Skip to main content
Log in

Pathways to suicide or collections of vicious cycles? Understanding the complexity of suicide through causal mapping

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

Suicide is the second leading cause of death among youth ages 10–19 in the USA. While suicide has long been recognized as a multifactorial issue, there is limited understanding regarding the complexities linking adverse childhood experiences (ACEs) to suicide ideation, attempt, and fatality among youth. In this paper, we develop a map of these complex linkages to provide a decision support tool regarding key issues in policymaking and intervention design, such as identifying multiple feedback loops (e.g., involving intergenerational effects) or comprehensively examining the rippling effects of an intervention. We use the methodology of systems mapping to structure the complex interrelationships of suicide and ACEs based on the perceptions of fifteen subject matter experts. Specifically, systems mapping allows us to gain insight into the feedback loops and potential emergent properties of ACEs and youth suicide. We describe our methodology and the results of fifteen one-on-one interviews, which are transformed into individual maps that are then aggregated and simplified to produce our final causal map. Our map is the largest to date on ACEs and suicide among youth, totaling 361 concepts and 946 interrelationships. Using a previously developed open-source software to navigate the map, we are able to explore how trauma may be perpetuated through familial, social, and historical concepts. In particular, we identify connections and pathways between ACEs and youth suicide that have not been identified in prior research, and which are of particular interest for youth suicide prevention efforts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Availability of data and materials

Our data are provided on a third-party repository by the Open Science Framework (OSF), which can be publicly accessed without registration at https://osf.io/7nxp4/In particular, the online repository includes the individual map from each SME, the questionnaires used to assign weights to causal relationships, the overall map, and the software used to visualize the map.

Notes

  1. ACEs are preventable, potentially traumatic events that occur in childhood such as neglect, experiencing or witnessing violence, and having a family member attempt or die by suicide. This also includes aspects of a child’s environment that can undermine their sense of safety, stability, and bonding, such as growing up in a household with substance use, mental health problems, or instability due to parental separation or incarceration of a parent, sibling or other member of the household. ACEs have complex and negative influences on individual health outcomes throughout the life course, including increased risk of suicidal behaviors (World Health Organization 2014).

  2. Fuzzy Cognitive Mapping (FCM) is “a powerful means to represent knowledge domains that are characterized by high complexity, by widespread knowledge sources that usually only have partial knowledge, by qualitative information that frequently changes, and by a lack of a commonly accepted ‘theory’ or ‘truth’” (Jetter 2006).

  3. Fuzzy Logic can produce highly accurate estimates, as demonstrated by the use of FCMs in settings highly sensitive to errors such as radiotherapy treatment, diagnostics of brain tumor (where the system was above 90% accurate) (Amirkhani et al. 2017), or drug therapy management (Bevilacqua et al. 2018).

  4. For the mathematical foundations of Fuzzy Logic and standard operations involved (Mamdani, Algebraic Sum, Center of Gravity), we refer the reader to (Xu and Da 2003; Kruse et al. 2016; Mamdani 1977).

Abbreviations

ACEs:

Adverse childhood experiences

CDC:

Centers for disease control and prevention

FCM:

Fuzzy cognitive map

SME:

Subject matter expert

References

  • Allender S, Brown AD, Bolton KA, Fraser P, Lowe J, Hovmand P (2019) Translating systems thinking into practice for community action on childhood obesity. Obes Rev 20:179–184

    Google Scholar 

  • Allender S, Owen B, Kuhlberg J, Lowe J, Nagorcka-Smith P, Whelan J, Bell C (2015) A community based systems diagram of obesity causes. PLoS ONE 10(7):e0129683

    Google Scholar 

  • Amirkhani A, Papageorgiou EI, Mohseni A, Mosavi MR (2017) A review of fuzzy cognitive maps in medicine: taxonomy, methods, and applications. Comput Methods Programs Biomed 142:129–145

    Google Scholar 

  • Andersen DF, Richardson GP, Vennix JA (1997) Group model building: adding more science to the craft. Syst Dyn Rev: J Syst Dyn Soc 13(2):187–201

    Google Scholar 

  • Anjum M, Voinov A, Taghikhah F, Pileggi SF (2021) Discussoo: towards an intelligent tool for multi-scale participatory modeling. Environ Model Softw 140:105044

    Google Scholar 

  • Axelrod R (ed.) (2015) Structure of decision: the cognitive maps of political elites. Princeton University Press

  • Bakeman R, Gottman JM (1997) Observing interaction: an introduction to sequential analysis. Cambridge University Press

