Recommendations for accelerating open preprint peer review to improve the culture of science.

Peer review is an important part of the scientific process, but traditional peer review at journals is coming under increased scrutiny for its inefficiency and lack of transparency. As preprints become more widely used and accepted, they raise the possibility of rethinking the peer-review process. Preprints are enabling new forms of peer review that have the potential to be more thorough, inclusive, and collegial than traditional journal peer review, and to thus fundamentally shift the culture of peer review toward constructive collaboration. In this Consensus View, we make a call to action to stakeholders in the community to accelerate the growing momentum of preprint sharing and provide recommendations to empower researchers to provide open and constructive peer review for preprints.

Short report: Vaccine attitudes in the age of COVID-19 for a population of children with mitochondrial disease.

BACKGROUND: Children with developmental disabilities are vulnerable to morbidity associated with COVID-19. AIMS: To understand attitudes toward routine childhood vaccinations versus the COVID-19 vaccine in a population of families affected by mitochondrial disease (MtD), a form of developmental disability. METHODS AND PROCEDURES: An online survey was administered via several advocacy groups for children with MtD. OUTCOMES AND RESULT: Eighty-six percent of families reported being up to date with the childhood vaccine schedule and seventy percent reported that their affected child receives the annual flu shot. However, only fifty percent reported that the benefits of the COVID-19 vaccine outweighed the risk for their affected child. One quarter of families expressed concern that their child may become sick or deteriorate after the COVID-19 vaccine. In comparison to other routine childhood vaccines, families expressed less confidence in the COVID-19 vaccine. CONCLUSIONS AND IMPLICATIONS: Families affected by this population of developmental disabilities are more comfortable with the vaccines included in the routine childhood immunization schedule than with the newly introduced COVID-19 vaccine, even despite this group's vulnerability.

Risk mitigation behaviors to prevent infection in the mitochondrial disease community during the COVID-19 pandemic.

BACKGROUND: A challenge during the COVID-19 pandemic has been widespread adherence to risk-reducing behaviors. Individuals with mitochondrial disease (MtD) are special population with an increased risk of morbidity associated with infection. PURPOSE: To measure risk mitigation behaviors (RMBs) in families affected by MtD and identify factors that may influence these behaviors. METHODS: An online questionnaire was distributed in April and June 2020. Individuals with MtD or their caregivers completed the survey. RESULTS: We received 529 eligible responses with n = 312 completing all questions for our multivariate regression model. The most common RMBs were increased hand washing (96%), social distancing (94%), and avoiding public gatherings (93%). Higher numbers of recent healthcare visits (b = 0.62, p < 0.05) and expressed fear of the MtD patient contracting COVID-19 (b = 0.92, p < 0.05) were associated with more RMBs. Living in a rural community (b = -0.99,p < 0.05) and a history of COVID-19 testing (b = -2.14,p < 0.01) were associated with fewer RMBs. CONCLUSIONS: Our results suggest that during the COVID-19 pandemic, families affected by MtD have near universal adherence to basic RMBs. This may be motivated by fear of the severe morbidity associated with infection in MtD. Patients with frequent healthcare visits may be sicker and therefore take more precautions. Living in a rural community may also impact these behaviors. People who practice fewer RMBs may be more likely to seek testing. Our findings may generalize to other chronic diseases.

Interpersonal Correlates of Dementia Caregivers' Emotional Support Networks: Considering Family History.

Caregiving networks of individuals with Alzheimer's disease and related dementias (ADRD) are comprised of family and friends directly involved in caregiving activities and those supporting these activities. The purpose of this study was to investigate whether caregiving-related behaviors and interactions (i.e., uplift, malfeasance, and nonfeasance), kinship tie (i.e., friend, family), and family history of ADRD were associated with caregivers' emotional support networks. Seventy-one caregivers across 30 families provided information about 776 network members. Perceived emotional support and interactions representing uplift, malfeasance, and nonfeasance were assessed. Results indicated that uplift and friendship were associated with increased, whereas nonfeasance was associated with decreased, likelihood of perceived emotional support. Caregivers with a family history of ADRD were particularly more likely to report emotional support from friends and uplifting network members. Findings suggest the need for differential strategies based on families' prior caregiving experience to facilitate positive and minimize negative interactions within caregiving networks.

