This 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations (CoSTR) for neonatal life support includes evidence from 7 systematic reviews, 3 scoping reviews, and 12 evidence updates. The Neonatal Life Support Task Force generally determined by consensus the type of evidence evaluation to perform; the topics for the evidence updates followed consultation with International Liaison Committee on Resuscitation member resuscitation councils. The 2020 CoSTRs for neonatal life support are published either as new statements or, if appropriate, reiterations of existing statements when the task force found they remained valid. Evidence review topics of particular interest include the use of suction in the presence of both clear and meconium-stained amniotic fluid, sustained inflations for initiation of positive-pressure ventilation, initial oxygen concentrations for initiation of resuscitation in both preterm and term infants, use of epinephrine (adrenaline) when ventilation and compressions fail to stabilize the newborn infant, appropriate routes of drug delivery during resuscitation, and consideration of when it is appropriate to redirect resuscitation efforts after significant efforts have failed. All sections of the Neonatal Resuscitation Algorithm are addressed, from preparation through to postresuscitation care. This document now forms the basis for ongoing evidence evaluation and reevaluation, which will be triggered as further evidence is published. Over 140 million babies are born annually worldwide ( https://ourworldindata.org/grapher/births-and-deaths-projected-to-2100 ). If up to 5% receive positive-pressure ventilation, this evidence evaluation is relevant to more than 7 million newborn infants every year. However, in terms of early care of the newborn infant, some of the topics addressed are relevant to every single baby born.

This 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With TreatmentRecommendations (CoSTR) for neonatal life support includes evidence from 7 systematic reviews, 3 scoping reviews, and 12 evidence updates.The Neonatal Life Support Task Force generally determined by consensus the type of evidence evaluation to perform; the topics for the evidence updates followed consultation with International Liaison Committee on Resuscitation member resuscitation councils. The 2020 CoSTRs for neonatal life support are published either as new statements or, if appropriate, reiterations of existing statements when the task force found they remained valid.Evidence review topics of particular interest include the use of suction in the presence of both clear and meconium-stained amniotic fluid, sustained inflations for initiation of positive-pressure ventilation, initial oxygen concentrations for initiation of resuscitation in both preterm and term infants, use of epinephrine (adrenaline) when ventilation and compressions fail to stabilize the newborn infant, appropriate routes of drug delivery during resuscitation, and consideration of when it is appropriate to redirect resuscitation efforts after significant efforts have failed.All sections of the Neonatal Resuscitation Algorithm are addressed, from preparation through to postresuscitation care. This document now forms the basis for ongoing evidence evaluation and reevaluation, which will be triggered as further evidence is published.Over 140 million babies are born annually worldwide (https://ourworldindata.org/grapher/births-and-deaths-projected-to-2100). If up to 5% receive positive-pressure ventilation, this evidence evaluation is relevant to more than 7 million newborn infants every year. However, in terms of early care of the newborn infant, some of the topics addressed are relevant to every single baby born.

Background The interaction between COVID-19, non-communicable diseases, and chronic infectious diseases such as HIV and tuberculosis is unclear, particularly in low-income and middle-income countries in Africa. South Africa has a national HIV prevalence of 19% among people aged 15-49 years and a tuberculosis prevalence of 0•7% in people of all ages. Using a nationally representative hospital surveillance system in South Africa, we aimed to investigate the factors associated with in-hospital mortality among patients with COVID-19. MethodsIn this cohort study, we used data submitted to DATCOV, a national active hospital surveillance system for COVID-19 hospital admissions, for patients admitted to hospital with laboratory-confirmed SARS-CoV-2 infection between March 5, 2020, and March 27, 2021. Age, sex, race or ethnicity, and comorbidities (hypertension, diabetes, chronic cardiac disease, chronic pulmonary disease and asthma, chronic renal disease, malignancy in the past 5 years, HIV, and past and current tuberculosis) were considered as risk factors for COVID-19-related in-hospital mortality. COVID-19 in-hospital mortality, the main outcome, was defined as a death related to COVID-19 that occurred during the hospital stay and excluded deaths that occurred because of other causes or after discharge from hospital; therefore, only patients with a known in-hospital outcome (died or discharged alive) were included. Chained equation multiple imputation was used to account for missing data and random-effects multivariable logistic regression models were used to assess the role of HIV status and underlying comorbidities on COVID-19 in-hospital mortality. FindingsAmong the 219 265 individuals admitted to hospital with laboratory-confirmed SARS-CoV-2 infection and known in-hospital outcome data, 51 037 (23•3%) died. Most commonly observed comorbidities among individuals with available data were hypertension in 61 098 (37•4%) of 163 350, diabetes in 43 885 (27•4%) of 159 932, and HIV in 13 793 (9•1%) of 151 779. Tuberculosis was reported in 5282 (3•6%) of 146 381 individuals. Increasing age was the strongest predictor of COVID-19 in-hospital mortality. Other factors associated were HIV infection (adjusted odds ratio 1•34, 95% CI 1•27-1•43), past tuberculosis (1•26, 1•15-1•38), current tuberculosis (1•42, 1•22-1•64), and both past and current tuberculosis (1•48, 1•32-1•67) compared with never tuberculosis, as well as other described risk factors for COVID-19, such as male sex; non-White race; underlying hypertension, diabetes, chronic cardiac disease, chronic renal disease, and malignancy in the past 5 years; and treatment in the public health sector. After adjusting for other factors, people with HIV not on antiretroviral therapy (ART; adjusted odds ratio 1•45, 95% CI 1•22-1•72) were more likely to die in hospital than were people with HIV on ART. Among people with HIV, the prevalence of other comorbidities was 29•2% compared with 30•8% among HIV-uninfected individuals. Increasing number of comorbidities was associated with...

