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Patients with chronic obstructive pulmonary disease complicated with coronary heart disease are a major public health problem, but it has not been widely accepted by the public or health professionals, the purpose of this study is to conduct a meta-analysis of the literature reports on the risk of coronary heart disease in patients with chronic obstructive pulmonary disease. Data sources are PubMed and Web of Science searched up to August 2021. Design is meta-analysis. Literature searches yielded 8877 records, meta-analysis showed that the risk of coronary heart disease in chronic obstructive pulmonary disease patients was 1.24 times higher than that in non-chronic obstructive pulmonary disease patients (HR=1.24,95% CL 1.16-1.32). The findings suggest that patients with chronic obstructive pulmonary disease are at a higher risk of developing coronary heart disease than non-chronic obstructive pulmonary disease patients.

This commentary discusses the potential utility of research syntheses for evidence-based policy- and decision-making, examining the papers that comprise the special issue on modern meta-analytic methods. Evidence and data have the potential to play a critical role in the development of policies and in the administration of programs that meet the social and economic needs of children and families. Novel, innovative, and methodologically rigorous methods that allow for comprehensive and systematic research synthesis, such as those disseminated in this special issue, can help inform the work of the federal government and the prevention science field at large. Overall, the papers hold promise for strengthening the rigor of existing approaches, illustrate novel approaches, and demonstrate the utility of information that research syntheses can produce. Collectively, the studies in this special issue advance the available toolbox of methods that can be used to support evidence-based policy- and decision-making.

Background Accurate parental perceptions of their children’s underweight status are needed to prevent overlooking potential disordered eating patterns or health conditions affecting growth. Purpose The aim of this study is to determine overall proportion of parents who misperceive children’s underweight status and correlates of such misperceptions. Methods Original studies published to January 2013 were chosen through a literature search in established databases. Studies included assessed parental perceptions of their children’s underweight and then compared perceptions to recognized standards for defining underweight based on anthropometric measures. Random- and mixed-effects models were used. Results Thirty-seven articles (representing 39 studies; N  = 4,039) were included. Pooled effect sizes indicated that 46.58 % (95 % CI 40.90–52.35 %) of parents misperceive their children’s underweight status, though the extent of misperceptions depended on a number of moderators. Conclusions Nearly half of parents perceive their underweight children as weighing more than they actually do. Health care professionals are well positioned to take steps to remedy misperceptions and encourage healthy behaviors.

In managed carnivore populations, natural mortality rate (d) is difficult to estimate directly, and context-specific data are typically weakly informative about it. Nevertheless, natural mortality is potentially an important component of total mortality, particularly if additive to harvest or culling mortality. The natural mortality rate exhibits allometric or life-history relationships that are invariant across diverse taxonomic groups, and it is valuable to derive estimates on this basis to serve as priors in later Bayesian models, steering parameter uncertainty towards biologically plausible values, and leading to more reliable model predictions and improved management recommendations. We used Bayesian hierarchical modeling and data from the literature to establish informative priors for instantaneous d as predictions scaled from body mass or maximum age. Posterior mean estimates of the scaling parameters of these models were −0.27 (body mass) and −1.07 (maximum age), respectively, conforming to expected values of −0.25 and −1.00. Direct estimates of d from published studies of coyotes (Canis latrans) in southern Texas, fisher (Pekania pennanti) in Sierra Nevada, and slender mongoose (Galerella sanguinea) in the Kalahari Desert were within the credible intervals of predictions for d using both models. We also compared survivorship curves based on model predictions with observed survivorship of red fox (Vulpes vulpes) from a variety of studies in Britain, among which intensity of culling varied markedly. Across all species, there was better support for the d prediction from maximum age, than from body mass. We thus recommend use of maximum age data to establish informative priors for d where possible. Posterior median predictions of d from maximum age were within 0.01–0.14/year of the direct estimates, whereas the differences between direct estimates and predictions from body mass were 0.04–0.27/year. Sensitivity analysis showed trivial effects of between-sex differences in body mass, and age-specific mortality, on predictions of d. Differences between body mass and maximum age model predictions were attributed to the relative importance of intrinsic and extrinsic mortality factors in the 2 approaches (i.e., maximum age predictions allowed for extrinsic factors to affect predicted mortality).

