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Family-based treatment (FBT) is the first-line treatment for adolescent anorexia nervosa (AN). Yet, remission is not achieved for about half of adolescents with AN receiving FBT. Understanding patient- and parent-level factors that predict FBT response may inform treatment development and improve outcomes.
Network analysis was used to identify the most central symptoms of AN in adolescents who completed the Eating Disorder Examination (EDE) prior to FBT (N = 409). Bridge pathways between adolescent AN and parental self-efficacy in facilitating their child's recovery from AN were identified in a subset of participants (n = 184). Central and bridge symptoms were tested as predictors of early response (⩾2.4 kg weight gain by the fourth session of FBT) and end-of-treatment weight restoration [⩾95% expected body weight (EBW)] and full remission (⩾95% EBW and EDE score within 1 standard deviation of norms).
The most central symptoms of adolescent AN included desiring weight loss, dietary restraint, and feeling fat. These symptoms predicted early response, but not end-of-treatment outcomes. Bridge symptoms were parental beliefs about their responsibility to renourish their child, adolescent discomfort eating in front of others, and adolescent dietary restraint. Bridge symptoms predicted end-of-treatment weight restoration, but not early response nor full remission.
Findings highlight the prognostic utility of core symptoms of adolescent AN. Parent beliefs about their responsibility to renourish their child may maintain associations between parental self-efficacy and AN psychopathology. These findings could inform strategies to adapt FBT and improve outcomes.
This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data.
Individual patient data from all six eligible randomised controlled trials were used to develop (k = 3, n = 1722) and test (k = 3, n = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1–3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3–4 months.
Models 1–7 all outperformed the null model and model 8. Model performance was very similar across models 1–6, meaning that differential weights applied to the baseline sum scores had little impact.
Any of the modelling techniques (models 1–7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.
Antibiotic residues have entered into the environment owing to the unreasonable use and disposal of antibiotics. The emergence of antibiotic resistance poses a huge threat to ecosystems and human health. In this study, the network analysis method was used to compare publications on antibiotics in water, soil and sediment from the aspects of countries, institutes, journals, subject categories and keywords based on Web of Science Core Collection. The results indicated that the United States of America and China had dominant positions of studies on antibiotics. The Chinese Academy of Sciences published the most articles on antibiotic research. ‘Chemosphere’, ‘Science of the Total Environment’, ‘Environmental Science and Technology’ and ‘Applied and Environmental Microbiology’ all appeared in the top six journals. ‘Environmental Sciences and Ecology’ was the core subject category of antibiotic research. Further analysis results depicted that ‘Antibiotics’, ‘Tetracycline’ and ‘Antibiotic Resistance’ were found as the research hotspots. Tetracycline and oxytetracycline all showed in the top 50 keywords of antibiotics research in water, soil and sediment. However, chlortetracycline, sulfadiazine and tylosin all emerged only in the top 50 keywords of antibiotics study in soil. In future, more attention should be paid to antibiotic resistance genes and antibiotic resistance bacteria in antibiotics research.
Thematic analysis of personal networks involves identifying regularities in network structure and content, and grouping networks into types/clusters, to allow for a holistic understanding of social complexities. We propose an inductive approach to network thematic analysis, applying the learnings from qualitative coding, fused mixed-methods analysis, and typology development. It involves framing (changing focus by magnifying, aggregating, and graphical configuration), pattern detection (identification of underlying dimensions, sorting, and clustering), labeling, and triangulating (confirmation and fine-tuning using quantitative and qualitative approaches); applied repeatedly and emergently. We describe this approach utilized in two cases of studying support networks of caregivers.
