Magnesium sulphate at 30 to 34 weeks' gestational age: neuroprotection trial (MAGENTA) -study protocol

Extent: 9 p.BACKGROUND: Magnesium sulphate is currently recommended for neuroprotection of preterm infants for women at risk of preterm birth at less than 30 weeks’ gestation, based on high quality evidence of benefit. However there remains uncertainty as to whether these benefits apply at higher gestational ages. The aim of this randomised controlled trial is to assess whether giving magnesium sulphate compared with placebo to women immediately prior to preterm birth between 30 and 34 weeks’ gestation reduces the risk of death or cerebral palsy in their children at two years’ corrected age. METHODS/DESIGN: DESIGN: Randomised, multicentre, placebo controlled trial. INCLUSION CRITERIA: Women, giving informed consent, at risk of preterm birth between 30 to 34 weeks’ gestation, where birth is planned or definitely expected within 24 hours, with a singleton or twin pregnancy and no contraindications to the use of magnesium sulphate. TRIAL ENTRY & RANDOMISATION: Eligible women will be randomly allocated to receive either magnesium sulphate or placebo. TREATMENT GROUPS: Women in the magnesium sulphate group will be administered 50 ml of a 100 ml infusion bag containing 8 g magnesium sulphate heptahydrate [16 mmol magnesium ions]. Women in the placebo group will be administered 50 ml of a 100 ml infusion bag containing isotonic sodium chloride solution (0.9%). Both treatments will be administered through a dedicated IV infusion line over 30 minutes. PRIMARY STUDY OUTCOME: Death or cerebral palsy measured in children at two years’ corrected age. SAMPLE SIZE: 1676 children are required to detect a decrease in the combined outcome of death or cerebral palsy, from 9.6% with placebo to 5.4% with magnesium sulphate (two-sided alpha 0.05, 80% power, 5% loss to follow up, design effect 1.2). DISCUSSION: Given the magnitude of the protective effect in the systematic review, the ongoing uncertainty about benefits at later gestational ages, the serious health and cost consequences of cerebral palsy for the child, family and society, a trial of magnesium sulphate for women at risk of preterm birth between 30 to 34 weeks’ gestation is both important and relevant for clinical practice globally. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry - ACTRN12611000491965Caroline A. Crowther, Philippa F. Middleton, Dominic Wilkinson, Pat Ashwood and Ross Haslam for the MAGENTA Study Grou

The Global Trachoma Mapping Project: Methodology of a 34-Country Population-Based Study.

PURPOSE: To complete the baseline trachoma map worldwide by conducting population-based surveys in an estimated 1238 suspected endemic districts of 34 countries. METHODS: A series of national and sub-national projects owned, managed and staffed by ministries of health, conduct house-to-house cluster random sample surveys in evaluation units, which generally correspond to "health district" size: populations of 100,000-250,000 people. In each evaluation unit, we invite all residents aged 1 year and older from h households in each of c clusters to be examined for clinical signs of trachoma, where h is the number of households that can be seen by 1 team in 1 day, and the product h × c is calculated to facilitate recruitment of 1019 children aged 1-9 years. In addition to individual-level demographic and clinical data, household-level water, sanitation and hygiene data are entered into the purpose-built LINKS application on Android smartphones, transmitted to the Cloud, and cleaned, analyzed and ministry-of-health-approved via a secure web-based portal. The main outcome measures are the evaluation unit-level prevalence of follicular trachoma in children aged 1-9 years, prevalence of trachomatous trichiasis in adults aged 15 + years, percentage of households using safe methods for disposal of human feces, and percentage of households with proximate access to water for personal hygiene purposes. RESULTS: In the first year of fieldwork, 347 field teams commenced work in 21 projects in 7 countries. CONCLUSION: With an approach that is innovative in design and scale, we aim to complete baseline mapping of trachoma throughout the world in 2015

A synthesis of evidence for policy from behavioural science during COVID-19

Scientific evidence regularly guides policy decisions, with behavioural science increasingly part of this process. In April 2020, an influential paper proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization

