Advancing Interoperability of Patient-level Social Determinants of Health Data to Support COVID-19 Research.

Including social determinants of health (SDoH) data in health outcomes research is essential for studying the sources of healthcare disparities and developing strategies to mitigate stressors. In this report, we describe a pragmatic design and approach to explore the encoding needs for transmitting SDoH screening tool responses from a large safety-net hospital into the National Covid Cohort Collaborative (N3C) OMOP dataset. We provide a stepwise account of designing data mapping and ingestion for patient-level SDoH and summarize the results of screening. Our approach demonstrates that sharing of these important data - typically stored as non-standard, EHR vendor specific codes - is feasible. As SDoH screening gains broader use nationally, the approach described in this paper could be used for other screening instruments and improve the interoperability of these important data.

International Collaboration to Develop a Remote Monitoring Web App for COVID-19 Patients in Armenia: Design and Development With Agile Methodology.

BACKGROUND: COVID-19 has led to over 500 million cases and 6.2 million deaths around the world. Low- and middle-income countries (LMICs) like Armenia face unique infrastructure, financial, and capacity challenges that in many cases result in worse outcomes. Health care facilities across Armenia experienced a shortage of resources, including hospital beds and oxygen, which was further exacerbated by the war with neighboring Azerbaijan. Without a framework for home-based care, health care facilities were severely strained by COVID-19 patients who had prolonged oxygen requirements but were otherwise clinically stable. OBJECTIVE: This paper describes our approach to establishing an international collaboration to develop a web app to support home monitoring of patients with COVID-19 with persistent oxygen requirements. METHODS: The app was developed using a rapid, coordinated, and collaborative approach involving an international group of clinicians, developers, and collaborators. Health screening, monitoring, and discharge forms were developed into a lightweight OpenMRS web app and customized for the local Armenian context. RESULTS: The software was designed and developed over 2 months using human-centered design and agile sprints. Once live, 5087 patient records were created for 439 unique patients. CONCLUSIONS: This project suggests a promising framework for designing and implementing remote monitoring programs in LMICs, despite pandemic and geopolitical challenges.

The human toll and humanitarian crisis of the Russia-Ukraine war: the first 162 days.

BACKGROUND: We examined the human toll and subsequent humanitarian crisis resulting from the Russian invasion of Ukraine, which began on 24 February 2022. METHOD: We extracted and analysed data resulting from Russian military attacks on Ukrainians between 24 February and 4 August 2022. The data tracked direct deaths and injuries, damage to healthcare infrastructure and the impact on health, the destruction of residences, infrastructure, communication systems, and utility services - all of which disrupted the lives of Ukrainians. RESULTS: As of 4 August 2022, 5552 civilians were killed outright and 8513 injured in Ukraine as a result of Russian attacks. Local officials estimate as many as 24 328 people were also killed in mass atrocities, with Mariupol being the largest (n=22 000) such example. Aside from wide swaths of homes, schools, roads, and bridges destroyed, hospitals and health facilities from 21 cities across Ukraine came under attack. The disruption to water, gas, electricity, and internet services also extended to affect supplies of medications and other supplies owing to destroyed facilities or production that ceased due to the war. The data also show that Ukraine saw an increase in cases of HIV/AIDS, tuberculosis, and Coronavirus (COVID-19). CONCLUSIONS: The 2022 Russia-Ukraine War not only resulted in deaths and injuries but also impacted the lives and safety of Ukrainians through destruction of healthcare facilities and disrupted delivery of healthcare and supplies. The war is an ongoing humanitarian crisis given the continuing destruction of infrastructure and services that directly impact the well-being of human lives. The devastation, trauma and human cost of war will impact generations of Ukrainians to come.

Issues With Variability in Electronic Health Record Data About Race and Ethnicity: Descriptive Analysis of the National COVID Cohort Collaborative Data Enclave.

