Skip to main content
SpringerLink
Log in

Model-Based Policy Analysis to Mitigate Post-Traumatic Stress Disorder

  • Chapter
  • First Online:
Policy Analytics, Modelling, and Informatics

Part of the book series: Public Administration and Information Technology ((PAIT,volume 25))

Abstract

A wide range of modeling methods have been used to inform health policies. In this chapter, we describe three models for understanding the complexities of post-traumatic stress disorder (PTSD), a major mental disorder. The models are: (1) a qualitative model describing the social and psychological complexities of PTSD treatment; (2) a system dynamics model of a population of PTSD patients in the military and the Department of Veterans Affairs (VA); and (3) a Monte Carlo simulation model of PTSD prevalence and clinical demand over time among the OEF/OIF population. These models have two characteristics in common. First, they take systems approaches. In all models, we set a large boundary and look at the whole system, incorporating both military personnel and veterans. Second, the models are informed by a wide range of qualitative and quantitative data. Model I is rooted in qualitative data, and models II and III are calibrated to several data sources. These models are used to analyze the effects of different policy alternatives, such as more screening, more resiliency, and better recruitment procedures, on PTSD prevalence. They also provide analysis of healthcare costs in the military and the VA for each policy. Overall, the developed models offer examples of modeling techniques that incorporate a wide range of data sources and inform policy makers in developing programs for mitigating PTSD, a major premise of policy informatics.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Abbreviations

DOD:

Department of defense

OEF:

Operation enduring freedom

OIF:

Operation Iraqi freedom

PTSD:

Post-traumatic stress disorder

VA:

Veterans affairs

References

  • Bonanno GA (2004) Loss, trauma, and human resilience: have we underestimated the human capacity to thrive after extremely aversive events? Am Psychol 59(1):20

    Article  Google Scholar 

  • Box GE, Draper NR (1987) Empirical model-building and response surfaces, vol 424. Wiley, New York

    Google Scholar 

  • Breslau N, Davis GC, Schultz LR (2003) Posttraumatic stress disorder and the incidence of nicotine, alcohol, and other drug disorders in persons who have experienced trauma. Arch Gen Psychiatry 60(3):289–294

    Article  Google Scholar 

  • Campbell DG, Felker BL, Liu C-F, Yano EM, Kirchner JE, Chan D, Rubenstein LV, Chaney EF (2007) Prevalence of depression–PTSD comorbidity: implications for clinical practice guidelines and primary care-based interventions. J Gen Intern Med 22(6):711–718

    Article  Google Scholar 

  • Department of Defense Task Force on Mental Health (2007) An achievable vision: Report of the Department of Defense Task Force on Mental Health.ES 1–2

    Google Scholar 

  • Department of the Army (2009) Manual 6-22.5: combat and operational stress control manual for leaders and soldiers. US Department of the Army, Washington, p 111

    Google Scholar 

  • Fallah-Fini S, Rahmandad H, Huang TT-K, Bures RM, Glass TA (2014) Modeling US adult obesity trends: a system dynamics model for estimating energy imbalance gap. Am J Public Health 104(7):1230–1239

    Article  Google Scholar 

  • Fingerhut HA (2015) A systems framework and modeling approach to predict and manage the mental health effects of a military conflict. Masters Thesis, Massachusetts Institute of Technology. https://dspace.mit.edu/handle/1721.1/103570. Accessed 4 Aug 2016

  • Finkelstein SN, Larson RC, Nigmatulina K, Teytelman A (2015) Engineering effective responses to influenza outbreaks. Serv Sci 7(2):119–131

    Article  Google Scholar 

  • Ghaffarzadegan N, Ebrahimvandi A, Jalali MS (2016) A dynamic model of post-traumatic stress disorder for military personnel and veterans. PLoS One 11(10):e0161405. doi:10.1371/journal.pone.0161405

    Article  Google Scholar 

  • Ghaffarzadegan N, Hawley J, Desai A (2014) Research workforce diversity: the case of balancing national versus international postdocs in US biomedical research. Syst Res Behav Sci 31(2):301–315

    Article  Google Scholar 

  • Ghaffarzadegan N, Larson R (2015) Posttraumatic stress disorder: five vicious cycles that inhibit effective treatment. US Army Med Dep J (4–15):8–13

    Google Scholar 

  • Ghaffarzadegan N, Lyneis J, Richardson GP (2015) Policy informatics with small system dynamics models: how small models can help the public policy process. In: Johnston EW (ed) Governance in the information era: theory and practice of policy informatics. Routledge Press, New York

