Multiple-membership multiple-classification models for social network and group dependences

The European livestock resistome

Metagenomic sequencing has proven to be a powerful tool in the monitoring of antimicrobial resistance (AMR). Here, we provide a comparative analysis of the resistome from pigs, poultry, veal calves, turkey, and rainbow trout, for a total of 538 herds across nine European countries. We calculated the effects of per-farm management practices and antimicrobial usage (AMU) on the resistome in pigs, broilers, and veal calves. We also provide an in-depth study of the associations between bacterial diversity, resistome diversity, and AMR abundances as well as co-occurrence analysis of bacterial taxa and antimicrobial resistance genes (ARGs) and the universality of the latter. The resistomes of veal calves and pigs clustered together, as did those of avian origin, while the rainbow trout resistome was different. Moreover, we identified clear core resistomes for each specific food-producing animal species. We identified positive associations between bacterial alpha diversity and both resistome alpha diversity and abundance. Network analyses revealed very few taxa-ARG associations in pigs but a large number for the avian species. Using updated reference databases and optimized bioinformatics, previously reported significant associations between AMU, biosecurity, and AMR in pig and poultry farms were validated. AMU is an important driver for AMR; however, our integrated analyses suggest that factors contributing to increased bacterial diversity might also be associated with higher AMR load. We also found that dispersal limitations of ARGs are shaping livestock resistomes, and future efforts to fight AMR should continue to emphasize biosecurity measures.IMPORTANCEUnderstanding the occurrence, diversity, and drivers for antimicrobial resistance (AMR) is important to focus future control efforts. So far, almost all attempts to limit AMR in livestock have addressed antimicrobial consumption. We here performed an integrated analysis of the resistomes of five important farmed animal populations across Europe finding that the resistome and AMR levels are also shaped by factors related to bacterial diversity, as well as dispersal limitations. Thus, future studies and interventions aimed at reducing AMR should not only address antimicrobial usage but also consider other epidemiological and ecological factors.

BISoN: A Bayesian Framework for Inference of Social Networks

Animal social networks are often constructed from point estimates of edge weights. In many contexts, edge weights are inferred from observational data, and the uncertainty around estimates can be affected by various factors. Though this has been acknowledged in previous work, methods that explicitly quantify uncertainty in edge weights have not yet been widely adopted, and remain undeveloped for many common types of data. Furthermore, existing methods are unable to cope with some of the complexities often found in observational data, and do not propagate uncertainty in edge weights to subsequent statistical analyses.We introduce a unified Bayesian framework for modelling social networks based on observational data. This framework, which we call BISoN, can accommodate many common types of observational social data, can capture confounds and model effects at the level of observations, and is fully compatible with popular methods used in social network analysis.We show how the framework can be applied to common types of data and how various types of downstream statistical analyses can be performed, including non-random association tests and regressions on network properties.Our framework opens up the opportunity to test new types of hypotheses, make full use of observational datasets, and increase the reliability of scientific inferences. We have made both an R package and example R scripts available to enable adoption of the framework.

Multiple teams, multiple projects, multiple groups at the intersection of (multiple) research fields: A bibliometric study

Multi-teaming is a concept studied across a variety of disciplines. While using a bibliometric approach on 255 research papers extracted from Web of Science, we aimed to depict the architecture of the multi-teaming concept across academic disciplines and time. Results of citation, co-citation and bibliographic coupling analyses identified four major fields looking at the concept of multi-teaming. The fields emerged over time from fragmentation to integration and acknowledging similarities. We identify gaps and propose (multi)-disciplinary research ideas that can benefit the field of multi-teaming.