BACKGROUND: The adverse impact of COVID-19 on marginalized and under-resourced communities of color has highlighted the need for accurate, comprehensive race and ethnicity data. However, a significant technical challenge related to integrating race and ethnicity data in large, consolidated databases is the lack of consistency in how data about race and ethnicity are collected and structured by health care organizations. OBJECTIVE: This study aims to evaluate and describe variations in how health care systems collect and report information about the race and ethnicity of their patients and to assess how well these data are integrated when aggregated into a large clinical database. METHODS: At the time of our analysis, the National COVID Cohort Collaborative (N3C) Data Enclave contained records from 6.5 million patients contributed by 56 health care institutions. We quantified the variability in the harmonized race and ethnicity data in the N3C Data Enclave by analyzing the conformance to health care standards for such data. We conducted a descriptive analysis by comparing the harmonized data available for research purposes in the database to the original source data contributed by health care institutions. To make the comparison, we tabulated the original source codes, enumerating how many patients had been reported with each encoded value and how many distinct ways each category was reported. The nonconforming data were also cross tabulated by 3 factors: patient ethnicity, the number of data partners using each code, and which data models utilized those particular encodings. For the nonconforming data, we used an inductive approach to sort the source encodings into categories. For example, values such as "Declined" were grouped with "Refused," and "Multiple Race" was grouped with "Two or more races" and "Multiracial." RESULTS: "No matching concept" was the second largest harmonized concept used by the N3C to describe the race of patients in their database. In addition, 20.7% of the race data did not conform to the standard; the largest category was data that were missing. Hispanic or Latino patients were overrepresented in the nonconforming racial data, and data from American Indian or Alaska Native patients were obscured. Although only a small proportion of the source data had not been mapped to the correct concepts (0.6%), Black or African American and Hispanic/Latino patients were overrepresented in this category. CONCLUSIONS: Differences in how race and ethnicity data are conceptualized and encoded by health care institutions can affect the quality of the data in aggregated clinical databases. The impact of data quality issues in the N3C Data Enclave was not equal across all races and ethnicities, which has the potential to introduce bias in analyses and conclusions drawn from these data. Transparency about how data have been transformed can help users make accurate analyses and inferences and eventually better guide clinical care and public policy.

Advancing Interoperability of Patient-level Social Determinants of Health Data to Support COVID-19 Research

Including social determinants of health (SDoH) data in health outcomes research is essential for studying the sources of healthcare disparities and developing strategies to mitigate stressors. In this report, we describe a pragmatic design and approach to explore the encoding needs for transmitting SDoH screening tool responses from a large safety-net hospital into the National Covid Cohort Collaborative (N3C) OMOP dataset. We provide a stepwise account of designing data mapping and ingestion for patient-level SDoH and summarize the results of screening. Our approach demonstrates that sharing of these important data - typically stored as non-standard, EHR vendor specific codes - is feasible. As SDoH screening gains broader use nationally, the approach described in this paper could be used for other screening instruments and improve the interoperability of these important data.

Pediatric Type 2 Diabetes Prevalence from Real-World Data in the United States

Background: The SEARCH for Diabetes in Youth Study has provided critical insights into the epidemiology of pediatric T2D. The 2021 report estimated an annual percent change (APC) in prevalence of 4.8% between 2009 to 2017. Multi-center longitudinal cohort studies generate robust data, but are time, resource, and effort intensive. Large real-world data repositories may offer cost-effective opportunities to validate and expand on these findings. Hypothesis: Youth-onset T2D prevalence is rising at a rate in excess of 5% per year in the US in the previous five years. Methods: We queried the Trinetx Research Network which includes EHR data from 58 institutions with 86,487,854 total patients. We included patients <22 years of age, who had at least one incidence of an E11 ICD-10 code for T2D associated with an ambulatory, emergency room, or inpatient encounter for each calendar year from 2016 to 2021. Patients with other diabetes ICD-10 codes (E08, E09, E10, or E13) were excluded. Summary statistics of the query cohort describing sex, race and ethnicity, labs, medications, and anthopometrics were extracted from the platform. Chi-squared test for trend was used to determine statistical significance in demographic change. Results: The mean APC in prevalence per center rose by 9.1% between 2016 to 2019 (2016: 127 cases/center, 5334 total cases;2019: 166.2 cases/center, 7919 total cases) , but subsequently declined by 6.6% between 2019 to 2020. There was a consistent increase in the median BMI percentile over time from 2016 to 2021. The percentage of black youth diagnosed with T2D rose (from 30 to 33%, P<0.0001) whereas that of Hispanic youth was unchanged (25%, P=0.73) . The proportion of male also increased over time (P<0.0001) . Conclusion: Real-world data from Trinetx demonstrates a rising incidence of youth-onset T2D that parallels an increase in median BMI percentile. The decrease in case rate in 2020 may be the result of decreased encounters during the Covid-19 pandemic. The rising prevalence of T2D in black youth and male gender warrants further analysis, to understand the driver behind this trend.

Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses.

