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2.
JAMA Netw Open ; 4(7): e2116901, 2021 07 01.
Article in English | MEDLINE | ID: covidwho-1306627

ABSTRACT

Importance: The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy. Objectives: To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. Design, Setting, and Participants: In a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). Main Outcomes and Measures: Patient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. Results: The cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, age (odds ratio [OR], 1.03 per year; 95% CI, 1.03-1.04), male sex (OR, 1.60; 95% CI, 1.51-1.69), liver disease (OR, 1.20; 95% CI, 1.08-1.34), dementia (OR, 1.26; 95% CI, 1.13-1.41), African American (OR, 1.12; 95% CI, 1.05-1.20) and Asian (OR, 1.33; 95% CI, 1.12-1.57) race, and obesity (OR, 1.36; 95% CI, 1.27-1.46) were independently associated with higher clinical severity. Conclusions and Relevance: This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission.


Subject(s)
COVID-19 , Databases, Factual , Forecasting , Hospitalization , Models, Biological , Severity of Illness Index , Adult , Aged , Aged, 80 and over , COVID-19/ethnology , COVID-19/mortality , Comorbidity , Extracorporeal Membrane Oxygenation , Female , Humans , Hydrogen-Ion Concentration , Male , Middle Aged , Pandemics , Respiration, Artificial , Retrospective Studies , Risk Factors , SARS-CoV-2 , United States , Young Adult
3.
Risk Hazards Crisis Public Policy ; 2021 Apr 27.
Article in English | MEDLINE | ID: covidwho-1222694

ABSTRACT

Evidence suggests that people vary in their desire to undertake protective actions during a health emergency, and that trust in authorities may influence decision making. We sought to examine how the trust in health experts and trust in White House leadership during the COVID-19 pandemic impacts individuals' decisions to adopt recommended protective actions such as mask-wearing. A mediation analysis was conducted using cross-sectional U.S. survey data collected between March 27 and 30, 2020, to elucidate how individuals' trust in health experts and White House leadership, their perceptions of susceptibility and severity to COVID-19, and perceived benefits of protecting against COVID-19, influenced their uptake of recommended protective actions. Trust in health experts was associated with greater perceived severity of COVID-19 and benefits of taking action, which led to greater uptake of recommended actions. Trust in White House leadership was associated with lower perceived susceptibility to COVID-19 and was not associated with taking recommended actions. Having trust in health experts is a greater predictor of individuals' uptake of protective actions than having trust in White House leadership. Public health messaging should emphasize the severity of COVID-19 and the benefits of protecting oneself while ensuring consistency and transparency to regain trust in health experts.


证据显示,人们在一场卫生紧急事件中就采取防护行动所具备的意愿有所差异,并且对政府机构的信任可能会影响决策。我们试图分析新冠肺炎(COVID­19)大流行期间对卫生专家的信任和对白宫领导力的信任如何影响个体在采纳例如佩戴口罩等推荐的防护行动方面的决策。对2020å¹´3月27­30日收集的美国调查的截面数据进行中介分析,以期阐明个体对卫生专家和对白宫领导力的信任、他们对新冠肺炎的易感染性和严重性的感知、以及对采取新冠肺炎防护措施的利益感知,如何影响他们对推荐防护行动的采纳。对卫生专家的信任与"新冠肺炎严重性感知的增强,和关于采取防护行动的利益感知的增强"相关,导致更高的推荐防护行动采取率。对白宫领导力的信任与新冠肺炎易感染性的较低感知相关,并且与采取推荐防护行动一事不相关。比起信任白宫领导力,信任卫生专家更能预测个体的防护行动采取率。公共卫生信息应强调新冠肺炎的严重性和保护自身的益处,同时确保一致性和透明性,以重获对卫生专家的信任。.La evidencia sugiere que las personas varían en su deseo de emprender acciones de protección durante una emergencia de salud y que la confianza en las autoridades puede influir en la toma de decisiones. Buscamos examinar cómo la confianza en los expertos en salud y la confianza en el liderazgo de la Casa Blanca durante la pandemia de COVID­19 impactan las decisiones de las personas para adoptar las acciones de protección recomendadas, como el uso de máscaras. Se realizó un análisis de mediación utilizando datos de encuestas transversales de EE. UU. Recopilados entre el 27 y el 30 de marzo de 2020 para dilucidar cómo la confianza de las personas en los expertos en salud y el liderazgo de la Casa Blanca, sus percepciones de susceptibilidad y gravedad al COVID­19, y los beneficios percibidos de protegerse contra COVID­19, influyó en su adopción de las acciones de protección recomendadas. La confianza en los expertos en salud se asoció con una mayor gravedad percibida de COVID­19 y los beneficios de tomar medidas, lo que llevó a una mayor aceptación de las acciones recomendadas. La confianza en el liderazgo de la Casa Blanca se asoció con una menor susceptibilidad percibida al COVID­19 y no con la adopción de las acciones recomendadas. Tener confianza en los expertos en salud es un factor de predicción mayor de la adopción de acciones de protección por parte de los individuos que tener confianza en el liderazgo de la Casa Blanca. Los mensajes de salud pública deben enfatizar la gravedad de COVID­19 y los beneficios de protegerse a sí mismo, al tiempo que se garantiza la coherencia y la transparencia para recuperar la confianza en los expertos en salud.

4.
J Am Med Inform Assoc ; 28(3): 427-443, 2021 03 01.
Article in English | MEDLINE | ID: covidwho-719257

ABSTRACT

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.


Subject(s)
COVID-19 , Data Science/organization & administration , Information Dissemination , Intersectoral Collaboration , Computer Security , Data Analysis , Ethics Committees, Research , Government Regulation , Humans , National Institutes of Health (U.S.) , United States
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