@article{nokey,

title = {Participatory modeling for high complexity, multi-system issues: challenges and recommendations for balancing qualitative understanding and quantitative questions},

author = {Arielle R. Deutsch and Leah Frerichs and Madeleine Perry and Mohammad S. Jalali},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2024/02/Deutsch_et_al_2024_SDR.pdf},

year  = {2024},

date = {2024-02-22},

journal = {System Dynamics Review},

abstract = {Community stakeholder participation can be incredibly valuable for the qualitative model development process. However, modelers often encounter challenges for participatory modeling projects focusing on high-complexity, synergistic interactions between multiple issues, systems, and granularity. The diverse stakeholder perspectives and volumes of information necessary for developing such models can yield qualitative models that are difficult to translate into quantitative simulation or clear insight for informed decision-making. There are few recommended best practices for developing high-complexity, participatory models. We use an ongoing project as a case study to highlight three practical challenges for tackling high-complexity, multi-system issues with system dynamics tools. These challenges include balanced and respectful stakeholder engagement, defining boundaries and levels of variable aggregation, and timing and processes for qualitative/quantitative model integration. Our five recommendations to address these challenges serve as a foundation for further research on methods for developing translatable qualitative multi-system models for informing actions for systemic change.},

keywords = {disparity and equity},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {How Funding Policy Maintains Structural Inequity Within Indigenous Community-Based Organizations},

author = {Arielle R. Deutsch and Leah Frerichs and Zeynep Hasgul and Foster Murphey and Addie K. Coleman and Annie Y. Bachand and Arlana Bettelyoun and Paul Forney and Gene Tyon and Mohammad S. Jalali},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2023/10/deutsch-et-al-2023.pdf},

year  = {2023},

date = {2023-10-02},

urldate = {2023-10-02},

journal = {Health Affairs},

volume = {42},

issue = {10},

abstract = {Despite efforts to increase investment in Indigenous health and well-being in the United States, disparities remain. The way in which health-promoting organizations are funded is one key mechanism driving the systemic, long-term health disparities experienced by Indigenous people in the US. Using Indigenous-led community-based organizations (ICBOs) that provide psychosocial care as a case study, we highlight multiple ways in which policies that regulate the external funding that ICBOs depend on must change to promote equity and allow the organizations to flourish and address unmet psychosocial needs for Indigenous community members. We use a system dynamics approach to discuss how “capability traps” arise from a misfit between external funding regulations and organizations’ needs for sustainability and effective care provision. We provide suggestions for reforming funding policies that focus on improving ICBO sustainability.},

keywords = {disparity and equity},

pubstate = {published},

tppubtype = {article}

}

@article{669757,

title = {Social Vulnerability Predictors of Drug Poisoning Mortality: A Machine Learning Analysis in the United States},

author = {Moosa Tatar and Mohammad R. Faraji and Katherine Keyes and Fernando A. Wilson and Mohammad S. Jalali},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2023/07/Tatar_et_al_AJA_2023.pdf},

year  = {2023},

date = {2023-05-01},

urldate = {2023-05-01},

journal = {The American Journal on Addictions},

abstract = {Background and Objectives

Drug poisoning is a leading cause of unintentional deaths in the United States. Despite the growing literature, there are a few recent analyses of a wide range of community-level social vulnerability features contributing to drug poisoning mortality. Current studies on this topic face three limitations: often studying a limited subset of vulnerability features, focusing on small sample sizes, or solely including local data. To address this gap, we conducted a national-level analysis to study the impacts of several social vulnerability features in predicting drug mortality rates in the United States.



Methods

We used machine learning to investigate the role of 16 social vulnerability features in predicting drug mortality rates for US counties in 2014, 2016, and 2018—the most recent available data. We estimated each vulnerability feature's gain relative contribution in predicting drug poisoning mortality.



Results

Among all social vulnerability features, the percentage of noninstitutionalized persons with a disability is the most influential predictor, with a gain relative contribution of 18.6%, followed by population density and the percentage of minority residents (13.3% and 13%, respectively). Percentages of households with no available vehicles, mobile homes, and persons without a high school diploma are the following features with gain relative contributions of 6.3%, 5.8%, and 5.1%, respectively.