BACKGROUND: There is a need to evaluate how the choice of time interval contributes to the lack of consistency of SDoH variables that appear as important to COVID-19 disease burden within an analysis for both case counts and death counts. METHODS: This study identified SDoH variables associated with U.S county-level COVID-19 cumulative case and death incidence for six different periods: the first 30, 60, 90, 120, 150, and 180 days since each county had COVID-19 one case per 10,000 residents. The set of SDoH variables were in the following domains: resource deprivation, access to care/health resources, population characteristics, traveling behavior, vulnerable populations, and health status. A generalized variance inflation factor (GVIF) analysis was used to identify variables with high multicollinearity. For each dependent variable, a separate model was built for each of the time periods. We used a mixed-effect generalized linear modeling of counts normalized per 100,000 population using negative binomial regression. We performed a Kolmogorov-Smirnov goodness of fit test, an outlier test, and a dispersion test for each model. Sensitivity analysis included altering the county start date to the day each county reached 10 COVID-19 cases per 10,000. RESULTS: Ninety-seven percent (3059/3140) of the counties were represented in the final analysis. Six features proved important for both the main and sensitivity analysis: adults-with-college-degree, days-sheltering-in-place-at-start, prior-seven-day-median-time-home, percent-black, percent-foreign-born, over-65-years-of-age, black-white-segregation, and days-since-pandemic-start. These variables belonged to the following categories: COVID-19 related, vulnerable populations, and population characteristics. Our diagnostic results show that across our outcomes, the models of the shorter time periods (30 days, 60 days, and 900 days) have a better fit. CONCLUSION: Our findings demonstrate that the set of SDoH features that are significant for COVID-19 outcomes varies based on the time from the start date of the pandemic and when COVID-19 was present in a county. These results could assist researchers with variable selection and inform decision makers when creating public health policy.

Social Determinants of Health Factors for Gene-Environment COVID-19 Research: Challenges and Opportunities.

The characteristics of a person's health status are often guided by how they live, grow, learn, their genetics, as well as their access to health care. Yet, all too often, studies examining the relationship between social determinants of health (behavioral, sociocultural, and physical environmental factors), the role of demographics, and health outcomes poorly represent these relationships, leading to misinterpretations, limited study reproducibility, and datasets with limited representativeness and secondary research use capacity. This is a profound hurdle in what questions can or cannot be rigorously studied about COVID-19. In practice, gene-environment interactions studies have paved the way for including these factors into research. Similarly, our understanding of social determinants of health continues to expand with diverse data collection modalities as health systems, patients, and community health engagement aim to fill the knowledge gaps toward promoting health and wellness. Here, a conceptual framework is proposed, adapted from the population health framework, socioecological model, and causal modeling in gene-environment interaction studies to integrate the core constructs from each domain with practical considerations needed for multidisciplinary science.

Diabetes Technology Meeting 2020.

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 12 to November 14, 2020. This meeting brought together speakers to cover various perspectives about the field of diabetes technology. The meeting topics included artificial intelligence, digital health, telemedicine, glucose monitoring, regulatory trends, metrics for expressing glycemia, pharmaceuticals, automated insulin delivery systems, novel insulins, metrics for diabetes monitoring, and discriminatory aspects of diabetes technology. A live demonstration was presented.

Extracorporeal Membrane Oxygenation for COVID-19-associated Severe Acute Respiratory Distress Syndrome in Chile: A Nationwide Incidence and Cohort Study.

Rationale: The role of and needs for extracorporeal membrane oxygenation (ECMO) at a population level during the coronavirus disease (COVID-19) pandemic have not been completely established. Objectives: To identify the cumulative incidence of ECMO use in the first pandemic wave and to describe the Nationwide Chilean cohort of ECMO-supported patients with COVID-19. Methods: We conducted a population-based study from March 3 to August 31, 2020, using linked data from national agencies. The cumulative incidence of ECMO use and mortality risk of ECMO-supported patients were calculated and age standardized. In addition, a retrospective cohort analysis was performed. Outcomes were 90-day mortality after ECMO initiation, ECMO-associated complications, and hospital length of stay. Cox regression models were used to explore risk factors for mortality in a time-to-event analysis. Measurements and Main Results: Ninety-four patients with COVID-19 were supported with ECMO (0.42 per population of 100,000, 14.89 per 100,000 positive cases, and 1.2% of intubated patients with COVID-19); 85 were included in the cohort analysis, and the median age was 48 (interquartile range [IQR], 41-55) years, 83.5% were men, and 42.4% had obesity. The median number of pre-ECMO intubation days was 4 (IQR, 2-7), the median PaO2/FiO2 ratio was 86.8 (IQR, 64-99) mm Hg, 91.8% of patients were prone positioned, and 14 patients had refractory respiratory acidosis. Main complications were infections (70.6%), bleeding (38.8%), and thromboembolism (22.4%); 52 patients were discharged home, and 33 died. The hospital length of stay was a median of 50 (IQR, 24-69) days. Lower respiratory system compliance and higher driving pressure before ECMO initiation were associated with increased mortality. A duration of pre-ECMO intubation ≥10 days was not associated with mortality. Conclusions: Documenting nationwide ECMO needs may help in planning ECMO provision for future COVID-19 pandemic waves. The 90-day mortality of the Chilean cohort of ECMO-supported patients with COVID-19 (38.8%) is comparable to that of previous reports.

