Incidence and Risk Factors for Severe Outcomes in Pediatric Patients With COVID-19.

OBJECTIVES: Throughout the pandemic, children with COVID-19 have experienced hospitalization, ICU admission, invasive respiratory support, and death. Using a multisite, national dataset, we investigate risk factors associated with these outcomes in children with COVID-19. METHODS: Our data source (Optum deidentified COVID-19 Electronic Health Record Dataset) included children aged 0 to 18 years testing positive for COVID-19 between January 1, 2020, and January 20, 2022. Using ordinal logistic regression, we identified factors associated with an ordinal outcome scale: nonhospitalization, hospitalization, or a severe composite outcome (ICU, intensive respiratory support, death). To contrast hospitalization for COVID-19 and incidental positivity on hospitalization, we secondarily identified patient factors associated with hospitalizations with a primary diagnosis of COVID-19. RESULTS: In 165 437 children with COVID-19, 3087 (1.8%) were hospitalized without complication, 2954 (1.8%) experienced ICU admission and/or intensive respiratory support, and 31 (0.02%) died. We grouped patients by age: 0 to 4 years old (35 088), and 5 to 11 years old (75 574), 12 to 18 years old (54 775). Factors positively associated with worse outcomes were preexisting comorbidities and residency in the Southern United States. In 0- to 4-year-old children, there was a nonlinear association between age and worse outcomes, with worse outcomes in 0- to 2-year-old children. In 5- to 18-year-old patients, vaccination was protective. Findings were similar in our secondary analysis of hospitalizations with a primary diagnosis of COVID-19, though region effects were no longer observed. CONCLUSIONS: Among children with COVID-19, preexisting comorbidities and residency in the Southern United States were positively associated with worse outcomes, whereas vaccination was negatively associated. Our study population was highly insured; future studies should evaluate underinsured populations to confirm generalizability.

Validation of a predictive model for hospital-acquired acute kidney injury with emergence of SARS-CoV-2 variants.

We previously developed and validated a model to predict acute kidney injury (AKI) in hospitalized coronavirus disease 2019 (COVID-19) patients and found that the variables with the highest importance included a history of chronic kidney disease and markers of inflammation. Here, we assessed model performance during periods when COVID-19 cases were attributable almost exclusively to individual variants. Electronic Health Record data were obtained from patients admitted to 19 hospitals. The outcome was hospital-acquired AKI. The model, previously built in an Inception Cohort, was evaluated in Delta and Omicron cohorts using model discrimination and calibration methods. A total of 9104 patients were included, with 5676 in the Inception Cohort, 2461 in the Delta cohort, and 967 in the Omicron cohort. The Delta Cohort was younger with fewer comorbidities, while Omicron patients had lower rates of intensive care compared with the other cohorts. AKI occurred in 13.7% of the Inception Cohort, compared with 13.8% of Delta and 14.4% of Omicron (Omnibus p = 0.84). Compared with the Inception Cohort (area under the curve (AUC): 0.78, 95% confidence interval (CI): 0.76-0.80), the model showed stable discrimination in the Delta (AUC: 0.78, 95% CI: 0.75-0.80, p = 0.89) and Omicron (AUC: 0.74, 95% CI: 0.70-0.79, p = 0.37) cohorts. Estimated calibration index values were 0.02 (95% CI: 0.01-0.07) for Inception, 0.08 (95% CI: 0.05-0.17) for Delta, and 0.12 (95% CI: 0.04-0.47) for Omicron cohorts, p = 0.10 for both Delta and Omicron vs Inception. Our model for predicting hospital-acquired AKI remained accurate in different COVID-19 variants, suggesting that risk factors for AKI have not substantially evolved across variants.

The Politicization of Ivermectin Tweets During the COVID-19 Pandemic.

Background: We explore the ivermectin discourse and sentiment in the United States with a special focus on political leaning through the social media blogging site Twitter. Methods: We used sentiment analysis and topic modeling to geospatially explore ivermectin Twitter discourse in the United States and compared it to the political leaning of a state based on the 2020 presidential election. Results: All modeled topics were associated with a negative sentiment. Tweets originating from democratic leaning states were more likely to be negative. Conclusions: Real-time analysis of social media content can identify public health concerns and guide timely public health interventions tackling disinformation.

Analyzing COVID-19 disinformation on Twitter using the hashtags #scamdemic and #plandemic: Retrospective study.