Understanding the role of species traits in mediating ecological interactions and shaping community structure is a key question in ecology. In this sense, parasite population parameters allow us to estimate the functional importance of traits in shaping the strength of interactions among hosts and parasites in a network. The aim of this study was to survey and analyse the small mammal-helminth network in a forest reserve of the Brazilian Atlantic Forest in order to understand (i) how functional traits (type of parasite life cycle, site of infection in their host, host and parasite body length, host diet, host locomotor habit and host activity period) and abundance influence host–parasite interactions, (ii) whether these traits explain species roles, and (iii) if this relationship is consistent across different parasite population parameters (presence and absence, mean abundance and prevalence). Networks were modular and their structural patterns did not vary among the population parameters. Functional traits and abundance shaped the interactions observed between parasites and hosts. Host species abundance, host diet and locomotor habit affected their centrality and/or vulnerability to parasites. For helminths, infection niche was the main trait determining their central roles in the networks.
Self-harm thoughts and behaviours (SHTBs) are a serious public health concern in young people. Emerging research suggests that pain may be an important correlate of SHTBs in young people. However, it remains unclear whether this association is driven by the shared association with other correlates of SHTBs. This study used network analysis to delineate the relationship between SHTBs, pain and other correlates of SHTBs in a population-based sample of young people.
We performed secondary analyses, using data from 7977 young people aged 5–16 years who participated in the British Child and Adolescent Mental Health Survey in 2004. We used χ2 tests and network analysis to examine the complex interplay between SHTBs, pain and other correlates of SHTBs, including psychiatric disorders, childhood trauma, stressful life events, parental distress, family dysfunction, peer problems and inhibitory control deficits.
Pain was associated with a doubled risk of SHTBs, and likewise, SHTBs were associated with a doubled risk of pain. Furthermore, network analysis showed that although pain was significantly associated with all measured correlates of SHTBs, except family dysfunction, pain was most strongly associated with SHTBs, after accounting for these measured correlates.
To the best of our knowledge, this is the first study to utilise network analysis to provide novel insights into the complex relationship between SHTBs, pain and other known correlates of SHTBs in young people. Results suggest that pain is an independent correlate of SHTBs. Future research should aim to identify underlying mechanisms.
Network approach has been applied to a wide variety of psychiatric disorders. The aim of the present study was to identify network structures of remitters and non-remitters in patients with first-episode psychosis (FEP) at baseline and the 6-month follow-up.
Participants (n = 252) from the Korean Early Psychosis Study (KEPS) were enrolled. They were classified as remitters or non-remitters using Andreasen's criteria. We estimated network structure with 10 symptoms (three symptoms from the Positive and Negative Syndrome Scale, one depressive symptom, and six symptoms related to schema and rumination) as nodes using a Gaussian graphical model. Global and local network metrics were compared within and between the networks over time.
Global network metrics did not differ between the remitters and non-remitters at baseline or 6 months. However, the network structure and nodal strengths associated with positive-self and positive-others scores changed significantly in the remitters over time. Unique central symptoms for remitters and non-remitters were cognitive brooding and negative-self, respectively. The correlation stability coefficients for nodal strength were within the acceptable range.
Our findings indicate that network structure and some nodal strengths were more flexible in remitters. Negative-self could be an important target for therapeutic intervention.
To investigate conditional dependence relationships of impulse dyscontrol symptoms in mild cognitive impairment (MCI) and subjective cognitive decline (SCD).
A prospective, observational study.
Two hundred and thirty-five patients with MCI (n = 159) or SCD (n = 76) from the Prospective Study for Persons with Memory Symptoms dataset.
Items of the Mild Behavioral Impairment Checklist impulse dyscontrol subscale.
Stubbornness/rigidity, agitation/aggressiveness, and argumentativeness were frequent and the most central symptoms in the network. Impulsivity, the fourth most central symptom in the network, served as the bridge between these common symptoms and less central and rare symptoms.
Impulse dyscontrol in at-risk states for dementia is characterized by closely connected symptoms of irritability, agitation, and rigidity. Compulsions and difficulties in regulating rewarding behaviors are relatively isolated symptoms.
The symptoms of obsessive−compulsive disorder (OCD) are highly heterogeneous and it is unclear what is the optimal way to conceptualize this heterogeneity. This study aimed to establish a comprehensive symptom structure model of OCD across the lifespan using factor and network analytic techniques.
