Publications
2022
Stringfellow, Erin J.; Lim, Tse Yang; Humphreys, Keith; DiGennaro, Catherine; Stafford, Celia; Beaulieu, Elizabeth; Homer, Jack; Wakeland, Wayne; Bearnot, Benjamin; McHugh, R. Kathryn; Kelly, John; Glos, Lukas; Eggers, Sara; Kazemi, Reza; Jalali, Mohammad S.
Reducing Opioid Use Disorder and Overdose in the United States: A Dynamic Modeling Analysis Journal Article
In: Science Advances, vol. 8, no. 25, 2022.
Abstract | Links | BibTeX | Tags: Simulation modeling, Substance use, Working papers
@article{Stringfellow2021,
title = {Reducing Opioid Use Disorder and Overdose in the United States: A Dynamic Modeling Analysis},
author = {Erin J. Stringfellow and Tse Yang Lim and Keith Humphreys and Catherine DiGennaro and Celia Stafford and Elizabeth Beaulieu and Jack Homer and Wayne Wakeland and Benjamin Bearnot and R. Kathryn McHugh and John Kelly and Lukas Glos and Sara Eggers and Reza Kazemi and Mohammad S. Jalali},
url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2022/12/Stringfellow_2022_SciAdv.pdf},
doi = {10.1126/sciadv.abm8147},
year = {2022},
date = {2022-06-24},
journal = {Science Advances},
volume = {8},
number = {25},
abstract = {Opioid overdose deaths remain a major public health crisis. We used a system dynamics simulation model of the U.S. opioid-using population age 12 and older to explore the impacts of 11 strategies on the prevalence of opioid use disorder (OUD) and fatal opioid overdoses from 2022 to 2032. These strategies spanned opioid misuse and OUD prevention, buprenorphine capacity, recovery support, and overdose harm reduction. By 2032, three strategies saved the most lives: (i) reducing the risk of opioid overdose involving fentanyl use, which may be achieved through fentanyl-focused harm reduction services; (ii) increasing naloxone distribution to people who use opioids; and (iii) recovery support for people in remission, which reduced deaths by reducing OUD. Increasing buprenorphine providers’ capacity to treat more people decreased fatal overdose, but only in the short term. Our analysis provides insight into the kinds of multifaceted approaches needed to save lives.
},
keywords = {Simulation modeling, Substance use, Working papers},
pubstate = {published},
tppubtype = {article}
}
Opioid overdose deaths remain a major public health crisis. We used a system dynamics simulation model of the U.S. opioid-using population age 12 and older to explore the impacts of 11 strategies on the prevalence of opioid use disorder (OUD) and fatal opioid overdoses from 2022 to 2032. These strategies spanned opioid misuse and OUD prevention, buprenorphine capacity, recovery support, and overdose harm reduction. By 2032, three strategies saved the most lives: (i) reducing the risk of opioid overdose involving fentanyl use, which may be achieved through fentanyl-focused harm reduction services; (ii) increasing naloxone distribution to people who use opioids; and (iii) recovery support for people in remission, which reduced deaths by reducing OUD. Increasing buprenorphine providers’ capacity to treat more people decreased fatal overdose, but only in the short term. Our analysis provides insight into the kinds of multifaceted approaches needed to save lives.
Horvat, Andrijana; Luning, Pieternel A; DiGennaro, Catherine; Rommens, Edien; van Daalen, Els; Koene, Miriam; Jalali, Mohammad S.
In: Food Control, vol. 114, 2022.
Abstract | Links | BibTeX | Tags: Simulation modeling
@article{Horvat2022,
title = {The impacts of biosecurity measures on Campylobacter contamination in broiler houses and slaughterhouses in the Netherlands: A simulation modelling approach},
author = {Horvat, Andrijana and Luning, Pieternel A and DiGennaro, Catherine and Rommens, Edien and van Daalen, Els and Koene, Miriam and Mohammad S. Jalali},
url = {https://mj-lab.mgh.harvard.edu/campydynamics/},
year = {2022},
date = {2022-03-01},
urldate = {2022-03-01},
journal = {Food Control},
volume = {114},
abstract = {Intestinal campylobacteriosis, caused by Campylobacter ingestion, is the most reported zoonosis in the EU; it is societally costly and can lead to more severe sequelae. To reduce Campylobacter infections, biosecurity measures at both farms and slaughterhouses are warranted. However, the potential improvements achieved by these interventions have not been quantified. We used a systems science approach to develop a simulation model, synthesizing information from interviews with stakeholders in the Dutch broiler production industry and the current literature. The model includes both farms and slaughterhouses in a “system of systems,” helping to clarify the complexity of interrelated components of these systems and analyse the impact of various interventions. Insects, transportation crates, farm personnel, and catchers were identified as potential Campylobacter sources and modelled as elements of feedback loops. Insect control, farm hygiene, visitor control, thinning, and transportation control interventions were analysed. The model was shown to accurately describe the seasonality of Campylobacter, which supports its validity. Model simulation revealed that insect control interventions had the strongest impacts, followed by combined farm hygiene and visitor control, and combined thinning and transportation control. Insect control interventions alone reduced the peak percentage of contaminated chickens from 51% to 26% and the peak percentage of highly contaminated (>1000 CFU/g) neck samples of chicken carcasses from 13% to 8%. Implementing all interventions concurrently reduced the peak percentages of contaminated chickens to 5% and highly contaminated chicken neck samples to 2%. These results suggest that multiple biosecurity measures must be implemented to reduce Campylobacter contamination.
},
keywords = {Simulation modeling},
pubstate = {published},
tppubtype = {article}
}
Intestinal campylobacteriosis, caused by Campylobacter ingestion, is the most reported zoonosis in the EU; it is societally costly and can lead to more severe sequelae. To reduce Campylobacter infections, biosecurity measures at both farms and slaughterhouses are warranted. However, the potential improvements achieved by these interventions have not been quantified. We used a systems science approach to develop a simulation model, synthesizing information from interviews with stakeholders in the Dutch broiler production industry and the current literature. The model includes both farms and slaughterhouses in a “system of systems,” helping to clarify the complexity of interrelated components of these systems and analyse the impact of various interventions. Insects, transportation crates, farm personnel, and catchers were identified as potential Campylobacter sources and modelled as elements of feedback loops. Insect control, farm hygiene, visitor control, thinning, and transportation control interventions were analysed. The model was shown to accurately describe the seasonality of Campylobacter, which supports its validity. Model simulation revealed that insect control interventions had the strongest impacts, followed by combined farm hygiene and visitor control, and combined thinning and transportation control. Insect control interventions alone reduced the peak percentage of contaminated chickens from 51% to 26% and the peak percentage of highly contaminated (>1000 CFU/g) neck samples of chicken carcasses from 13% to 8%. Implementing all interventions concurrently reduced the peak percentages of contaminated chickens to 5% and highly contaminated chicken neck samples to 2%. These results suggest that multiple biosecurity measures must be implemented to reduce Campylobacter contamination.
2021
Jalali, Mohammad S; DiGennaro, Catherine; Guitar, Abby; Lew, Karen; Rahmandad, Hazhir
Evolution and Reproducibility of Simulation Modeling in Epidemiology and Health Policy over Half a Century Journal Article
In: Epidemiologic Reviews, 2021.
Abstract | Links | BibTeX | Tags: Simulation modeling
@article{643800,
title = {Evolution and Reproducibility of Simulation Modeling in Epidemiology and Health Policy over Half a Century},
author = {Mohammad S Jalali and Catherine DiGennaro and Abby Guitar and Karen Lew and Hazhir Rahmandad},
url = {https://scholar.harvard.edu/files/jalali/files/simulation_modeling_in_health_policy.pdf},
year = {2021},
date = {2021-07-29},
urldate = {2021-07-29},
journal = {Epidemiologic Reviews},
abstract = {Background: Simulation models are increasingly used to inform health policy. We provide an overview of applications of simulation models in health policy, analyze the use of best reporting practices, and assess the reproducibility of existing studies.
Method: Studies that used simulation modeling as the core method to address any health policy questions were included. Health policy domain distribution and changes in quality over time were well-characterized using MeSH terms and model characteristics, respectively. Reproducibility was assessed using predefined, categorical criteria.
Findings: 1,613 studies were analyzed. We found an exponential growth in the number of studies over the past half century, with the highest growth in dynamic modeling approaches. The largest subset of studies is focused on disease policy models (70%), within which pathological conditions, viral diseases, neoplasms, and cardiovascular diseases account for one-third of the articles. Nearly half of the studies do not report the details of their models. A subset of 100 articles (50 highly cited and 50 random) were selected to analyze in-depth criteria for reporting quality and reproducibility. Significant gaps between best modeling practices could be found in both the random and highly cited samples; only seven of 26 in-depth evaluation criteria were satisfied by more than 80% of samples. We found no evidence that the highly cited samples adhered better to the modeling best practices.
Interpretation: Our results suggest crucial areas for increased applications of simulation modeling, and opportunities to enhance the rigor and documentation in the conduct and reporting of simulation modeling in health policy.},
keywords = {Simulation modeling},
pubstate = {published},
tppubtype = {article}
}
Background: Simulation models are increasingly used to inform health policy. We provide an overview of applications of simulation models in health policy, analyze the use of best reporting practices, and assess the reproducibility of existing studies.
Method: Studies that used simulation modeling as the core method to address any health policy questions were included. Health policy domain distribution and changes in quality over time were well-characterized using MeSH terms and model characteristics, respectively. Reproducibility was assessed using predefined, categorical criteria.
Findings: 1,613 studies were analyzed. We found an exponential growth in the number of studies over the past half century, with the highest growth in dynamic modeling approaches. The largest subset of studies is focused on disease policy models (70%), within which pathological conditions, viral diseases, neoplasms, and cardiovascular diseases account for one-third of the articles. Nearly half of the studies do not report the details of their models. A subset of 100 articles (50 highly cited and 50 random) were selected to analyze in-depth criteria for reporting quality and reproducibility. Significant gaps between best modeling practices could be found in both the random and highly cited samples; only seven of 26 in-depth evaluation criteria were satisfied by more than 80% of samples. We found no evidence that the highly cited samples adhered better to the modeling best practices.
Interpretation: Our results suggest crucial areas for increased applications of simulation modeling, and opportunities to enhance the rigor and documentation in the conduct and reporting of simulation modeling in health policy.
Method: Studies that used simulation modeling as the core method to address any health policy questions were included. Health policy domain distribution and changes in quality over time were well-characterized using MeSH terms and model characteristics, respectively. Reproducibility was assessed using predefined, categorical criteria.
Findings: 1,613 studies were analyzed. We found an exponential growth in the number of studies over the past half century, with the highest growth in dynamic modeling approaches. The largest subset of studies is focused on disease policy models (70%), within which pathological conditions, viral diseases, neoplasms, and cardiovascular diseases account for one-third of the articles. Nearly half of the studies do not report the details of their models. A subset of 100 articles (50 highly cited and 50 random) were selected to analyze in-depth criteria for reporting quality and reproducibility. Significant gaps between best modeling practices could be found in both the random and highly cited samples; only seven of 26 in-depth evaluation criteria were satisfied by more than 80% of samples. We found no evidence that the highly cited samples adhered better to the modeling best practices.
Interpretation: Our results suggest crucial areas for increased applications of simulation modeling, and opportunities to enhance the rigor and documentation in the conduct and reporting of simulation modeling in health policy.
2020
Jalali, Mohammad S; DiGennaro, Catherine; Sridhar, Devi
Transparency Assessment of COVID-19 Models Journal Article
In: The Lancet Global Health, vol. 8, no. 12, pp. E1459-E1460, 2020.
Abstract | Links | BibTeX | Tags: Infectious diseases, Simulation modeling
@article{651959,
title = {Transparency Assessment of COVID-19 Models},
author = {Mohammad S Jalali and Catherine DiGennaro and Devi Sridhar},
url = {https://scholar.harvard.edu/files/jalali/files/transparency_assessment_covid19.pdf
https://scholar.harvard.edu/files/jalali/files/transparency_assessment_covid19_appendix.pdf},
year = {2020},
date = {2020-01-01},
journal = {The Lancet Global Health},
volume = {8},
number = {12},
pages = {E1459-E1460},
abstract = {As the COVID-19 pandemic has caused major societal unrest, modelers have worked to project future trends of COVID-19 and predict upcoming challenges and impacts of policy action. These models, alone or in aggregate, are influential for decision-makers at every level. Therefore, the method and documentation of COVID-19 models must be highly transparent to ensure that projections and consequential policies put forth have sound epistemological grounds. We evaluated 29 COVID-19 models receiving high attention levels within the scientific community and/or informing government responses. We evaluated these models against 27 transparency criteria. We found high levels of transparency in model documentation aspects such as reporting uncertainty analysis; however, about half of the models do not share code and a quarter do not report equations. These discrepancies underscore the need for transparency and reproducibility to be at the forefront of researcherstextquoteright priorities, especially during a global health crisis when stakes are critically high.},
keywords = {Infectious diseases, Simulation modeling},
pubstate = {published},
tppubtype = {article}
}
As the COVID-19 pandemic has caused major societal unrest, modelers have worked to project future trends of COVID-19 and predict upcoming challenges and impacts of policy action. These models, alone or in aggregate, are influential for decision-makers at every level. Therefore, the method and documentation of COVID-19 models must be highly transparent to ensure that projections and consequential policies put forth have sound epistemological grounds. We evaluated 29 COVID-19 models receiving high attention levels within the scientific community and/or informing government responses. We evaluated these models against 27 transparency criteria. We found high levels of transparency in model documentation aspects such as reporting uncertainty analysis; however, about half of the models do not share code and a quarter do not report equations. These discrepancies underscore the need for transparency and reproducibility to be at the forefront of researcherstextquoteright priorities, especially during a global health crisis when stakes are critically high.
Jalali, Mohammad S; Botticelli, Michael; Hwang, Rachael; Koh, Howard K; McHugh, Kathryn R
The opioid crisis: need for systems science research Journal Article
In: Health Research Policy and Systems, vol. 18, no. 88, 2020.
