To date, the practice of global emergency medicine (GEM) has involved being “on the ground” supporting in‐country training of local learners, conducting research, and providing clinical care. This face‐to‐face interaction has been understood as critically important for developing partnerships and building trust. The COVID‐19 pandemic has brought significant uncertainty worldwide, including international travel restrictions of indeterminate permanence. Following the 2020 Society for Academic Emergency Medicine meeting, the Global Emergency Medicine Academy (GEMA) sought to enhance collective understanding of best practices in GEM training with a focus on multidirectional education and remote collaboration in the setting of COVID‐19. GEMA members led an initiative to outline thematic areas deemed most pertinent to the continued implementation of impactful GEM programming within the physical and technologic confines of a pandemic. Eighteen GEM practitioners were divided into four workgroups to focus on the following themes: advances in technology, valuation, climate impacts, skill translation, research/scholastic projects, and future challenges. Several opportunities were identified: broadened availability of technology such as video conferencing, Internet, and smartphones; online learning; reduced costs of cloud storage and printing; reduced carbon footprint; and strengthened local leadership. Skills and knowledge bases of GEM practitioners, including practicing in resource‐poor settings and allocation of scarce resources, are translatable domestically. The COVID‐19 pandemic has accelerated a paradigm shift in the practice of GEM, identifying a previously underrecognized potential to both strengthen partnerships and increase accessibility. This time of change has provided an opportunity to enhance multidirectional education and remote collaboration to improve global health equity.

Diarrhea remains a leading cause of morbidity and mortality in patients worldwide. The objective of this study was to determine the relative inter-rater reliability and usability of standard and Mobile health (mHealth)-supported World Health Organization (WHO) algorithms for dehydration assessment in patients with acute diarrhea in a rural, low-income country hospital. Two nurses blinded to each other's examinations assessed dehydration status on patients soon after hospital arrival using either the standard WHO algorithm printed on a laminated card or an mHealth-supported WHO algorithm downloaded onto a smartphone. The assignment of assessment tool was based on odd or even enrollment date. The inter-rater reliability for dehydration assessment between the two nurses was calculated using Cohen's K statistic for each study group. A total of 496 patients (< 5 years = 349,> 5 years = 147) were enrolled in the study; 132 (27%) had some or severe dehydration, and 364 (73%) had no dehydration on arrival. Cohen's K statistic demonstrated greater reliability for the mHealth-supported dehydration assessment (0.59) compared with the standard assessment (0.50) in the overall population ( < 0.0001), as well as in the pediatric (0.43 versus 0.37, < 0.0001) and adult (0.79 versus 0.57, < 0.0001) populations individually. This is the first study to show that mHealth can improve the reliability of nursing dehydration assessment in patients with acute diarrhea and the first to report on the reliability of the WHO algorithm in adult patients specifically. Future studies should focus on the impact of mHealth-supported dehydration assessment on patient-centered outcomes and examine its reliability in different settings worldwide.

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Introduction: The recent spread of coronavirus disease 2019 (COVID-19) has disproportionately impacted racial and ethnic minority groups; however, the impact of healthcare utilization on outcome disparities remains unexplored. Our study examines racial and ethnic disparities in hospitalization, medication usage, intensive care unit (ICU) admission and in-hospital mortality for COVID-19 patients. Methods: In this retrospective cohort study, we analyzed data for adult patients within an integrated healthcare system in New York City between February 28–August 28, 2020, who had a lab-confirmed COVID-19 diagnosis. Primary outcome was likelihood of inpatient admission. Secondary outcomes were differences in medication administration, ICU admission, and in-hospital mortality. Results: Of 4717 adult patients evaluated in the emergency department (ED), 3219 (68.2%) were admitted to an inpatient setting. Black patients were the largest group (29.1%), followed by Hispanic/Latinx (29.0%), White (22.9%), Asian (3.86%), and patients who reported “other” race-ethnicity (19.0%). After adjusting for demographic, clinical factors, time, and hospital site, Hispanic/Latinx patients had a significantly lower adjusted rate of admission compared to White patients (odds ratio [OR] 0.51; 95% confidence interval [CI] 0.34-0.76). Black (OR 0.60; 95% CI 0.43-0.84) and Asian patients (OR 0.47; 95% CI 0.25 - 0.89) were less likely to be admitted to the ICU. We observed higher rates of ICU admission (OR 2.96; 95% CI 1.43-6.15, and OR 1.83; 95% CI 1.26-2.65) and in-hospital mortality (OR 4.38; 95% CI 2.66-7.24; and OR 2.96; 95% CI 2.12-4.14) at two community-based academic affiliate sites relative to the primary academic site. Conclusion: Non-White patients accounted for a disproportionate share of COVID-19 patients seeking care in the ED but were less likely to be admitted. Hospitals serving the highest proportion of minority patients experienced the worst outcomes, even within an integrated health system with shared resources. Limited capacity during the COVID-19 pandemic likely exacerbated pre-existing health disparities across racial and ethnic minority groups.

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Background: People have both individual and neighborhood level social determinants that may confer health risks or health benefits. While these are often aligned, this is not always the case. An affluent individual may live in a neighborhood with a high median income (concordance) or an affluent individual may live in a neighborhood with a low median income (discordance).Methods: We propose a method to operationalize the concepts of concordance and discordance between individual-level and neighborhood-level social determinants and provide two examples. We first consider a factor that is measured with a continuous variable–household income. We then consider race/ethnicity—a categorical variable. To illustrate the proposed method, we apply our operational definitions to a small dataset that contains self-reported individual data paired with geocoded publicly available neighborhood-level data based on residential address.Results: In our cohort, 38% of participants lived in neighborhoods that were racial/ethnically concordant; 23% of participants in neighborhood that were racial/ethnically discordant, and 39% of participants in neighborhoods that were Neutral.Conclusion: We propose an approach to defining the concepts of concordance and discordance of individual characteristics with neighborhood-level social determinants. Our proposed approach will enable more robust investigations of social determinants of health and their impact on health. Such investigations are needed to inform the growing use of social determinants of health data from various sources for a variety of purposes.

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