  • Ballesteros MF, Sumner SA, Law R, Wolkin A, Jones C (2020) Advancing injury and violence prevention through data science. J Saf Res 73:189–193

    Google Scholar 

  • Bevilacqua M, Ciarapica FE, Mazzuto G (2018) Fuzzy cognitive maps for adverse drug event risk management. Saf Sci 102:194–210

    Google Scholar 

  • Bilsen J (2018) Suicide and youth: risk factors. Front Psych 9:540

    Google Scholar 

  • Blacketer MP, Brownlee MT, Baldwin ED, Bowen BB (2021) Fuzzy cognitive maps of social-ecological complexity: applying mental modeler to the Bonneville salt flats. Ecol Complex 47:100950

    Google Scholar 

  • Brenas JH, Shaban-Nejad A (2020) Health intervention evaluation using semantic explainability and causal reasoning. IEEE Access 8:9942–9952

    Google Scholar 

  • Brenas JH, Shin EK, Shaban-Nejad A (2019) Adverse childhood experiences ontology for mental health surveillance, research, and evaluation: advanced knowledge representation and semantic web techniques. JMIR Ment Health 6(5):e13498

    Google Scholar 

  • Broido AD, Clauset A (2019) Scale-free networks are rare. Nat Commun 10(1):1–10

    Google Scholar 

  • Bryan CJ, Butner JE, May AM, Rugo KF, Harris JA, Oakey DN et al (2020) Nonlinear change processes and the emergence of suicidal behavior: a conceptual model based on the fluid vulnerability theory of suicide. New Ideas Psychol 57:100758

    Google Scholar 

  • Cash SJ, Bridge JA (2009) Epidemiology of youth suicide and suicidal behavior. Curr Opin Pediatr 21(5):613

    Google Scholar 

  • Centers for Disease Control and Prevention (2015) The social-ecological model: a framework for prevention. CDC, Atlanta. https://www.cdc.gov/violenceprevention/publichealthissue/social-ecologicalmodel.html. Accessed 9 May 2015

  • Centers for Disease Control and Prevention (2018) Vital signs: suicide rising across the US. https://www.cdc.gov/vitalsigns/pdf/vs-0618-suicide-H.pdfCenters for Disease Control and Prevention. NCHS Data Brief, No. 362. National Center for Health Statistics, Hyattsville. Accessed June 2018

  • Chu C, Buchman-Schmitt JM, Stanley IH, Hom MA, Tucker RP, Hagan CR et al (2017) The interpersonal theory of suicide: a systematic review and meta-analysis of a decade of cross-national research. Psychol Bull 143(12):1313

    Google Scholar 

  • Chu JP, Goldblum P, Floyd R, Bongar B (2010) The cultural theory and model of suicide. Appl Prev Psychol 14(1–4):25–40

    Google Scholar 

  • Chung SY (2016) Suicide attempts from adolescence into young adulthood: a system dynamics perspective for intervention and prevention. Dissertation, Washington University in St Louis

  • Cleary M, Visentin DC, Neil A, West S, Kornhaber R, Large M (2019) Complexity of youth suicide and implications for health services. J Adv Nurs 75(10):2056–2058

    Google Scholar 

  • De Pinho, H (2017) Generation of systems maps. In: El-Sayed AM, Galea S (eds) Systems science and population health. Oxford University Press, Oxford, United Kingdom, p 61–76

    Google Scholar 

  • Düspohl M, Döll P (2016) Causal networks and scenarios: participatory strategy development for promoting renewable electricity generation. J Clean Prod 121:218–230

    Google Scholar 

  • Eden C, Ackermann F, Cropper S (1992) The analysis of cause maps. J Manage Stud 29(3):309–324

    Google Scholar 

  • Epstein JM (2008) Why model? J Artif Soc Soc Simul 11(4):12

    Google Scholar 

  • Finegood DT, Merth TD, Rutter H (2010) Implications of the foresight obesity system map for solutions to childhood obesity. Obesity 18(n1s):S13

    Google Scholar 

  • Firmansyah HS, Supangkat SH, Arman AA, Giabbanelli PJ (2019) Identifying the components and interrelationships of smart cities in Indonesia: Supporting policymaking via fuzzy cognitive systems. IEEE Access 7:46136–46151

    Google Scholar 

  • Freund AJ, Giabbanelli PJ (2021) Automatically combining conceptual models using semantic and structural information. In: 2021 annual modeling and simulation conference (ANNSIM). IEEE, pp 1–12

