1
|
Junus A, Yip PSF. Evaluating potential effects of distress symptoms' interventions on suicidality: Analyses of in silico scenarios. J Affect Disord 2024; 347:352-363. [PMID: 37992776 DOI: 10.1016/j.jad.2023.11.060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/23/2023] [Accepted: 11/17/2023] [Indexed: 11/24/2023]
Abstract
BACKGROUND Complexity science perspectives like the network approach to psychopathology have emerged as a prominent methodological toolkit to generate novel hypotheses on complex etiologies surrounding various mental health problems and inform intervention targets. Such approach may be pivotal in advancing early intervention of suicidality among the younger generation (10-35 year-olds), the increasing burden of which needs to be reversed within a limited window of opportunity to avoid massive long-term repercussions. However, the network approach currently lends limited insight into the potential extent of proposed intervention targets' effectiveness, particularly for target outcomes in comorbid conditions. METHODS This paper proposes an in silico (i.e., computer-simulated) intervention approach that maps symptoms' complex interactions onto dynamic processes and analyzes their evolution. The proposed methodology is applied to investigate potential effects of changes in 1968 community-dwelling individuals' distress symptoms on their suicidal ideation. Analyses on specific subgroups were conducted. Results were also compared with centrality indices employed in typical network analyses. RESULTS Findings concur with symptom networks' centrality indices in suggesting that timely deactivating hopelessness among distressed individuals may be instrumental in preventing distress to develop into suicidal ideation. Additionally, however, they depict nuances beyond those provided by centrality indices, e.g., among young adults, reducing nervousness and tension may have similar effectiveness as deactivating hopeless in reducing suicidal ideation. LIMITATIONS Caution is warranted when generalizing findings here to the general population. CONCLUSION The proposed methodology may help facilitate timely agenda-setting in population mental health measures, and may also be augmented for future co-creation projects.
Collapse
Affiliation(s)
- Alvin Junus
- Centre for Urban Mental Health, University of Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam UMC location AMC, University of Amsterdam, The Netherlands
| | - Paul S F Yip
- Department of Social Work and Social Administration, The University of Hong Kong, Hong Kong; The Hong Kong Jockey Club Centre for Suicide Research and Prevention, The University of Hong Kong, Hong Kong.
| |
Collapse
|
2
|
Fallah N, Hong HA, Wang D, Humphreys S, Parsons J, Walden K, Street J, Charest-Morin R, Cheng CL, Cheung CJ, Noonan VK. Network analysis of multimorbidity and health outcomes among persons with spinal cord injury in Canada. Front Neurol 2024; 14:1286143. [PMID: 38249735 PMCID: PMC10797060 DOI: 10.3389/fneur.2023.1286143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 10/18/2023] [Indexed: 01/23/2024] Open
Abstract
Introduction Multimorbidity, defined as the coexistence of two or more health conditions, is common in persons with spinal cord injury (SCI). Network analysis is a powerful tool to visualize and examine the relationship within complex systems. We utilized network analysis to explore the relationship between 30 secondary health conditions (SHCs) and health outcomes in persons with traumatic (TSCI) and non-traumatic SCI (NTSCI). The study objectives were to (1) apply network models to the 2011-2012 Canadian SCI Community Survey dataset to identify key variables linking the SHCs measured by the Multimorbidity Index-30 (MMI-30) to healthcare utilization (HCU), health status, and quality of life (QoL), (2) create a short form of the MMI-30 based on network analysis, and (3) compare the network-derived MMI to the MMI-30 in persons with TSCI and NTSCI. Methods Three network models (Gaussian Graphical, Ising, and Mixed Graphical) were created and analyzed using standard network measures (e.g., network centrality). Data analyzed included demographic and injury variables (e.g., age, sex, region of residence, date, injury severity), multimorbidity (using MMI-30), HCU (using the 7-item HCU questionnaire and classified as "felt needed care was not received" [HCU-FNCNR]), health status (using the 12-item Short Form survey [SF-12] Physical and Mental Component Summary [PCS-12 and MCS-12] score), and QoL (using the 11-item Life Satisfaction questionnaire [LiSAT-11] first question and a single item QoL measure). Results Network analysis of 1,549 participants (TSCI: 1137 and NTSCI: 412) revealed strong connections between the independent nodes (30 SHCs) and the dependent nodes (HCU-FNCNR, PCS-12, MCS-12, LiSAT-11, and the QoL score). Additionally, network models identified that cancer, deep vein thrombosis/pulmonary embolism, diabetes, high blood pressure, and liver disease were isolated. Logistic regression analysis indicated the network-derived MMI-25 correlated with all health outcome measures (p <0.001) and was comparable to the MMI-30. Discussion The network-derived MMI-25 was comparable to the MMI-30 and was associated with inadequate HCU, lower health status, and poor QoL. The MMI-25 shows promise as a follow-up screening tool to identify persons living with SCI at risk of having poor health outcomes.
