A Research Note on Community Resilience Estimates: New U.S. Census Bureau Data With an Application to Excess Deaths From COVID-19.

In this research note, we describe the results of the first validation study of the U.S. Census Bureau's new Community Resilience Estimates (CRE), which uses Census microdata to develop a tract-level vulnerability index for the United States. By employing administrative microdata to link Social Security Administration mortality records to CRE, we show that CRE quartiles provide more stable predictions of COVID-19 excess deaths than single demographic categorizations such as race or age, as well as other vulnerability measures including the U.S. Centers for Disease Control and Prevention's Social Vulnerability Index (SVI) and the Federal Emergency Management Agency's National Risk Index (NRI). We also use machine learning techniques to show that CRE provides more predictive power of COVID-19 excess deaths than standard socioeconomic predictors of vulnerability such as poverty and unemployment, as well as SVI and NRI. We find that a 10-percentage-point increase in a key CRE risk measure is associated with one additional death per neighborhood during the initial outbreak of COVID-19 in the United States. We conclude that, compared with alternative measures, CRE provides a more accurate predictor of community vulnerability to a disaster such as a pandemic.

The Effect of Prehospital Clinical Trial-Related Procedures on Scene Interval, Cognitive Load, and Error: A Randomized Simulation Study.

INTRODUCTION: Globally, very few settings have undertaken prehospital randomized controlled trials. Given this lack of experience, there is a risk that such trials in these settings may result in protocol deviations, increased prehospital intervals, and increased cognitive load, leading to error. Ultimately, this may affect patient safety and mortality. The aim of this study was to assess the effect of trial-related procedures on simulated scene interval, self-reported cognitive load, medical errors, and time to action. METHODS: This was a prospective simulation study. Using a cross-over design, ten teams of prehospital clinicians were allocated to three separate simulation arms in a random order. Simulations were: (1) Eligibility assessment and administration of freeze-dried plasma (FDP) and a hemoglobin-based oxygen carrier (HBOC), (2) Eligibility assessment and administration of HBOC, (3) Eligibility assessment and standard care. All simulations also required clinical management of hemorrhagic shock. Simulated scene interval, error rates, cognitive load (measured by NASA Task Load Index), and competency in clinical care (assessed using the Simulation Assessment Tool Limiting Assessment Bias (SATLAB)) were measured. Mean differences between simulations with and without trial-related procedures were sought using one-way ANOVA or Kruskal-Wallis test. A p-value of <0.05 within the 95% confidence interval was considered significant. RESULTS: Thirty simulations were undertaken, representing our powered sample size. The mean scene intervals were 00:16:56 for Simulation 1 (FDP and HBOC), 00:17:22 for Simulation 2 (HBOC only), and 00:14:24 for Simulation 3 (standard care). Scene interval did not differ between the groups (p = 0.27). There were also no significant differences in error rates (p = 0.28) or cognitive load (p = 0.67) between the simulation groups. There was no correlation between cognitive load and error rates (r = 0.15, p = 0.42). Competency was achieved in all the assessment criteria for all simulation groups. CONCLUSION: In a simulated environment, eligibility screening, performance of trial-related procedures, and clinical management of patients with hemorrhagic shock can be completed competently by prehospital advanced life support clinicians without delaying transport or emergency care. Future prehospital clinical trials may use a similar approach to help ensure graded and cautious implementation of clinical trial procedures into prehospital emergency care systems.

Emergency medicine doctoral education in Africa: a scoping review of the published literature.

BACKGROUND: While Africa accounts for a significant proportion of world population, and disease and injury burden, it produces less than 1% of the total research output within emergency care. Emergency care research capacity in Africa may be expanded through the development of doctoral programmes that aim to upskill the PhD student into an independent scholar, through dedicated support and structured learning. This study therefore aims to identify the nature of the problem of doctoral education in Africa, thereby informing a general needs assessment within the context of academic emergency medicine. METHODS: A scoping review, utilising an a priori, piloted search strategy was conducted (Medline via PubMed and Scopus) to identify literature published between 2011 and 2021 related to African emergency medicine doctoral education. Failing that, an expanded search was planned that focused on doctoral education within health sciences more broadly. Titles, abstracts, and full texts were screened for inclusion in duplicate, and extracted by the principal author. The search was rerun in September 2022. RESULTS: No articles that focused on emergency medicine/care were found. Following the expanded search, a total of 235 articles were identified, and 27 articles were included. Major domains identified in the literature included specific barriers to PhD success, supervision practices, transformation, collaborative learning, and research capacity improvement. CONCLUSIONS: African doctoral students are hindered by internal academic factors such as limited supervision and external factors such as poor infrastructure e.g. internet connectivity. While not always feasible, institutions should offer environments that are conducive to meaningful learning. In addition, doctoral programmes should adopt and enforce gender policies to help alleviate the gender differences noted in PhD completion rates and research publication outputs. Interdisciplinary collaborations are potential mechanisms to develop well-rounded and independent graduates. Post-graduate and doctoral supervision experience should be a recognised promotion criterion to assist with clinician researcher career opportunities and motivation. There may be little value in attempting to replicate the programmatic and supervision practices of high-income countries. African doctoral programmes should rather focus on creating contextual and sustainable ways of delivering excellent doctoral education.

Estimating determinants of healthcare establishment locations with restricted federal administrative data.

We model the locational determinants of nine categories of healthcare services in the contiguous United States using restricted access federal establishment data. These data enable close examination of rural health services, which are subject to suppression in publicly published data sources. After reviewing differences in public and unsuppressed restricted data and testing underlying data generation processes for each healthcare industry, including the Poisson, negative binomial, and their zero-inflated counterparts, we estimate marginal effects for four categories of independent variables: place-based factors, financial access, characteristics of population, and industry interdependencies. Findings show establishments are less likely to be found with high concentrations of Medicare and Medicaid recipients, while agglomerations are associated with more establishments. Nonemployer establishments serve a broader spectrum of people, but the rural poor still experience less access to health care.

When to use commuting zones? An empirical description of spatial autocorrelation in U.S. counties versus commuting zones.

U.S. Commuting Zones (CZs) are an aggregation of county-level data that researchers commonly use to create less arbitrary spatial entities and to reduce spatial autocorrelation. However, by further aggregating data, researchers lose point data and the associated detail. Thus, the choice between using counties or CZs often remains subjective with insufficient empirical evidence guiding researchers in the choice. This article categorizes regional data as entrepreneurial, economic, social, demographic, or industrial and tests for the existence of local spatial autocorrelation in county and CZ data. We find CZs often reduce-but do not eliminate and can even increase-spatial autocorrelation for variables across categories. We then test the potential for regional variation in spatial autocorrelation with a series of maps and find variation based on the variable of interest. We conclude that the use of CZs does not eliminate the need to test for spatial autocorrection, but CZs may be useful for reducing spatial autocorrelation in many cases.