    Google Scholar 

  • Ginzburg K, Ein-Dor T, Solomon Z (2010) Comorbidity of posttraumatic stress disorder, anxiety and depression: a 20-year longitudinal study of war veterans. J Affect Disord 123(1):249–257

    Article  Google Scholar 

  • Hendin H, Haas AP (1991) Suicide and guilt as manifestations of PTSD. Am J Psychiatr 148(5):586–591

    Article  Google Scholar 

  • Henning KR, Frueh BC (1997) Combat guilt and its relationship to PTSD symptoms. J Clin Psychol 53(8):801–808

    Article  Google Scholar 

  • Hoge CW, Riviere LA, Wilk JE, Herrell RK, Weathers FW (2014) The prevalence of post-traumatic stress disorder (PTSD) in US combat soldiers: a head-to-head comparison of DSM-5 versus DSM-IV-TR symptom criteria with the PTSD checklist. Lancet Psychiatr 1(4):269–277

    Article  Google Scholar 

  • Homer JB, Hirsch GB (2006) System dynamics modeling for public health: background and opportunities. Am J Public Health 96(3):452–458

    Article  Google Scholar 

  • Jakupcak M, Cook J, Imel Z, Fontana A, Rosenheck R, McFall M (2009) Posttraumatic stress disorder as a risk factor for suicidal ideation in Iraq and Afghanistan war veterans. J Trauma Stress 22(4):303–306

    Article  Google Scholar 

  • Johnston EW (2015) Governance in the information era: theory and practice of policy informatics. Routledge, New York

    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

    Article  Google Scholar 

  • Kim H, MacDonald RH, Andersen DF (2013) Simulation and managerial decision making: a double-loop learning framework. Public Adm Rev 73(2):291–300

    Article  Google Scholar 

  • Luna-Reyes LF, Andersen DL (2003) Collecting and analyzing qualitative data for system dynamics: methods and models. Syst Dyn Rev 19(4):271–296

    Article  Google Scholar 

  • McFall ME, Mackay PW, Donovan DM (1992) Combat-related posttraumatic stress disorder and severity of substance abuse in Vietnam veterans. J Stud Alcohol 53(4):357–363

    Article  Google Scholar 

  • McFarland L, Milstein B, Hirsch G, Homer J, Andersen D, Irving R, Reineke E, Niles RD, Cawvey E, Desai A (2016) The NASPAA student simulation competition: reforming the US health care system within a simulated environment. J Public Aff Edu 22(3):363–380

    Google Scholar 

  • Milstein B, Homer J, Briss P, Burton D, Pechacek T (2011) Why behavioral and environmental interventions are needed to improve health at lower cost. Health Aff 30(5):823–832

    Article  Google Scholar 

  • Najavits LM, Ryngala D, Back SE, Bolton E, Mueser KT, Brady KT (2009) Treatment of PTSD and comorbid disorders. In: Foa EB, Keane TM, Friedman MJ, Cohen J (eds.) Effective treatments for PTSD: practice guidelines from the International Society for Traumatic Stress Studies. Guilford Press, New York, pp 508–535

    Google Scholar 

  • Phelan JC (2005) Geneticization of deviant behavior and consequences for stigma: the case of mental illness. J Health Soc Behav 46(4):307–322

    Article  Google Scholar 

  • Pietrzak RH, Johnson DC, Goldstein MB, Malley JC, Rivers AJ, Morgan CA, Southwick SM (2010) Psychosocial buffers of traumatic stress, depressive symptoms, and psychosocial difficulties in veterans of operations enduring Freedom and Iraqi Freedom: the role of resilience, unit support, and postdeployment social support. J Affect Disord 120(1):188–192

    Article  Google Scholar 

  • Sabounchi NS, Hovmand PS, Osgood ND, Dyck RF, Jungheim ES (2014) A novel system dynamics model of female obesity and fertility. Am J Public Health 104(7):1240–1246

    Article  Google Scholar 

  • Solomon Z, Snir A, Fingerhut H, Rosenberg M (2015) Long-term trajectories and recovery from PTSD. In: Bromet EJ (ed) Long-term outcomes in psychopathology research: rethinking the scientific Agenda. Oxford University Press, Oxford, pp 187–204

