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Individual participant data (IPD) meta-analyses that obtain \"raw\" data from studies rather than summary data typically adopt a \"two-stage\" approach to analysis whereby IPD within trials generate summary measures, which are combined using standard meta-analytical methods. Recently, a range of \"one-stage\" approaches which combine all individual participant data in a single meta-analysis have been suggested as providing a more powerful and flexible approach. However, they are more complex to implement and require statistical support. This study uses a dataset to compare \"two-stage\" and \"one-stage\" models of varying complexity, to ascertain whether results obtained from the approaches differ in a clinically meaningful way. We included data from 24 randomised controlled trials, evaluating antiplatelet agents, for the prevention of pre-eclampsia in pregnancy. We performed two-stage and one-stage IPD meta-analyses to estimate overall treatment effect and to explore potential treatment interactions whereby particular types of women and their babies might benefit differentially from receiving antiplatelets. Two-stage and one-stage approaches gave similar results, showing a benefit of using anti-platelets (Relative risk 0.90, 95% CI 0.84 to 0.97). Neither approach suggested that any particular type of women benefited more or less from antiplatelets. There were no material differences in results between different types of one-stage model. For these data, two-stage and one-stage approaches to analysis produce similar results. Although one-stage models offer a flexible environment for exploring model structure and are useful where across study patterns relating to types of participant, intervention and outcome mask similar relationships within trials, the additional insights provided by their usage may not outweigh the costs of statistical support for routine application in syntheses of randomised controlled trials. Researchers considering undertaking an IPD meta-analysis should not necessarily be deterred by a perceived need for sophisticated statistical methods when combining information from large randomised trials.

A key goal of conservation is to protect biodiversity by supporting the long-term persistence of viable, natural populations of wild species. Conservation practice has long been guided by genetic, ecological and demographic indicators of risk. Emerging evidence of animal culture across diverse taxa and its role as a driver of evolutionary diversification, population structure and demographic processes may be essential for augmenting these conventional conservation approaches and decision-making. Animal culture was the focus of a ground-breaking resolution under the Convention on the Conservation of Migratory Species of Wild Animals (CMS), an international treaty operating under the UN Environment Programme. Here, we synthesize existing evidence to demonstrate how social learning and animal culture interact with processes important to conservation management. Specifically, we explore how social learning might influence population viability and be an important resource in response to anthropogenic change, and provide examples of how it can result in phenotypically distinct units with different, socially learnt behavioural strategies. While identifying culture and social learning can be challenging, indirect identification and parsimonious inferences may be informative. Finally, we identify relevant methodologies and provide a framework for viewing behavioural data through a cultural lens which might provide new insights for conservation management.

Understanding the rich social lives of animals benefits international conservation efforts Animal culture, defined as “information or behavior—shared within a community—which is acquired from conspecifics through some form of social learning” ( 1 ), can have important consequences for the survival and reproduction of individuals, social groups, and potentially, entire populations ( 1 , 2 ). Yet, until recently, conservation strategies and policies have focused primarily on broad demographic responses and the preservation of genetically defined, evolutionarily significant units. A burgeoning body of evidence on cultural transmission and other aspects of sociality ( 3 ) is now affording critical insights into what should be conserved (going beyond the protection of genetic diversity, to consider adaptive aspects of phenotypic variation), and why specific conservation programs succeed (e.g., through facilitating the resilience of cultural diversity) while others fail (e.g., by neglecting key repositories of socially transmitted knowledge). Here, we highlight how international legal instruments, such as the Convention on the Conservation of Migratory Species of Wild Animals (CMS), can facilitate smart, targeted conservation of a wide range of taxa, by explicitly considering aspects of their sociality and cultures.

Funnel plots are widely used to investigate possible publication bias in meta-analyses. There has, however, been little formal assessment of whether a visual inspection of a funnel plot is sufficient to identify publication bias. Visual assessment of bias in a funnel plot is quantified using two new statistics: the Imbalance and the Asymmetry Distance, both intended to replicate how a funnel plot is typically assessed. A simulation study was performed to assess the performance of these two statistics for identifying publication bias. The two statistics both have high type I error and low statistical power, unless the number of studies in the meta-analysis is very large. These results suggest that visual inspection of a funnel plot is unlikely to lead to a valid assessment of publication bias. In most systematic reviews, visual inspection of a funnel plot may give a misleading impression of the presence or absence of publication bias. Formal statistical tests for bias should generally be preferred.