Abstract | Links | BibTeX | Tags: Simulation modeling, Substance use
@article{661337,
title = {The opioid crisis: need for systems science research},
author = {Mohammad S Jalali and Michael Botticelli and Rachael Hwang and Howard K Koh and Kathryn R McHugh},
url = {https://scholar.harvard.edu/files/jalali/files/opioid_crisis_systems_science.pdf},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Health Research Policy and Systems},
volume = {18},
number = {88},
abstract = {The opioid epidemic in the United States has had a devastating impact on millions of people as well as on their families and communities. The increased prevalence of opioid misuse, use disorder and overdose in recent years has highlighted the need for improved public health approaches for reducing the tremendous harms of this illness. In this paper, we explain and call for the need for more systems science approaches, which can uncover the complexities of the opioid crisis, and help evaluate, analyse and forecast the effectiveness of ongoing and new policy interventions. Similar to how a stream of systems science research helped policy development in infectious diseases and obesity, more systems science research is needed in opioids.},
keywords = {Simulation modeling, Substance use},
pubstate = {published},
tppubtype = {article}
}
The opioid epidemic in the United States has had a devastating impact on millions of people as well as on their families and communities. The increased prevalence of opioid misuse, use disorder and overdose in recent years has highlighted the need for improved public health approaches for reducing the tremendous harms of this illness. In this paper, we explain and call for the need for more systems science approaches, which can uncover the complexities of the opioid crisis, and help evaluate, analyse and forecast the effectiveness of ongoing and new policy interventions. Similar to how a stream of systems science research helped policy development in infectious diseases and obesity, more systems science research is needed in opioids.
Namin, Amir T; Vahdat, Vahab; DiGennaro, Catherine; Amid, Roham; Jalali, Mohammad S
Adoption of new medical technologies: The effects of insurance coverage vs continuing medical education Journal Article
In: Health Policy and Technology, vol. 9, no. 1, pp. 31-41, 2020.
Abstract | Links | BibTeX | Tags: Adoption dynamics, Simulation modeling
@article{647807,
title = {Adoption of new medical technologies: The effects of insurance coverage vs continuing medical education},
author = {Amir T Namin and Vahab Vahdat and Catherine DiGennaro and Roham Amid and Mohammad S Jalali},
url = {https://scholar.harvard.edu/files/jalali/files/adoption_insurance_vs_education.pdf},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Health Policy and Technology},
volume = {9},
number = {1},
pages = {31-41},
abstract = {Medical technologies innovate rapidly and responsively to patient needs, but the adoption of the latest technologies in practice can be delayed by lack of knowledge and ability to pay. Customized individually made (CIM) knee implants potentially provide an option for individuals to maintain moderate to high activity levels with fewer surgical revisions following a total knee replacement, however they are costlier upfront. Not only is the technology more expensive, but insurance typically covers around 50% (versus 90% for older off-the-shelf knee implants). We used a recent simulation model and analyzed the effects on overall adoption of CIM through 2026 and found that continuing medical education (CME)—a common intervention to increase the adoption of new medical technologies through increasing practitioner knowledge and comfort with the new technologies—can increase the adoption of CIM to 48% in the short term, but increasing insurance coverage to be equal to OTS knee replacement coverage increases the adoption to 87% in the sustained long term. Efforts to implement CME are well-placed and will increase the rate of adoption, however the combination of CME and increased insurance coverage provides the most benefit, with the technology reaching 80% of the population undergoing total knee replacement by 2021.},
keywords = {Adoption dynamics, Simulation modeling},
pubstate = {published},
tppubtype = {article}
}
Medical technologies innovate rapidly and responsively to patient needs, but the adoption of the latest technologies in practice can be delayed by lack of knowledge and ability to pay. Customized individually made (CIM) knee implants potentially provide an option for individuals to maintain moderate to high activity levels with fewer surgical revisions following a total knee replacement, however they are costlier upfront. Not only is the technology more expensive, but insurance typically covers around 50% (versus 90% for older off-the-shelf knee implants). We used a recent simulation model and analyzed the effects on overall adoption of CIM through 2026 and found that continuing medical education (CME)—a common intervention to increase the adoption of new medical technologies through increasing practitioner knowledge and comfort with the new technologies—can increase the adoption of CIM to 48% in the short term, but increasing insurance coverage to be equal to OTS knee replacement coverage increases the adoption to 87% in the sustained long term. Efforts to implement CME are well-placed and will increase the rate of adoption, however the combination of CME and increased insurance coverage provides the most benefit, with the technology reaching 80% of the population undergoing total knee replacement by 2021.
2019
Jalali, Mohammad S.; Kaiser, Jessica P; Siegel, Michael; Madnick, Stuart
The Internet of Things Promises New Benefits and Risks: A Systematic Analysis of Adoption Dynamics of IoT Products Journal Article
In: IEEE Security and privacy, vol. 17, no. 2, pp. 39-48, 2019.
Links | BibTeX | Tags: Adoption dynamics, Cybersecurity, Simulation modeling
@article{631218,
title = {The Internet of Things Promises New Benefits and Risks: A Systematic Analysis of Adoption Dynamics of IoT Products},
author = {Mohammad S. Jalali and Jessica P Kaiser and Michael Siegel and Stuart Madnick},
url = {https://scholar.harvard.edu/files/jalali/files/adoption_dynamics_of_iot_products.pdf},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {IEEE Security and privacy},
volume = {17},
number = {2},
pages = {39-48},
keywords = {Adoption dynamics, Cybersecurity, Simulation modeling},
pubstate = {published},
tppubtype = {article}
}
Jalali, Mohammad S; Rahmandad, Hazhir; Bullock, Sally Lawrence; Lee-Kwan, Seung Hee; Gittelsohn, Joel; Ammerman, Alice
Dynamics of intervention adoption, implementation, and maintenance inside organizations: The case of an obesity prevention initiative Journal Article
In: Social Science & Medicine, vol. 224, pp. 67-76, 2019.