    Google Scholar 

  • Sterman J (2014) Interactive web-based simulations for strategy and sustainability: the MIT Sloan Learning Edge management flight simulators, part I. Syst Dyn Rev 30(1–2):89–121

    Article  Google Scholar 

  • Sterman J, Fiddaman T, Franck T, Jones A, McCauley S, Rice P, Sawin E, Siegel L (2012) Climate interactive: the C-ROADS climate policy model. Syst Dyn Rev 28(3):295–305

    Article  Google Scholar 

  • Sterman JD (1992) Teaching takes off. OR/MS Today 35(3):40–44

    Google Scholar 

  • Teytelman A, Larson RC (2013) Multiregional dynamic vaccine allocation during an influenza epidemic. Serv Sci 5(3):197–215

    Article  Google Scholar 

  • US Department Of Veterans Affairs (2016a) PTSD: National Center for PTSD. http://www.ptsd.va.gov/professional/PTSD-overview/index.asp. Accessed 7 Apr 2016

  • US Department Of Veterans Affairs (2016b) How Common is PTSD?. http://www.ptsd.va.gov/public/PTSD-overview/basics/how-common-is-ptsd.asp. Accessed 7 Apr 2016

  • Vogt D (2011) Mental health-related beliefs as a barrier to service use for military personnel and veterans: a review. Psychiatr Serv 62(2):135–142

    Article  Google Scholar 

  • Wittenborn A, Rahmandad H, Rick J, Hosseinichimeh N (2016) Depression as a systemic syndrome: mapping the feedback loops of major depressive disorder. Psychol Med 46(03):551–562

    Article  Google Scholar 

  • Zagonel AA, Rohrbaugh J, Richardson GP, Andersen DF (2004) Using simulation models to address “what if” questions about welfare reform. J Policy Anal Manage 23(4):890–901

    Article  Google Scholar 

Download references

Acknowledgments

This chapter is based upon MIT work supported, in part, by the Office of the Assistant Secretary of Defense for Health Affairs, under Award No. W81XWH-12-0016. The US Army Medical Research Acquisition Activity, 820 Chandler Street, Fort Detrick MD 21702-5014, is the awarding and administering acquisition office. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the Department of Defense, MIT, or Virginia Tech.

Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Virginia Tech, 250 Durham Hall (MC 0118), 1145 Perry Street, Blacksburg, VA, 24061, USA

    Navid Ghaffarzadegan & Alireza Ebrahimvandi

  2. Institute for Data, Systems, and Society, Massachusetts Institute of Technology, 77 Massachusetts Ave., E17-365, Cambridge, MA, 02139, USA

    Richard C. Larson

  3. Institute for Data, Systems, and Society, Massachusetts Institute of Technology, 50 Ames Street, E18-407A, Cambridge, MA, 02142, USA

    Henry Fingerhut

  4. Sloan School of Management, Massachusetts Institute of Technology, 245 First Street, E94-1568D, Cambridge, MA, 02142, USA

    Mohammad S. Jalali

  5. Sloan School of Management, Massachusetts Institute of Technology, 100 Main Street, E62-490, Cambridge, MA, 02142, USA

    Anne Quaadgras

  6. Sloan School of Management, Massachusetts Institute of Technology, 100 Main Street, E62-334, Cambridge, MA, 02142, USA

    Thomas Kochan

Authors
  1. Navid Ghaffarzadegan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Richard C. Larson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Henry Fingerhut
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammad S. Jalali
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alireza Ebrahimvandi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anne Quaadgras
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Thomas Kochan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Navid Ghaffarzadegan .

Editor information

Editors and Affiliations

  1. Center for Technology in Government & Rockefeller College of Public Affairs and Policy, University at Albany, State University of New York, Albany, New York, USA

    J Ramon Gil-Garcia

  2. Center for Technology in Government & Rockefeller College of Public Affairs and Policy, University at Albany, State University of New York, Albany, New York, USA

    Theresa A. Pardo

  3. Center for Technology in Government & Rockefeller College of Public Affairs and Policy, University at Albany, State University of New York, Albany, New York, USA

    Luis F. Luna-Reyes

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Ghaffarzadegan, N. et al. (2018). Model-Based Policy Analysis to Mitigate Post-Traumatic Stress Disorder. In: Gil-Garcia, J., Pardo, T., Luna-Reyes, L. (eds) Policy Analytics, Modelling, and Informatics. Public Administration and Information Technology, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-61762-6_17

Download citation

Publish with us

Policies and ethics

Search

Navigation