Measles outbreak during the COVID-19 pandemic in Mexico.

Measles is an exanthematous disease caused by a virus of the Morbillivirus genus. On February 23, 2020, the exanthema began in the first confirmed case of measles this year. At the same time, on February 28, 2020, the first case of the new coronavirus disease (COVID-19) was confirmed. Up to June 6, 2020, 176 measles cases have been confirmed: 137 in Mexico City, 37 in the state of Mexico, and two in the state of Campeche. In Mexico City, municipalities with more cases were Gustavo A. Madero, Miguel Hidalgo, and Iztapalapa; in the State of Mexico were Ecatepec, Tlalnepantla, and Netzahualcoyotl; in Campeche, there are only two cases reported. On the other hand, 7,065,133 cases of COVID-19 have been registered worldwide with a global case fatality rate of 5.7%. In Mexico, there have been 113,619 confirmed cases and 13,511 deaths, while in Mexico City, there have been 30,223 cases and 3062 deaths. Iztapalapa and Gustavo A. Madero are the locations with more cases reported. Hence, a campaign of social distancing started as part of the strategies to control the spread of the infection. The potential threat is that as social confinement measures are relaxed and mobility is initiated, both viruses could continue to spread. It is expected that due to the time that has passed since the last reported measles case, control of this disease will be achieved.

El sarampión es una enfermedad exantemática causada por un virus del género Morbilivirus. El 23 de febrero de 2020 inició el exantema en el primer caso confirmado en este año; el 28 de febrero de 2020 se confirmó el primer caso de enfermedad por un nuevo coronavirus (COVID-19). Hasta el 6 de junio de 2020 se han confirmado 176 casos de sarampión: 137 en la Ciudad de México, 37 en el Estado de México y 2 en Campeche. En la Ciudad de México, las alcaldías con más casos son Gustavo A. Madero, Miguel Hidalgo e Iztapalapa; en el Estado de México, la mayoría de los casos se presentan en Ecatepec, Tlalnepantla y Netzahualcóyotl; en el estado de Campeche solo se han reportado dos casos. Hasta el 6 de junio de 2020 se habían registrado en todo el mundo 7,065,33 casos de COVID-19, con una letalidad global del 5.7%. En México se han presentado 113,619 casos confirmados y 13,511 muertes; de ellos, 30,223 casos y 3,062 muertes en la Ciudad de México, siendo las alcaldías de Iztapalapa y Gustavo A. Madero las que han tenido el mayor número de casos. Como parte de las estrategias para controlar la diseminación de la infección, se decidió iniciar la campaña de distanciamiento social. La amenaza latente es que, al disminuir las medidas de confinamiento social e iniciar la movilidad paulatina, podrían continuar diseminándose ambos virus. Se espera que debido al tiempo transcurrido desde el último caso de sarampión reportado se logre el control de esta enfermedad.

COVID-19 pandemic: challenges ahead.

Since December 2019, health systems worldwide have faced the pandemic caused by the new severe acute respiratory syndrome coronavirus 2. The pandemic began in China and has spread throughout the world. This new coronavirus has a high transmission capacity and elevated lethality in people over 60 years old and in those with risk factors (obesity, diabetes, and systemic arterial hypertension); those characteristics have a different proportion in each country. At present, there is no specific, effective, and safe treatment to treat this virus. In this review, an analysis is made on the differences in epidemiological aspects of the disease and its presentation in pediatric patients; the poorly-based recommendation for using an empirical combination of antimalarials plus antimicrobials as antiviral treatment; the indication of intravenous steroids; and the possible influence of antihypertensive drugs on the course of the disease.