Group B Streptococcus (GBS) is a leading cause of invasive disease in infants, causing mortality and neurodevelopmental impairment (NDI) in survivors. This article estimates the percentage of survivors of infant GBS disease with NDI.

Background The first wave of COVID-19 in South Africa peaked in July, 2020, and a larger second wave peaked in January, 2021, in which the SARS-CoV-2 501Y.V2 (Beta) lineage predominated. We aimed to compare in-hospital mortality and other patient characteristics between the first and second waves.Methods In this prospective cohort study, we analysed data from the DATCOV national active surveillance system for COVID-19 admissions to hospital from March 5, 2020, to March 27, 2021. The system contained data from all hospitals in South Africa that have admitted a patient with COVID-19. We used incidence risk for admission to hospital and determined cutoff dates to define five wave periods: pre-wave 1, wave 1, post-wave 1, wave 2, and post-wave 2. We compared the characteristics of patients with COVID-19 who were admitted to hospital in wave 1 and wave 2, and risk factors for in-hospital mortality accounting for wave period using random-effect multivariable logistic regression.Findings Peak rates of COVID-19 cases, admissions, and in-hospital deaths in the second wave exceeded rates in the first wave: COVID-19 cases, 240•4 cases per 100 000 people vs 136•0 cases per 100 000 people; admissions, 27•9 admissions per 100 000 people vs 16•1 admissions per 100 000 people; deaths, 8•3 deaths per 100 000 people vs 3•6 deaths per 100 000 people. The weekly average growth rate in hospital admissions was 20% in wave 1 and 43% in wave 2 (ratio of growth rate in wave 2 compared with wave 1 was 1•19, 95% CI 1•18-1•20). Compared with the first wave, individuals admitted to hospital in the second wave were more likely to be age 40-64 years (adjusted odds ratio [aOR] 1•22, 95% CI 1•14-1•31), and older than 65 years (aOR 1•38, 1•25-1•52), compared with younger than 40 years; of Mixed race (aOR 1•21, 1•06-1•38) compared with White race; and admitted in the public sector (aOR 1•65, 1•41-1•92); and less likely to be Black (aOR 0•53, 0•47-0•60) and Indian (aOR 0•77, 0•66-0•91), compared with White; and have a comorbid condition (aOR 0•60, 0•55-0•67).For multivariable analysis, after adjusting for weekly COVID-19 hospital admissions, there was a 31% increased risk of in-hospital mortality in the second wave (aOR 1•31, 95% CI 1•28-1•35). In-hospital case-fatality risk increased from 17•7% in weeks of low admission (<3500 admissions) to 26•9% in weeks of very high admission (>8000 admissions; aOR 1•24, 1•17-1•32).Interpretation In South Africa, the second wave was associated with higher incidence of COVID-19, more rapid increase in admissions to hospital, and increased in-hospital mortality. Although some of the increased mortality can be explained by admissions in the second wave being more likely in older individuals, in the public sector, and by the increased health system pressure, a residual increase in mortality of patients admitted to hospital could be related to the new Beta lineage.