Labor and Delivery Unit Closures in Rural Georgia from 2012 to 2016 and the Impact on Black Women: A Mixed-Methods Investigation.

BACKGROUND: Obstetric provider coverage in rural Georgia has worsened, with nine rural labor and delivery units (LDUs) closing outside the Atlanta Metropolitan Statistical Area from 2012 to 2016. Georgia consistently has one of the highest maternal mortality rates in the nation and faces increased adverse health consequences from this decline in obstetric care. OBJECTIVE: This study explores what factors may be associated with rural hospital LDU closures in Georgia from 2012 to 2016. METHODS: This study describes differences between rural Georgia hospitals based on LDU closure status through a quantitative analysis of 2011 baseline regional, hospital, and patient data, and a qualitative analysis of newspaper articles addressing the closures. RESULTS: LDUs that closed had higher proportions of Black female residents in their Primary Care Service Areas (PCSAs), of Black birthing patients, and of patients with Medicaid, self-pay or other government insurance; lower LDU birth volume; more women giving birth within their PCSA of residence; fewer obstetricians and obstetric provider equivalents per LDU; and fewer average annual births per obstetric provider. Qualitative results indicate financial distress primarily contributed to closures, but also suggest that low birth volume and obstetric provider shortage impacted closures. CONCLUSIONS FOR PRACTICE: Rural LDU closure in Georgia has a disproportionate impact on Black and low-income women and may be prevented through funding maternity healthcare, financing LDUs, and addressing provider shortages.

Preventing extreme polarization of political attitudes.

Extreme polarization can undermine democracy by making compromise impossible and transforming politics into a zero-sum game. "Ideological polarization"-the extent to which political views are widely dispersed-is already strong among elites, but less so among the general public [N. McCarty, Polarization: What Everyone Needs to Know, 2019, pp. 50-68]. Strong mutual distrust and hostility between Democrats and Republicans in the United States, combined with the elites' already strong ideological polarization, could lead to increasing ideological polarization among the public. The paper addresses two questions: 1) Is there a level of ideological polarization above which polarization feeds upon itself to become a runaway process? 2) If so, what policy interventions could prevent such dangerous positive feedback loops? To explore these questions, we present an agent-based model of ideological polarization that differentiates between the tendency for two actors to interact ("exposure") and how they respond when interactions occur, positing that interaction between similar actors reduces their difference, while interaction between dissimilar actors increases their difference. Our analysis explores the effects on polarization of different levels of tolerance to other views, responsiveness to other views, exposure to dissimilar actors, multiple ideological dimensions, economic self-interest, and external shocks. The results suggest strategies for preventing, or at least slowing, the development of extreme polarization.

Programming active cohesive granular matter with mechanically induced phase changes.

At the macroscale, controlling robotic swarms typically uses substantial memory, processing power, and coordination unavailable at the microscale, e.g., for colloidal robots, which could be useful for fighting disease, fabricating intelligent textiles, and designing nanocomputers. To develop principles that can leverage physical interactions and thus be used across scales, we take a two-pronged approach: a theoretical abstraction of self-organizing particle systems and an experimental robot system of active cohesive granular matter that intentionally lacks digital electronic computation and communication, using minimal (or no) sensing and control. As predicted by theory, as interparticle attraction increases, the collective transitions from dispersed to a compact phase. When aggregated, the collective can transport non-robot "impurities," thus performing an emergent task driven by the physics underlying the transition. These results reveal a fruitful interplay between algorithm design and active matter robophysics that can result in principles for programming collectives without the need for complex algorithms or capabilities.