A large multinational cohort of well-characterized children, adolescents, and adults diagnosed with OCD (N = 1366) participated in the study. All completed the Dimensional Yale-Brown Obsessive−Compulsive Scale, which contains an expanded checklist of 87 distinct OCD symptoms. Exploratory and confirmatory factor analysis were used to outline empirically supported symptom dimensions, and interconnections among the resulting dimensions were established using network analysis. Associations between dimensions and sociodemographic and clinical variables were explored using structural equation modeling (SEM).
Thirteen first-order symptom dimensions emerged that could be parsimoniously reduced to eight broad dimensions, which were valid across the lifespan: Disturbing Thoughts, Incompleteness, Contamination, Hoarding, Transformation, Body Focus, Superstition, and Loss/Separation. A general OCD factor could be included in the final factor model without a significant decline in model fit according to most fit indices. Network analysis showed that Incompleteness and Disturbing Thoughts were most central (i.e. had most unique interconnections with other dimensions). SEM showed that the eight broad dimensions were differentially related to sociodemographic and clinical variables.
Future research will need to establish if this expanded hierarchical and multidimensional model can help improve our understanding of the etiology, neurobiology and treatment of OCD.
People living in precarious housing or homelessness have higher than expected rates of psychotic disorders, persistent psychotic symptoms, and premature mortality. Psychotic symptoms can be modeled as a complex dynamic system, allowing assessment of roles for risk factors in symptom development, persistence, and contribution to premature mortality.
The severity of delusions, conceptual disorganization, hallucinations, suspiciousness, and unusual thought content was rated monthly over 5 years in a community sample of precariously housed/homeless adults (n = 375) in Vancouver, Canada. Multilevel vector auto-regression analysis was used to construct temporal, contemporaneous, and between-person symptom networks. Network measures were compared between participants with (n = 219) or without (n = 156) history of psychotic disorder using bootstrap and permutation analyses. Relationships between network connectivity and risk factors including homelessness, trauma, and substance dependence were estimated by multiple linear regression. The contribution of network measures to premature mortality was estimated by Cox proportional hazard models.
Delusions and unusual thought content were central symptoms in the multilevel network. Each psychotic symptom was positively reinforcing over time, an effect most pronounced in participants with a history of psychotic disorder. Global connectivity was similar between those with and without such a history. Greater connectivity between symptoms was associated with methamphetamine dependence and past trauma exposure. Auto-regressive connectivity was associated with premature mortality in participants under age 55.
Past and current experiences contribute to the severity and dynamic relationships between psychotic symptoms. Interrupting the self-perpetuating severity of psychotic symptoms in a vulnerable group of people could contribute to reducing premature mortality.
We live in a networked world. Online social networking platforms and the World Wide Web have changed how society thinks about connectivity. Because of the technological nature of such networks, their study has predominantly taken place within the domains of computer science and related scientific fields. But arts and humanities scholars are increasingly using the same kinds of visual and quantitative analysis to shed light on aspects of culture and society hitherto concealed. This Element contends that networks are a category of study that cuts across traditional academic barriers, uniting diverse disciplines through a shared understanding of complexity in our world. Moreover, we are at a moment in time when it is crucial that arts and humanities scholars join the critique of how large-scale network data and advanced network analysis are being harnessed for the purposes of power, surveillance, and commercial gain. This title is also available as Open Access on Cambridge Core.
Few factor analyses and no network analyses have examined the structure of DSM phobic fears or tested the specificity of the relationship between panic disorder and agoraphobic fears.
Histories of 21 lifetime phobic fears, coded as four-level ordinal variables (no fear to fear with major interference) were assessed at personal interview in 7514 adults from the Virginia Twin Registry. We estimated Gaussian Graphical Models on individual phobic fears; compared network structures of women and men using the Network Comparison Test; used community detection to determine the number and nature of groups in which phobic fears hang together; and validated the anticipated specific relationship between panic disorder and agoraphobia.