Within-individual changes reveal increasing social selectivity with age in rhesus macaques

Accumulating evidence in humans and other mammals suggests older individuals tend to have smaller social networks. Uncovering the cause of these declines can inform how changes in social relationships with age affect health and fitness in later life. While age-based declines in social networks have been thought to be detrimental, physical and physiological limitations associated with age may lead older individuals to adjust their social behavior and be more selective in partner choice. Greater selectivity with age has been shown in humans, but the extent to which this phenomenon occurs across the animal kingdom remains an open question. Using longitudinal data from a population of rhesus macaques on Cayo Santiago, we provide compelling evidence in a nonhuman animal for within-individual increases in social selectivity with age. Our analyses revealed that adult female macaques actively reduced the size of their networks as they aged and focused on partners previously linked to fitness benefits, including kin and partners to whom they were strongly and consistently connected earlier in life. Females spent similar amounts of time socializing as they aged, suggesting that network shrinkage does not result from lack of motivation or ability to engage, nor was this narrowing driven by the deaths of social partners. Furthermore, females remained attractive companions and were not isolated by withdrawal of social partners. Taken together, our results provide rare empirical evidence for social selectivity in nonhumans, suggesting that patterns of increasing selectivity with age may be deeply rooted in primate evolution.

Common permutation methods in animal social network analysis do not control for non-independence

The non-independence of social network data is a cause for concern among behavioural ecologists conducting social network analysis. This has led to the adoption of several permutation-based methods for testing common hypotheses. One of the most common types of analysis is nodal regression, where the relationships between node-level network metrics and nodal covariates are analysed using a permutation technique known as node-label permutations. We show that, contrary to accepted wisdom, node-label permutations do not automatically account for the non-independences assumed to exist in network data, because regression-based permutation tests still assume exchangeability of residuals. The same assumption also applies to the quadratic assignment procedure (QAP), a permutation-based method often used for conducting dyadic regression. We highlight that node-label permutations produce the same p-values as equivalent parametric regression models, but that in the presence of non-independence, parametric regression models can also produce accurate effect size estimates. We also note that QAP only controls for a specific type of non-independence between edges that are connected to the same nodes, and that appropriate parametric regression models are also able to account for this type of non-independence. Based on this, we suggest that standard parametric models could be used in the place of permutation-based methods. Moving away from permutation-based methods could have several benefits, including reducing over-reliance on p-values, generating more reliable effect size estimates, and facilitating the adoption of causal inference methods and alternative types of statistical analysis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00265-022-03254-x.

Modern applications of cross-classified random effects models in social and behavioral research: Illustration with R package PLmixed

Cross-classified random effects models (CCREMs) have been developed for appropriately analyzing data with a cross-classified structure. Despite its flexibility and the prevalence of cross-classified data in social and behavioral research, CCREMs have been under-utilized in applied research. In this article, we present CCREMs as a general and flexible modeling framework, and present a wide range of existing models designed for different purposes as special instances of CCREMs. We also introduce several less well-known applications of CCREMs. The flexibility of CCREMs allows these models to be easily extended to address substantive questions. We use the free R package PLmixed to illustrate the estimation of these models, and show how the general language of the CCREM framework can be translated into specific modeling contexts.

“Title does not dictate behavior”: Associations of formal, structural, and behavioral brokerage with school staff members’ professional well-being

Individuals in brokerage positions are vital when further developing complex organizations with multiple subgroups only loosely coupled to each other. Network theorists have conceptualized an individual’s brokerage as the degree to which a person occupies a bridging position between disconnected others. Research outside the school context has indicated for quite some time that an individual’s social capital in the form of brokerage is positively associated with professional development—not only on a collective but also on an individual level. Schools are without any doubt complex organizations with multiple loosely connected stakeholders involved when further developing their educational practice. Thus, it is not surprising that in recent years, the concept of brokerage has gained interest in research on school improvement as well. Up to now, in school improvement research brokerage has been operationalized in different ways: as individuals’ formal entitlement to act as intermediaries (formal brokerage), their position within a social network (structural brokerage), or their behavior when linking disconnected groups of staff members (behavioral brokerage). As these perspectives have often been examined separately, this study, as a first step, aimed to simultaneously assess school staff members’ formal, structural, and behavioral brokerage, and examine their degree of interrelatedness. In a second step, associations of brokerage with professional well-being were analyzed. Even though there is evidence for the positive impact of brokerage on professional development, only little is known about its associations with professional well-being. In a third step, interaction effects were examined when formal brokerage is congruent or incongruent with other facets of brokerage. Based on a sample of 1,316 school staff members at 51 primary schools in the German-speaking part of Switzerland, we conducted both bivariate correlational and multiple-group structural equation modeling analyses. The findings revealed that formal, structural, and behavioral brokerage are interrelated facets. However, formal entitlement did not determine either structural position or behavior. Moreover, brokerage within schools was only partially related to professional well-being. In the discussion section, the study’s key contributions and practical implications are presented in detail.