  • Giabbanelli PJ, Baniukiewicz M (2018) Navigating complex systems for policymaking using simple software tools. In: Advanced data analytics in health. Springer, Cham, pp 21–40

  • Giabbanelli PJ, Flarsheim R, Vesuvala C, Drasic L (2016) Developing technology to support policymakers in taking a systems science approach to obesity and well-being: T6: S41: 31. Obes Rev 17:194–195

    Google Scholar 

  • Giabbanelli PJ, Galgoczy MC, Nguyen DM, Foy R, Rice KL, Nataraj N, Brown MM, Harper CR (2021) Mapping the complexity of suicide by combining participatory modeling and network science. In: Proceedings of the IEEE/ACM international conference on advances in social network analysis and mining (ASONAM)

  • Giabbanelli PJ, Tawfik AA (2019) Overcoming the PBL assessment challenge: Design and development of the incremental thesaurus for assessing causal maps (ITACM). Technol Knowl Learn 24(2):161–168

    Google Scholar 

  • Giabbanelli PJ, Tawfik AA (2020) Reducing the gap between the conceptual models of students and experts using graph-based adaptive instructional systems. In: International conference on human-computer interaction. Springer, Cham, pp 538–556

  • Giabbanelli PJ, Tawfik AA (2021) How perspectives of a system change based on exposure to positive or negative evidence. Systems 9(2):23

    Google Scholar 

  • Giabbanelli PJ, Tawfik AA, Gupta VK (2019) Learning analytics to support teachers’ assessment of problem solving: a novel application for machine learning and graph algorithms. In Utilizing learning analytics to support study success, pp 175–199

  • Giabbanelli PJ, Torsney-Weir T, Mago VK (2012) A fuzzy cognitive map of the psychosocial determinants of obesity. Appl Soft Comput 12(12):3711–3724

    Google Scholar 

  • Giles BG, Findlay CS, Haas G, LaFrance B, Laughing W, Pembleton S (2007) Integrating conventional science and aboriginal perspectives on diabetes using fuzzy cognitive maps. Soc Sci Med 64(3):562–576

    Google Scholar 

  • Gray SA, Gray S, Cox LJ, Henly-Shepard S (2013) Mental modeler: a fuzzy-logic cognitive mapping modeling tool for adaptive environmental management. In: 2013 46th Hawaii international conference on system sciences. IEEE, pp 965–973

  • Gray S, Hilsberg J, McFall A, Arlinghaus R (2015) The structure and function of angler mental models about fish population ecology: the influence of specialization and target species. J Outdoor Recreat Tour 12:1–13

    Google Scholar 

  • Gray S, Sterling EJ, Aminpour P, Goralnik L, Singer A, Wei C et al (2019) Assessing (social-ecological) systems thinking by evaluating cognitive maps. Sustainability 11(20):5753

    Google Scholar 

  • Gupta VK, Giabbanelli PJ, Tawfik AA (2018) An online environment to compare students’ and expert solutions to ill-structured problems. In: International conference on learning and collaboration technologies. Springer, Cham, pp 286–307

  • Hall KD, Sacks G, Chandramohan D, Chow CC, Wang YC, Gortmaker SL, Swinburn BA (2011) Quantification of the effect of energy imbalance on bodyweight. Lancet 378(9793):826–837

    Google Scholar 

  • Hayward J, Morton S, Johnstone M, Creighton D, Allender S (2020) Tools and analytic techniques to synthesise community knowledge in CBPR using computer-mediated participatory systemmodelling. NPJ Digit Med 3(1):1–6

    Google Scholar 

  • Hedegaard H, Curtin SC, Margaret W (2020) Increase in suicide mortality in the United States, 1999–2018

  • Hedelin B, Gray S, Woehlke S, Todd KB, Alison S, Jordan R, Moira Z et al (2021) What's left before participatory modeling can fully support real-world environmental planning processes: A case study review. Environ Model Softw 143:105073

  • Henly-Shepard S, Gray SA, Cox LJ (2015) The use of participatory modeling to promote social learning and facilitate community disaster planning. Environ Sci Policy 45:109–122

    Google Scholar 

  • Heron MP (2019) Deaths: leading causes for 2017 [USA]. In: National vital statistics reports: from the Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System, vol 68, no 6, pp 1–77

  • Hester PT, Adams KM (2017) Complex systems modeling. In: Systemic decision making. Springer, Cham, pp 101–125

