Many individuals with severe mental disorders have difficulties in vocational and social functioning, which are regarded the most important outcomes, together with clinical symptoms. To understand ...the underlying mechanisms, research is increasingly focused on factors influencing functional outcomes. One established association has been shown between cognition and community functioning with negative symptoms as a possible mediator. Although it has been shown that negative symptoms consist of two subdomains, thus far negative symptoms have been assessed as one unitary construct. This study considers for the first time subdomains of negative symptoms as putative mediators (expressive deficits, amotivation) of the association between cognition (neuro- and social cognition) and functional outcome (living situation, occupation, social functioning). We expected that specific subdomains of negative symptoms (e.g. amotivation) would mediate the effect of cognition on specific functional outcomes (e.g. social functioning) independently from illness duration. To assess this, we included two independent cohorts, consisting of participants with different illness duration.
These two independent cohorts consisted of patients with a recent-onset psychotic disorder: PROGR-S (first time treated; N = 1129) and GROUP (illness duration preferably <5 years; N = 1200). Using linear regression, mediation analyses were performed with two cognition domains (neurocognition and social cognition) as predictors, negative symptoms (Expressive deficits and Amotivation as indexed with items from the Positive and Negative Syndrome Scale) as mediators and three measures of functional outcomes (living situation, occupation and social functioning) as outcome measures. The analyses were repeated with the same outcome measures three years later.
Three main results were obtained. I) Both in the cross-sectional and longitudinal analyses, the associations of neurocognition (both cohorts) and social cognition (GROUP) with social functioning were mediated by amotivation. II) The association between cognition and living situation was mediated by Expressive deficits in one cohort (GROUP) but not in the cohort assessing first-episode psychosis (PROGR-S). III) The association between cognition and occupation was mediated by Amotivation in PROGR-S and by Expressive deficits in GROUP.
The current results show a less robust mediating role for specific negative symptom domains regarding the associations of cognition with occupation and living situation that may depend on the duration of psychotic illness. However, Amotivation, mediates the association between cognition and social functioning, which holds true for patients experiencing a first-onset and patients with a longer illness duration alike. The results may have implications for the development of therapeutic approaches focusing on amotivation to improve social functioning.
This study stresses the importance of distinguishing subdomains of negative symptoms, cognition and functioning. Our results show that specific negative symptom dimensions mediate the effects of cognition on specific functional outcomes.
This research proposes guidelines for hypothesizing and assessing mediation concepts for tourism and hospitality research. The mediation analysis plays a significant role in theory development and ...advancement in social science disciplines and is significantly increasing in recent years. However, following questionable hypothesis development, statistical assessment, and interpretation of results cause biased and unreliable understanding of mediation analysis and questionable findings for theory building and development. Drawing from a systematic review of 536 mediation papers published in five recent years (2016–2020) in top tier tourism and hospitality journals, the findings revealed several critical issues in different mediation analysis steps, including hypothesis development, mediation assessment and interpretation of results. The results highlighted the common methodological mistakes and misconceptions for mediation analysis. This paper provides robust guidelines for mediation analysis by highlighting those methodological issues and has significant theoretical and methodological contributions in the tourism and hospitality field.
Mediation analysis is crucial for diagnosing indirect causal relations in many scientific fields. However, mediation analysis of nominal variables requires examining and comparing multiple total ...effects and their corresponding direct/indirect causal effects derived from mediation models. This process is tedious and challenging to achieve with classical analysis tools such as Excel tables. In this study, we worked closely with experts from two scientific domains to design MediVizor, a visualization system that enables experts to conduct visual mediation analysis of nominal variables. The visualization design allows users to browse and compare multiple total effects together with the direct/indirect effects that compose them. The design also allows users to examine to what extent the positive and negative direct/indirect effects contribute to and reduce the total effects, respectively. We conducted two case studies separately with the experts from the two domains, sports and communication science,and a user study with common users to evaluate the system and design.The positive feedback from experts and common users demonstrates the effectiveness and generalizability of the system.
In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only ...estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently used to assess potential causal mechanisms. The mediation package implements a comprehensive suite of statistical tools for conducting such an analysis. The package is organized into two distinct approaches. Using the model-based approach, researchers can estimate causal mediation effects and conduct sensitivity analysis under the standard research design. Furthermore, the design-based approach provides several analysis tools that are applicable under different experimental designs. This approach requires weaker assumptions than the model-based approach. We also implement a statistical method for dealing with multiple (causally dependent) mediators, which are often encountered in practice. Finally, the package also offers a methodology for assessing causal mediation in the presence of treatment noncompliance, a common problem in randomized trials.
