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Although meta‐analysis has become an essential tool in ecology and evolution, reporting of meta‐analytic results can still be much improved. To aid this, we have introduced the orchard plot, which presents not only overall estimates and their confidence intervals, but also shows corresponding heterogeneity (as prediction intervals) and individual effect sizes. Here, we have added significant enhancements by integrating many new functionalities into orchaRd 2.0. This updated version allows the visualisation of heteroscedasticity (different variances across levels of a categorical moderator), marginal estimates (e.g. marginalising out effects other than the one visualised), conditional estimates (i.e. estimates of different groups conditioned upon specific values of a continuous variable) and visualisations of all types of interactions between two categorical/continuous moderators. orchaRd 2.0 has additional functions which calculate key statistics from multilevel meta‐analytic models such as I2 and R2. Importantly, orchaRd 2.0 contributes to better reporting by complying with PRISMA‐EcoEvo (preferred reporting items for systematic reviews and meta‐analyses in ecology and evolution). Taken together, orchaRd 2.0 can improve the presentation of meta‐analytic results and facilitate the exploration of previously neglected patterns. In addition, as a part of a literature survey, we found that graphical packages are rarely cited (~3%). We plea that researchers credit developers and maintainers of graphical packages, for example, by citations in a figure legend, acknowledging the use of relevant packages. Streszczenie Chociaż metaanaliza stała się podstawowym narzędziem w ekologii i ewolucji, raportowanie wyników metaanalizy trudne. Aby je ułatwić, wprowadziliśmy wykres orchard, który przedstawia nie tylko ogólne (średnie) oszacowanie efektu i jego przedziały ufności, ale także pokazuje odpowiadającą mu heterogeniczność (jako przedziały predykcji) i wielkości efektów z poszczególnych prób. W drugiej wersji pakietu, orchaRd 2.0 dodaliśmy znaczące ulepszenia, wbudowując w niego wiele nowych funkcjonalności. Zaktualizowana wersja pakietu pozwala na wizualizację heteroskedastyczności (uwzględniającej różne wariancje na poziomach moderatora kategorycznego), średnich brzegowych (np. przy marginalizacji efektów innych niż wizualizowany), średnich warunkowych (np. oszacowań różnych grup dla konkretnych wartości zmiennej ciągłej) oraz wizualizacje wszystkich interakcji pomiędzy dwoma moderatorami, zarówno kategorycznymi jak i ciągłymi. orchaRd 2.0 posiada dodatkowe funkcje obliczające kluczowe statystyki z wielopoziomowych modeli metaanalitycznych, takie jak I2 i R2. Co ważne, orchaRd 2.0 przyczynia się do lepszego raportowania wyników poprzez zgodność z PRISMA‐EcoEvo (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses in Ecology and Evolution). W efekcie, orchaRd 2.0 może poprawić prezentację wyników metaanalitycznych i ułatwić eksplorację wcześniej pomijanych wzorców. Dodatkowo, w ramach systematycznego przeglądu literatury, stwierdziliśmy, że pakiety graficzne są rzadko cytowane (~3%). Zwracamy się z prośbą do badaczy, aby docenili twórców i osoby rozwijające pakiety graficzne, np. poprzez cytowanie wykorzystanych pakietów w opisie wykresów.

Meta-analysis is a common tool for synthesizing results of multiple studies. Among methods for performing meta-analysis, the approach known as 'fixed effects' or Inverse variance weighting' is popular and widely used. A common interpretation of this method is that it assumes that the underlying effects in contributing studies are identical, and for this reason it is sometimes dismissed by practitioners. However, other interpretations of fixed effects analyses do not make this assumption, yet appear to be little known in the literature. We review these alternative interpretations, describing both their strengths and their limitations. We also describe how heterogeneity of the underlying effects can be addressed, with the same minimal assumptions, through either testing or meta-regression. Recommendations for the practice of meta-analysis are given; it is hoped that these will foster more direct connection of the questions that meta-analysts wish to answer with the statistical methods they choose.

Meta-analysis is the gold standard for synthesis in ecology and evolution. Together with estimating overall effect magnitudes, meta-analyses estimate differences between effect sizes via heterogeneity statistics. It is widely hypothesized that heterogeneity will be present in ecological/evolutionary meta-analyses due to the system-specific nature of biological phenomena. Despite driving recommended best practices, the generality of heterogeneity in ecological data has never been systematically reviewed. We reviewed 700 studies, finding 325 that used formal meta-analysis, of which total heterogeneity was reported in fewer than 40%. We used second-order meta-analysis to collate heterogeneity statistics from 86 studies. Our analysis revealed that the median and mean heterogeneity, expressed as I², are 84.67% and 91.69%, respectively. These estimates are well above \"high\" heterogeneity (i.e., 75%), based on widely adopted benchmarks. We encourage reporting heterogeneity in the forms of I² and the estimated variance components (e.g., τ²) as standard practice. These statistics provide vital insights in to the degree to which effect sizes vary, and provide the statistical support for the exploration of predictors of effect-size magnitude. Along with standard meta-regression techniques that fit moderator variables, multi-level models now allow partitioning of heterogeneity among correlated (e.g., phylogenetic) structures that exist within data.