  • Ifenthaler D, Masduki I, Seel NM (2011) The mystery of cognitive structure and how we can detect it: tracking the development of cognitive structures over time. Instr Sci 39(1):41–61

    Google Scholar 

  • Isaac ME, Dawoe E, Sieciechowicz K (2009) Assessing local knowledge use in agroforestry management with cognitive maps. Environ Manage 43(6):1321–1329

    Google Scholar 

  • Ivey-Stephenson AZ (2020) Suicidal ideation and behaviors among high school students—youth risk behavior survey, USA. MMWR Suppl 69:47

    Google Scholar 

  • Jeong AC (2020) Developing computer-aided diagramming tools to mine, model and support students’ reasoning processes. Educ Tech Res Dev 68(6):3353–3369

    Google Scholar 

  • Jeong A (2016) Facilitating collaborative problem-solving with computer-supported causal mapping. In: Proceedings of the 19th ACM conference on computer supported cooperative work and social computing companion, pp 57–60

  • Jetter AJ (2006) Fuzzy cognitive maps for engineering and technology management: What works in practice? In: 2006 technology management for the global future-PICMET 2006 conference, vol 2. IEEE, pp 498–512

  • Jordan R, Gray S, Zellner M, Glynn PD, Voinov A, Hedelin B et al (2018) Twelve questions for the participatory modeling community. Earth’s Fut 6(8):1046–1057

    Google Scholar 

  • Kim H, Andersen DF (2012) Building confidence in causal maps generated from purposive text data: mapping transcripts of the Federal Reserve. Syst Dyn Rev 28(4):311–328

    MathSciNet  Google Scholar 

  • Kruse R, Borgelt C, Braune C, Mostaghim S, Steinbrecher M (2016) Introduction to fuzzy sets and fuzzy logic. In: Computational intelligence. Springer, London, pp 329–359

  • Lavin EA, Giabbanelli PJ, Stefanik AT, Gray SA, Arlinghaus R (2018) Should we simulate mental models to assess whether they agree? In: Proceedings of the annual simulation symposium, pp 1–12

  • Levy MA, Lubell MN, McRoberts N (2018) The structure of mental models of sustainable agriculture. Nat Sustain 1(8):413–420

    Google Scholar 

  • Mamdani EH (1977) Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans Comput 26(12):1182–1191

    MATH  Google Scholar 

  • McGlashan J, Hayward J, Brown A, Owen B, Millar L, Johnstone M et al (2018) Comparing complex perspectives on obesity drivers: action-driven communities and evidence-oriented experts. Obes Sci Pract 4(6):575–581

    Google Scholar 

  • McGlashan J, Johnstone M, Creighton D, de la Haye K, Allender S (2016) Quantifying a systems map: network analysis of a childhood obesity causal loop diagram. PLoS ONE 11(10):e0165459

    Google Scholar 

  • McNeese MD, Ayoub PJ (2011) Concept mapping in the analysis and design of cognitive systems: a historical review. Appl Concept Mapp, Captur, Anal Organ Knowl 47:3–21

    Google Scholar 

  • McPherson K, Marsh T, Brown M (2007) Foresight report on obesity. Lancet 370(9601):1755

    Google Scholar 

  • Merlin MMM, Mary MFJ, Aishwarya R (2020) A Pythagorean FCM Analysis on the impacts of adverse childhood experiences in learning of school children. Eur J Mol Clin Med 7(9):2020

    Google Scholar 

  • Mkhitaryan S, Giabbanelli PJ, de Vries NK, Crutzen R (2020) Dealing with complexity: How to use a hybrid approach to incorporate complexity in health behavior interventions. Intell-Based Med 3:100008

    Google Scholar 

  • Morris MA, Wilkins E, Timmins KA, Bryant M, Birkin M, Griffiths C (2018) Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map. Int J Obes 42(12):1963–1976

    Google Scholar 

  • Murray-Smith DJ (2012) Continuous system simulation. Springer

  • Naumann RB, Austin AE, Sheble L, Lich KH (2019) System dynamics applications to injury and violence prevention: a systematic review. Curr Epidemiol Rep 6(2):248–262

    Google Scholar 

  • Owen B, Brown AD, Kuhlberg J, Millar L, Nichols M, Economos C, Allender S (2018) Understanding a successful obesity prevention initiative in children under 5 from a systems perspective. PLoS ONE 13(3):e0195141

