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CONTEXT: Nonnutritive sweetener (NNS) consumption is increasing among children, yet its long-term health impact is unclear, particularly when exposure occurs during early life.OBJECTIVE: To synthesize evidence from prospective studies evaluating the association of early-life NNS exposure and long-term metabolic health.DATA SOURCES: Medline, Embase, and Cochrane Library (inception to July 2015). STUDY SELECTION:We aimed to include randomized controlled trials (RCTs) evaluating NNSbased interventions and prospective cohort studies reporting NNS exposure among pregnant women, infants, or children (<12 years of age), with a minimum study duration of 6 months. DATA EXTRACTION:The primary outcome was BMI; secondary outcomes included growth velocity, overweight/obesity, adiposity, and adverse metabolic effects. Study quality and risk of bias were evaluated using validated assessment tools. RESULTS:We identified 6 eligible cohort studies and 2 RCTs (n = 15 641 children). Half of the cohorts reported increasing weight gain or fat mass accumulation with increasing NNS intake, and pooled data from 2 cohorts showed a significant correlation with BMI gain (weighted mean correlation 0.023, 95% confidence interval 0.006 to 0.041). RCTs reported contradictory effects on weight change in children receiving NNSs. No eligible studies evaluated prenatal or infant NNS exposure.LIMITATIONS: Meta-analysis was limited because of the small number of eligible studies and heterogeneity of populations and outcomes. CONCLUSIONS:There is limited and inconsistent evidence of the long-term metabolic effects of NNS exposure during gestation, infancy, and childhood. Further research is needed to inform recommendations for the use of NNSs in this sensitive population.

Stroke is the leading cause of death in many countries of Latin America. Population studies are necessary in this region.Objectives:To evaluate the prevalence of stroke and its risk factors in a population of vulnerable communities of southern Brazil.Methods:Population-based crosssectional study with systematic sampling. Individuals aged 20 and over were included (n=3,391). Individuals with previous diagnosis of stroke or identified by a validate stroke questionnaire were compared with those without stroke in many variables.Results:285 individuals (8.4%) had previous stroke. The group without stroke showed greater average of years of study than the group with stroke (p≪0.001). Multivariable analysis identified as risk factors for stroke (p≪0.05): age from 40 to 59, age from 60 to 79, widowhood, present smoking, previous smoking, hypertension and ischemic heart disease.Conclusion:The findings in this population indicate the need of preventive cost-effective public health policies in Brazil.

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ObjectivesTo provide a comprehensive systematic overview of current evidence from pooled analyses/meta-analyses and systematic reviews (PMASRs) pertaining to dairy consumption and incident cancer and/or all-cause or cancer-specific mortality.DesignOverview of reviews.SettingCommunity setting.ParticipantsThe unit of analysis is PMASRs. A total of 42 PMASRs was included in this overview of reviews.Interventions/exposuresAny dairy product consumption (eg, milk, yogurt, etc).Primary and secondary outcomes measuresPrimary outcome measure is development of any type of cancer. Secondary outcome measures are all-cause mortality and cancer-specific mortality.ResultsFrom 9693 citations identified, we included 42 PMASRs (52 study reports) published between 1991 and 2017. Thirty-one (74%) of these was pooled analyses/meta analyses, and only 11 (26%) were systematic reviews and meta-analyses. There was a wide variability in the type of study designs included within the other PMASRs, thus contributing to variable and, in instances, divergent estimates of cancer risk for several cancer subtypes. For example, only one systematic review and meta-analysis exclusively included prospective study designs. Most PMASRs were of low to moderate quality based on the Assessing the Methodological Quality of Systematic Reviews (AMSTAR) scores. The median AMSTAR score was 5 (IQR 2–7). Our overview identified conflicting evidence from PMASRs on association between dairy consumption and incident cancers or mortality. Heterogeneity in summary estimates reflected the inclusion of variable study designs and overall low methodological quality of individual PMASRs.ConclusionsThe association between dairy consumption and cancer risk has been explored in PMASRs with a variety of study designs and of low to moderate quality. To fully characterise valid associations between dairy consumption and risk of cancer and/or mortality rigorously conducted, PMASRs including only high-quality prospective study designs are required.Trial registration numberCRD42017078463.