Exponential-Family Random Graph Models for Multi-Layer Networks.

Multi-layer networks arise when more than one type of relation is observed on a common set of actors. Modeling such networks within the exponential-family random graph (ERG) framework has been previously limited to special cases and, in particular, to dependence arising from just two layers. Extensions to ERGMs are introduced to address these limitations: Conway-Maxwell-Binomial distribution to model the marginal dependence among multiple layers; a "layer logic" language to translate familiar ERGM effects to substantively meaningful interactions of observed layers; and nondegenerate triadic and degree effects. The developments are demonstrated on two previously published datasets.

Primary Caregivers in a Network Context.

OBJECTIVES: Caregiving to individuals affected by Alzheimer's disease and related dementia (ADRD) is a family-systems process where tasks are distributed between multiple caregivers in a network. We evaluate the extent to which multiple network members nominate one another as filling primary caregiver (PCG) roles and factors associated with nomination. METHOD: Data come from the Caregiving Roles and Expectations Networks project, which aimed to characterize the caregiving networks of families affected by ADRD. All persons affected by ADRD were either full-time residents in residential care facilities or community-dwelling adult day-care participants. Generalized Poisson regression was used to model the count of incoming PCG nominations of each network member. RESULTS: On average, there were multiple network members identified as PCGs across different network contexts. Network members who were perceived to perform essential caregiving tasks, such as making decisions on behalf of and spending time with the care recipient, received more primary caregiving nominations from their network peers, adjusting for personal attributes, and the context of care. DISCUSSION: Having multiple PCGs in a network may result in lack of consensus in who fills those roles, potentially putting families at risk for interpersonal conflicts. Future work aimed at intervention development should fully assess the social contexts surrounding caregiving processes in order to better understand how network composition might impact outcomes.

The structure of emotional support networks in families affected by Lynch syndrome.

Genetic risk is particularly salient for families and testing for genetic conditions is necessarily a family-level process. Thus, risk for genetic disease represents a collective stressor shared by family members. According to communal coping theory, families may adapt to such risk vis-a-vis interpersonal exchange of support resources. We propose that communal coping is operationalized through the pattern of supportive relationships observed between family members. In this study, we take a social network perspective to map communal coping mechanisms to their underlying social interactions and include those who declined testing or were not at risk for Lynch Syndrome. Specifically, we examine the exchange of emotional support resources in families at risk of Lynch Syndrome, a dominantly inherited cancer susceptibility syndrome. Our results show that emotional support resources depend on the testing-status of individual family members and are not limited to the bounds of the family. Network members from within and outside the family system are an important coping resource in this patient population. This work illustrates how social network approaches can be used to test structural hypotheses related to communal coping within a broader system and identifies structural features that characterize coping processes in families affected by Lynch Syndrome.

Intergenerational exchange of healthful eating encouragement: Consideration of family ancestry and disease history.

INTRODUCTION: Improving diet is a prime target for the prevention and management of chronic disease. The communal coping model suggests that families can mitigate shared risk of chronic disease through encouragement of healthful eating, eliciting preventive behaviors. METHOD: Using network data from 69 Australian families across three ancestry groups (Anglo, Italian, and Asian) with varied family health histories, the present study applied social network analysis to identify patterns of intergenerational encouragement of healthful eating behavior within families, and assess whether patterns varied by family ancestry or disease density. RESULTS: Findings indicated variation in patterns of health encouragement by ancestry such that Asian-Australian families were most distinct from the other ancestry groups. While there was no main effect of familial disease history, it moderated the effect of Italian ancestry on intergenerational encouragement patterns. DISCUSSION: These results provide important context for future family based interventions that leverage normative patterns of intergenerational exchange of encouragement or aim to modify such patterns in an effort to improve family health. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

A Bayesian hierarchical logistic regression model of multiple informant family health histories.