A partir de diciembre de 2019, los sistemas de salud de todos los países se han enfrentado a la pandemia causada por un nuevo coronavirus (SARS-CoV-2), el cual fue notificado por primera vez en China y se ha esparcido por todo el mundo. Este nuevo coronavirus posee una alta capacidad para transmitirse. A escala mundial la letalidad ha sido más alta en la población mayor de 60 años y en aquellos que tienen factores de riesgo (obesidad, diabetes e hipertensión arterial sistémica). Sin embargo, estas características varían en proporción en cada país. Hasta el momento no hay un tratamiento específico, eficaz y seguro para combatir este virus. En este artículo se realiza un análisis sobre las diferencias globales en los aspectos ­epidemiológicos y con relación a su presentación en pacientes pediátricos, así como de la recomendación, con pobre fundamento, del uso de la combinación de antimaláricos y antimicrobianos empíricos como antivirales. También se analizan la indicación de esteroides intravenosos y la posible influencia de los fármacos antihipertensivos en el curso de la enfermedad.

How to Best Protect People With Diabetes From the Impact of SARS-CoV-2: Report of the International COVID-19 and Diabetes Summit.

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has rapidly involved the entire world and exposed the pressing need for collaboration between public health and other stakeholders from the clinical, scientific, regulatory, pharmaceutical, and medical device and technology communities. To discuss how to best protect people with diabetes from serious outcomes from COVID-19, Diabetes Technology Society, in collaboration with Sansum Diabetes Research Institute, hosted the "International COVID-19 and Diabetes Virtual Summit" on August 26-27, 2020. This unique, unprecedented real-time conference brought together physicians, scientists, government officials, regulatory experts, industry representatives, and people with diabetes from six continents to review and analyze relationships between COVID-19 and diabetes. Over 800 attendees logged in. The summit consisted of five sessions: (I) Keynotes, (II) Preparedness, (III) Response, (IV) Recovery, and (V) Surveillance; eight parts: (A) Background, (B) Resilience, (C) Outpatient Care, (D) Inpatient Care, (E) Resources, (F) High-Risk Groups, (G) Regulation, and (H) The Future; and 24 sections: (1) Historic Pandemics and Impact on Society, (2) Pathophysiology/Risk Factors for COVID-19, (3) Social Determinants of COVID-19, (4) Preparing for the Future, (5) Medications and Vaccines, (6) Psychology of Patients and Caregivers, (7) Outpatient Treatment of Diabetes Mellitus and Non-Pharmacologic Intervention, (8) Technology and Telehealth for Diabetes Outpatients, (9) Technology for Inpatients, (10) Management of Diabetes Inpatients with COVID-19, (11) Ethics, (12) Accuracy of Diagnostic Tests, (13) Children, (14) Pregnancy, (15) Economics of Care for COVID-19, (16) Role of Industry, (17) Protection of Healthcare Workers, (18) People with Diabetes, (19) International Responses to COVID-19, (20) Government Policy, (21) Regulation of Tests and Treatments, (22) Digital Health Technology, (23) Big Data Statistics, and 24) Patient Surveillance and Privacy. The two keynote speeches were entitled (1) COVID-19 and Diabetes-Meeting the Challenge and (2) Knowledge Gaps and Research Opportunities for Diabetes and COVID-19. While there was an emphasis on diabetes and its interactions with COVID-19, the panelists also discussed the COVID-19 pandemic in general. The meeting generated many novel ideas for collaboration between experts in medicine, science, government, and industry to develop new technologies and disease treatment paradigms to fight this global pandemic.