Background In view of many unanswered clinical questions regarding treatment of COVID-19 with remdesivir, we systematically identified, critically appraised and summarized the findings from randomized controlled trials (RCTs) of remdesivir for COVID-19. Methods We searched relevant databases/websites (up to September 2020) and selected English-language RCT publications of remdesivir for COVID-19. We conducted meta-analysis using an inverse variance, random-effects model in addition to trial sequential analysis (TSA) for the efficacy outcomes: all-cause mortality, viral burden and clinical progression. Safety outcomes were diarrhoea, nausea, and vomiting. We calculated the relative risk (RR) and 95% confidence interval (CI) for all outcomes. Statistical heterogeneity was calculated using the I 2 statistic. Results We included five RCTs (7540 participants) from 7237 citations. Most (80%) were of an unclear to high risk of bias. There was no evidence of a significant improvement with remdesivir (100 mg, 10 days) regarding all-cause mortality (RR 0.94, CI 0.82–1.07; I 2 = 0%; 4 RCTs; 7143 patients), clinical progression (RR 1.08, CI 0.99–1.18; I 2 = 70.4%; 3 RCTs; 1692 patients), or diarrhoea (RR 0.82, CI 0.40–1.66; I 2 = 0%; 2 RCTs; 630 patients). Nausea occurred more often with remdesivir (RR 2.77, CI 1.28–6.03; I 2 = 0%; 2 RCTs; 630 patients). TSA showed that the required information size was not reached for firm conclusions to be drawn. Conclusions and relevance There is insufficient evidence to support the use of remdesivir for treatment of COVID-19. More high-quality RCTs are needed for a stronger evidence. Until then, remdesivir should remain an experimental drug for COVID-19.

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Background: The Cochrane Bias Methods Group recently developed the "Risk of Bias (ROB) in Non-randomized Studies of Interventions" (ROBINS-I) tool to assess ROB for non-randomized studies of interventions (NRSI). It is important to establish consistency in its application and interpretation across review teams. In addition, it is important to understand if specialized training and guidance will improve the reliability of the results of the assessments. Therefore, the objective of this cross-sectional study is to establish the inter-rater reliability (IRR), interconsensus reliability (ICR), and concurrent validity of ROBINS-I. Furthermore, as this is a relatively new tool, it is important to understand the barriers to using this tool (e.g., time to conduct assessments and reach consensusevaluator burden). Methods: Reviewers from four participating centers will appraise the ROB of a sample of NRSI publications using the ROBINS-I tool in two stages. For IRR and ICR, two pairs of reviewers will assess the ROB for each NRSI publication. In the first stage, reviewers will assess the ROB without any formal guidance. In the second stage, reviewers will be provided customized training and guidance. At each stage, each pair of reviewers will resolve conflicts and arrive at a consensus. To calculate the IRR and ICR, we will use Gwet's AC 1 statistic. For concurrent validity, reviewers will appraise a sample of NRSI publications using both the New-castle Ottawa Scale (NOS) and ROBINS-I. We will analyze the concordance between the two tools for similar domains and for the overall judgments using Kendall's tau coefficient. To measure the evaluator burden, we will assess the time taken to apply the ROBINS-I (without and with guidance), and the NOS. To assess the impact of customized training and guidance on the evaluator burden, we will use the generalized linear models. We will use Microsoft Excel and SAS 9.4 to manage and analyze study data, respectively. Discussion: The quality of evidence from systematic reviews that include NRS depends partly on the study-level ROB assessments. The findings of this study will contribute to an improved understanding of the ROBINS-I tool and how best to use it.