The Use of Social Capital in Teacher Research: A Necessary Clarification

In this paper, we present a critical reflection on the concept of social capital. We argue that there is no such idea of an umbrella concept of social capital. Instead, two overarching conceptualizations of social capital exist, namely individual social capital and collective social capital. As these conceptualizations of social capital are completely different, we emphasize that studies using social capital as a theoretical lens should clarify the concept as well as be consistent in the interpretation of the concept, from its definition to its methodological operationalization. In this article, we first map the two different conceptualizations of social capital. Next, these conceptualizations are illustrated with well-known teacher research studies, followed by examples of studies in which individual and collective social capital are mixed. Finally, we discuss the consequences of the use and the mix of these different conceptualizations in terms of measurement methods. Additionally, implications for teacher education are presented.

Entropy of Co-Enrolment Networks Reveal Disparities in High School STEM Participation

The current study uses a network analysis approach to explore the STEM pathways that students take through their final year of high school in Aotearoa New Zealand. By accessing individual-level microdata from New Zealand’s Integrated Data Infrastructure, we are able to create a co-enrolment network comprised of all STEM assessment standards taken by students in New Zealand between 2010 and 2016. We explore the structure of this co-enrolment network though use of community detection and a novel measure of entropy. We then investigate how network structure differs across sub-populations based on students’ sex, ethnicity, and the socio-economic-status (SES) of the high school they attended. Results show the structure of the STEM co-enrolment network differs across these sub-populations, and also changes over time. We find that, while female students were more likely to have been enrolled in life science standards, they were less well represented in physics, calculus, and vocational (e.g., agriculture, practical technology) standards. Our results also show that the enrollment patterns of Asian students had lower entropy, an observation that may be explained by increased enrolments in key science and mathematics standards. Through further investigation of differences in entropy across ethnic group and high school SES, we find that ethnic group differences in entropy are moderated by high school SES, such that sub-populations at higher SES schools had lower entropy. We also discuss these findings in the context of the New Zealand education system and policy changes that occurred between 2010 and 2016.

The influence of firearms trafficking on gunshot injuries in a co-offending network

Individuals at the greatest risk of gunshot victimization are often prohibited from legally acquiring guns in the U.S. due to prior felony convictions or other disqualifications. Prohibited persons often rely on others - such as friends, family members, fellow gang members, and gun brokers - to acquire firearms. This study examines whether the sources of guns recovered from high-risk individuals differ relative to the sources of guns recovered more generally in a major U.S. city, and whether illegally-diverted guns are associated with increased gunshot victimization risk. Using official data, we recreate the co-offending network of individuals in Boston who were arrested or contacted by the police with at least one other person between 2007 and 2014. Firearms trace data are then used to develop measures of the shortest distance between individuals and firearms in their immediate network. Results suggest guns with markers of illegal diversion are more likely to be recovered in the highest risk sector of the network and that the probability of gunshot victimization increases with decreased distance to an individual linked to firearms with markers of illegal trafficking.