A synthesis of evidence for policy from behavioural science during COVID-19

Scientifc evidence regularly guides policy decisions1 , with behavioural science increasingly part of this process2 . In April 2020, an infuential paper3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to eforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams fnding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy efectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed efects and there were no efects for highlighting individual benefts or protecting others. No available evidence existed to assess any distinct diferences in efects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientifc evidence in policy formulation and prioritization.Fil: Ruggeri, Kai. New York Air National Guard; Estados Unidos. Columbia University Mailman School of Public Health; Estados Unidos. University of Cambridge; Estados UnidosFil: Stock, Friederike. Max Planck Institute for Human Development; Alemania. Humboldt-Universität zu Berlin; AlemaniaFil: Haslam, S. Alexander. University of Queensland; AustraliaFil: Capraro, Valerio. Università degli Studi di Milano; ItaliaFil: Boggio, Paulo. Universidade Presbiteriana Mackenzie; Brasil. National Institute of Science and Technology on Social and Affective Neuroscience; BrasilFil: Ellemers, Naomi. Utrecht University; Países Bajos. University of Utrecht; Países BajosFil: Cichocka, Aleksandra. University Of Kent; Reino UnidoFil: Douglas, Karen M.. University Of Kent; Reino UnidoFil: Rand, David G.. Massachusetts Institute of Technology; Estados UnidosFil: van der Linden, Sander. University of Cambridge; Estados UnidosFil: Cikara, Mina. Harvard University; Estados UnidosFil: Finkel, Eli J.. Northwestern University; Estados UnidosFil: Druckman, James N.. Northwestern University; Estados UnidosFil: Wohl, Michael J. A.. Carleton University; CanadáFil: Petty, Richard E.. Ohio State University; Estados UnidosFil: Tucker, Joshua A.. University of New York; Estados UnidosFil: Shariff, Azim. University of British Columbia; CanadáFil: Gelfand, Michele. University of Stanford; Estados UnidosFil: Packer, Dominic. Lehigh University; Estados UnidosFil: Jetten, Jolanda. University of Queensland; AustraliaFil: Van Lange, Paul A. M.. Universitat zu Köln; Alemania. Vrije Universiteit Amsterdam; Países BajosFil: Pennycook, Gordon. Cornell University; Estados UnidosFil: Peters, Ellen. University of Oregon; Estados UnidosFil: Navajas Ahumada, Joaquin Mariano. Universidad Torcuato Di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Papa, Francesca. Organisation for Economic Co-operation and Development; FranciaFil: Galizzi, Matteo M.. The London School of Economics and Political Science; Reino UnidoFil: Milkman, Katherine L.. University of Pennsylvania; Estados UnidosFil: Petrović, Marija. University of Belgrade; SerbiaFil: Van Bavel, Jay J.. University of New York; Estados UnidosFil: Willer, Robb. University of Stanford; Estados Unido

A synthesis of evidence for policy from behavioural science during COVID-19

Scientific evidence regularly guides policy decisions, with behavioural science increasingly part of this process. In April 2020, an influential paper proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization

A synthesis of evidence for policy from behavioural science during COVID-19

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization

A synthesis of evidence for policy from behavioural science during COVID-19

Scientific evidence regularly guides policy decisions 1, with behavioural science increasingly part of this process 2. In April 2020, an influential paper 3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization

Age and frailty are independently associated with increased COVID-19 mortality and increased care needs in survivors: results of an international multi-centre study

INTRODUCTION: Increased mortality has been demonstrated in older adults with COVID-19, but the effect of frailty has been unclear.METHODS: This multi-centre cohort study involved patients aged 18years and older hospitalised with COVID-19, using routinely collected data. We used Cox regression analysis to assess the impact of age, frailty, and delirium on the risk of inpatient mortality, adjusting for sex, illness severity, inflammation, and co-morbidities. We used ordinal logistic regression analysis to assess the impact of age, Clinical Frailty Scale (CFS), and delirium on risk of increased care requirements on discharge, adjusting for the same variables.RESULTS: Data from 5,711 patients from 55 hospitals in 12 countries were included (median age 74, IQR 54-83; 55.2% male). The risk of death increased independently with increasing age (>80 vs 18-49: HR 3.57, CI 2.54-5.02), frailty (CFS 8 vs 1-3: HR 3.03, CI 2.29-4.00) inflammation, renal disease, cardiovascular disease, and cancer, but not delirium. Age, frailty (CFS 7 vs 1-3: OR 7.00, CI 5.27-9.32), delirium, dementia, and mental health diagnoses were all associated with increased risk of higher care needs on discharge. The likelihood of adverse outcomes increased across all grades of CFS from 4 to 9.CONCLUSIONS: Age and frailty are independently associated with adverse outcomes in COVID-19. Risk of increased care needs was also increased in survivors of COVID-19 with frailty or older age

A synthesis of evidence for policy from behavioural science during COVID-19

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations ('claims') detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms 'physical distancing' and 'social distancing'. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization