Methods and Results from Parameter Estimation Exercises Used in 2-Day Group Modeling Session for Ohio Infant Mortality Study

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. 1. Adding a Quantitative Emphasis to Group Model-Building Techniques Over the past three decades, system dynamics group model-building (GMB) has evolved by introduction of multiple scripts and applying them to diverse projects. These scripts combine the related group decision-making methods and system dynamics modeling approach to address issues arising when clients are directly involved in the development of a system dynamics model. The use of scripts in group model building was initiated by Andersen and Richardson (1997) and was extended by others (Hovmand et al., 2012; Luna-Reyes et al., 2006). Current scripts have been designed to define model boundaries and to conceptualize overall model structure. Parameter estimations were retained with specific values determined after the GMB. However, the GMB participants are subject matter experts and policy makers who are likely to contribute good estimates of parameter values or know where the values can be found. This article presents three scripts intended to elicit rough estimates of parameter values from participants, identify data sources, and set the scope of the model. These scripts were tested in a GMB conducted at The Ohio State University (OSU) for the Infant Mortality Research Partnership (IMRP) project. We apply the framework suggested by Hovmand et al. (2012) to document these scripts and explain the sequence in which they should be used. The paper is organized into six sections. The second section provides an overview of the IMRP and the third section summarizes the two-day GMB session conducted in Ohio. A description of the scripts used in the GMB is provided in the fourth section. The fifth section presents the results of a parameter estimation exercise and the final section provides concluding remarks and suggest next steps. 2. Infant Mortality in the state of Ohio Ohio ranks 45th out of 50 in the United States with an overall infant mortality rate (IMR) of 6.8 per 1,000 live births in 2014. More alarmingly is the racial disparity in terms of IMR. The rate among White and Black babies was 5.3 and 14.3, respectively. These dramatic statistics have incentivized Ohio to carry out various initiatives at multiple levels to reduce the overall IMR and remove racial inequities in birth outcomes and infant mortality among Ohioans. At the state level, initiatives have mainly focused on the following: i) increased sharing of data across multiple agencies, such as the Ohio Department of Medicaid, Ohio Department of Health and Ohio Department of Jobs and Family Services, ii) addressing the social determinants of health, iii) implementation of evidence-based community interventions, and iv) quality improvement initiatives in the health care system. At the county level, public health departments have implemented several maternal and child health programs to improve the coordination of care and allocation of resources. One example is the Celebrate One initiative, which is a partnership with Columbus Public Health that aims to educate pregnant women and connect them with resources before, during and after the birth of their baby to reduce the risk of adverse outcomes, such as preterm birth and post-neonatal death. At the city level, there are more focused programs such as Moms2B, which is a group prenatal care education initiative targeting very low income Black women in Columbus, Ohio through clinics in the poorest neighborhoods. The integration of these interventions may improve the effectiveness of individual and multi-pronged approaches to reduce infant mortality throughout the state. In March 2016, the State of Ohio released a request for proposal that sought research in multiple areas related to infant mortality using various methodologies. This broad research program, the Infant Mortality Research Partnership (IMRP), is administered and managed by the Government Research Center (GRC), a biomedical research center within OSU’s Wexner Medical Center. The IMRP consists of four projects briefly described, below: • Systems modeling of infant mortality to identify optimal interventions for reducing infant mortality in Ohio; • Predictive (statistical) modeling of clinical and non-clinical factors to predict the probability of preterm birth, neonatal death and post-neonatal death; • Spatial analysis of infant mortality cases in Ohio to identify hotspots and geographical risk factors; and, • Evaluation of a home-visiting program for prenatal care among vulnerable communities. Our team was funded to conduct research in the area of systems modeling of infant mortality. The three primary objectives of our proposal include: 1. Conduct a group model building workshop and develop a system dynamics model of infant mortality in Ohio, 2. Develop a causal structural model of infant mortality interventions, and 3. Develop an agent-based model of infant mortality in Ohio. Members of our team include one principal investigator and multiple co-investigators, leading the research for each of the three objectives. The team also consists of graduate students and postdoctoral researchers. The diversity of research expertise of our team members was perhaps the greatest strength of our proposal. This expertise includes knowledge of multiple modeling methodologies (e.g., system dynamics, agent-based modeling, and causal modeling), numerous disciplines (e.g., perinatal epidemiology, social work, public policy, and education) and broad ranging experiences (e.g. working with state agencies, federal programs, and healthcare partners). This diversity in expertise has also served our team members in being able to translate and bridge gaps in our individual knowledge regarding modeling and the clinical and nonclinical determinants of infant mortality. 3. Two-day Group Modeling Session for Infant Mortality Project The GMB in Ohio was conducted to collaboratively develop a system dynamics model of infant mortality in the state for examining the effects of different interventions intended to reduce infant mortality. To achieve this purpose, the two-day session was designed in alignment with GMB best practices (Andersen & Richardson, 1997; Luna-Reyes et al., 2006; Richardson, 2013). The number of participants was limited to 12-15 experts with relevant background/disciplines on the topic of infant mortality to encourage direct engagement. The project team strove to achieve balanced representation from relevant stakeholder groups, and to ensure that individuals with different content expertise relevant to infant mortality and its contributing factors were present. The facility chosen for the GMB event provided a flexible, convenient, and supportive space for the sessions. Members of the research team were assigned specific roles a priori, to ensure that the process and documentation of the model building session were seamless. In preparing for the GMB, the research team consulted with the client to identify appropriate candidates to recruit as participants, thereby assuring key stakeholder constituency representation. Individuals selected for invitation included those invested in addressing Ohio’s infant mortality problem in one of three sectors: medical (including researchers and practitioners), policy, and community-based/non-profit organizations. Every effort was made to balance representation between these three groups, such that no stakeholder group was overrepresented. Additionally, consideration was given to the content expertise of each participant. Given the scope of the project it was critical that expert knowledge was inclusive of both medical contributors to infant mortality and social determinants of health, as both mechanisms are considered to be priorities by the IMRP. Thirteen content experts, or “core participants,” were present during the full two days. The client and funder also requested the presence of seven additional attendees, or “participants. ” Participants observed activities early on the first day and at the conclusion of the second day. Integrated technology and the availability of white boards were prioritized in the facility selection process; the room had white boards spanning three walls and had two large screens for projecting (Andersen & Richardson, 1997). Tables were arranged in small clusters throughout the room to provide participants with ample working space while also fostering social interaction (Hovmand, Rouwette, Anderson, Richardson, & Kraus, 2013). Facility selection for the GMB session was designed to encourage all-day attendance by being somewhat removed from attendees’ workplaces, but still convenient for the majority of participants to travel to the session. Prior to the two-day model building session, interviews were held with three major constituency groups. Representatives from Medicaid and the Ohio Department of Job and Family Services were engaged, as well as a nationally renowned clinical researcher with content expertise relevant to infant mortality. These interviews had several