A Multilevel Analysis of Neighbourhood, School, Friend and Individual-Level Variation in Primary School Children's Physical Activity

Physical activity is influenced by individual, inter-personal and environmental factors. In this paper, we explore the variability in children's moderate-to-vigorous physical activity (MVPA) at different individual, parent, friend, school and neighbourhood levels. Valid accelerometer data were collected for 1077 children aged 9, and 1129 at age 11, and the average minutes of MVPA were derived for weekdays and weekends. We used a multiple-membership, multiple-classification model (MMMC) multilevel model to compare the variation in physical activity outcomes at each of the different levels. There were differences in the proportion of variance attributable to the different levels between genders, for weekdays and weekends, at ages 9 and 11. The largest proportion of variability in MVPA was attributable to individual variation, accounting for half of the total residual variability for boys, and two thirds of the variability for girls. MVPA clustered within friendship groups, with friends influencing peer MVPA. Including covariates at the different levels explained only small amounts (3%-13%) of variability. There is a need to enhance our understanding of individual level influences on children's physical activity.

Joint spatial mapping of childhood anemia and malnutrition in sub-Saharan Africa: a cross-sectional study of small-scale geographical disparities

BACKGROUND

In epidemiological studies, several diseases share common risk factors or co-exist in their spatial prevalence. Disease mapping allows the health practitioners and epidemiologists to hypothesize the disease aetiology and gain better understanding of the geographical prevalence of the disease risks.

OBJECTIVE

This paper investigates the differences in small scale geographical variations and the underlying risk factors of child's health outcomes (anemia, stunting and wasting) in Sub-Saharan Africa using spatial epidemiology.

METHOD

The study first carried out an independent univariate analysis on each malnutrition indicator to identify underlying risk factors. A multivariate conditional autoregressive prior was explored to jointly model the spatial correlation between the undernutrition indicators and the small area-geographical disparities at sub-national levels in two sub-Saharan African countries.

RESULTS

The approach was implemented on data from National cross-sectional household- based demographic and health surveys conducted in 17,307 under-five children in Burkina Faso and Mozambique in 2010-012. Out of these children, 31.8% are found to be stunted, 15.5% wasted and 30.9% had anemia among Burkina Faso children, while 42.5% of Mozambican children were stunted, 5.9% wasted and 30.9% suffered from iron-deficiency anemia. The multivariate analysis revealed that the spatial prevalence existed across regions in Burkina Faso with geographical variations in stunting estimated as: 0.7549, CI (0.4693, 1.264); wasting 0.9197; (95%CI: 0.535, 1.591)and anemia: 0.734; (0.4606, 1.214). In additin, the spatial correlation between stunting and wasting was negatively correlated: -0.998; 95% CI (-1.000, -0.984), and a perfect negative correlation;(-1) between stunting and anemia, and positive for wasting and anemia: 0.997; (0.978, 1.000). The spatial occurrence across provinces in Mozambique indicated that there was strong positive correlation between stunting and wasting; 0.986; (0.899, 1.000) and a significant negative correlation between stunting and anemia: -0.720, (-0.934, -0.308) and wasting and anemia: -0.640; (-0.903 -0.174) with individual geographical variability in child stunting: 1427, (913.6, 2268); wasting:1751, (1117, 2803) and anemia: 556, (279.5, 978.9). These extra random effect parameters computed in our multivariate approach would outperform a univariate analysis in similar studies. Our model further detected high prevalent of malnutrition and anemia in the northern Burkina Faso, but high anemia prevalent found in central Mozambique, and high stunting and wasting identified Southern Mozambique. In addition, the risk factors of malnutrition and iron deficiency anemia included household poverty, morbidity, short birth interval (less 18 months), breast feeding, antenatal attendance and maternal literacy.

CONCLUSION

The statistical relevance of the identified risk factors in this study is useful to target specific individual interventions and the maps of the geographical inequalities in sub-national region can be used for designing nutrition interventions and allocation of scarce health resources.

