A class of distribution-free models for longitudinal mediation analysis

Urinary Glyphosate, 2,4-D and DEET Biomarkers in Relation to Neurobehavioral Performance in Ecuadorian Adolescents in the ESPINA Cohort

BACKGROUND

Herbicides are the most used class of pesticides worldwide, and insect repellents are widely used globally. Yet, there is a dearth of studies characterizing the associations between these chemical groups and human neurobehavior. Experimental studies suggest that glyphosate and 2,4-dichlorophenoxyacetic acid (2,4-D) herbicides can affect neurobehavior and the cholinergic and glutamatergic pathways in the brain. We aim to assess whether herbicides and insect repellents are associated with neurobehavioral performance in adolescents.

METHODS

We assessed 519 participants (11-17 years of age) living in agricultural communities in Ecuador. We quantified urinary concentrations of glyphosate, 2,4-D, and two N,N-diethyl-meta-toluamide (DEET) insect repellent metabolites [3-(diethylcarbamoyl)benzoic acid (DCBA) and 3-(ethylcarbamoyl)benzoic acid (ECBA)] using isotope-dilution mass spectrometry. We assessed neurobehavioral performance using 9 subtests across 5 domains (attention/inhibitory control, memory/learning, language, visuospatial processing, and social perception). We characterized the associations using generalized estimating equations and multiple imputation for metabolites below detection limits. Models were adjusted for demographic and anthropometric characteristics, urinary creatinine, and sexual maturation. Mediation by salivary cortisol, dehydroepiandrosterone, 17 β -estradiol , and testosterone was assessed using structural equation modeling.

RESULTS

The mean of each neurobehavioral domain score was between 7.0 and 8.7 [standard deviation (SD) range: 2.0-2.3]. Glyphosate was detected in 98.3% of participants, 2,4-D in 66.2%, DCBA in 63.3%, and ECBA in 33.4%. 2,4-D was negatively associated with all neurobehavioral domains, but statistically significant associations were observed with attention/inhibition [score difference per 50% higher metabolite concentration ( β ) = - 0.19 95% confidence interval (CI): - 0.31 , - 0.07 ], language [β = - 0.12 (95% CI: - 0.23 , - 0.01 )], and memory/learning [β = - 0.11 (95% CI: - 0.22 , 0.01)]. Glyphosate had a statistically significant negative association only with social perception [β = - 0.08 (95% CI: - 0.14 , - 0.01 )]. DEET metabolites were not associated with neurobehavioral performance. Mediation by gender and adrenal hormones was not observed.

CONCLUSION

This study describes worse neurobehavioral performance associated with herbicide exposures in adolescents, particularly with 2,4-D. Replication of these findings among other pediatric and adult populations is needed. https://doi.org/10.1289/EHP11383.

Dimensions of inattention: Cognitive, behavioral, and affective consequences

Inattention to one's on-going task leads to well-documented cognitive, behavioral, and physiological consequences. At the same time, the reliable association between mind-wandering and negative mood has suggested that there are affective consequences to task inattention as well. We examined this potential relationship between inattention and mood in the following study. Six hundred and fifty-five participants completed self-report questionnaires related to inattentive thinking (i.e., attentional lapses, daydreaming, mindfulness, rumination, reflection, worry, postevent processing, inattentiveness, and counterfactual thinking), a questionnaire about depressive symptoms, and a questionnaire about anxiety symptoms. First, an exploratory factor analysis was conducted to identify potential underlying constructs of types of inattentive thinking. Using ordinary least squares extraction and Oblimin rotation, a three-factor model demonstrated suitable fit, broadly representing mind-wandering/inattentive consequences, repetitive negative thinking, and reflective/introspective thinking. Second, after eliminating measures that did not strongly load on any factor, structural equation modeling was conducted and found that the relationship between mind-wandering and depression was partially explained by repetitive negative thinking, whereas the relationship between mind-wandering and anxiety was fully explained by repetitive negative thinking. The present findings suggest that understanding how inattentive thoughts are interrelated not only influences mood and affect but also reveals important considerations of intentionality, executive functioning, and qualitative styles of these thoughts.