    Google Scholar 

  • Page A, Atkinson JA, Heffernan M, McDonnell G, Hickie IB (2017) A decision-support tool to inform Australian strategies for preventing suicide and suicidal behaviour. Public Health Res Pract 27(2):e2721717

    Google Scholar 

  • Page A, Atkinson JA, Heffernan M, McDonnell G, Prodan A, Osgood N, Hickie I (2018) Static metrics of impact for a dynamic problem: The need for smarter tools to guide suicide prevention planning and investment. Aust N Z J Psych 52(7):660–667

    Google Scholar 

  • Papageorgiou E,  Areti K (2012) Using fuzzy cognitive mapping in environmental decision making and management: a methodological primer and an application. In: Young S, Silvern S (eds) International perspectives on global environmental change. InTech, Rijeka, Croatia, p 427–450

  • Penn AS, Knight CJ, Lloyd DJ, Avitabile D, Kok K, Schiller F et al (2013) Participatory development and analysis of a fuzzy cognitive map of the establishment of a bio-based economy in the Humber region. PLoS ONE 8(11):e78319

    Google Scholar 

  • Pillutla VS, Giabbanelli PJ (2019) Iterative generation of insight from text collections through mutually reinforcing visualizations and fuzzy cognitive maps. Appl Soft Comput 76:459–472

    Google Scholar 

  • Plemmons G, Matthew H, Stephanie D, James G, Charlotte B, Whitney B, Robert C et al (2018) Hospitalization for suicide ideation or attempt: 2008–2015. Pediatrics 141(6):e20172426

  • Reddy T, Giabbanelli PJ, Mago VK (2019) The artificial facilitator: guiding participants in developing causal maps using voice-activated technologies. In: International conference on human-computer interaction. Springer, Cham, pp 111–129

  • Reddy T, Srivastava G, Mago V (2020) Testing the causal map builder on Amazon Alexa. In: World conference on information systems and technologies. Springer, Cham, pp 449–461

  • Rogers ML, Joiner TE (2019) Exploring the temporal dynamics of the interpersonal theory of suicide constructs: a dynamic systems modeling approach. J Consult Clin Psychol 87(1):56

    Google Scholar 

  • Rozenfeld HD, Kirk JE, Bollt EM, Ben-Avraham D (2005) Statistics of cycles: How loopy is your network? J Phys a: Math Gen 38(21):4589

    MathSciNet  MATH  Google Scholar 

  • Ruiz-Primo MA (2000) On the use of concept maps as an assessment tool in science: What we have learned so far. REDIE Rev Electrón Invest Educ 2(1):29–53

    Google Scholar 

  • Siokou C, Morgan R, Shiell A (2014) Group model building: a participatory approach to understanding and acting on systems. Public Health Res Pract 25(1):e2511404

    Google Scholar 

  • Ulijaszek S (2015) With the benefit of Foresight: obesity, complexity and joined-up government. BioSocieties 10(2):213–228

    Google Scholar 

  • van Vliet M, Kok K, Veldkamp T (2010) Linking stakeholders and modellers in scenario studies: the use of Fuzzy Cognitive Maps as a communication and learning tool. Futures 42(1):1–14

    Google Scholar 

  • Vasslides JM, Jensen OP (2016) Fuzzy cognitive mapping in support of integrated ecosystem assessments: developing a shared conceptual model among stakeholders. J Environ Manage 166:348–356

    Google Scholar 

  • Voinov A, Jenni K, Gray S, Kolagani N, Glynn PD, Bommel P et al (2018) Tools and methods in participatory modeling: selecting the right tool for the job. Environ Model Softw 109:232–255

    Google Scholar 

  • Waqa G, Moodie M, Snowdon W, Latu C, Coriakula J, Allender S, Bell C (2017) Exploring the dynamics of food-related policymaking processes and evidence use in Fiji using systems thinking. Health Res Policy Syst 15(1):1–8

    Google Scholar 

  • White E, Mazlack LJ (2011) Discerning suicide notes causality using fuzzy cognitive maps. In: 2011 IEEE international conference on fuzzy systems (FUZZ-IEEE 2011). IEEE, pp 2940–2947

  • World Health Organization (2014) Preventing suicide: a global imperative. World Health Organization, Geneva

    Google Scholar 

  • Xu Z, Da QL (2003) An overview of operators for aggregating information. Int J Intell Syst 18(9):953–969

    MATH  Google Scholar 

  • Yoon BS, Jetter AJ (2016) Comparative analysis for fuzzy cognitive mapping. In: 2016 Portland international conference on management of engineering and technology (PICMET). IEEE, pp 1897–1908

Download references

Acknowledgements

We are indebted to all fifteen subject-matter experts for engaging in this process and sharing their perspectives: Sarah Bacon, Margaret Brown, Eric Caine, Monica Chambers, Alexander Crosby, Beverly Fortson, Christopher Harper, Kristin Holland, Asha Ivey-Stephenson, Cheryl King, Melissa Merrick, Marilyn Metzler, Kelly Quinn, Deb Stone, and Elizabeth Swedo. The complete list including affiliations and fields of expertise is provided in Appendix A.