Following a request from the European Commission, the EFSA Panel on Dietetic Products, Nutrition and Allergies (NDA) derived dietary reference values (DRVs) for vitamin D. The Panel considers that serum 25(OH)D concentration, which reflects the amount of vitamin D attained from both cutaneous synthesis and dietary sources, can be used as a biomarker of vitamin D status in adult and children populations. The Panel notes that the evidence on the relationship between serum 25(OH)D concentration and musculoskeletal health outcomes in adults, infants and children, and adverse pregnancy‐related health outcomes, is widely variable. The Panel considers that Average Requirements and Population Reference Intakes for vitamin D cannot be derived, and therefore defines adequate intakes (AIs), for all population groups. Taking into account the overall evidence and uncertainties, the Panel considers that a serum 25(OH)D concentration of 50 nmol/L is a suitable target value for all population groups, in view of setting the AIs. For adults, an AI for vitamin D is set at 15 μg/day, based on a meta‐regression analysis and considering that, at this intake, the majority of the population will achieve a serum 25(OH)D concentration near or above the target of 50 nmol/L. For children aged 1–17 years, an AI for vitamin D is set at 15 μg/day, based on the meta‐regression analysis. For infants aged 7–11 months, an AI for vitamin D is set at 10 μg/day, based on trials in infants. For pregnant and lactating women, the Panel sets the same AI as for non‐pregnant non‐lactating women, i.e. 15 μg/day. The Panel underlines that the meta‐regression was done on data collected under conditions of assumed minimal cutaneous vitamin D synthesis. In the presence of cutaneous vitamin D synthesis, the requirement for dietary vitamin D is lower or may even be zero. This publication is linked to the following EFSA Supporting Publications article: http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2016.EN-1078/full

Aim: The pattern of increasing biological diversity from high latitudes to the equator [latitudinal diversity gradient (LDG)] has been recognized for > 200 years. Empirical studies have documented this pattern across many different organisms and locations. Our goal was to quantify the evidence for the global LDG and the associated spatial, taxonomic and environmental factors. We performed a meta-analysis on a large number of individual LDGs that have been published in the 14 years since Hillebrand's ground-breaking meta-analysis of the LDG, using meta-analysis and meta-regression approaches largely new to the fields of ecology and biogeography. Location: Global. Time period: January 2003–September 2015. Major taxa studied: Bacteria, protists, plants, fungi and animals. Methods: We synthesized the outcomes of 389 individual cases of LDGs from 199 papers published since 2003, using hierarchical mixed-effects meta-analysis and multiple meta-regression. Additionally, we re-analysed Hillebrand's original dataset using modern methods. Results: We confirmed the generality of the LDG, but found the pattern to be weaker than was found in Hillebrand's study. We identified previously unreported variation in LDG strength and slope across longitude, with evidence that the LDG is strongest in the Western Hemisphere. Locational characteristics, such as habitat and latitude range, contributed significantly to LDG strength, whereas organismal characteristics, including taxonomic group and trophic level, did not. Modern meta-analytical models that incorporate hierarchical structure led to more conservative and sometimes contrasting effect size estimates relative to Hillebrand's initial analysis, whereas meta-regression revealed underlying patterns in Hillebrand's dataset that were not apparent with a traditional analysis. Main conclusions: We present evidence of global latitudinal, longitudinal and habitat-based patterns in the LDG, which are apparent across both marine and terrestrial realms and over a broad taxonomic range of organisms, from bacteria to plants and vertebrates.

Experiencing pain and insufficient relief can be devastating and negatively affect a patient’s quality of life. Developments in oncology such as new treatments and adjusted pain management guidelines may have influenced the prevalence of cancer pain and severity in patients. This review aims to provide an overview of the prevalence and severity of pain in cancer patients in the 2014–2021 literature period. A systematic literature search was performed using the databases PubMed, Embase, CINAHL, and Cochrane. Titles and abstracts were screened, and full texts were evaluated and assessed on methodological quality. A meta-analysis was performed on the pooled prevalence and severity rates. A meta-regression analysis was used to explore differences between treatment groups. We identified 10,637 studies, of which 444 studies were included. The overall prevalence of pain was 44.5%. Moderate to severe pain was experienced by 30.6% of the patients, a lower proportion compared to previous research. Pain experienced by cancer survivors was significantly lower compared to most treatment groups. Our results imply that both the prevalence of pain and pain severity declined in the past decade. Increased attention to the assessment and management of pain might have fostered the decline in the prevalence and severity of pain.

In prevention science and related fields, large meta-analyses are common, and these analyses often involve dependent effect size estimates. Robust variance estimation (RVE) methods provide a way to include all dependent effect sizes in a single meta-regression model, even when the exact form of the dependence is unknown. RVE uses a working model of the dependence structure, but the two currently available working models are limited to each describing a single type of dependence. Drawing on flexible tools from multilevel and multivariate meta-analysis, this paper describes an expanded range of working models, along with accompanying estimation methods, which offer potential benefits in terms of better capturing the types of data structures that occur in practice and, under some circumstances, improving the efficiency of meta-regression estimates. We describe how the methods can be implemented using existing software (the “metafor” and “clubSandwich” packages for R), illustrate the proposed approach in a meta-analysis of randomized trials on the effects of brief alcohol interventions for adolescents and young adults, and report findings from a simulation study evaluating the performance of the new methods.