Measles outbreak during the COVID-19 pandemic in Mexico

Measles is an exanthematous disease caused by a virus of the Morbillivirus genus. On February 23, 2020, the exanthema began in the first confirmed case of measles this year. At the same time, on February 28, 2020, the first case of the new coronavirus disease (COVID-19) was confirmed. Up to June 6, 2020, 176 measles cases have been confirmed: 137 in Mexico City, 37 in the state of Mexico, and two in the state of Campeche. In Mexico City, municipalities with more cases were Gustavo A. Madero, Miguel Hidalgo, and Iztapalapa;in the State of Mexico were Ecatepec, Tlalnepantla, and Netzahualcoyotl;in Campeche, there are only two cases reported. On the other hand, 7,065,133 cases of COVID-19 have been registered worldwide with a global case fatality rate of 5.7%. In Mexico, there have been 113,619 confirmed cases and 13,511 deaths, while in Mexico City, there have been 30,223 cases and 3062 deaths. Iztapalapa and Gustavo A. Madero are the locations with more cases reported. Hence, a campaign of social distancing started as part of the strategies to control the spread of the infection. The potential threat is that as social confinement measures are relaxed and mobility is initiated, both viruses could continue to spread. It is expected that due to the time that has passed since the last reported measles case, control of this disease will be achieved. Resumen El sarampión es una enfermedad exantemática causada por un virus del género Morbilivirus. El 23 de febrero de 2020 inició el exantema en el primer caso confirmado en este año;el 28 de febrero de 2020 se confirmó el primer caso de enfermedad por un nuevo coronavirus (COVID-19). Hasta el 6 de junio de 2020 se han confirmado 176 casos de sarampión: 137 en la Ciudad de México, 37 en el Estado de México y 2 en Campeche. En la Ciudad de México, las alcaldías con más casos son Gustavo A. Madero, Miguel Hidalgo e Iztapalapa;en el Estado de México, la mayoría de los casos se presentan en Ecatepec, Tlalnepantla y Netzahualcóyotl;en el estado de Campeche solo se han reportado dos casos. Hasta el 6 de junio de 2020 se habían registrado en todo el mundo 7,065,33 casos de COVID-19, con una letalidad global del 5.7%. En México se han presentado 113,619 casos confirmados y 13,511 muertes;de ellos, 30,223 casos y 3,062 muertes en la Ciudad de México, siendo las alcaldías de Iztapalapa y Gustavo A. Madero las que han tenido el mayor número de casos. Como parte de las estrategias para controlar la diseminación de la infección, se decidió iniciar la campaña de distanciamiento social. La amenaza latente es que, al disminuir las medidas de confinamiento social e iniciar la movilidad paulatina, podrían continuar diseminándose ambos virus. Se espera que debido al tiempo transcurrido desde el último caso de sarampión reportado se logre el control de esta enfermedad.

COVID-19 pandemic: challenges ahead

Since December 2019, health systems worldwide have faced the pandemic caused by the new severe acute respiratory syndrome coronavirus 2. The pandemic began in China and has spread throughout the world. This new coronavirus has a high transmission capacity and elevated lethality in people over 60 years old and in those with risk factors (obesity, diabetes, and systemic arterial hypertension);those characteristics have a different proportion in each country. At present, there is no specific, effective, and safe treatment to treat this virus. In this review, an analysis is made on the differences in epidemiological aspects of the disease and its presentation in pediatric patients;the poorly-based recommendation for using an empirical combination of antimalarials plus antimicrobials as antiviral treatment;the indication of intravenous steroids;and the possible influence of antihypertensive drugs on the course of the disease. Resumen A partir de diciembre de 2019, los sistemas de salud de todos los países se han enfrentado a la pandemia causada por un nuevo coronavirus (SARS-CoV-2), el cual fue notificado por primera vez en China y se ha esparcido por todo el mundo. Este nuevo coronavirus posee una alta capacidad para transmitirse. A escala mundial la letalidad ha sido más alta en la población mayor de 60 años y en aquellos que tienen factores de riesgo (obesidad, diabetes e hipertensión arterial sistémica). Sin embargo, estas características varían en proporción en cada país. Hasta el momento no hay un tratamiento específico, eficaz y seguro para combatir este virus. En este artículo se realiza un análisis sobre las diferencias globales en los aspectos epidemiológicos y con relación a su presentación en pacientes pediátricos, así como de la recomendación, con pobre fundamento, del uso de la combinación de antimaláricos y antimicrobianos empíricos como antivirales. También se analizan la indicación de esteroides intravenosos y la posible influencia de los fármacos antihipertensivos en el curso de la enfermedad.

A Guide to Chatbots for COVID-19 Screening at Pediatric Health Care Facilities.

The coronavirus disease 2019 (COVID-19) outbreak has required institutions to rapidly adapt to changing public health circumstances. The Centers for Disease Control and Prevention has encouraged health care facilities to explore novel health care delivery modes. However, many institutions may not be prepared to begin offering digital health and telehealth services. Chatbots are one digital health tool that can help evolve triage and screening processes in a scalable manner. Here, we present a decision-making and implementation framework for deploying COVID-19 screening chatbots at pediatric health care facilities.