All networks were densely and positively inter-connected; networks of women and men were structurally similar. Our most frequent and stable solution identified four phobic clusters: (i) blood-injection, (ii) social-agoraphobia, (iii) situational, and (iv) animal-disease. Fear of public restrooms and of diseases clustered with animal and not, respectively, social and blood-injury phobias. When added to the network, the three strongest connections with lifetime panic disorder were all agoraphobic fears: being in crowds, going out of the house alone, and being in open spaces
Using network analyses applied to a large epidemiologic twin sample, we broadly validated the DSM-IV typography but did not entirely support the distinction of agoraphobic and social phobic fears or the DSM placements for fears of public restrooms and diseases. We found strong support for the specificity of the relationship between panic disorder and agoraphobic fears.
For decades confirmatory factor analysis (CFA) has been the preeminent method to study the underlying structure of posttraumatic stress disorder (PTSD); however, methodological limitations of CFA have led to the emergence of other analytic approaches. In particular, network analysis has become a gold standard to investigate the structure and relationships between PTSD symptoms. A key methodological limitation, however, which has significant clinical implications, is the lack of data on the potential impact of item order effects on the conclusions reached through network analyses.
The current study, involving a large sample (N = 5055) of active duty army soldiers following deployment to Iraq, assessed the vulnerability of network analyses and prevalence rate to item order effects. This was done by comparing symptom networks of the DSM-IV PTSD checklist items to these same items distributed in random order. Half of the participants rated their symptoms on traditionally ordered items and half the participants rated the same items, but in random order and interspersed between items from other validated scales. Differences in prevalence rate and network composition were examined.
The prevalence rate differed between the ordered and random item samples. Network analyses using the ordered survey closely replicated the conclusions reached in the existing network analyses literature. However, in the random item survey, network composition differed considerably.
Order effects appear to have a significant impact on conclusions reached from PTSD network analysis. Prevalence rates were also impacted by order effects. These findings have important diagnostic and clinical treatment implications.
We propose a new methodology for inferring political actors’ latent memberships in communities of collective activity that drive their observable interactions. Unlike existing methods, the proposed Bipartite Link Community Model (biLCM) (1) applies to two groups of actors, (2) takes into account that actors may be members of more than one community, and (3) allows a pair of actors to interact in more than one way. We apply this method to characterize legislative communities of special interest groups and politicians in the 113th U.S. Congress. Previous empirical studies of interest group politics have been limited by the difficulty of observing the ties between interest groups and politicians directly. We therefore first construct an original dataset that connects the politicians who sponsor congressional bills with the interest groups that lobby on those bills based on more than two million textual descriptions of lobbying activities. We then use the biLCM to make quantitative measurements of actors’ community memberships ranging from narrow targeted interactions according to industry interests and jurisdictional committee membership to broad multifaceted connections across multiple policy domains.
Childhood exposure to interpersonal violence (IPV) may be linked to distinct manifestations of mental illness, yet the nature of this change remains poorly understood. Network analysis can provide unique insights by contrasting the interrelatedness of symptoms underlying psychopathology across exposed and non-exposed youth, with potential clinical implications for a treatment-resistant population. We anticipated marked differences in symptom associations among IPV-exposed youth, particularly in terms of ‘hub’ symptoms holding outsized influence over the network, as well as formation and influence of communities of highly interconnected symptoms.
Participants from a population-representative sample of youth (n = 4433; ages 11–18 years) completed a comprehensive structured clinical interview assessing mental health symptoms, diagnostic status, and history of violence exposure. Network analytic methods were used to model the pattern of associations between symptoms, quantify differences across diagnosed youth with (IPV+) and without (IPV–) IPV exposure, and identify transdiagnostic ‘bridge’ symptoms linking multiple disorders.
Symptoms organized into six ‘disorder’ communities (e.g. Intrusive Thoughts/Sensations, Depression, Anxiety), that exhibited considerably greater interconnectivity in IPV+ youth. Five symptoms emerged in IPV+ youth as highly trafficked ‘bridges’ between symptom communities (11 in IPV– youth).