Collapse
Affiliation(s)
- Nader Fallah
- Praxis Spinal Cord Institute, Vancouver, BC, Canada
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | | | - Di Wang
- Praxis Spinal Cord Institute, Vancouver, BC, Canada
| | | | | | | | - John Street
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
- Department of Orthopaedics, Vancouver Spine Surgery Institute, University of British Columbia, Vancouver, BC, Canada
| | - Raphaele Charest-Morin
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
- Department of Orthopaedics, Vancouver Spine Surgery Institute, University of British Columbia, Vancouver, BC, Canada
| | | | | | - Vanessa K. Noonan
- Praxis Spinal Cord Institute, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
| |
Collapse
|
3
|
Zavlis O, Matheou A, Bentall R. Identifying the bridge between depression and mania: A machine learning and network approach to bipolar disorder. Bipolar Disord 2023; 25:571-582. [PMID: 36869637 DOI: 10.1111/bdi.13316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
OBJECTIVES Although the cyclic nature of bipolarity is almost by definition a network system, no research to date has attempted to scrutinize the relationship of the two bipolar poles using network psychometrics. We used state-of-the-art network and machine learning methodologies to identify symptoms, as well as relations thereof, that bridge depression and mania. METHODS Observational study that used mental health data (12 symptoms for depression and 12 for mania) from a large, representative Canadian sample (the Canadian Community Health Survey of 2002). Complete data (N = 36,557; 54.6% female) were analysed using network psychometrics, in conjunction with a random forest algorithm, to examine the bidirectional interplay of depressive and manic symptoms. RESULTS Centrality analyses pointed to symptoms relating to emotionality and hyperactivity as being the most central aspects of depression and mania, respectively. The two syndromes were spatially segregated in the bipolar model and four symptoms appeared crucial in bridging them: sleep disturbances (insomnia and hypersomnia), anhedonia, suicidal ideation, and impulsivity. Our machine learning algorithm validated the clinical utility of central and bridge symptoms (in the prediction of lifetime episodes of mania and depression), and suggested that centrality, but not bridge, metrics map almost perfectly onto a data-driven measure of diagnostic utility. CONCLUSIONS Our results replicate key findings from past network studies on bipolar disorder, but also extend them by highlighting symptoms that bridge the two bipolar poles, while also demonstrating their clinical utility. If replicated, these endophenotypes could prove fruitful targets for prevention/intervention strategies for bipolar disorders.
Collapse
Affiliation(s)
- Orestis Zavlis
- University of Manchester, Department of Social Statistics, Manchester, UK
| | - Andreas Matheou
- University of Manchester, Manchester Medical School, Manchester, UK
| | - Richard Bentall
- University of Sheffield, Department of Clinical Psychology, Sheffield, UK
| |
Collapse
|
4
|
Šablatúrová N, Rečka K, Blinka L. Validation of the Social Media Disorder Scale using network analysis in a large representative sample of Czech adolescents. Front Public Health 2022; 10:907522. [PMID: 36072384 PMCID: PMC9441909 DOI: 10.3389/fpubh.2022.907522] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 07/29/2022] [Indexed: 01/22/2023] Open
Abstract
Background The importance of studying the excessive use of social media in adolescents is increasing and so is the need for in-depth evaluations of the psychometric properties of the measurement tools. This study investigated the properties of the Social Media Disorder Scale (SMDS) in a large representative sample of Czech adolescents. Methods We analyzed the representative sample of 13,377 Czech adolescents (50.9% boys), 11-16 years old, who participated in the Health Behavior in School-aged Children (HBSC) survey (2017-18), using confirmatory factor analysis (CFA) and network models. Furthermore, we evaluated the measurement invariance and constructed the validity of the SMDS. Results We found support for a single dominant factor but not for strict unidimensionality. Several residual correlations were identified. The strongest were for: problems-conflicts-deceptions; persistence-escape; and preoccupation-tolerance-withdrawal. Girls, particularly 13- and 15-year-olds, scored higher than boys in the same age group, and 13- and 15-year-olds achieved higher scores than 11-year-olds, although some items were not invariant between the groups. The SMDS was positively related to other online activities, screen time, and falling asleep late, but negatively related to well-being and mental health. Discussion and conclusions The SMDS showed solid psychometric properties and construct validity. However, small violations of measurement invariance were detected. Furthermore, the network analysis showed important residual relationships between the items.
