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Published on: January 12, 2018
Understanding and Addressing Severe Maternal Morbidity Among Black Women in Texas: Findings From a System Dynamics
Michael K Lemke1, Kyrah K Brown2, Saeideh Fallah-Fini3
1Department of Environmental & Occupational Health Sciences, Southwest Center for Occupational and Environmental Health, University of Texas Health Science Center at Houston School of Public Health, 7411 John Smith Drive, San Antonio, TX, 78229, USA. Michael.K.Lemke@uth.tmc.edu.
Introduction:
To understand the dynamically complex causes of severe maternal mortality (SMM) among Black women in Texas, this study sought to answer the following questions: (1) What are the most important factors and forces that shape these outcomes?; (2) How have those factors and forces changed over time, and what caused them to change?; (3) How are those factors and forces related to one another over time, perhaps in nonlinear or cyclical ways?; and (4) Where are the leverage points for introducing interventions?
Methods:
Two participatory system dynamics group modeling building (SD GMB) sessions (2.5 total days) were held during March 2024 in Texas with 17 community-based stakeholders. Session activities elicited key variables, behavior-over-time graphs, causal loop diagrams, and targets for action.
Results:
Stakeholders identified 351 key variables: 72 results, 83 roles, 74 relationships, 43 rules, and 79 resources. Cumulatively, 75 themes emerged across these variables. Stakeholders generated 16 behavior-over-time graphs that exhibited high degrees of uniqueness. The two causal loop diagrams featured 15 reinforcing and 27 balancing loops, and 31 reinforcing and 6 balancing loops, respectively. Finally, 36 targets for action were identified.
Conclusions:
This study provided insights into the most important factors that shape SMM outcomes, how those factors may have changed over time, what may have caused them to change, and how they function in nonlinear and circular ways. Initial interventions were also identified. These findings bolster complex systems-grounded approaches, such as SD GMB, in maternal health by demonstrating their value for generating novel insights.
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