Funding

This publication was supported by the Centers for Disease Control and Prevention (CDC), National Center for Injury Prevention and Control (NCIPC), Intergovernmental Personnel Act (IPA) Assignment Agreement 20IPA2009427.

Author information

Authors and Affiliations

Authors

Contributions

KLR managed and coordinated responsibilities for the research activity planning and execution. KLR and PJG conceptualized and designed the study. PJG designed the methods. KLR recruited participants. PJG and KLR acquired data. DMN visualized the data for analysis. MCG, PJG, KLR, MMB combined individual datasets into one map. KLR, PJG, NN, MMB, CRH analyzed and interpreted the data. PJG and KLR directed the first draft. KLR, PJG, NN, MMB, CRH reviewed and edited the manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Corresponding author

Correspondence to Philippe J. Giabbanelli.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Consent for publication

The manuscript received clearance from publication from relevant divisions within the Centers for Disease Control and Prevention (CDC): the National Center for Injury Prevention and Control (NCIPC) Division of Injury Prevention (DIP)—Data Analytics Branch (DAB), and the National Center for Injury Prevention and Control (NCIPC) Office of Strategy and Innovation (OSI). The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Ethics approval and consent to participate

The study protocol was examined given the CDC guidelines to determine the need for approval by the Institutional Review Board (IRB) and Office of Management and Budget (OMB). The study received exemption from both as it does not involve clinical investigations (e.g., drugs, biologics, devices) and the number of non-federal interviewees included in the study is under the threshold requiring a review by the Human Research Protection Office. Each interview was conducted and recorded using WebEx video conferencing after receiving informed consent from the SME.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix A: Affiliations of the subject matter experts (SMEs) who were interviewed

Appendix A: Affiliations of the subject matter experts (SMEs) who were interviewed

Sarah Bacon, PhD.

Behavioral Scientist.

Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Office of the Director.

Margaret Brown, DrPH.

Behavioral Scientist.

Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Division of Injury Prevention, Suicide Prevention Team.

Eric Caine, MD.

Psychiatrist.

University of Rochester Medical Center.

Monica Chambers, MS.

Mental Health Counselor.

Odyssey Family Counseling Center.

Alexander Crosby, MD, MPH.

Medical Epidemiologist.

Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Office of the Director.

Beverly Fortson, PhD.

Behavioral Scientist.

U.S. Department of Defense, Sexual Assault Prevention and Response Office.

Christopher Harper, PhD.

Behavioral Scientist.

Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Division of Violence Prevention, Child Abuse Neglect and Adversity Team.

Kristin Holland, PhD, MPH.

Health Scientist.

Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Division of Overdose Prevention, Office of the Director.

Asha Ivey-Stephenson, PhD.

Behavioral Scientist.

Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Division of Injury Prevention, Suicide Prevention Team.

Cheryl King, PhD.

Professor.

University of Michigan, Youth and Young Adult Depression and Suicide Prevention Research Program.

Melissa Merrick, PhD.

President and CEO.

Prevent Child Abuse America.

Marilyn Metzler, MPH.

Public Health Analyst.

Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Division of Violence Prevention, Office of the Director.

Kelly Quinn, PhD, MPH.

Behavioral Scientist.

Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Division of Violence Prevention, Child Abuse Neglect and Adversity Team.

Deb Stone, ScD, MSW, MPH.

Behavioral Scientist.

Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Division of Injury Prevention, Suicide Prevention Team.

Elizabeth Swedo, MD, MPH.

Physician.

Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Division of Violence Prevention, Morbidity and Behavioral Surveillance Team.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Giabbanelli, P.J., Rice, K.L., Galgoczy, M.C. et al. Pathways to suicide or collections of vicious cycles? Understanding the complexity of suicide through causal mapping. Soc. Netw. Anal. Min. 12, 60 (2022). https://doi.org/10.1007/s13278-022-00886-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-022-00886-9

Keywords

Navigation