Reducing Opioid Use Disorder and Overdose in the United States: Model Development and Policy Analysis

Mohammad Jalali, PhD
Harvard University

Wayne Wakeland, PhD
Portland State University

ABSTRACT:
The opioid crisis is one of the most pressing public health issues in the U.S. today. Opioid overdoses are the proverbial “tip of the iceberg,” arising within a complex adaptive system characterized by rapidly changing dynamics combined with significant time lags and large uncertainties in the data. System dynamics modeling is a critical tool to guide policymaking and avoid unintended consequences. We developed a simulation model of the opioid system, spanning from medical use of prescription opioids to opioid misuse and heroin use, use disorder, treatment, and remission. The model aims to help policymakers address the crisis by aiding in policy analysis and decision-making under uncertainty. We project the effects of several policies to reduce opioid use disorder and overdose, and analyze intended and unintended effects of the policies over the next 10 years. Model simulations suggest most policies implemented on their own will achieve only modest reduction in either fatal overdoses or prevalence of OUD.

High-Fidelity Agent-Based Modeling to Support Prevention Decision-making

Wouter Vermeer, PhD
Northwestern University

ABSTRACT:
Preventing adverse health outcomes is complex due to the multilevel contexts and social systems in which these phenomena occur. To capture both the systemic effects, local determinants, and individual-level risks and protective factors simultaneously, the prevention field has called for adoption of system science methods in general, and agent-based models (ABMs) specifically. While these models can provide unique and timely insight into the potential of prevention strategies, an ABM’s ability to do so depends strongly on its accuracy in capturing the phenomenon. What is more, to support what we call model-based decision-making, these models they need to be accepted by and available to decision-makers and other stakeholders. In this presentation we will present a set of recommendations for adopting and using this novel method. We recommend ways to include stakeholders throughout the modeling process, as well as ways to conduct model verification, validation, and replication.

Agent-based Modeling of COVID-19 to Support Public Health Decision Making

Jonathan Ozik, Ph.D.
The University of Chicago

Anna Hotton, Ph.D., MPH, BS
The University of Chicago

ABSTRACT:
The COVID-19 pandemic has highlighted the need for detailed modeling approaches that can capture the myriad complexities of emerging infectious diseases. In response, our group has developed CityCOVID, an agent-based model capable of tracking COVID-19 transmission in large, urban areas. Through partnerships between Argonne National Laboratory, the University of Chicago, the Chicago Department of Public Health, and the Illinois COVID-19 Modeling Task Force we combined multiple data sources to develop a locally informed, realistic, and statistically representative synthetic agent population, with attributes and processes that reflect real-world social and biomedical aspects of transmission. We model all 2.7 million individual residents of Chicago, as they go to and from 1.2 million different places according to their individual hourly schedules. The places include locations such as households, workplaces, schools, and hospitals, and, as individuals congregate with other individuals in these places over the course of their daily routines, they are exposed to potential infection from other infectious people who are also at those places. Transitions between disease states depend on agent attributes and exposure to infected individuals, placed-based risks, and protective behaviors. This detailed modeling approach allows us to implement very specific and realistic mitigation strategies that are being considered by stakeholders, and which have been evolving over the course of the pandemic. We continue to apply CityCOVID to examine the impact of non-pharmaceutical interventions, SARS-CoV-2 variants of concern, vaccination deployment strategies, and to understand the impacts of social determinants of health on disease outcomes. In this presentation we will describe CityCOVID, including how the synthetic population was developed, what agent-based modeling and high-performance computing technologies were required, and our efforts in supporting local public health stakeholders in understanding, responding to and planning for the current and future population health emergencies.

Mixed Up: Modeling for Context

Hendricks Brown, PhD
Northwestern University Feinberg School of Medicine

Daniel Almirall, PhD
University of Michigan

Robert Gibbons, PhD
University of Chicago

Don Hedeker, PhD
University of Chicago, Public Health Sciences

Carlos Gallo, PhD
Northwestern University Feinberg School of Medicine

Naihua Duan, PhD
Columbia University

ABSTRACT:
This presentation provides a background into design and analysis of interventions or implementation strategies that are initially randomized, then afterwards are conducted in group or network settings where the units randomized can no longer be treated as independent. Such designs include individually randomized group assigned trials, where the group context is an active ingredient in delivering one arm of the trial. Also included are implementation trials that involve formal learning collaboratives where the sites interact with one another. A wide variation of such designs occur, including trials with rolling entrances and exits to groups, network based interventions, and so-called rollout trials. It is important to take into account such non-independence in analysis, because otherwise the critical values ordinarily used in test statistics are too small and therefore erroneously finding significance more often than they should. Examples are given in multiple contexts, and appropriate statistical procedures are given. To increase appropriate statistical testing, we provide tools to conduct such analyses across different statistical platforms. A shiny R program that accounts for some of these procedures is demonstrated.

What multi-level modeling can teach us about single-level modeling and vice versa: The case of latent transition analysis

Bengt Muthén, PhD
UCLA, Professor Emeritus

ABSTRACT:
This talk discusses modeling connections between multi- and single-level analysis in the contexts of latent trait-state modeling, cross-lagged panel modeling, factor analysis, latent class analysis, and latent transition analysis.  Both continuous and categorical outcomes are considered.  A key point is that single-level, wide-format modeling of longitudinal data is sometimes carried out in a way that is different from what one would do in a two-level context.  The single-level modeling can benefit from two-level thinking.  An example of this is latent transition analysis which has been lacking an important two-level component.  Two-level modeling can also benefit from single-level thinking, an example of which is time series analysis of intensive longitudinal data.