Exposome recognizes that humans are constantly exposed to multiple environmental factors, and elucidating the health effects of complex exposure mixtures places greater demands on analytical methods.
...We aimed to explore the association between mixed exposure to metals and hyperuricemia (HUA), and highlight the potential of explainable machine learning (EML) and causal mediation analysis (CMA) for application in the analysis of exposome data.
Pre-pandemic data from the National Health and Nutrition Examination Survey (NHANES) 2011–2020 and a total of 13780 individuals were included. We first used traditional statistical models (multiple logistic regression (MLR) and restricted cubic spline regression (RCS)) and EML to explore associations between mixed metals exposures and HUA, followed by the CMA using the 4-way decomposition method to analyze the interaction and mediation effects among BMI or estimated glomerular filtration rate (eGFR), metals and HUA.
The prevalence of HUA was 18.91% (2606/13780). The MLR showed that mercury (Q4 vs Q1: OR = 1.08, 95% CI:1.02–1.14) and lead (Q4 vs Q1: OR = 1.23, 95% CI:1.13–1.34) were generally positively associated with HUA. Higher concentrations of lead, mercury, selenium and manganese were associated with the increased odds of HUA, and BMI and eGFR were the top two variables attributable to the risk of developing HUA in the EML. Subgroup analyses from the MLR and EML consistently demonstrated the positive relationship between exposure to lead, mercury and selenium in participants with BMI <25 kg/m2 and BMI ≥30 kg/m2. BMI mediated 32.12% of the association between lead exposure and HUA, and the interaction between BMI and lead accounted for 3.88% of the association in the CMA.
Heavy metals can increase the HUA risk and BMI or eGFR can mediate and interact with metals to cause HUA. Future studies based on exposome can attempt to utilize the EML and CMA.
Display omitted
•The widest possible range of data on metals and covariates form NHANES were involved that were openly available.•Two innovative methods (explainable machine learning and causal mediation analysis) were applied to exposome data.•Higher concentrations of lead, mercury, selenium and manganese were associated with the higher risk of hyperuricemia.•BMI and eGFR can mediate and interact with metals to cause hyperuricemia, especially for Pb and Hg.
Mendelian randomization (MR) is the use of genetic variants associated with an exposure to estimate the causal effect of that exposure on an outcome. Mediation analysis is the method of decomposing ...the effects of an exposure on an outcome, which act directly, and those that act via mediating variables. These effects are decomposed through the use of multivariable analysis to estimate the causal effects between three types of variables: exposures, mediators, and an outcome. Multivariable MR (MVMR) is a recent extension to MR that uses genetic variants associated with multiple, potentially related exposures to estimate the effect of each exposure on a single outcome. MVMR allows for equivalent analysis to mediation within the MR framework and therefore can also be used to estimate mediation effects. This approach retains the benefits of using genetic instruments for causal inference, such as avoiding bias due to confounding, while allowing for estimation of the different effects required for mediation analysis. This review explains MVMR, what is estimated when one exposure is a mediator of another in an MVMR estimation, and how MR and MVMR can therefore be used to estimate mediated effects. This review then goes on to consider the advantages and limitations of using MR and MVMR to conduct mediation analysis.
In a rapidly urbanizing world, many people have little contact with natural environments, which may affect health and well-being. Existing reviews generally conclude that residential greenspace is ...beneficial to health. However, the processes generating these benefits and how they can be best promoted remain unclear.
During an Expert Workshop held in September 2016, the evidence linking greenspace and health was reviewed from a transdisciplinary standpoint, with a particular focus on potential underlying biopsychosocial pathways and how these can be explored and organized to support policy-relevant population health research.
Potential pathways linking greenspace to health are here presented in three domains, which emphasize three general functions of greenspace: reducing harm (e.g. reducing exposure to air pollution, noise and heat), restoring capacities (e.g. attention restoration and physiological stress recovery) and building capacities (e.g. encouraging physical activity and facilitating social cohesion). Interrelations between among the three domains are also noted. Among several recommendations, future studies should: use greenspace and behavioural measures that are relevant to hypothesized pathways; include assessment of presence, access and use of greenspace; use longitudinal, interventional and (quasi)experimental study designs to assess causation; and include low and middle income countries given their absence in the existing literature. Cultural, climatic, geographic and other contextual factors also need further consideration.
