Background Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous publications have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for specific pathogen-drug combinations in select locations. To our knowledge, this study presents the most comprehensive estimates of AMR burden to date. MethodsWe estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 23 pathogens and 88 pathogen-drug combinations in 204 countries and territories in 2019. We obtained data from systematic literature reviews, hospital systems, surveillance systems, and other sources, covering 471 million individual records or isolates and 7585 study-location-years. We used predictive statistical modelling to produce estimates of AMR burden for all locations, including for locations with no data. Our approach can be divided into five broad components: number of deaths where infection played a role, proportion of infectious deaths attributable to a given infectious syndrome, proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antibiotic of interest, and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden based on two counterfactuals: deaths attributable to AMR (based on an alternative scenario in which all drugresistant infections were replaced by drug-susceptible infections), and deaths associated with AMR (based on an alternative scenario in which all drug-resistant infections were replaced by no infection). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We present final estimates aggregated to the global and regional level. FindingsOn the basis of our predictive statistical models, there were an estimated 4•95 million (3•62-6•57) deaths associated with bacterial AMR in 2019, including 1•27 million (95% UI 0•911-1•71) deaths attributable to bacterial AMR. At the regional level, we estimated the all-age death rate attributable to resistance to be highest in western sub-Saharan Africa, at 27•3 deaths per 100 000 (20•9-35•3), and lowest in Australasia, at 6•5 deaths (4•3-9•4) per 100 000. Lower respiratory infections accounted for more than 1•5 million deaths associated with resistance in 2019, making it the most burdensome infectious syndrome. The six leading pathogens for deaths associated with resistance (Escherichia coli, followed by Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa) were responsible for 929 000 (660 000-1 270 000) deaths attributable to AMR and 3•57 million (2•62-4•78) deaths associated with AMR in 2019. One pathogen-drug combination, meticillinresistant S aureus, caused more than 100 000 deaths attributa...

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Clinical details are presented on 151 individuals with Noonan syndrome (83 males and 68 females, mean age 12-6 years). Polyhydramnios complicated 33% of affected pregnancies. The commonest cardiac lesions were pulmonary stenosis (62%), and hypertrophic cardiomyopathy (20%), with a normal echocardiogram present in only 12-5% of all cases. Significant feeding difficulties during infancy were present in 76% of the group. Although the children were short (50% with a height <3rd centile), and underweight (43% with a weight <3rd centile), the mean head circumference of the group was on the 50th centile. Motor milestone delay was usual, the cohort having a mean age of sitting unsupported of 10 months and walking of 21 months. Abnormal vision (94%) and hearing (40%) were frequent findings, but 89% of the group were attending normal primary or secondary schools. Other associations included undescended testicles (77%), hepatosplenomegaly (50%), and evidence of abnormal bleeding (56%). The mean age at diagnosis of Noonan syndrome in this group was 9 0 years. Earlier diagnosis of this common condition would aid both clinical management and genetic counselling.

Background Aetiological data for neonatal infections are essential to inform policies and programme strategies, but such data are scarce from sub-Saharan Africa. We therefore completed a systematic review and meta-analysis of available data from the African continent since 1980, with a focus on regional differences in aetiology and antimicrobial resistance (AMR) in the past decade (2008-18). Methods We included data for microbiologically confirmed invasive bacterial infection including meningitis and AMR among neonates in sub-Saharan Africa and assessed the quality of scientific reporting according to Strengthening the Reporting of Observational Studies in Epidemiology for Newborn Infection (STROBE-NI) checklist. We calculated pooled proportions for reported bacterial isolates and AMR. Findings We included 151 studies comprising data from 84 534 neonates from 26 countries, almost all of which were hospital-based. Of the 82 studies published between 2008 and 2018, insufficient details were reported regarding most STROBE-NI items. Regarding culture positive bacteraemia or sepsis, Staphylococcus aureus, Klebsiella spp, and Escherichia coli accounted for 25% (95% CI 21-29), 21% (16-27), and 10% (8-10) respectively. For meningitis, the predominant identified causes were group B streptococcus 25% (16-33), Streptococcus pneumoniae 17% (9-6), and S aureus 12% (3-25). Resistance to WHO recommended β-lactams was reported in 614 (68%) of 904 cases and resistance to aminoglycosides in 317 (27%) of 1176 cases. Interpretation Hospital-acquired neonatal infections and AMR are a major burden in Africa. More population-based neonatal infection studies and improved routine surveillance are needed to improve clinical care, plan health systems approaches, and address AMR. Future studies should be reported according to standardised reporting guidelines, such as STROBE-NI, to aid comparability and reduce research waste.

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