People living in rural regions in the United States face more health challenges than their non-rural counterparts which could put them at additional risks during the COVID-19 pandemic. Few studies have examined if rurality is associated with additional mortality risk among those hospitalized for COVID-19. We studied a retrospective cohort of 3,991 people hospitalized with SARS-CoV-2 infections discharged between March 1 and September 30, 2020 in one of 17 hospitals in North Carolina that collaborate as a clinical data research network. Patient demographics, comorbidities, symptoms and laboratory data were examined. Logistic regression was used to evaluate associations of rurality with a composite outcome of death/hospice discharge. Comorbidities were more common in the rural patient population as were the number of comorbidities per patient. Overall, 505 patients died prior to discharge and 63 patients were discharged to hospice. Among rural patients, 16.5% died or were discharged to hospice vs. 13.3% in the urban cohort resulting in greater odds of death/hospice discharge (OR 1.3, 95% CI 1.1, 1.6). This estimate decreased minimally when adjusted for age, sex, race/ethnicity, payer, disease comorbidities, presenting oxygen levels and cytokine levels (adjusted model OR 1.2, 95% CI 1.0, 1.5). This analysis demonstrated a higher COVID-19 mortality risk among rural residents of NC. Implementing policy changes may mitigate such disparities going forward.

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Long waiting lists are a symbol of inefficiencies of hospital services. The dynamics of waiting lists are complex, especially when trying to understand how the lists grow due to the demand of a particular treatment relative to a hospital's capacity. Understanding the uncertainty of forecasting growth/decline of waiting lists could help hospital managers with capacity planning. We address this uncertainty through the use of statistical tolerance intervals, which are intervals that contain a specified proportion of the sampled population at a given confidence level. Tolerance intervals are available for numerous settings, however, the approaches for autoregressive models are far more limited. This article fills that gap and establishes tolerance intervals for general AR(p) models, which may also have a mean or trend component present. A rigorous development of tolerance intervals in this setting is presented. Extensive simulation studies identify that good coverage properties are achieved when the AR process is stationary and the parameters of the AR model are well within the stationarity constraints. Otherwise, a bootstrap-based correction can be applied to improve the coverage probabilities. Finally, the method is applied to the monthly number of patients on hospital waiting lists in England. K E Y W O R D Sk-factor, bootstrap, capacity planning, coverage probability, forecasting, regression 1 268

Background Creationist religious views have a large influence on the public’s views and learning related to evolution, especially human evolution. Creationism has been shown to reinforce students’ design teleological stance, which creates a challenging conceptual obstacle for learning evolution. The purpose of the current study was to determine if students with creationist views responded differently to education intended to directly challenge design teleological reasoning in the context of a human evolution course, compared to students with naturalist views. In a convergent mixed methods design this study combined pre- and post-semester quantitative survey data (N = 48) on student endorsement of teleological reasoning, acceptance of evolution (Inventory of Student Evolution Acceptance), and understanding of natural selection (Conceptual Inventory of Natural Selection), with a thematic analysis of student reflective writing on their understanding and acceptance of natural selection and teleological reasoning. Results This study found that students with creationist views had higher levels of design teleological reasoning and lower levels of acceptance of evolution at the beginning of the semester, compared to students with naturalist views (p < 0.01). Students with creationist views experienced significant (p < 0.01) improvements in teleological reasoning and acceptance of human evolution. While the changes in teleological reasoning, understanding and acceptance experienced by students with creationist views were similar in magnitude to changes in students with naturalist views, creationists never achieved levels of evolution understanding and acceptance seen in students with naturalist views. Multiple linear regression showed that student religiosity was a significant predictor of understanding of evolution, while having creationist views was a predictor of acceptance of evolution. Thematic analysis revealed that more students believed that religion and evolution are incompatible than compatible. However, more than one-third of students expressed openness to learning about evolution alongside their religious views. Conclusions Students with creationist views made gains on nearly all measures, but significantly underperformed their counterparts with natural views. For many students, religiosity and creationism challenge their thinking about evolution. This paper describes pedagogical practices to help students understand their own teleological reasoning and support students with creationist views who are learning about evolution.

Background SARS-CoV-2 infection has caused variable clinical outcomes including hospitalization and death. We analyzed state-level data from the North Carolina COVID-19 Surveillance System (NC COVID) to describe demographics of those infected with SARS-CoV-2 and to describe factors associated with infection-fatality in North Carolina. Methods This was a retrospective cohort study using surveillance data on positive SARS-CoV-2-infected individuals (N = 214,179) identified between March 1, 2020, and September 30, 2020. We present descriptive statistics and associations among demographics, medical comorbidities, and SARS-CoV-2 infection-fatality. Results Median age for residents with reported SARS-CoV-2 was 38 (IQR 23–54). Age was strongly correlated with SARS-CoV-2 infection-fatality. Greater infection-fatality was noted among those who identified as Black across all comorbidities. Coexisting chronic disease was associated with greater infection-fatality, with kidney disease demonstrating the strongest association. Limitations A high percentage of missing data for race/ethnicity and comorbidities limits the interpretation of our findings. Data were not available for socioeconomic measures that could aid in better understanding inequities associated with SARS-CoV-2 infection-fatality. Conclusions Among North Carolinians identified with SARS-CoV-2 via surveillance efforts, age, race, and comorbidities were associated with infection-fatality; these findings are similar to those of studies using different source populations in the United States. In addition to age and other nonmodifiable variables, systematic differences in social conditions and opportunity may increase the risk of SARS-CoV-2 infection-fatality among Black Americans compared to other races/ethnicities.