International guidelines recommend the routine use of oxygen in the initial treatment of myocardial infarction, yet it is uncertain what effect this might have on physiologic and clinical outcomes. We undertook a systematic search of Medline, Cochrane Database of Systematic Reviews, EMBASE, and CINHAL using the key words “oxygen,” “coronary blood flow,” “hyperoxia,” and “coronary circulation” to identify human studies involving a measure of coronary blood flow while breathing oxygen and room air. The primary outcome measure was coronary blood flow; secondary outcomes included coronary vascular resistance and myocardial oxygen consumption. From 2,072 potential publications, there were 6 studies from 4 publications that met the inclusion criteria, with 6 healthy subjects and 61 subjects with cardiac disease. It was not possible to undertake a meta-analysis due to methodological limitations. In the 6 studies, high-concentration oxygen therapy resulted in hyperoxia, with a range in mean Pa o 2 of 273 to 425 mm Hg. Hyperoxia caused a significant reduction in coronary blood flow (mean change −7.9% to −28.9%, n = 6 studies). Hyperoxia caused a significant increase in coronary vascular resistance (mean change 21.5% to 40.9%, n = 4 studies) and a significant reduction in myocardial oxygen consumption (mean change −15.3% to −26.9%, n = 3 studies). Hyperoxia from high-concentration oxygen therapy causes a marked reduction in coronary blood flow and myocardial oxygen consumption. These physiologic effects may have the potential to cause harm and are relevant to the use of high-concentration oxygen therapy in the treatment of cardiac and other disorders.

The conservation of harbor porpoises ( Phocoena phocoena ) appears to be failing in Europe. There are particular concerns about this species in the Baltic Proper, Black, and Mediterranean Seas, as well as in the Northeast Atlantic, including the Iberian population, off the Spanish and Portuguese coasts. The Baltic Proper porpoise is “critically endangered,” with a population only in the low hundreds, and the Scientific Committee of the International Whaling Commission has repeatedly called for action to ensure its survival. In 2020, the Committee issued a series of recommendations relating to it and the Iberian population. Similarly, the Black Sea harbor porpoise, Phocoena phocoena ssp. relicta , is classified by the IUCN as endangered. Another population which may be genetically distinct is the West Greenland harbor porpoise, which is hunted without quotas or close seasons. European cetaceans and their habitats are covered by a number of international and regional conventions and agreements and, under European Union law, are “highly protected.” In practice, however, these legal protections have failed to generate effective conservation. For example, Special Areas of Conservation (SACs) are required for them and, although sites have been designated in some marine areas/countries, in the absence of appropriate management plans, SACs cannot be expected to help improve the harbor porpoise's conservation status. Compared to many other species, porpoises are relatively long-lived with low reproductive capacity and only poor public recognition. Conservation and management efforts are caught up in a complicated nexus of interactions involving a web of commitments under international conventions and agreements, European environmental laws, and European fisheries policy. However, public disinterest, lack of political will to implement conservation measures, and complicated fishing-related issues hinder any real progress. More positively, recent advice from the International Council for the Exploration of the Seas (ICES) provides a new scientific foundation for conservation action to address fisheries bycatch in the Baltic Proper harbor porpoise population. Populations of other porpoise species (family Phocoenidae) are also threatened, most notably the global population of the critically endangered vaquita, or Gulf of California porpoise ( Phocoena sinus ). The common threats and factors affecting porpoise populations are discussed and recommendations offered.

Background High-throughput non-invasive prenatal testing (NIPT) for fetal Rhesus D (RhD) status could avoid unnecessary treatment with anti-D immunoglobulin for RhD-negative women found to be carrying an RhD-negative fetus. We aimed to assess the diagnostic accuracy of high-throughput NIPT for fetal RhD status in RhD-negative women not known to be sensitized to the RhD antigen, by performing a systematic review and meta-analysis. Methods Prospective cohort studies of high-throughput NIPT used to determine fetal RhD status were included. The eligible population were pregnant women who were RhD negative and not known to be sensitized to RhD antigen. The index test was high-throughput, NIPT cell-free fetal DNA tests of maternal plasma used to determine fetal RhD status. The reference standard considered was serologic cord blood testing at birth. Databases including MEDLINE, EMBASE, and Science Citation Index were searched up to February 2016. Two reviewers independently screened titles and abstracts and assessed full texts identified as potentially relevant. Risk of bias was assessed using QUADAS-2. The bivariate and hierarchical summary receiver-operating characteristic (HSROC) models were fitted to calculate summary estimates of sensitivity, specificity, false positive and false negative rates, and the associated 95% confidence intervals (CIs). Results A total of 3921 references records were identified through electronic searches. Eight studies were included in the systematic review. Six studies were judged to be at low risk of bias. The HSROC models demonstrated high diagnostic performance of high-throughput NIPT testing for women tested at or after 11 weeks gestation. In the primary analysis for diagnostic accuracy, women with an inconclusive test result were treated as having tested positive. The false negative rate (incorrectly classed as RhD negative) was 0.34% (95% CI 0.15 to 0.76) and the false positive rate (incorrectly classed as RhD positive) was 3.86% (95% CI 2.54 to 5.82). There was limited evidence for non-white women and multiple pregnancies. Conclusions High-throughput NIPT is sufficiently accurate to detect fetal RhD status in RhD-negative women and would considerably reduce unnecessary treatment with routine anti-D immunoglobulin. The applicability of these findings to non-white women and women with multiple pregnancies is uncertain.