Objectives Cardiovascular disease (CVD) is a major cause of mortality; periodontal disease (PD) affects up to 50% of the world's population. Observational evidence has demonstrated association between CVD and PD. Absent from the literature is a systematic review and meta‐analysis of longitudinal cohort studies quantifying CVD risk in PD populations compared to non‐PD populations. To examine the risk of incident CVD in people with PD in randomised controlled trials and longitudinal cohort studies. Material and Methods We searched Medline, EMBASE and Cochrane databases up to 9th Oct 2019 using keywords and MeSH headings using the following concepts: PD, CVD, longitudinal and RCT study design. CVD outcomes included but were not restricted to any CVD, myocardial infarction, coronary heart disease (CHD) and stroke. Diagnosis method and severity of PD were measured either clinically or by self‐report. Studies comparing incident CVD in PD and non‐PD populations were included. Meta‐analysis and meta‐regression was performed to determine risk of CVD in PD populations and examine the effects of PD diagnosis method, PD severity, gender and study region. Results Thirty‐two longitudinal cohort studies were included after full text screening; 30 were eligible for meta‐analysis. The risk of CVD was significantly higher in PD compared to non‐PD (relative risk [RR]: 1.20, 95% CI: 1.14–1.26). CVD risk did not differ between clinical or self‐reported PD diagnosis (RR = 0.97, 95% CI: 0.87–1.07,). CVD risk was higher in men (RR: 1.16, 95% CI: 1.08–1.25) and severe PD (RR: 1.25, 95% CI: 1.15–1.35). Among all types of CVD, the risk of stroke was highest (RR = 1.24; 95% CI:1.12–1.38), the risk of CHD was also increased (RR = 1.14; 95% CI:1.08–1.21). Conclusion This study demonstrated modest but consistently increased risk of CVD in PD populations. Higher CVD risk in men and people with severe PD suggests population‐targeted interventions could be beneficial.

Determine age-specific infection fatality rates for COVID-19 to inform public health policies and communications that help protect vulnerable age groups. Studies of COVID-19 prevalence were collected by conducting an online search of published articles, preprints, and government reports that were publicly disseminated prior to 18 September 2020. The systematic review encompassed 113 studies, of which 27 studies (covering 34 geographical locations) satisfied the inclusion criteria and were included in the meta-analysis. Age-specific IFRs were computed using the prevalence data in conjunction with reported fatalities 4 weeks after the midpoint date of the study, reflecting typical lags in fatalities and reporting. Meta-regression procedures in Stata were used to analyze the infection fatality rate (IFR) by age. Our analysis finds a exponential relationship between age and IFR for COVID-19. The estimated age-specific IFR is very low for children and younger adults (e.g., 0.002% at age 10 and 0.01% at age 25) but increases progressively to 0.4% at age 55, 1.4% at age 65, 4.6% at age 75, and 15% at age 85. Moreover, our results indicate that about 90% of the variation in population IFR across geographical locations reflects differences in the age composition of the population and the extent to which relatively vulnerable age groups were exposed to the virus. These results indicate that COVID-19 is hazardous not only for the elderly but also for middle-aged adults, for whom the infection fatality rate is two orders of magnitude greater than the annualized risk of a fatal automobile accident and far more dangerous than seasonal influenza. Moreover, the overall IFR for COVID-19 should not be viewed as a fixed parameter but as intrinsically linked to the age-specific pattern of infections. Consequently, public health measures to mitigate infections in older adults could substantially decrease total deaths.

Findings from previous studies reporting the levels of serum brain-derived neurotrophic factor (BDNF) in patients with Alzheimer’s disease (AD) and individuals with mild cognitive impairment (MCI) have been conflicting. Hence, we performed a meta-analysis to examine the aggregate levels of serum BDNF in patients with AD and individuals with MCI, in comparison with healthy controls. Fifteen studies were included for the comparison between AD and healthy control (HC) (n = 2067). Serum BDNF levels were significantly lower in patients with AD (SMD: −0.282; 95% confidence interval [CI]: −0.535 to −0.028; significant heterogeneity: I2 = 83.962). Meta-regression identified age (p < 0.001) and MMSE scores (p < 0.001) to be the significant moderators that could explain the heterogeneity in findings in these studies. Additionally, there were no significant differences in serum BDNF levels between patients with AD and MCI (eight studies, n = 906) and between MCI and HC (nine studies, n = 5090). In all, patients with AD, but not MCI, have significantly lower serum BDNF levels compared to healthy controls. This meta-analysis confirmed the direction of change in serum BDNF levels in dementia. This finding suggests that a significant change in peripheral BDNF levels can only be detected at the late stage of the dementia spectrum. Molecular mechanisms, implications on interventional trials, and future directions for studies examining BDNF in dementia were discussed.