Conclusion and Scientific Significance

We identified social vulnerability features that are most predictive of drug poisoning mortality. Public health interventions and policies targeting vulnerable communities may increase the resilience of these communities and mitigate the overdose death and drug misuse crisis.},

keywords = {Artificial intelligence, disparity and equity, Substance use},

pubstate = {published},

tppubtype = {article}

}

@article{Dong2022b,

title = {Racial and Ethnic Disparities in Buprenorphine Treatment Duration in the US},

author = {Huiru Dong and Erin J. Stringfellow and W. Alton Russell and Mohammad S. Jalali},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2022/12/Dong-2022-jamapsychiatry.pdf},

doi = {10.1001/jamapsychiatry.2022.3673},

year  = {2023},

date = {2023-02-01},

urldate = {2023-03-01},

journal = {JAMA Psychiatry},

keywords = {disparity and equity, Substance use},

pubstate = {published},

tppubtype = {article}

}

@article{Deutsch2022,

title = {Equitable Policies Need Equitable Practices: Alcohol-and Substance-Exposed Pregnancy as a Case Study},

author = {Arielle R Deutsch and Mohammad S Jalali and Sarah Stout and Leah Frerichs},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2022/12/Deutsch_2022_HPP.pdf},

doi = {10.1177/15248399221107605},

year  = {2022},

date = {2022-05-11},

journal = {Health Promotion Practice},

keywords = {disparity and equity},

pubstate = {published},

tppubtype = {article}

}

@article{Liao2022,

title = {Racial Disparities in Opioid Overdose Deaths in Massachusetts},

author = {Che-Yi Liao and Gian-Gabriel P. Garcia and Catherine DiGennaro and Mohammad S. Jalali},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2022/12/Liao_2022_JAMA_no.pdf},

doi = {10.1001/jamanetworkopen.2022.9081},

year  = {2022},

date = {2022-05-01},

journal = {JAMA Network Open},

keywords = {disparity and equity, Substance use, Working papers},

pubstate = {published},

tppubtype = {article}

}

@article{Weiner2022,

title = {Evaluating disparities in prescribing of naloxone after emergency department treatment of opioid overdose},

author = {Scott G Weiner and Aleta D Carroll and Nicholas M Brisbon and Claudia P Rodriguez and Charles Covahey and Erin J Stringfellow and Catherine DiGennaro and Mohammad S Jalali and Sarah E Wakeman},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2022/12/Weiner_2022_JSAT.pdf},

doi = {10.1016/j.jsat.2022.108785},

year  = {2022},

date = {2022-04-22},

journal = {Journal of Substance Abuse Treatment},

volume = {139},

pages = {108785},

keywords = {disparity and equity, Substance use},

pubstate = {published},

tppubtype = {article}

}

@article{651231,

title = {Community-based System Dynamics Modeling of Sensitive Public Health Issues: Maximizing Diverse Representation of Individuals with Personal Experiences},

author = {Arielle R Deutsch and Rebecca Lustfield and Mohammad S Jalali},

url = {https://scholar.harvard.edu/files/jalali/files/community_based_sd_modeling.pdf},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

abstract = {Community-based system dynamics (CBSD) models enhance our understanding of stigmatized public health issues and related health disparities. The accuracy and usefulness of these models depend upon the individuals who take part in group modeling sessions. Marginalized individuals that are personally impacted by these health issues are critical in the function and development of the models. However, the extent of inclusion varies between studies since such individuals are often hard to recruit. There is substantial diversity in how individuals experience a stigmatized public health issue and with the underrepresentation of individuals with personal experience, research may conclude in biased model development. The purpose of this study was to explore a method that would increase representation for individuals with personal experience of stigmatized issues in model development. We used a case study from a CBSD project on the association between alcohol misuse (AM) and intimate partner violence (IPV) within a Northern Plains American Indian community. Group model building sessions were held at three community organizations: a faith-based re-entry program, a substance use rehabilitation program for pregnant women and mothers, and a domestic violence shelter. Session participants (clients of these organizations) were quick to understand the systems method and were engaged in the modeling process. There were few similarities between the three CBSD models. Each model contributed unique system components, and a consolidated model provided a rich picture of the complex AM-IPV system, as well as the ways in which health disparities are maintained. Coupled with an emphasis on transparency and trust building between researchers and modelers, our approach illuminated the diversity of ways in which individuals with personal experience can perceive AM-IPV systems. Using similar strategies for model building can complement existing efforts to build representative models for stigmatized public health issues within communities.},

keywords = {disparity and equity},

pubstate = {published},

tppubtype = {article}

}