Background The economic and social costs of autism are significant. This study evaluates the cost-effectiveness of early intensive Applied Behaviour Analysis (ABA)-based interventions for autistic pre-school children in the UK. Methods A de novo economic analysis was developed in Microsoft Excel comparing early intensive ABA-based interventions compared with treatment as usual (TAU). The analysis used 15.5-year time horizon, with costs and benefits discounted a 3.5%. The model structure was based on cohort structure to capture changes in adaptive behaviour and cognitive ability over time. The analysis was informed by an individual patient data (IPD) meta-analysis of available evidence. Results Adopting a public sector perspective, early intensive ABA-based therapies were associated with greater incremental costs and greater benefits. When pessimistic assumptions were made regarding the long-term effects of treatment incremental costs were £46,103 and incremental quality-adjusted life years (QALYs) were 0.24, resulting in an incremental cost-effectiveness ratio (ICER) of £189,122 per quality-adjusted life year (QALY). When optimistic assumptions were made about long-term effects, incremental costs were £39,233 with incremental benefits of 0.84 QALYs. The resulting ICER was £46,768 per QALY. Scenario analyses emphasised the importance of assumptions made regarding adult outcomes and type of school attended, both of which significantly affect the results of the analysis. Conclusions The results of this economic analysis suggest that early intensive ABA-based interventions are unlikely to represent value for money, based on a £20,000 to £30,000 per QALY threshold typically adopted to inform UK healthcare funding decisions. However, important gaps in the available evidence, limit the strength of the conclusions that can be drawn from the presented analysis. Further research, focusing on the trajectory of autistic children following intervention is likely to be highly beneficial to resolving some of these uncertainties.

Easily identifiable risk factors including: obesity and ethnicity at high risk of diabetes are commonly used to indicate which women should be offered the oral glucose tolerance test (OGTT) to diagnose gestational diabetes (GDM). Evidence regarding these risk factors is limited however. We conducted a systematic review (SR) and meta-analysis and individual participant data (IPD) analysis to evaluate the performance of risk factors in identifying women with GDM. We searched MEDLINE, Medline in Process, Embase, Maternity and Infant Care and the Cochrane Central Register of Controlled Trials (CENTRAL) up to August 2016 and conducted additional reference checking. We included observational, cohort, case-control and cross-sectional studies reporting the performance characteristics of risk factors used to identify women at high risk of GDM. We had access to IPD from the Born in Bradford and Atlantic Diabetes in Pregnancy cohorts, all pregnant women in the two cohorts with data on risk factors and OGTT results were included. Twenty nine published studies with 211,698 women for the SR and a further 14,103 women from two birth cohorts (Born in Bradford and the Atlantic Diabetes in Pregnancy study) for the IPD analysis were included. Six studies assessed the screening performance of guidelines; six examined combinations of risk factors; eight evaluated the number of risk factors and nine examined prediction models or scores. Meta-analysis using data from published studies suggests that irrespective of the method used, risk factors do not identify women with GDM well. Risk factor screening methods are poor predictors of which pregnant women will be diagnosed with GDM. A simple approach of offering an OGTT to women 25 years or older and/or with a BMI of 25kg/m.sup.2 or more is as good as more complex risk prediction models. Research to identify more accurate (bio)markers is needed.

Medical interventions may be more effective in some types of individuals than others and identifying characteristics that modify the effectiveness of an intervention is a cornerstone of precision or stratified medicine. The opportunity for detailed examination of treatment-covariate interactions can be an important driver for undertaking an individual participant data (IPD) meta-analysis, rather than a meta-analysis using aggregate data. A number of recent modelling approaches are available. We apply these methods to the Perinatal Antiplatelet Review of International Studies (PARIS) Collaboration IPD dataset and compare estimates between them. We discuss the practical implications of applying these methods, which may be of interest to aid meta-analysists in the use of these, often complex models. Models compared included the two-stage meta-analysis of interaction terms and one-stage models which fit multiple random effects and separate within and between trial information. Models were fitted for nine covariates and five binary outcomes and results compared. Interaction terms produced by the methods were generally consistent. We show that where data are sparse and there is low heterogeneity in the covariate distributions across trials, the meta-analysis of interactions may produce unstable estimates and have issues with convergence. In this IPD dataset, varying assumptions by using multiple random effects in one-stage models or using only within trial information made little difference to the estimates of treatment-covariate interaction. Method choice will depend on datasets characteristics and individual preference.