Browsing by Author "Ebrahimvandi, Alireza"

Now showing 1 - 6 of 6
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    A Dynamic Model of Post-Traumatic Stress Disorder for Military Personnel and Veterans
    Ghaffarzadegan, Navid; Ebrahimvandi, Alireza; Jalali, Mohammad S. (PLOS, 2016-10-07)
    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 "system of systems." 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.
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    Group Model Building Techniques for Rapid Elicitation of Parameter Values and Effect-Size-Driven Formulations
    Hosseinichimeh, Niyousha; MacDonald, Rod; Hyder, Ayaz; Ebrahimvandi, Alireza; Porter, Lauren; Reno, Becky; Maurer, Julie A.; Andersen, Deborah; Richardson, George; Hawley, Josh; Andersen, David F. (2017-02)
    Three parts to this presentation: Group Model Building and the Ohio Infant Mortality Project More Details on Rapid Elicitation of Parameters and Size Effects Advance Look at the Resultant Model as of Feb. 2017
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    Identifying the Early Signs of Preterm Birth from U.S. Birth Records Using Machine Learning Techniques
    Ebrahimvandi, Alireza; Hosseinichimeh, Niyousha; Kong, Zhenyu James (MDPI, 2022-06-25)
    Preterm birth (PTB) is the leading cause of infant mortality in the U.S. and globally. The goal of this study is to increase understanding of PTB risk factors that are present early in pregnancy by leveraging statistical and machine learning (ML) techniques on big data. The 2016 U.S. birth records were obtained and combined with two other area-level datasets, the Area Health Resources File and the County Health Ranking. Then, we applied logistic regression with elastic net regularization, random forest, and gradient boosting machines to study a cohort of 3.6 million singleton deliveries to identify generalizable PTB risk factors. The response variable is preterm birth, which includes spontaneous and indicated PTB, and we performed a binary classification. Our results show that the most important predictors of preterm birth are gestational and chronic hypertension, interval since last live birth, and history of a previous preterm birth, which explains 10.92, 5.98, and 5.63% of the predictive power, respectively. Parents’ education is one of the influential variables in predicting PTB, explaining 7.89% of the predictive power. The relative importance of race declines when parents are more educated or have received adequate prenatal care. The gradient boosting machines outperformed with an AUC of 0.75 (sensitivity: 0.64, specificity: 0.73) for the validation dataset. In this study, we compare our results with seminal and most related studies to demonstrate the superiority of our results. The application of ML techniques improved the performance measures in the prediction of preterm birth. The results emphasize the importance of socioeconomic factors such as parental education as one of the most important indicators of preterm birth. More research is needed on these mechanisms through which socioeconomic factors affect biological responses.
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    Methods and Results from Parameter Estimation Exercises Used in 2-Day Group Modeling Session for Ohio Infant Mortality Study
    Hosseinichimeh, Niyousha; MacDonald, Rod; Hyder, Ayaz; Ebrahimvandi, Alireza; Porter, Lauren; Reno, Becky; Maurer, Julie A.; Andersen, Deborah; Richardson, George; Hawley, Josh; Andersen, David F. (2017-10)
    Much of the existing group model-building literature focuses on approaches to defining model boundary and conceptualizing overall model structure including the creation of a backbone stock-and-flow structure as well as mapping key feedback structures into the model. Detailed formulation and parameter estimates are often left to back room techniques. This paper reports on the use of three scripts designed to engage groups in tasks related to model parameterization. The paper describes the context of the study—a group model building session hosted by the Ohio State University and focusing on infant mortality in the state of Ohio—and then proceeds to lay out the agenda for the two-day group modeling project. We discuss in detail how three scripts were used and also present a summary of the data that was collected to parameterize the final model. The paper sketches how that data has been used to support the rapid prototyping of the first phase of a running simulation model.
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    A Stakeholder Analysis of Infant Mortality in Ohio: Key Behaviors and Their Formulations
    Hosseinichimeh, Niyousha; Kim, Hyunjung; Ebrahimvandi, Alireza; Iams, Jay; Andersen, David F. (2017-11-27)
    This document reports stakeholder behaviors considered for modeling the impact of progesterone therapy on infant mortality in the state of Ohio and describes the formula used in the model. In part I, we present seven classes of stakeholder behaviors. For each stakeholder, first, we explain the behavior in Ohio. Then we provide evidence from the literature. Finally, we state how the behavior was captured in the system dynamics model. In part II, we show the structure of the model and the formula used to simulate the model.
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    Three Essays on Analysis of U.S. Infant Mortality Using Systems and Data Science Approaches
    Ebrahimvandi, Alireza (Virginia Tech, 2020-01-02)
    High infant mortality (IM) rates in the U.S. have been a major public health concern for decades. Many studies have focused on understanding causes, risk factors, and interventions that can reduce IM. However, death of an infant is the result of the interplay between many risk factors, which in some cases can be traced to the infancy of their parents. Consequently, these complex interactions challenge the effectiveness of many interventions. The long-term goal of this study is to advance the common understanding of effective interventions for improving health outcomes and, in particular, infant mortality. To achieve this goal, I implemented systems and data science methods in three essays to contribute to the understanding of IM causes and risk factors. In the first study, the goal was to identify patterns in the leading causes of infant mortality across states that successfully reduced their IM rates. I explore the trends at the state-level between 2000 and 2015 to identify patterns in the leading causes of IM. This study shows that the main drivers of IM rate reduction is the preterm-related mortality rate. The second study builds on these findings and investigates the risk factors of preterm birth (PTB) in the largest obstetric population that has ever been studied in this field. By applying the latest statistical and machine learning techniques, I study the PTB risk factors that are both generalizable and identifiable during the early stages of pregnancy. A major finding of this study is that socioeconomic factors such as parent education are more important than generally known factors such as race in the prediction of PTB. This finding is significant evidence for theories like Lifecourse, which postulate that the main determinants of a health trajectory are the social scaffolding that addresses the upstream roots of health. These results point to the need for more comprehensive approaches that change the focus from medical interventions during pregnancy to the time where mothers become vulnerable to the risk factors of PTB. Therefore, in the third study, I take an aggregate approach to study the dynamics of population health that results in undesirable outcomes in major indicators like infant mortality. Based on these new explanations, I offer a systematic approach that can help in addressing adverse birth outcomes—including high infant mortality and preterm birth rates—which is the central contribution of this dissertation. In conclusion, this dissertation contributes to a better understanding of the complexities in infant mortality and health-related policies. This work contributes to the body of literature both in terms of the application of statistical and machine learning techniques, as well as in advancing health-related theories.