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Objective U.S. hospitals that care for vulnerable populations, “safety-net hospitals” (SNHs), are more likely to incur penalties under the Hospital Readmissions Reduction Program (HRRP), which penalizes hospitals with higher-than-expected readmissions. Understanding whether SNHs face unique barriers to reducing readmissions or whether they underuse readmission-prevention strategies is important. Design We surveyed leadership at 1,600 U.S. acute care hospitals, of whom 980 participated, between June 2013–January 2014. Responses on 28 questions on readmission-related barriers and strategies were compared between SNHs and non-SNHs, adjusting for non-response and sampling strategy. We further compared responses between high-performing SNHs and low-performing SNHs. Results We achieved a 62% response rate. SNHs were more likely to report patient-related barriers, including lack of transportation, homelessness, and language barriers compared to non-SNHs (p-values<0.001). Despite reporting more barriers, SNHs were less likely to use e-tools to share discharge summaries (70.1% vs. 73.7%, p<0.04) or verbally communicate (31.5% vs. 39.8%, p<0.001) with outpatient providers, track readmissions by race/ethnicity (23.9% vs. 28.6%, p<0.001), or enroll patients in post-discharge programs (13.3% vs. 17.2%, p<0.001). SNHs were also less likely to use discharge coordinators, pharmacists, and post-discharge programs. When we examined the use of strategies within SNHs, we found trends to suggest that high-performing SNHs were more likely to use several readmission strategies. Conclusions Despite reporting more barriers to reducing readmissions, SNHs were less likely to use readmission-reduction strategies. This combination of higher barriers and lower use of strategies may explain why SNHs have higher rates of readmissions and penalties under the HRRP.

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Hospitals with greater reported use of care coordination and transition strategies have better patient experience than hospitals with fewer reported strategies. Strategies that most directly involve patients have the strongest association with better experience.

We apply tools from functional data analysis to model cumulative trajectories of COVID-19 cases across countries, establishing a framework for quantifying and comparing cases and deaths across countries longitudinally. It emerges that a country’s trajectory during an initial first month “priming period” largely determines how the situation unfolds subsequently. We also propose a method for forecasting case counts, which takes advantage of the common, latent information in the entire sample of curves, instead of just the history of a single country. Our framework facilitates to quantify the effects of demographic covariates and social mobility on doubling rates and case fatality rates through a time-varying regression model. Decreased workplace mobility is associated with lower doubling rates with a roughly 2 week delay, and case fatality rates exhibit a positive feedback pattern.