium tuberculosis Complex species, leading to an underestimated burden of the disease. Here, Whole-Genome Sequencing of Mycobacterium bovis isolated from tissues with TB-like lesions obtained from cattle and buffalos at Marajó Island, Brazil, demonstrates that recent transmission of M. bovis is ongoing at distinct sites. Moreover, the M. bovis epidemiology in this setting is herein found to be dominated by an endemic and unique clade composed of strains evolved from a common ancestor that are now genetically differentiated from other M. bovis clades. Additionally, envisioning a rapid strain differentiation and tracing across multiple settings, 28 globally validated strain-specific SNPs were identified, three of which considered as robust markers for the M. bovis Marajó strain. In conclusion, this study contributes with data regarding the identification of a novel M. bovis phylogenetic clade responsible for ongoing transmission events in both cattle and buffalo species in Brazil, provides a framework to investigate the dissemination of this highly prevalent strain and, holds the potential to inform TB control strategies that may help to prevent the spread of bovine and zoonotic TB

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BACKGROUND: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. METHODS: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING: Bill & Melinda Gates Foundation

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Evaluation of drug susceptibility profile of mycobacterium tuberculosis lineage 1 from brazil based on whole genome sequencing and phenotypic methods

CNPq (207,071/2014-4), CAPES (Finance Code 001), Swiss National Science Foundation (grants 310030_166687 and IZRJZ3_164171)Universidade do Estado do Pará. Instituto de Ciências Biológicas e da Saúde. Pós-Graduação em Biologia Parasitária na Amazônia. Belém, PA, Brasil / Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.International Institute of Information Technology. Department of Data Science. Bangalore, India.Universidade do Estado do Pará. Instituto de Ciências Biológicas e da Saúde. Pós-Graduação em Biologia Parasitária na Amazônia. Belém, PA, Brasil.University of Basel. Basel, Switzerland / Swiss Tropical & Public Health Institute. Basel, Switzerland.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Centro de Inovações Tecnológicas. Ananindeua, PA, Brasil.Universidade do Estado do Pará. Instituto de Ciências Biológicas e da Saúde. Pós-Graduação em Biologia Parasitária na Amazônia. Belém, PA, Brasil / Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Universidade Federal do Espírito Santo. Núcleo de Doenças Infecciosas. Vitória, ES, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Genética Molecular de Microrganismos. Rio de Janeiro, RJ, Brasil.Universidade do Estado do Pará. Instituto de Ciências Biológicas e da Saúde. Pós-Graduação em Biologia Parasitária na Amazônia. Belém, PA, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Professor Paulo de Góes. Laboratório de Micobactérias. Rio de Janeiro, RJ, Brasil.University of Basel. Basel, Switzerland / Swiss Tropical & Public Health Institute. Basel, Switzerland.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Biologia Molecular Aplicada a Micobactérias. Rio de Janeiro, RJ, Brasil.Universidade do Estado do Pará. Instituto de Ciências Biológicas e da Saúde. Pós-Graduação em Biologia Parasitária na Amazônia. Belém, PA, Brasil / Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Biologia Molecular Aplicada a Micobactérias. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Programa de Pós-Graduação em Pesquisa Clínica e Doenças Infecciosas. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Bacteriologia e Bioensaios em Micobactérias. Rio de Janeiro, RJ, Brasil.BACKGROUND The evaluation of procedures for drug susceptibility prediction of Mycobacterium tuberculosis based on genomic data against the conventional reference method test based on culture is realistic considering the scenario of growing number of tools proposals based on whole-genome sequences (WGS). OBJECTIVES This study aimed to evaluate drug susceptibility testing (DST) outcome based on WGS tools and the phenotypic methods performed on isolates of M. tuberculosis Lineage 1 from the state of Pará, Brazil, generally associated with low levels of drug resistance. METHODOLOGY Culture based DST was performed using the Proportion Method in Löwenstein-Jensen medium on 71 isolates that had been submitted to WGS. We analysed the seven main genome sequence-based tools for resistance and lineage prediction applied to M. tuberculosis and for comparison evaluation we have used the Kappa concordance test. FINDINGS When comparing the WGS-based tools against the DST, we observed the highest level of agreement using TBprofiler. Among the tools, TB-profiler, KvarQ and Mykrobe were those which identified the largest number of TB-MDR cases. Comparing the four most sensitive tools regarding resistance prediction, agreement was observed for 43 genomes. MAIN CONCLUSIONS Drug resistance profiling using next-generation sequencing offers rapid assessment of resistanceassociated mutations, therefore facilitating rapid access to effective treatment