Collapse
|
5
|
Lunansky G, Naberman J, van Borkulo CD, Chen C, Wang L, Borsboom D. Intervening on psychopathology networks: Evaluating intervention targets through simulations. Methods 2021; 204:29-37. [PMID: 34793976 DOI: 10.1016/j.ymeth.2021.11.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/30/2021] [Accepted: 11/11/2021] [Indexed: 01/16/2023] Open
Abstract
Identifying the different influences of symptoms in dynamic psychopathology models may hold promise for increasing treatment efficacy in clinical applications. Dynamic psychopathology models study the behavioral patterns of symptom networks, where symptoms mutually enforce each other. Interventions could be tailored to specific symptoms that are most effective at lowering symptom activity or that hinder the further development of psychopathology. Simulating interventions in psychopathology network models fits in a novel tradition where symptom-specific perturbations are used as in silico interventions. Here, we present the NodeIdentifyR algorithm (NIRA) to identify the projected most efficient, symptom-specific intervention target in a network model (i.e., the Ising model). We implemented NIRA in a freely available R package. The technique studies the projected effects of symptom-specific interventions by simulating data while symptom parameters (i.e., thresholds) are systematically altered. The projected effect of these interventions is defined in terms of the expected change in overall symptom activity across simulations. With this algorithm, it is possible to study (1) whether symptoms differ in their projected influence on the behavior of the symptom network and, if so, (2) which symptom has the largest projected effect in lowering or increasing overall symptom activation. As an illustration, we apply the algorithm to an empirical dataset containing Post-Traumatic Stress Disorder symptom assessments of participants who experienced the Wenchuan earthquake in 2008. The most important limitations of the method are discussed, as well as recommendations for future research, such as shifting towards modeling individual processes to validate these types of simulation-based intervention methods.
Collapse
Affiliation(s)
- Gabriela Lunansky
- Department of Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands.
| | - Jasper Naberman
- Department of Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands
| | - Claudia D van Borkulo
- Department of Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands; Centre for Urban Mental Health, University of Amsterdam, The Netherlands
| | - Chen Chen
- Laboratory for Traumatic Stress Studies, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Li Wang
- Laboratory for Traumatic Stress Studies, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands
| |
Collapse
|
6
|
Abstract
The “Ising model” refers to both the statistical and the theoretical use of the same equation. In this article, we introduce both uses and contrast their differences. We accompany the conceptual introduction with a survey of Ising-related software packages in R. Since the model’s different uses are best understood through simulations, we make this process easily accessible with fully reproducible examples. Using simulations, we show how the theoretical Ising model captures local-alignment dynamics. Subsequently, we present it statistically as a likelihood function for estimating empirical network models from binary data. In this process, we give recommendations on when to use traditional frequentist estimators as well as novel Bayesian options.
Collapse
|
7
|
Lunansky G, van Borkulo CD, Haslbeck JMB, van der Linden MA, Garay CJ, Etchevers MJ, Borsboom D. The Mental Health Ecosystem: Extending Symptom Networks With Risk and Protective Factors. Front Psychiatry 2021; 12:640658. [PMID: 33815173 PMCID: PMC8012560 DOI: 10.3389/fpsyt.2021.640658] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 02/22/2021] [Indexed: 12/27/2022] Open
Abstract
Inspired by modeling approaches from the ecosystems literature, in this paper, we expand the network approach to psychopathology with risk and protective factors to arrive at an integrated analysis of resilience. We take a complexity approach to investigate the multifactorial nature of resilience and present a system in which a network of interacting psychiatric symptoms is targeted by risk and protective factors. These risk and protective factors influence symptom development patterns and thereby increase or decrease the probability that the symptom network is pulled toward a healthy or disorder state. In this way, risk and protective factors influence the resilience of the network. We take a step forward in formalizing the proposed system by implementing it in a statistical model and translating different influences from risk and protective factors to specific targets on the node and edge parameters of the symptom network. To analyze the behavior of the system under different targets, we present two novel network resilience metrics: Expected Symptom Activity (ESA, which indicates how many symptoms are active or inactive) and Symptom Activity Stability (SAS, which indicates how stable the symptom activity patterns are). These metrics follow standard practices in the resilience literature, combined with ideas from ecology and physics, and characterize resilience in terms of the stability of the system's healthy state. By discussing the advantages and limitations of our proposed system and metrics, we provide concrete suggestions for the further development of a comprehensive modeling approach to study the complex relationship between risk and protective factors and resilience.
Collapse
Affiliation(s)
- Gabriela Lunansky
- Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands
| | - Claudia D. van Borkulo
- Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands
| | - Jonas M. B. Haslbeck
- Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands
| | - Max A. van der Linden
- Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands
| | - Cristian J. Garay
- Faculty of Psychology, University of Buenos Aires, Buenos Aires, Argentina
| | | | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands
| |
Collapse
|