Abstract | Links | BibTeX | Tags: Adoption dynamics, Obesity, Simulation modeling
@article{631231,
title = {Dynamics of intervention adoption, implementation, and maintenance inside organizations: The case of an obesity prevention initiative},
author = {Mohammad S Jalali and Hazhir Rahmandad and Sally Lawrence Bullock and Seung Hee Lee-Kwan and Joel Gittelsohn and Alice Ammerman},
url = {https://scholar.harvard.edu/files/jalali/files/dynamics_of_intervention_adoption.pdf, dynamics of intervention adoption.pdf
https://scholar.harvard.edu/files/jalali/files/vensimfiles.zip, Vensim Files},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {Social Science & Medicine},
volume = {224},
pages = {67-76},
abstract = {Overall impact of public health prevention interventions relies not only on the average efficacy of an intervention, but also on the successful adoption, implementation, and maintenance (AIM) of that intervention. In this study, we aim to understand the dynamics that regulate AIM of organizational level intervention programs. We focus on two well-documented obesity prevention interventions, implemented in food carry-outs and stores in low-income urban areas of Baltimore, Maryland, which aimed to improve dietary behaviour for adults by providing access to healthier foods and point-of-purchase promotions. Building on data from field observations, in-depth interviews, and data discussed in previous publications, as well as the strategy and organizational behaviour literature, we developed a system dynamics model of the key processes of AIM. With simulation analysis, we show several reinforcing mechanisms that span stakeholder motivation, communications, and implementation quality and costs can turn small changes in the process of AIM into big difference in the overall impact of the intervention. Specifically, small changes in the allocation of resources to communication with stakeholders of intervention could have a nonlinear long-term impact if those additional resources can turn stakeholders into allies of the intervention, reducing the erosion rates and enhancing sustainability. We present how the dynamics surrounding communication, motivation, and erosion can create significant heterogeneity in the overall impact of otherwise similar interventions. Therefore, careful monitoring of how those dynamics unfold, and timely adjustments to keep the intervention on track are critical for successful implementation and maintenance.},
keywords = {Adoption dynamics, Obesity, Simulation modeling},
pubstate = {published},
tppubtype = {article}
}
Overall impact of public health prevention interventions relies not only on the average efficacy of an intervention, but also on the successful adoption, implementation, and maintenance (AIM) of that intervention. In this study, we aim to understand the dynamics that regulate AIM of organizational level intervention programs. We focus on two well-documented obesity prevention interventions, implemented in food carry-outs and stores in low-income urban areas of Baltimore, Maryland, which aimed to improve dietary behaviour for adults by providing access to healthier foods and point-of-purchase promotions. Building on data from field observations, in-depth interviews, and data discussed in previous publications, as well as the strategy and organizational behaviour literature, we developed a system dynamics model of the key processes of AIM. With simulation analysis, we show several reinforcing mechanisms that span stakeholder motivation, communications, and implementation quality and costs can turn small changes in the process of AIM into big difference in the overall impact of the intervention. Specifically, small changes in the allocation of resources to communication with stakeholders of intervention could have a nonlinear long-term impact if those additional resources can turn stakeholders into allies of the intervention, reducing the erosion rates and enhancing sustainability. We present how the dynamics surrounding communication, motivation, and erosion can create significant heterogeneity in the overall impact of otherwise similar interventions. Therefore, careful monitoring of how those dynamics unfold, and timely adjustments to keep the intervention on track are critical for successful implementation and maintenance.
Namin, Amir T; Jalali, Mohammad S; Vahdat, Vahab; Bedair, Hany S; O'Connor, Mary I; Kamarthi, Sagar; Isaacs, Jacqueline A
The Adoption of New Medical Technologies: The Case of Customized Individually Made Knee Implants Journal Article
In: Value in Health, vol. 22, no. 4, pp. 423-430, 2019.