BACKGROUND: Family health history (FHH) inherently involves collecting proxy reports of health statuses of related family members. Traditionally, such information has been collected from a single informant. More recently, research has suggested that a multiple informant approach to collecting FHH results in improved individual risk assessments. Likewise, recent work has emphasized the importance of incorporating health-related behaviors into FHH-based risk calculations. Integrating both multiple accounts of FHH with behavioral information on family members represents a significant methodological challenge as such FHH data is hierarchical in nature and arises from potentially error-prone processes. METHODS: In this paper, we introduce a statistical model that addresses these challenges using informative priors for background variation in disease prevalence and the effect of other, potentially correlated, variables while accounting for the nested structure of these data. Our empirical example is drawn from previously published data on families with a history of diabetes. RESULTS: The results of the comparative model assessment suggest that simply accounting for the structured nature of multiple informant FHH data improves classification accuracy over the baseline and that incorporating family member health-related behavioral information into the model is preferred over alternative specifications. CONCLUSIONS: The proposed modelling framework is a flexible solution to integrate multiple informant FHH for risk prediction purposes.

Using hospital administrative data to infer patient-patient contact via the consistent co-presence algorithm.

In health care settings, patients who are physically proximate to other patients (co-presence) for a meaningful amount of time may have differential health outcomes depending on who they are in contact with. How to best measure this co-presence, however is an open question and previous approaches have limitations that may make them inappropriate for complex health care settings. Here, we introduce a novel method which we term "consistent co-presence", that implicitly models the many complexities of patient scheduling and movement through a hospital by randomly perturbing the timing of patients' entry time into the health care system. This algorithm generates networks that can be employed in models of patient outcomes, such as 1-year mortality, and are preferred over previously established alternative algorithms from a model comparison perspective. These results indicate that consistent co-presence retains meaningful information about patient-patient interaction, which may affect outcomes relevant to health care practice. Furthermore, the generalizabiity of this approach allows it to be applied to a wide variety of complex systems.

Genetic, Psychological, and Personal Network Factors Associated With Changes in Binge Drinking Over 2 Years Among Mexican Heritage Adolescents in the USA.

Background: Despite prevalent binge drinking and alcohol-dependent symptoms among Hispanics, few studies have examined how multidimensional factors influence Hispanic adolescents' binge drinking. Purpose This study examines the effects of genetic, psychological, and social network factors on binge drinking over time among Mexican heritage adolescents in the USA and whether there are correlations among genetic variants that are associated with binge drinking and psychological and network characteristics. Methods: Mexican heritage adolescents (n = 731) participated in a longitudinal study, which included genetic testing at baseline, alcohol use assessments at first and second follow-ups, and questionnaires on sensation seeking, impulsivity, and peer and family network characteristics at second follow-up. Logistic regression and Spearman correlation analyses were performed. Results: After adjusting for demographic characteristics, underlying genetic clustering, and binge drinking at first follow-up, two genetic variants on tryptophan hydroxylase 2 (TPH2; rs17110451, rs7963717), sensation seeking and impulsivity, and having a greater fraction of peers who drink or encourage drinking alcohol were associated with greater risk whereas another genetic variant on TPH2 (rs11178999) and having a greater fraction of close family relationships were associated with reduced risk for binge drinking at second follow-up. Genetic variants in TPH1 (rs591556) were associated with sensation seeking and impulsivity, while genetic variants in TPH2 (rs17110451) were associated with the fraction of drinkers in family. Conclusions: Results reveal that genetic variants in the serotonin pathway, behavioral disinhibition traits, and social networks exert joint influences on binge drinking in Mexican heritage adolescents in the USA.

Modeling Dynamic Food Choice Processes to Understand Dietary Intervention Effects.