The Co-evolution of Organizational and Network Structure: The Role of Multilevel Mixing and Closure Mechanisms

We present a dynamic multilevel framework for analyzing the mutual dependence of change in interorganizational networks and internal organizational structure. Change occurring at the former (interorganizational) level involves decisions to change the portfolio of network ties to external partners. Change occurring in the latter (intraorganizational) level involves decisions to change the portfolio of internal activities. We estimate a recently derived class of stochastic actor-oriented models (SAOMs) that we adopt and adapt to specify how decisions to change internal portfolios of activities and external portfolios of partners are connected by theoretically derived multilevel mechanisms that link organizational and network structures. We show that statistical models for multilevel networks reproduce with high fidelity the structural regularities observed in the distribution of (a) activities within organizations, (b) network ties between organizations, and (c) knowledge available in the organizational field. We discuss the implications of the study for theory development, and for empirical research on interorganizational and other kinds of multilevel networks.

The Importance of External Contacts in Job Performance: A Study in Healthcare Organizations Using Social Network Analysis

There is evidence that relations between physicians and nurses within healthcare institutions might be shaped by informal aspects of such relations and by links to people external to the organization, with an impact on work performance. Social network analysis is underutilized in exploring such associations. The paper aims to describe physicians’ and nurses’ relationships outside their clinical units and to explore what kind of ties are related to job performance. A network analysis was performed on cross-sectional data. The study population consisted of 196 healthcare employees working in a public hospital and a primary healthcare centre in Spain. Relational data were analysed using the UCINET software package. Measures included: (i) sample characteristics; (ii) social network variables; and (iii) team performance ratings. Descriptive statistics (means, medians, percentages) were used to characterize staff and performance ratings. A correlational analysis was conducted to examine the strength of relationships between four different types of ties. Our findings suggest that external ties only contribute to improving the performance of physicians at both the individual and team level. They are focused on the decision-making process about the therapeutic plan and, therefore, might need to seek advice outside the workplace. In contrast, external ties are not relevant for the work performance of nurses, as they need to find solutions to immediate problems in a short period of time, having strong ties in the workplace. Social network analysis can illuminate relations within healthcare organizations and inform the development of innovative interventions.

Relationship between a network's indicators and basic factors with high-risk behavior of injection among injecting drug users (IDU) via the multiple membership multilevel model

Background

Among various methods and types of drug abuse, injection receives a great deal of importance because of its related dangerous behavior and health consequences. It seemed that some of the network’s indicators affect dangerous behavior of injection.

Objective

To determine the relationship between a network’s indicators and basic factors with high-risk behavior of injection among injecting drug users (IDU) via the multiple memberships multilevel model.

Methods

In this cross-sectional study, the data related to 147 IDUs in Kerman province, who were interviewed from October 2013 through March 2014, were used, and these addicts were chosen for interview from specific resorts used for common injection. In this study, for analyzing data, multiple membership multilevel model and MLwiN 2.02 software were used.

Results

In this study, the mean age of people, who were mostly men, was 37.2±9.02. Based on the result, it becomes obvious that variables of in-degree with OR=1.49 (p=0.006) and the whole number of people related to the person with OR=1.18 (p=0.003) influences the high-risk behavior of injection. Also, none of the demographic variables influenced the high-risk behavior of injection.

Conclusion

Totally based on the results of this study, one can find a suitable method in the social network of IDUs in order to create essential strategies, reducing the risk throughout the country. In addition, in minimum time with fewer expenses, aggravation of dangerous behavior especially high-risk behavior of injection can be prevented.

Comparing multilevel and multiscale convolution models for small area aggregated health data

In spatial epidemiology, data are often arrayed hierarchically. The classification of individuals into smaller units, which in turn are grouped into larger units, can induce contextual effects. On the other hand, a scaling effect can occur due to the aggregation of data from smaller units into larger units. In this paper, we propose a shared multilevel model to address the contextual effects. In addition, we consider a shared multiscale model to adjust for both scale and contextual effects simultaneously. We also study convolution and independent multiscale models, which are special cases of shared multilevel and shared multiscale models, respectively. We compare the performance of the models by applying them to real and simulated data sets. We found that the shared multiscale model was the best model across a range of simulated and real scenarios as measured by the deviance information criterion (DIC) and the Watanabe Akaike information criterion (WAIC).