Abstract | Links | BibTeX | Tags: Adoption dynamics, Simulation modeling
@article{631232,
title = {The Adoption of New Medical Technologies: The Case of Customized Individually Made Knee Implants},
author = {Amir T Namin and Mohammad S Jalali and Vahab Vahdat and Hany S Bedair and Mary I O'Connor and Sagar Kamarthi and Jacqueline A Isaacs},
url = {https://scholar.harvard.edu/files/jalali/files/theadoptionofnewmedicaltechnologies.pdf
https://scholar.harvard.edu/files/jalali/files/supporting_information.pdf
https://scholar.harvard.edu/files/jalali/files/modeling_documentation_and_instruction_for_reproducibility_mdir.pdf},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {Value in Health},
volume = {22},
number = {4},
pages = {423-430},
abstract = {Objectives: To investigate the impact of insurance coverage on the adoption of customized individually made (CIM) knee implants, and to compare patient outcomes and cost-effectiveness of off-the-shelf (OTS) and CIM implants.Study Design: A system dynamics simulation model is developed to study adoption dynamics of CIM and meet the research objectives.Methods: The model reproduced the historical data on primary and revision knee replacement implants obtained from the literature and the Nationwide Inpatient Sample. Then, the dynamics of adoption of CIM implants were simulated from 2018 to 2026. The rate of 90-day readmission, 3-year revision surgery, recovery period, time savings in operating rooms, and the associated cost within three years of primary knee replacement implants were used as performance metrics.Results: The simulation results indicate that, by 2026, an adoption rate of 90% for CIM implants can reduce the number of readmissions and revision surgeries by 62% and 39%, respectively, and can save hospitals and surgeons 6% on procedure time, and cut down cumulative healthcare costs by approximately $38 billion.Conclusions: CIM implants have the potential to deliver high-quality care while decreasing overall healthcare costs, but their adoption requires the expansion of current insurance coverage. This work presents a first systematic study to understand the dynamics of adoption of CIM knee implants and instrumentation. More broadly, the current modeling approach and systems thinking perspective could be utilized to consider the adoption of any emerging customized therapies for personalized medicine.},
keywords = {Adoption dynamics, Simulation modeling},
pubstate = {published},
tppubtype = {article}
}
Objectives: To investigate the impact of insurance coverage on the adoption of customized individually made (CIM) knee implants, and to compare patient outcomes and cost-effectiveness of off-the-shelf (OTS) and CIM implants.Study Design: A system dynamics simulation model is developed to study adoption dynamics of CIM and meet the research objectives.Methods: The model reproduced the historical data on primary and revision knee replacement implants obtained from the literature and the Nationwide Inpatient Sample. Then, the dynamics of adoption of CIM implants were simulated from 2018 to 2026. The rate of 90-day readmission, 3-year revision surgery, recovery period, time savings in operating rooms, and the associated cost within three years of primary knee replacement implants were used as performance metrics.Results: The simulation results indicate that, by 2026, an adoption rate of 90% for CIM implants can reduce the number of readmissions and revision surgeries by 62% and 39%, respectively, and can save hospitals and surgeons 6% on procedure time, and cut down cumulative healthcare costs by approximately $38 billion.Conclusions: CIM implants have the potential to deliver high-quality care while decreasing overall healthcare costs, but their adoption requires the expansion of current insurance coverage. This work presents a first systematic study to understand the dynamics of adoption of CIM knee implants and instrumentation. More broadly, the current modeling approach and systems thinking perspective could be utilized to consider the adoption of any emerging customized therapies for personalized medicine.
2018
Hosseinichimeh, Niyousha; Wittenborn, Andrea K; Rick, Jennifer; Jalali, Mohammad S; Rahmandad, Hazhir
Modeling and Estimating the Feedback Mechanisms among Depression, Rumination, and Stressors in Adolescents Journal Article
In: PLOS ONE, vol. 13, no. 9, pp. e0204389, 2018.
Abstract | Links | BibTeX | Tags: Simulation modeling
@article{631225,
title = {Modeling and Estimating the Feedback Mechanisms among Depression, Rumination, and Stressors in Adolescents},
author = {Niyousha Hosseinichimeh and Andrea K Wittenborn and Jennifer Rick and Mohammad S Jalali and Hazhir Rahmandad},
url = {https://scholar.harvard.edu/files/jalali/files/modeling_depression.pdf},
year = {2018},
date = {2018-01-01},
journal = {PLOS ONE},
volume = {13},
number = {9},
pages = {e0204389},
abstract = {The systemic interactions among depressive symptoms, rumination, and stress are important to understanding depression but have not yet been quantified. In this article, we present a system dynamics simulation model of depression that captures the reciprocal relationships among stressors, rumination, and depression. Building on the response styles theory, this model formalizes three interdependent mechanisms: 1) Rumination contributes to textquoteleftkeeping stressors alivetextquoteright; 2) Rumination has a direct impact on depressive symptoms; and 3) Both textquoteleftstressors kept alivetextquoteright and current depressive symptoms contribute to rumination. The strength of these mechanisms is estimated using data from 661 adolescents (353 girls and 308 boys) from two middle schools (grades 6–8). These estimates indicate that rumination contributes to depression by keeping stressors textquoteleftalivetextquoteright—and the individual activated—even after the stressor has ended. This mechanism is stronger among girls than boys, increasing their vulnerability to a rumination reinforcing loop. Different profiles of depression emerge over time depending on initial levels of depressive symptoms, rumination, and stressors as well as the occurrence rate for stressors; levels of rumination and occurrence of stressors are stronger contributors to long-term depression. Our systems model is a steppingstone towards a more comprehensive understanding of depression in which reinforcing feedback mechanisms play a significant role. Future research is needed to expand this simulation model to incorporate other drivers of depression and provide a more holistic tool for studying depression.},
keywords = {Simulation modeling},
pubstate = {published},
tppubtype = {article}
}
The systemic interactions among depressive symptoms, rumination, and stress are important to understanding depression but have not yet been quantified. In this article, we present a system dynamics simulation model of depression that captures the reciprocal relationships among stressors, rumination, and depression. Building on the response styles theory, this model formalizes three interdependent mechanisms: 1) Rumination contributes to textquoteleftkeeping stressors alivetextquoteright; 2) Rumination has a direct impact on depressive symptoms; and 3) Both textquoteleftstressors kept alivetextquoteright and current depressive symptoms contribute to rumination. The strength of these mechanisms is estimated using data from 661 adolescents (353 girls and 308 boys) from two middle schools (grades 6–8). These estimates indicate that rumination contributes to depression by keeping stressors textquoteleftalivetextquoteright—and the individual activated—even after the stressor has ended. This mechanism is stronger among girls than boys, increasing their vulnerability to a rumination reinforcing loop. Different profiles of depression emerge over time depending on initial levels of depressive symptoms, rumination, and stressors as well as the occurrence rate for stressors; levels of rumination and occurrence of stressors are stronger contributors to long-term depression. Our systems model is a steppingstone towards a more comprehensive understanding of depression in which reinforcing feedback mechanisms play a significant role. Future research is needed to expand this simulation model to incorporate other drivers of depression and provide a more holistic tool for studying depression.
