Publications
2023
Collins, Reagan A; Herman, Tianna; Snyder, Rebecca A; Haines, Krista L; Stey, Anne; Arora, Tania K; Geervarghese, Sunil; Philips, Joseph D; Vicente, Diego; Griggs, Cornelia L; McElroy, Imani E; Wall, Anji E; Hughes, Tasha M; Sen, Srijan; Valinejad, Jaber; Alban, Andres; Swan, J. Shannon; Mercaldo, Nathaniel; Jalali, Mohammad S.; Chhatwal, Jagpreet; Gazelle, G. Scott; Rangel, Erika; Yang, Chi-Fu Jeffrey; Donelan, Karen; Gold, Jessica A; West, Colin P; Cunningham, Carrie
Unspoken Truths: Mental Health Among Academic Surgeons Journal Article
In: Annals of Surgery, 2023.
Abstract | Links | BibTeX | Tags: Mental health
@article{nokey,
title = {Unspoken Truths: Mental Health Among Academic Surgeons},
author = {Reagan A Collins and Tianna Herman and Rebecca A Snyder and Krista L Haines and Anne Stey and Tania K Arora and Sunil Geervarghese and Joseph D Philips and Diego Vicente and Cornelia L Griggs and Imani E McElroy and Anji E Wall and Tasha M Hughes and Srijan Sen and Jaber Valinejad and Andres Alban and J. Shannon Swan and Nathaniel Mercaldo and Mohammad S. Jalali and Jagpreet Chhatwal and G. Scott Gazelle and Erika Rangel and Chi-Fu Jeffrey Yang and Karen Donelan and Jessica A Gold and Colin P West and Carrie Cunningham},
url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2023/12/unspoken_truths__2023.pdf},
year = {2023},
date = {2023-11-15},
urldate = {2023-11-15},
journal = {Annals of Surgery},
abstract = {Objective:
To characterize the current state of mental health within the surgical workforce in the United States (US).
Summary Background Data:
Mental illness and suicide is a growing concern in the medical community; however, the current state is largely unknown.
Methods:
Cross-sectional survey of the academic surgery community assessing mental health, medical error, and suicidal ideation. The odds of suicidal ideation adjusting for sex, prior mental health diagnosis, and validated scales screening for depression, anxiety, post-traumatic stress disorder (PTSD), and alcohol use disorder were assessed.
Results:
Of 622 participating medical students, trainees, and surgeons (estimated response rate=11.4-14.0%), 26.1% (141/539) reported a previous mental health diagnosis. 15.9% (83/523) of respondents screened positive for current depression, 18.4% (98/533) for anxiety, 11.0% (56/510) for alcohol use disorder, and 17.3% (36/208) for PTSD. Medical error was associated with depression (30.7% vs. 13.3%, P<0.001), anxiety (31.6% vs. 16.2%, P=0.001), PTSD (12.8% vs. 5.6%, P=0.018), and hazardous alcohol consumption (18.7% vs. 9.7%, P=0.022). 13.2% (73/551) of respondents reported suicidal ideation in the past year and 9.6% (51/533) in the past two weeks. On adjusted analysis, a previous history of a mental health disorder (aOR: 1.97, 95% CI: 1.04-3.65, P=0.033), and screening positive for depression (aOR: 4.30, 95% CI: 2.21-8.29, P<0.001) or PTSD (aOR: 3.93, 95% CI: 1.61-9.44, P=0.002) were associated with increased odds of suicidal ideation over the past 12 months.
Conclusions:
Nearly 1 in 7 respondents reported suicidal ideation in the past year. Mental illness and suicidal ideation are significant problems among the surgical workforce in the US.},
keywords = {Mental health},
pubstate = {published},
tppubtype = {article}
}
Objective:
To characterize the current state of mental health within the surgical workforce in the United States (US).
Summary Background Data:
Mental illness and suicide is a growing concern in the medical community; however, the current state is largely unknown.
Methods:
Cross-sectional survey of the academic surgery community assessing mental health, medical error, and suicidal ideation. The odds of suicidal ideation adjusting for sex, prior mental health diagnosis, and validated scales screening for depression, anxiety, post-traumatic stress disorder (PTSD), and alcohol use disorder were assessed.