Life history factors, personality and the social clustering of sexual experience in adolescents

Adolescent sexual behaviour may show clustering in neighbourhoods, schools and friendship networks. This study aims to assess how experience with sexual intercourse clusters across the social world of adolescents and whether predictors implicated by life history theory or personality traits can account for its between-individual variation and social patterning. Using data on 2877 adolescents from the Avon Longitudinal Study of Parents and Children, we ran logistic multiple classification models to assess the clustering of sexual experience by approximately 17.5 years in schools, neighbourhoods and friendship networks. We examined how much clustering at particular levels could be accounted for by life history predictors and Big Five personality factors. Sexual experience exhibited substantial clustering in friendship networks, while clustering at the level of schools and neighbourhoods was minimal, suggesting a limited role for socio-ecological influences at those levels. While life history predictors did account for some variation in sexual experience, they did not explain clustering in friendship networks. Personality, especially extraversion, explained about a quarter of friends' similarity. After accounting for life history factors and personality, substantial unexplained similarity among friends remained, which may reflect a tendency to associate with similar individuals or the social transmission of behavioural norms.

Temperament, age and weather predict social interaction in the sheep flock

The aim of the current study was to investigate the social relationships between individual sheep, and factors that influence this, through the novel application of the statistical multiple membership multiple classification (MMMC) model. In study one 49 ewes (ranging between 1 and 8 years old) were fitted with data loggers, which recorded when pairs of sheep were within 4m or less of each other, within a social group, for a total of 6days. In study two proximity data were collected from 45 ewes over 17days, as were measures of ewe temperament, weight and weather. In study 1 age difference significantly influenced daily contact time, with sheep of the same age spending an average of 20min 43s together per day, whereas pairs with the greatest difference in age spent 16min 33s together. Maximum daily temperature also significantly affected contact time, being longer on hotter days (34min 40s hottest day vs. 18min 17s coolest day), as did precipitation (29min 33s wettest day vs. 10min 32s no rain). Vocalisation in isolation, as a measure of temperament, also affected contacts, with sheep with the same frequency of vocalisations spending more time together (27min 16s) than those with the greatest difference in vocalisations (19min 36s). Sheep behaviour in the isolation box test (IBT) was also correlated over time, but vocalisations and movement were not correlated. Influences of age, temperature and rain on social contact are all well-established and so indicate that MMMC modelling is a useful way to analyse social structures of the flock. While it has been demonstrated that personality factors affect social relationships in non-human animals, the finding that vocalisation in isolation influences pair social contact in sheep is a novel one.

Multilevel Modelling with Spatial Interaction Effects with Application to an Emerging Land Market in Beijing, China

This paper develops a methodology for extending multilevel modelling to incorporate spatial interaction effects. The motivation is that classic multilevel models are not specifically spatial. Lower level units may be nested into higher level ones based on a geographical hierarchy (or a membership structure—for example, census zones into regions) but the actual locations of the units and the distances between them are not directly considered: what matters is the groupings but not how close together any two units are within those groupings. As a consequence, spatial interaction effects are neither modelled nor measured, confounding group effects (understood as some sort of contextual effect that acts ‘top down’ upon members of a group) with proximity effects (some sort of joint dependency that emerges between neighbours). To deal with this, we incorporate spatial simultaneous autoregressive processes into both the outcome variable and the higher level residuals. To assess the performance of the proposed method and the classic multilevel model, a series of Monte Carlo simulations are conducted. The results show that the proposed method performs well in retrieving the true model parameters whereas the classic multilevel model provides biased and inefficient parameter estimation in the presence of spatial interactions. An important implication of the study is to be cautious of an apparent neighbourhood effect in terms of both its magnitude and statistical significance if spatial interaction effects at a lower level are suspected. Applying the new approach to a two-level land price data set for Beijing, China, we find significant spatial interactions at both the land parcel and district levels.