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.
Jalali, Mohammad S; Siegel, Michael; Madnick, Stuart
Decision-Making and Biases in Cybersecurity Capability Development: Evidence from a Simulation Game Experiment Journal Article
In: Journal of Strategic Information Systems, 2018.
Abstract | Links | BibTeX | Tags: Cybersecurity, Simulation modeling
@article{631229,
title = {Decision-Making and Biases in Cybersecurity Capability Development: Evidence from a Simulation Game Experiment},
author = {Mohammad S Jalali and Michael Siegel and Stuart Madnick},
url = {https://scholar.harvard.edu/files/jalali/files/decision-making_in_cybersecurity.pdf},
year = {2018},
date = {2018-01-01},
journal = {Journal of Strategic Information Systems},
abstract = {We developed a simulation game to study the effectiveness of decision-makers in overcoming two complexities in building cybersecurity capabilities: potential delays in capability development; and uncertainties in predicting cyber incidents. Analyzing 1479 simulation runs, we compared the performances of a group of experienced professionals with those of an inexperienced control group. Experienced subjects did not understand the mechanisms of delays any better than inexperienced subjects; however, experienced subjects were better able to learn the need for proactive decision-making through an iterative process. Both groups exhibited similar errors when dealing with the uncertainty of cyber incidents. Our findings highlight the importance of training for decision-makers with a focus on systems thinking skills, and lay the groundwork for future research on uncovering mental biases about the complexities of cybersecurity.},
keywords = {Cybersecurity, Simulation modeling},
pubstate = {published},
tppubtype = {article}
}
We developed a simulation game to study the effectiveness of decision-makers in overcoming two complexities in building cybersecurity capabilities: potential delays in capability development; and uncertainties in predicting cyber incidents. Analyzing 1479 simulation runs, we compared the performances of a group of experienced professionals with those of an inexperienced control group. Experienced subjects did not understand the mechanisms of delays any better than inexperienced subjects; however, experienced subjects were better able to learn the need for proactive decision-making through an iterative process. Both groups exhibited similar errors when dealing with the uncertainty of cyber incidents. Our findings highlight the importance of training for decision-makers with a focus on systems thinking skills, and lay the groundwork for future research on uncovering mental biases about the complexities of cybersecurity.
Azghandi, Rana; Griffin, Jacqueline; Jalali, Mohammad S
Minimization of Drug Shortages in Pharmaceutical Supply Chains: A Simulation-Based Analysis of Drug Recall Patterns and Inventory Policies Journal Article
In: Complexity, pp. 1-14, 2018.