IPV exposure may alter mutually reinforcing symptom co-occurrence in youth, thus contributing to greater psychiatric comorbidity and treatment resistance. The presence of a condensed and unique set of bridge symptoms suggests trauma-enriched nodes which could be therapeutically targeted to improve outcomes in violence-exposed youth.
Major advances in biology and ecology have sharpened our understanding of what the goals of biodiversity conservation might be, but less progress has been made on how to achieve conservation in the complex, multi-sectoral world of human affairs. The failure to deliver conservation outcomes is especially severe in the rapidly changing landscapes of tropical low-income countries. We describe five techniques we have used to complement and strengthen long-term attempts to achieve conservation outcomes in the landscapes and seascapes of such regions; these are complex social-ecological systems shaped by interactions between biological, ecological and physical features mediated by the actions of people. Conservation outcomes occur as a result of human decisions and the governance arrangements that guide change. However, much conservation science in these countries is not rooted in a deep understanding of how these social-ecological systems work and what really determines the behaviour of the people whose decisions shape the future of landscapes. We describe five scientific practices that we have found to be effective in building relationships with actors in landscapes and influencing their behaviour in ways that reconcile conservation and development. We have used open-ended inductive enquiry, theories of change, simulation models, network analysis and multi-criteria analysis. These techniques are all widely known and well tested, but seldom figure in externally funded conservation projects. We have used these techniques to complement and strengthen existing interventions of international conservation agencies. These five techniques have proven effective in achieving deeper understanding of context, engagement with all stakeholders, negotiation of shared goals and continuous learning and adaptation.
At the height of its political power between c.350 BC and AD 350, the Royal City of Meroe was a cosmopolitan trade hub and a melting pot of external influences. In addition to controlling vast regions of land and ruling through the imposition of Kingship sanctioned by a complex religious system, the sovereigns of the Kingdom of Kush built on the economic success of their predecessors by ensuring a flow of trade goods and exotic items through their capital for hundreds of years. Local industry, providing essential objects such as weapons and tools, as well as luxury items, also played a paramount role at Meroe, with external and local influences visible, notably in the ceramics found at the site. Meroitic material culture and the industrial remains provide key insights into the past interconnectedness of the wider region as well as local traditions and innovations. This chapter examines what the Meroitic material culture and iron technology tells us about Meroe’s place and connections within its immediate and more distant landscapes, and how network analysis can help position it in this geographical framework.
The parties as networks approach has become a critical component of understanding American political parties. Research on it has so far mainly focused on variation in the placement of candidates within a network at the national level. This is in part due to a lack of data on state-level party networks. In this article, I fill that gap by developing state party networks for 47 states from 2000 to 2016 using candidate donation data. To do this, I introduce a backboning network analysis method not yet used in political science to infer relationships among donors at the state level. Finally, I validate these state networks and then show how parties have varied across states and over time. The networks developed here will be made publicly available for future research. Being able to quantify variation in party network structure will be important for understanding variation in party-policy linkages at the state level.
The 2-degrees target of the Paris Agreement and Sustainable Development Goal 7 on energy are intrinsically intertwined and highlight the urgency of an effective and integrated approach on climate change and energy. However, there are over a hundred international and transnational institutions with different characteristics and priorities that aim to address climate and energy-related targets. While prior research has contributed useful insights into the complexity of climate and energy governance, respectively, an integrated and coherent analysis of the climate-energy nexus is lacking. This chapter therefore maps, visualizes, and analyzes this nexus, i.e. institutions that seek to govern climate change and energy simultaneously. In addition, the chapter zooms in on three specific subsets of institutions: renewable energy, fossil fuel subsidy reform, and carbon pricing. The mapping and analysis are based on a new dataset and provide first insights into the gaps, overlaps, and varying degrees of complexity of the climate-energy nexus and across its subfields. Moreover, the chapter serves as the empirical basis for further analyses of coherence, management, legitimacy, and effectiveness, and as the first step in creating a knowledge base to guide actors who seek to navigate the institutionally complex landscape of the climate-energy nexus.