Results:
Of 622 participating medical students, trainees, and surgeons (estimated response rate=11.4-14.0%), 26.1% (141/539) reported a previous mental health diagnosis. 15.9% (83/523) of respondents screened positive for current depression, 18.4% (98/533) for anxiety, 11.0% (56/510) for alcohol use disorder, and 17.3% (36/208) for PTSD. Medical error was associated with depression (30.7% vs. 13.3%, P<0.001), anxiety (31.6% vs. 16.2%, P=0.001), PTSD (12.8% vs. 5.6%, P=0.018), and hazardous alcohol consumption (18.7% vs. 9.7%, P=0.022). 13.2% (73/551) of respondents reported suicidal ideation in the past year and 9.6% (51/533) in the past two weeks. On adjusted analysis, a previous history of a mental health disorder (aOR: 1.97, 95% CI: 1.04-3.65, P=0.033), and screening positive for depression (aOR: 4.30, 95% CI: 2.21-8.29, P<0.001) or PTSD (aOR: 3.93, 95% CI: 1.61-9.44, P=0.002) were associated with increased odds of suicidal ideation over the past 12 months.
Conclusions:
Nearly 1 in 7 respondents reported suicidal ideation in the past year. Mental illness and suicidal ideation are significant problems among the surgical workforce in the US.
To characterize the current state of mental health within the surgical workforce in the United States (US).
Summary Background Data:
Mental illness and suicide is a growing concern in the medical community; however, the current state is largely unknown.
Methods:
Cross-sectional survey of the academic surgery community assessing mental health, medical error, and suicidal ideation. The odds of suicidal ideation adjusting for sex, prior mental health diagnosis, and validated scales screening for depression, anxiety, post-traumatic stress disorder (PTSD), and alcohol use disorder were assessed.
Results:
Of 622 participating medical students, trainees, and surgeons (estimated response rate=11.4-14.0%), 26.1% (141/539) reported a previous mental health diagnosis. 15.9% (83/523) of respondents screened positive for current depression, 18.4% (98/533) for anxiety, 11.0% (56/510) for alcohol use disorder, and 17.3% (36/208) for PTSD. Medical error was associated with depression (30.7% vs. 13.3%, P<0.001), anxiety (31.6% vs. 16.2%, P=0.001), PTSD (12.8% vs. 5.6%, P=0.018), and hazardous alcohol consumption (18.7% vs. 9.7%, P=0.022). 13.2% (73/551) of respondents reported suicidal ideation in the past year and 9.6% (51/533) in the past two weeks. On adjusted analysis, a previous history of a mental health disorder (aOR: 1.97, 95% CI: 1.04-3.65, P=0.033), and screening positive for depression (aOR: 4.30, 95% CI: 2.21-8.29, P<0.001) or PTSD (aOR: 3.93, 95% CI: 1.61-9.44, P=0.002) were associated with increased odds of suicidal ideation over the past 12 months.
Conclusions:
Nearly 1 in 7 respondents reported suicidal ideation in the past year. Mental illness and suicidal ideation are significant problems among the surgical workforce in the US.
2018
Ghaffarzadegan, Navid; Larson, Richard C; Fingerhut, Henry; Jalali, Mohammad S.; Ebrahimvandi, Alireza; Quaadgras, Anne; Kochan, Thomas
Model-Based Policy Analysis to Mitigate Post-Traumatic Stress Disorder Book Chapter
In: Policy Analytics, Modelling, and Informatics, vol. 24, pp. 387-406, Springer, 2018.
Abstract | Links | BibTeX | Tags: Mental health, Simulation modeling
@inbook{631224,
title = {Model-Based Policy Analysis to Mitigate Post-Traumatic Stress Disorder},
author = {Navid Ghaffarzadegan and Richard C Larson and Henry Fingerhut and Mohammad S. Jalali and Alireza Ebrahimvandi and Anne Quaadgras and Thomas Kochan},
url = {https://scholar.harvard.edu/files/jalali/files/ptsd_modeling_book_chapter.pdf},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Policy Analytics, Modelling, and Informatics},
volume = {24},
pages = {387-406},
publisher = {Springer},
organization = {Springer},
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.},
keywords = {Mental health, Simulation modeling},
pubstate = {published},
tppubtype = {inbook}
}
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.