The helping older people engage (HOPE) study: Protocol & COVID modifications for a randomized trial

Objectives

Evidence-based strategies to reduce loneliness in later life are needed because loneliness impacts all domains of health, functioning, and quality of life. Volunteering is a promising strategy, as a large literature of observational studies documents associations between volunteering and better health and well-being. However, relatively few studies have used randomized controlled trials (RCTs) to examine benefits of volunteering, and none have examined loneliness. The primary objective of the Helping Older People Engage (HOPE) study is to examine the social-emotional benefits of a social volunteering program for lonely older adults. This manuscript describes the rationale and design of the trial.

Methods

We are randomly assigning adults aged 60 or older (up to 300) who report loneliness to 12 months of either AmeriCorps Seniors volunteering program or an active control (self-guided life review). Co-primary outcomes are assessed via self-report-loneliness (UCLA Loneliness Scale) and quality of life (WHOQOL-Bref). Enrollment was completed in May 2022 and follow-up assessments will continue through May 2023, with completion of primary outcomes soon thereafter.

Conclusions

Since older adults who report loneliness are less likely to actively seek out volunteering opportunities, if results support efficacy of volunteering for reducing loneliness, dissemination and scaling up efforts may involve connecting primary care patients who are lonely with AmeriCorps Seniors through aging services agencies.This RCT is registered at clinicaltrials.gov (NCT03343483).

How does inquisitiveness matter for generativity and happiness?

Inquisitiveness (curiosity & acceptance to something and someone different) is the main engine for one person to initiate some relation, and the literature has established that maintaining nice relationships with friends, family and general others contributes to generativity and happiness. However, little is known about how generativity and happiness are characterized by inquisitiveness. We hypothesize that inquisitiveness is a fundamental determinant for generativity and happiness, empirically examining the relationships along with cognitive, noncognitive and sociodemographic factors. We conduct questionnaire surveys with 400 Japanese subjects, applying quantile regression and structural equation modeling to the data. First, the analysis identifies the importance of inquisitiveness in characterizing generativity in that people with high inquisitiveness tend to be generative. Second, people are identified to be happy as they have high generativity and inquisitiveness, demonstrating two influential roles of inquisitiveness as direct and indirect determinants through a mediator of generativity. Overall, the results suggest that inquisitiveness shall be a key element of people’s happiness through intergenerational and intragenerational communications or relations.

A semiparametric marginalized zero-inflated model for analyzing healthcare utilization panel data with missingness

Zero-inflated count outcomes arise quite often in research and practice. Parametric models such as the zero-inflated Poisson and zero-inflated negative binomial are widely used to model such responses. However, interpretations of those models focus on the at-risk subpopulation of a two-component population mixture and fail to provide direct inference about marginal effects for the overall population. Recently, new approaches have been proposed to facilitate such marginal inferences for count responses with excess zeros. However, they are likelihood based and impose strong assumptions on data distributions. In this paper, we propose a new distribution-free, or semiparametric, alternative to provide robust inference for marginal effects when population mixtures are defined by zero-inflated count outcomes. The proposed method also applies to longitudinal studies with missing data following the general missing at random mechanism. The proposed approach is illustrated with both simulated and real study data.

Distribution-free models for latent mixed population responses in a longitudinal setting with missing data

Many biomedical and psychosocial studies involve population mixtures, which consist of multiple latent subpopulations. Because group membership cannot be observed, standard methods do not apply when differential treatment effects need to be studied across subgroups. We consider a two-group mixture in which membership of latent subgroups is determined by structural zeroes of a zero-inflated count variable and propose a new approach to model treatment differences between latent subgroups in a longitudinal setting. It has also been incorporated with the inverse probability weighted method to address data missingness. As the approach builds on the distribution-free functional response models, it requires no parametric distribution model and thereby provides a robust inference. We illustrate the approach with both real and simulated data.