Background: Meal construction is largely governed by nonconscious and habit-based processes that can be represented as a collection of in dividual, micro-level food choices that eventually give rise to a final plate. Despite this, dietary behavior intervention research rarely captures these micro-level food choice processes, instead measuring outcomes at aggregated levels. This is due in part to a dearth of analytic techniques to model these dynamic time-series events. Purpose: The current article addresses this limitation by applying a generalization of the relational event framework to model micro-level food choice behavior following an educational intervention. Method: Relational event modeling was used to model the food choices that 221 mothers made for their child following receipt of an information-based intervention. Participants were randomized to receive either (a) control information; (b) childhood obesity risk information; (c) childhood obesity risk information plus a personalized family history-based risk estimate for their child. Participants then made food choices for their child in a virtual reality-based food buffet simulation. Results: Micro-level aspects of the built environment, such as the ordering of each food in the buffet, were influential. Other dynamic processes such as choice inertia also influenced food selection. Among participants receiving the strongest intervention condition, choice inertia decreased and the overall rate of food selection increased. Conclusions: Modeling food selection processes can elucidate the points at which interventions exert their influence. Researchers can leverage these findings to gain insight into nonconscious and uncontrollable aspects of food selection that influence dietary outcomes, which can ultimately improve the design of dietary interventions.

Unmet Expectations in Alzheimer's Family Caregiving: Interactional Characteristics Associated With Perceived Under-Contribution.

Background and Objectives: Perceptions about family members not contributing enough to caregiving are documented to create psychological stress among caregivers. This study investigated whether individuals' perception that family members are under-contributing in caregiving processes was associated with their psychological well-being and explored the factors that may contribute to such perception borrowing concepts from a previous study: malfeasance, nonfeasance, and uplift. Research Design and Methods: Seventy-two members of 30 families recruited through residential and adult daycare settings provided information about 960 familial network members (e.g., family, friends). Perceived levels of participation in caregiving about each network member, whether the level met respondents' expectations, and interactions representing malfeasance, nonfeasance, and uplift were assessed. Results: Number of family members respondents identified as under-contributing in caregiving was associated with higher, whereas numbers of family participating in caregiving and supportive staff were associated with lower distress (Center for Epidemiologic Studies Depression Scale [CES-D]). Factor analyses identified a set of social interactions among familial network members capturing three constructs: malfeasance, nonfeasance, and uplift. Network members for whom respondents reported higher levels of nonfeasance were more likely to be identified as under-contributing in direct care (odds ratio [OR] = 1.92), care decision making (OR = 1.89), and social support (OR = 1.74) compared with those identified as contributing enough. Members with higher levels of malfeasance were more likely to be identified as under-contributing in direct care (OR = 1.19) than those identified as contributing enough. Discussion and Implications: Social interactions characterized as nonfeasance may explain the perception of unmet expectations in caregiving within families and may represent a potential focus of family-level interventions.

Multi-relational measurement for latent construct networks.

Multiple item scales have long been used to measure latent constructs on individual-level data. This is appropriate when an otherwise unobserved construct is indirectly measured by combining observable correlated characteristics that are thought to measure slightly different dimensions of that construct. Network data, which consist of observations on the relationships between a set of actors, however, are typically drawn from single-relation measurements. While this approach is sufficient for learning about discrete relations (communication, coauthorship, etc.), multi-item measurement of extemporaneous valued relationships, such as cohesion and conflict, may be of common interest in psychology and related sciences. In this article, we evaluate the use of multirelational network measurement in inferring valued latent construct networks. In particular, we present a psychometric framework for developing multirelational measures of latent construct networks, evaluating their reliability and construct validity, and identification of appropriate scaling approaches for these construct-level networks. (PsycINFO Database Record

Social influence on 5-year survival in a longitudinal chemotherapy ward co-presence network.