This chapter contains section titled: Summary Points Getting Started Commands to Perform a Standard Meta—Analysis Cumulative Meta—Analysis Examining the Influence of Individual Studies Funnel Plots and Tests for Funnel Plot Asymmetry Meta—Regression

This chapter contains sections titled: Introduction Overview of meta‐analytic methods Issues in meta‐analyses of addiction interventions Limitations Conclusion References Recommended readings Technological assistance

To collect reasons for selecting the methods for meta-analysis of diagnostic accuracy from authors of systematic reviews and improve guidance on recommended methods. Online survey in authors of recently published meta-analyses of diagnostic accuracy. We identified 100 eligible reviews, of which 40 had used more advanced methods of meta-analysis (hierarchical random-effects approach), 52 more traditional methods (summary receiver operating characteristic curve based on linear regression or a univariate approach), and 8 combined both. Fifty-nine authors responded to the survey; 29 (49%) authors had used advanced methods, 25 (42%) authors traditional methods, and 5 (9%) authors combined traditional and advanced methods. Most authors who had used advanced methods reported to do so because they believed that these methods are currently recommended (n = 27; 93%). Most authors who had used traditional methods also reported to do so because they believed that these methods are currently recommended (n = 18; 75%) or easy to understand (n = 18; 75%). Although more advanced methods for meta-analysis are recommended by The Cochrane Collaboration, both authors using these methods and those using more traditional methods responded that the methods they used were currently recommended. Clearer and more widespread dissemination of guidelines on recommended methods for meta-analysis of test accuracy data is needed.

Social exclusion refers to the experience of being disregarded or rejected by others and has wide-ranging negative consequences for well-being and cognition. Cyberball, a game where a ball is virtually tossed between players, then leads to the exclusion of the research participant, is a common method used to examine the experience of social exclusion. The neural correlates of social exclusion remain a topic of debate, particularly with regards to the role of the dorsal anterior cingulate cortex (dACC) and the concept of social pain. Here we conducted a quantitative meta-analysis using activation likelihood estimation (ALE) to identify brain activity reliably engaged by social exclusion during Cyberball task performance (Studies = 53; total N = 1,817 participants). Results revealed consistent recruitment in ventral anterior cingulate and posterior cingulate cortex, inferior and superior frontal gyri, posterior insula, and occipital pole. No reliable activity was observed in dACC. Using a probabilistic atlas to define dACC, fewer than 15% of studies reported peak coordinates in dACC. Meta-analytic connectivity mapping suggests patterns of co-activation are consistent with the topography of the default network. Reverse inference for cognition associated with reliable Cyberball activity computed in Neurosynth revealed social exclusion to be associated with cognitive terms Social, Autobiographical, Mental States, and Theory of Mind. Taken together, these findings highlight the role of the default network in social exclusion and warns against interpretations of the dACC as a key region involved in the experience of social exclusion in humans.

Cytoarchitectonic area 44 of Broca's region in the left inferior frontal gyrus is known to be involved in several functional domains including language, action and music processing. We investigated whether this functional heterogeneity is reflected in distinct modules within cytoarchitectonically defined left area 44 using meta-analytic connectivity-based parcellation (CBP). This method relies on identifying the whole-brain co-activation pattern for each area 44 voxel across a wide range of functional neuroimaging experiments and subsequently grouping the voxels into distinct clusters based on the similarity of their co-activation patterns. This CBP analysis revealed that five separate clusters exist within left area 44. A post-hoc functional characterization and functional connectivity analysis of these five clusters was then performed. The two posterior clusters were primarily associated with action processes, in particular with phonology and overt speech (posterior-dorsal cluster) and with rhythmic sequencing (posterior-ventral cluster). The three anterior clusters were primarily associated with language and cognition, in particular with working memory (anterior-dorsal cluster), with detection of meaning (anterior-ventral cluster) and with task switching/cognitive control (inferior frontal junction cluster). These five clusters furthermore showed specific and distinct connectivity patterns. The results demonstrate that left area 44 is heterogeneous, thus supporting anatomical data on the molecular architecture of this region, and provide a basis for more specific interpretations of activations localized in area 44. •Left area 44 of Broca's region is functionally heterogeneous.•Parcellation revealed five distinct clusters based on co-activation pattern.•Posterior clusters more linked with action (overt speech/rhythmic sequencing).•Anterior clusters more linked with language (semantics and meaning/working memory).•Inferior frontal junction cluster linked with cognitive control.

The UCLA (University of California, Los Angeles) Loneliness Scale is frequently employed instrument for assessing the structure of loneliness in psychology studies. Although many studies have tested the scale’s factor structure, the literature has produced inconsistent findings or different results. Some studies support a unidimensional structure, while other studies support a multidimensional structure including two-factor, three-factor, five-factor models. Moreover, these dimensions are denoted differently across studies. Hence, this study employs meta-analytical structural equation modeling (MASEM) to investigate the factor structure of the UCLA Loneliness Scale, MASEM provides more precise parameter estimations than conventional structural equation modeling and allows for the synthesis of conflicting findings concerning factor structure. Furthermore, MASEM provides an examination of the factor structure by utilizing correlation matrices from studies in which the scale has been employed but without a specific examination of its factor structures. Consequently, this study analyzed the correlation matrices from 52 studies encompassing 52 correlation matrices. The results of the meta-analysis revealed that the two-factor and second-order three-factor models provided the best fit for the pooled correlation matrix. Even when considering subgroup analyses based on sample size and sample age, which are possible variables that can explain heterogeneity, the three-factor structure of the scale remained consistent. This suggests that different variables may account for the observed heterogeneity.