Abstract | Links | BibTeX | Tags: Simulation modeling
@article{631223,
title = {Minimization of Drug Shortages in Pharmaceutical Supply Chains: A Simulation-Based Analysis of Drug Recall Patterns and Inventory Policies},
author = {Rana Azghandi and Jacqueline Griffin and Mohammad S Jalali},
url = {https://scholar.harvard.edu/files/jalali/files/drug_shortage.pdf},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Complexity},
pages = {1-14},
abstract = {The drug shortage crisis in the last decade not only increased health care costs but also jeopardized patientstextquoteright health across the United States. Ensuring that any drug is available to patients at health care centers is a problem that official health care administrators and other stakeholders of supply chains continue to face. Furthermore, managing pharmaceutical supply chains is very complex, as inevitable disruptions occur in these supply chains (exogenous factors), which are then followed by decisions members make after such disruptions (internal factors). Disruptions may occur due to increased demand, a product recall, or a manufacturer disruption, among which product recalls—which happens frequently in pharmaceutical supply chains—are least studied. We employ a mathematical simulation model to examine the effects of product recalls considering different disruption profiles, e.g., the propagation in time and space, and the interactions of decision makers on drug shortages to ascertain how these shortages can be mitigated by changing inventory policy decisions. We also measure the effects of different policy approaches on supply chain disruptions, using two performance measures: inventory levels and shortages of products at health care centers. We then analyze the results using an approach similar to data envelopment analysis to characterize the efficient frontier (best inventory policies) for varying cost ratios of the two performance measures as they correspond to the different disruption patterns. This analysis provides insights into the consequences of choosing an inappropriate inventory policy when disruptions take place.},
keywords = {Simulation modeling},
pubstate = {published},
tppubtype = {article}
}
The drug shortage crisis in the last decade not only increased health care costs but also jeopardized patientstextquoteright health across the United States. Ensuring that any drug is available to patients at health care centers is a problem that official health care administrators and other stakeholders of supply chains continue to face. Furthermore, managing pharmaceutical supply chains is very complex, as inevitable disruptions occur in these supply chains (exogenous factors), which are then followed by decisions members make after such disruptions (internal factors). Disruptions may occur due to increased demand, a product recall, or a manufacturer disruption, among which product recalls—which happens frequently in pharmaceutical supply chains—are least studied. We employ a mathematical simulation model to examine the effects of product recalls considering different disruption profiles, e.g., the propagation in time and space, and the interactions of decision makers on drug shortages to ascertain how these shortages can be mitigated by changing inventory policy decisions. We also measure the effects of different policy approaches on supply chain disruptions, using two performance measures: inventory levels and shortages of products at health care centers. We then analyze the results using an approach similar to data envelopment analysis to characterize the efficient frontier (best inventory policies) for varying cost ratios of the two performance measures as they correspond to the different disruption patterns. This analysis provides insights into the consequences of choosing an inappropriate inventory policy when disruptions take place.
2016
Hosseinichimeh, Niyousha; Rahmandad, Hazhir; Jalali, Mohammad S; Wittenborn, Andrea K
Estimating the parameters of system dynamics models using indirect inference Journal Article
In: System Dynamics Review, vol. 32, pp. 156-180, 2016, ISBN: 0883-7066.
Abstract | Links | BibTeX | Tags: Simulation modeling
@article{631215,
title = {Estimating the parameters of system dynamics models using indirect inference},
author = {Niyousha Hosseinichimeh and Hazhir Rahmandad and Mohammad S Jalali and Andrea K Wittenborn},
url = {https://scholar.harvard.edu/files/jalali/files/parameter_estimation_using_indirect_inference.pdf},
isbn = {0883-7066},
year = {2016},
date = {2016-01-01},
journal = {System Dynamics Review},
volume = {32},
pages = {156-180},
abstract = {There is limited methodological guidance for estimating system dynamics (SD) models using datasets common to social sciences that include few data points over time for many units under analysis. Here, we introduce indirect inference, a simulation-based estimation method that can be applied to common datasets and is applicable to SD models that often include intractable likelihood functions. In this method, the model parameters are found by ensuring that simulated data from the model and available empirical data produce similar auxiliary statistics. The method requires few assumptions about the structure of the model and error-generating processes and thus can be used in a variety of applications. We demonstrate the method in estimating an SD model of depression and rumination using a panel dataset. The overall results suggest that indirect inference can extend the application of SD models to new topics and leverage common panel datasets to provide unique insights.},
keywords = {Simulation modeling},
pubstate = {published},
tppubtype = {article}
}
There is limited methodological guidance for estimating system dynamics (SD) models using datasets common to social sciences that include few data points over time for many units under analysis. Here, we introduce indirect inference, a simulation-based estimation method that can be applied to common datasets and is applicable to SD models that often include intractable likelihood functions. In this method, the model parameters are found by ensuring that simulated data from the model and available empirical data produce similar auxiliary statistics. The method requires few assumptions about the structure of the model and error-generating processes and thus can be used in a variety of applications. We demonstrate the method in estimating an SD model of depression and rumination using a panel dataset. The overall results suggest that indirect inference can extend the application of SD models to new topics and leverage common panel datasets to provide unique insights.
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.
2014
Jalali, Mohammad S.; Rahmandad, Hazhir; Bullock, Sally Lawrence; Ammerman, Alice
Dynamics of Obesity Interventions inside Organizations Proceedings
System Dynamics Society, 2014.
BibTeX | Tags: Obesity, Simulation modeling
@proceedings{631208,
title = {Dynamics of Obesity Interventions inside Organizations},
author = {Mohammad S. Jalali and Hazhir Rahmandad and Sally Lawrence Bullock and Alice Ammerman},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
journal = {The 32nd International Conference of the System Dynamics Society},
pages = {69},
publisher = {System Dynamics Society},
keywords = {Obesity, Simulation modeling},
pubstate = {published},
tppubtype = {proceedings}
}
//[//tplist image=”left” image_size=”80″ entries_per_page=”200″]//