While the existing evidence affirms beneficial impacts of greenspace on health, much remains to be learned about the specific pathways and functional form of such relationships, and how these may vary by context, population groups and health outcomes. This Report provides guidance for further epidemiological research with the goal of creating new evidence upon which to develop policy recommendations.
•Although it appears that greenspace benefits health, the pathways are unclear.•We have organized pathways into three domains that emphasize greenspace functions.•Pathways likely intertwine and vary by context, populations and health outcomes.•We identify diverse challenges in measurement and analysis that require attention.•Research guided by our discussion will better efforts to enable greenspace-related health benefits.
Mediation and conditional process analyses have become popular approaches for examining the mechanisms by which effects operate and the factors that influence them. To estimate mediation models, ...researchers often augment their structural equation modeling (SEM) analyses with additional regression analyses using the PROCESS macro. This duality is surprising considering that research has long acknowledged the limitations of regression analyses when estimating models with latent variables. In this article, we argue that much of the confusion regarding SEM’s efficacy for mediation analyses results from a singular focus on factor-based methods, and there is no need for a tandem use of SEM and PROCESS. Specifically, we highlight that composite-based SEM methods overcome the limitations of both regression and factor-based SEM analyses when estimating even highly complex mediation models. We further conclude that composite-based SEM methods such as partial least squares (PLS-SEM) are the preferred and superior approach when estimating mediation and conditional process models, and that the PROCESS approach is not needed when mediation is examined with PLS-SEM.
Behavioral scientists use mediation analysis to understand the mechanism(s) by which an effect operates and moderation analysis to understand the contingencies or boundary conditions of effects. Yet ...how effects operate (i.e., the mechanism at work) and their boundary conditions (when they occur) are not necessarily independent, though they are often treated as such. Conditional process analysis is an analytical strategy that integrates mediation and moderation analysis with the goal of examining and testing hypotheses about how mechanisms vary as a function of context or individual differences. In this article, we provide a conceptual primer on conditional process analysis for those not familiar with the integration of moderation and mediation analysis, while also describing some recent advances and innovations for the more experienced conditional process analyst. After overviewing fundamental modeling principles using ordinary least squares regression, we discuss the extension of these fundamentals to models with more than one mediator and more than one moderator. We describe a differential dominance conditional process model and overview the concepts of partial, conditional, and moderated moderated mediation. We also discuss multilevel conditional process analysis and comment on implementation of conditional process analysis in statistical computing software.
Inadequate translation from theoretical to statistical models of the greenspace – health relationship may lead to incorrect conclusions about the importance of some pathways, which in turn may reduce ...the effectiveness of public health interventions involving urban greening. In this scoping review we aimed to: (1) summarize the general characteristics of approaches to intervening variable inference (mediation analysis) employed in epidemiological research in the field; (2) identify potential threats to the validity of findings; and (3) propose recommendations for planning, conducting, and reporting mediation analyses.
We conducted a scoping review, searching PubMed, Scopus, and Web of Science for peer-reviewed epidemiological studies published by December 31, 2019. The list of potential studies was continuously updated through other sources until March 2020. Narrative presentation of the results was coupled with descriptive summary of study characteristics.
We found 106 studies, most of which were cross-sectional in design. Most studies only had a spatial measure of greenspace. Mental health/well-being was the most commonly studied outcome, and physical activity and air pollution were the most commonly tested intervening variables. Most studies only conducted single mediation analysis, even when multiple potentially intertwined mediators were measured. The analytical approaches used were causal steps, difference-of-coefficients, product-of-coefficients, counterfactual framework, and structural equation modelling (SEM). Bootstrapping was the most commonly used method to construct the 95% CI of the indirect effect. The product-of-coefficients method and SEM as used to investigate serial mediation components were more likely to yield findings of indirect effect. In some cases, the causal steps approach thwarted tests of indirect effect, even though both links in an indirect effect were supported. In most studies, sensitivity analyses and proper methodological discussion of the modelling approach were missing.
We found a persistent pattern of suboptimal conduct and reporting of mediation analysis in epidemiological studies investigating pathways linking greenspace to health; however, recent years have seen improvements in these respects. Better planning, conduct, and reporting of mediation analyses are warranted.
•We summarized the characteristics of approaches to mediation analysis in the field.•Potential threats to the validity of findings were identified.•A persistent pattern of suboptimal conduct and reporting of mediation analysis was found.•We propose recommendations for better planning, conducting, and reporting mediation analyses.
PDF