Modern methods for longitudinal data analysis, capabilities, caveats and cautions

Longitudinal studies are used in mental health research and services studies. The dominant approaches for longitudinal data analysis are the generalized linear mixed-effects models (GLMM) and the weighted generalized estimating equations (WGEE). Although both classes of models have been extensively published and widely applied, differences between and limitations about these methods are not clearly delineated and well documented. Unfortunately, some of the differences and limitations carry significant implications for reporting, comparing and interpreting research findings. In this report, we review both major approaches for longitudinal data analysis and highlight their similarities and major differences. We focus on comparison of the two classes of models in terms of model assumptions, model parameter interpretation, applicability and limitations, using both real and simulated data. We discuss caveats and cautions when applying the two different approaches to real study data.

Variable selection for distribution-free models for longitudinal zero-inflated count responses

Zero-inflated count outcomes arise quite often in research and practice. Parametric models such as the zero-inflated Poisson and zero-inflated negative binomial are widely used to model such responses. Like most parametric models, they are quite sensitive to departures from assumed distributions. Recently, new approaches have been proposed to provide distribution-free, or semi-parametric, alternatives. These methods extend the generalized estimating equations to provide robust inference for population mixtures defined by zero-inflated count outcomes. In this paper, we propose methods to extend smoothly clipped absolute deviation (SCAD)-based variable selection methods to these new models. Variable selection has been gaining popularity in modern clinical research studies, as determining differential treatment effects of interventions for different subgroups has become the norm, rather the exception, in the era of patent-centered outcome research. Such moderation analysis in general creates many explanatory variables in regression analysis, and the advantages of SCAD-based methods over their traditional counterparts render them a great choice for addressing this important and timely issues in clinical research. We illustrate the proposed approach with both simulated and real study data. Copyright © 2016 John Wiley & Sons, Ltd.

On performance of parametric and distribution-free models for zero-inflated and over-dispersed count responses

Zero-inflated Poisson (ZIP) and negative binomial (ZINB) models are widely used to model zero-inflated count responses. These models extend the Poisson and negative binomial (NB) to address excessive zeros in the count response. By adding a degenerate distribution centered at 0 and interpreting it as describing a non-risk group in the population, the ZIP (ZINB) models a two-component population mixture. As in applications of Poisson and NB, the key difference between ZIP and ZINB is the allowance for overdispersion by the ZINB in its NB component in modeling the count response for the at-risk group. Overdispersion arising in practice too often does not follow the NB, and applications of ZINB to such data yield invalid inference. If sources of overdispersion are known, other parametric models may be used to directly model the overdispersion. Such models too are subject to assumed distributions. Further, this approach may not be applicable if information about the sources of overdispersion is unavailable. In this paper, we propose a distribution-free alternative and compare its performance with these popular parametric models as well as a moment-based approach proposed by Yu et al. [Statistics in Medicine 2013; 32: 2390-2405]. Like the generalized estimating equations, the proposed approach requires no elaborate distribution assumptions. Compared with the approach of Yu et al., it is more robust to overdispersed zero-inflated responses. We illustrate our approach with both simulated and real study data.

A class of distribution-free models for longitudinal mediation analysis

Mediation analysis constitutes an important part of treatment study to identify the mechanisms by which an intervention achieves its effect. Structural equation model (SEM) is a popular framework for modeling such causal relationship. However, current methods impose various restrictions on the study designs and data distributions, limiting the utility of the information they provide in real study applications. In particular, in longitudinal studies missing data is commonly addressed under the assumption of missing at random (MAR), where current methods are unable to handle such missing data if parametric assumptions are violated.In this paper, we propose a new, robust approach to address the limitations of current SEM within the context of longitudinal mediation analysis by utilizing a class of functional response models (FRM). Being distribution-free, the FRM-based approach does not impose any parametric assumption on data distributions. In addition, by extending the inverse probability weighted (IPW) estimates to the current context, the FRM-based SEM provides valid inference for longitudinal mediation analysis under the two most popular missing data mechanisms; missing completely at random (MCAR) and missing at random (MAR). We illustrate the approach with both real and simulated data.