2016
Ghaffarzadegan, Navid; Ebrahimvandi, Alireza; Jalali, Mohammad S.
A Dynamic Model of Post-Traumatic Stress Disorder for Military Personnel and Veterans Journal Article
In: PLOS ONE, vol. 11, pp. e0161405, 2016.
Abstract | Links | BibTeX | Tags: Mental health, Simulation modeling
@article{631213,
title = {A Dynamic Model of Post-Traumatic Stress Disorder for Military Personnel and Veterans},
author = {Navid Ghaffarzadegan and Alireza Ebrahimvandi and Mohammad S. Jalali},
url = {https://scholar.harvard.edu/files/jalali/files/a_dynamic_model_for_ptsd.pdf},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {PLOS ONE},
volume = {11},
pages = {e0161405},
abstract = {Post-traumatic stress disorder (PTSD) stands out as a major mental illness; however, little is known about effective policies for mitigating the problem. The importance and complexity of PTSD raise critical questions: What are the trends in the population of PTSD patients among military personnel and veterans in the postwar era? What policies can help mitigate PTSD? To address these questions, we developed a system dynamics simulation model of the population of military personnel and veterans affected by PTSD. The model includes both military personnel and veterans in a textquotedblleftsystem of systems.textquotedblright This is a novel aspect of our model, since many policies implemented at the military level will potentially influence (and may have side effects on) veterans and the Department of Veterans Affairs. The model is first validated by replicating the historical data on PTSD prevalence among military personnel and veterans from 2000 to 2014 (datasets from the Department of Defense, the Institute of Medicine, the Department of Veterans Affairs, and other sources). The model is then used for health policy analysis. Our results show that, in an optimistic scenario based on the status quo of deployment to intense/combat zones, estimated PTSD prevalence among veterans will be at least 10% during the next decade. The model postulates that during wars, resiliency-related policies are the most effective for decreasing PTSD. In a postwar period, current health policy interventions (e.g., screening and treatment) have marginal effects on mitigating the problem of PTSD, that is, the current screening and treatment policies must be revolutionized to have any noticeable effect. Furthermore, the simulation results show that it takes a long time, on the order of 40 years, to mitigate the psychiatric consequences of a war. Policy and financial implications of the findings are discussed.},
keywords = {Mental health, Simulation modeling},
pubstate = {published},
tppubtype = {article}
}
Post-traumatic stress disorder (PTSD) stands out as a major mental illness; however, little is known about effective policies for mitigating the problem. The importance and complexity of PTSD raise critical questions: What are the trends in the population of PTSD patients among military personnel and veterans in the postwar era? What policies can help mitigate PTSD? To address these questions, we developed a system dynamics simulation model of the population of military personnel and veterans affected by PTSD. The model includes both military personnel and veterans in a textquotedblleftsystem of systems.textquotedblright This is a novel aspect of our model, since many policies implemented at the military level will potentially influence (and may have side effects on) veterans and the Department of Veterans Affairs. The model is first validated by replicating the historical data on PTSD prevalence among military personnel and veterans from 2000 to 2014 (datasets from the Department of Defense, the Institute of Medicine, the Department of Veterans Affairs, and other sources). The model is then used for health policy analysis. Our results show that, in an optimistic scenario based on the status quo of deployment to intense/combat zones, estimated PTSD prevalence among veterans will be at least 10% during the next decade. The model postulates that during wars, resiliency-related policies are the most effective for decreasing PTSD. In a postwar period, current health policy interventions (e.g., screening and treatment) have marginal effects on mitigating the problem of PTSD, that is, the current screening and treatment policies must be revolutionized to have any noticeable effect. Furthermore, the simulation results show that it takes a long time, on the order of 40 years, to mitigate the psychiatric consequences of a war. Policy and financial implications of the findings are discussed.