Chemotherapy is often administered in openly designed hospital wards, where the possibility of patient-patient social influence on health exists. Previous research found that social relationships influence cancer patient's health; however, we have yet to understand social influence among patients receiving chemotherapy in the hospital. We investigate the influence of co-presence in a chemotherapy ward. We use data on 4,691 cancer patients undergoing chemotherapy in Oxfordshire, United Kingdom who average 59.8 years of age, and 44% are Male. We construct a network of patients where edges exist when patients are co-present in the ward, weighted by both patients' time in the ward. Social influence is based on total weighted co-presence with focal patients' immediate neighbors, considering neighbors' 5-year mortality. Generalized estimating equations evaluated the effect of neighbors' 5-year mortality on focal patient's 5-year mortality. Each 1,000-unit increase in weighted co-presence with a patient who dies within 5 years increases a patient's mortality odds by 42% (ß = 0.357, CI:0.204,0.510). Each 1,000-unit increase in co-presence with a patient surviving 5 years reduces a patient's odds of dying by 30% (ß = -0.344, CI:-0.538,0.149). Our results suggest that social influence occurs in chemotherapy wards, and thus may need to be considered in chemotherapy delivery.

Using the relational event model (REM) to investigate the temporal dynamics of animal social networks.

Social dynamics are of fundamental importance in animal societies. Studies on nonhuman animal social systems often aggregate social interaction event data into a single network within a particular time frame. Analysis of the resulting network can provide a useful insight into the overall extent of interaction. However, through aggregation, information is lost about the order in which interactions occurred, and hence the sequences of actions over time. Many research hypotheses relate directly to the sequence of actions, such as the recency or rate of action, rather than to their overall volume or presence. Here, we demonstrate how the temporal structure of social interaction sequences can be quantified from disaggregated event data using the relational event model (REM). We first outline the REM, explaining why it is different from other models for longitudinal data, and how it can be used to model sequences of events unfolding in a network. We then discuss a case study on the European jackdaw, Corvus monedula, in which temporal patterns of persistence and reciprocity of action are of interest, and present and discuss the results of a REM analysis of these data. One of the strengths of a REM analysis is its ability to take into account different ways in which data are collected. Having explained how to take into account the way in which the data were collected for the jackdaw study, we briefly discuss the application of the model to other studies. We provide details of how the models may be fitted in the R statistical software environment and outline some recent extensions to the REM framework.

Constructing and Modifying Sequence Statistics for relevent Using informR in 𝖱.

The informR package greatly simplifies the analysis of complex event histories in 𝖱 by providing user friendly tools to build sufficient statistics for the relevent package. Historically, building sufficient statistics to model event sequences (of the form a→b) using the egocentric generalization of Butts' (2008)relational event framework for modeling social action has been cumbersome. The informR package simplifies the construction of the complex list of arrays needed by the rem() model fitting for a variety of cases involving egocentric event data, multiple event types, and/or support constraints. This paper introduces these tools using examples from real data extracted from the American Time Use Survey.

Inter-generational contact from a network perspective.

Pathways for resource--or other--exchanges within families have long been known to be dependent on the structure of relations between generations (Agree et al., 2005; Fuller-Thomson et al., 1997; Silverstein, 2011; Treas & Marcum, 2011). Much life course research has theorized models of inter-generational exchange--including, the 'sandwich generation' (Miller, 1981) and the 'skipped generation' pathways (Chalfie, 1994)--but there is little work relating these theories to relevant network mechanisms such as liaison brokerage (Gould & Fernandez, 1989) and other triadic configurations (Davis & Leinhardt, 1972; Wasserman & Faust, 1994). To address this, a survey of models of resource allocation between members of inter-generational households from a network perspective is introduced in this paper. Exemplary data come from health discussion networks among Mexican-origin multi-generational households.

Age Differences in Daily Social Activities.

The extent to which older and younger people do different activities when they are with others and when they are alone is examined in this article. I leverage interpersonal data in combination with information on activities from the American Time Use Survey to shed light on the long held finding that older people have less social contact than younger people. The results show that, net of intervening factors, age is associated with declines in time spent with others for virtually all types of time use. However, the variety of activities that older and younger people do also differs. Using leisure activities to probe this finding reveals that, when older people spend time with others it tends to be during activities that are sui generis social activities-such as attending parties-but that this is not necessarily the case for younger people. The literature on time use and aging is discussed in light of these findings and a new hypothesis on agency in the life course is proposed.