INTRODUCTION: The use of social media during the COVID-19 pandemic has led to an "infodemic" of mis- and disinformation with potentially grave consequences. To explore means of counteracting disinformation, we analyzed tweets containing the hashtags #Scamdemic and #Plandemic. METHODS: Using a Twitter scraping tool called twint, we collected 419,269 English-language tweets that contained "#Scamdemic" or "#Plandemic" posted in 2020. Using the Twitter application-programming interface, we extracted the same tweets (by tweet ID) with additional user metadata. We explored descriptive statistics of tweets including their content and user profiles, analyzed sentiments and emotions, performed topic modeling, and determined tweet availability in both datasets. RESULTS: After removal of retweets, replies, non-English tweets, or duplicate tweets, 40,081 users tweeted 227,067 times using our selected hashtags. The mean weekly sentiment was overall negative for both hashtags. One in five users who used these hashtags were suspended by Twitter by January 2021. Suspended accounts had an average of 610 followers and an average of 6.7 tweets per user, while active users had an average of 472 followers and an average of 5.4 tweets per user. The most frequent tweet topic was "Complaints against mandates introduced during the pandemic" (79,670 tweets), which included complaints against masks, social distancing, and closures. DISCUSSION: While social media has democratized speech, it also permits users to disseminate potentially unverified or misleading information that endangers people's lives and public health interventions. Characterizing tweets and users that use hashtags associated with COVID-19 pandemic denial allowed us to understand the extent of misinformation. With the preponderance of inaccessible original tweets, we concluded that posters were in denial of the COVID-19 pandemic and sought to disperse related mis- or disinformation resulting in suspension. CONCLUSION: Leveraging 227,067 tweets with the hashtags #scamdemic and #plandemic in 2020, we were able to elucidate important trends in public disinformation about the COVID-19 vaccine.

Social media: A new tool for outbreak surveillance.

Social media platforms allow users to share news, ideas, thoughts, and opinions on a global scale. Data processing methods allow researchers to automate the collection and interpretation of social media posts for efficient and valuable disease surveillance. Data derived from social media and internet search trends have been used successfully for monitoring and forecasting disease outbreaks such as Zika, Dengue, MERS, and Ebola viruses. More recently, data derived from social media have been used to monitor and model disease incidence during the coronavirus disease 2019 (COVID-19) pandemic. We discuss the use of social media for disease surveillance.

Risk Prediction for Acute Kidney Injury in Patients Hospitalized With COVID-19.

Rationale & Objective: Acute kidney injury (AKI) is common in patients hospitalized with COVID-19, but validated, predictive models for AKI are lacking. We aimed to develop the best predictive model for AKI in hospitalized patients with coronavirus disease 2019 and assess its performance over time with the emergence of vaccines and the Delta variant. Study Design: Longitudinal cohort study. Setting & Participants: Hospitalized patients with a positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction result between March 1, 2020, and August 20, 2021 at 19 hospitals in Texas. Exposures: Comorbid conditions, baseline laboratory data, inflammatory biomarkers. Outcomes: AKI defined by KDIGO (Kidney Disease: Improving Global Outcomes) creatinine criteria. Analytical Approach: Three nested models for AKI were built in a development cohort and validated in 2 out-of-time cohorts. Model discrimination and calibration measures were compared among cohorts to assess performance over time. Results: Of 10,034 patients, 5,676, 2,917, and 1,441 were in the development, validation 1, and validation 2 cohorts, respectively, of whom 776 (13.7%), 368 (12.6%), and 179 (12.4%) developed AKI, respectively (P = 0.26). Patients in the validation cohort 2 had fewer comorbid conditions and were younger than those in the development cohort or validation cohort 1 (mean age, 54 ± 16.8 years vs 61.4 ± 17.5 and 61.7 ± 17.3 years, respectively, P < 0.001). The validation cohort 2 had higher median high-sensitivity C-reactive protein level (81.7 mg/L) versus the development cohort (74.5 mg/L; P < 0.01) and higher median ferritin level (696 ng/mL) versus both the development cohort (444 ng/mL) and validation cohort 1 (496 ng/mL; P < 0.001). The final model, which added high-sensitivity C-reactive protein, ferritin, and D-dimer levels, had an area under the curve of 0.781 (95% CI, 0.763-0.799). Compared with the development cohort, discrimination by area under the curve (validation 1: 0.785 [0.760-0.810], P = 0.79, and validation 2: 0.754 [0.716-0.795], P = 0.53) and calibration by estimated calibration index (validation 1: 0.116 [0.041-0.281], P = 0.11, and validation 2: 0.081 [0.045-0.295], P = 0.11) showed stable performance over time. Limitations: Potential billing and coding bias. Conclusions: We developed and externally validated a model to accurately predict AKI in patients with coronavirus disease 2019. The performance of the model withstood changes in practice patterns and virus variants.

Using dipstick urinalysis to predict development of acute kidney injury in patients with COVID-19.

BACKGROUND: Acute kidney injury (AKI) is a common complication in patients hospitalized with COVID-19 and may require renal replacement therapy (RRT). Dipstick urinalysis is frequently obtained, but data regarding the prognostic value of hematuria and proteinuria for kidney outcomes is scarce. METHODS: Patients with positive severe acute respiratory syndrome-coronavirus 2 (SARS-CoV2) PCR, who had a urinalysis obtained on admission to one of 20 hospitals, were included. Nested models with degree of hematuria and proteinuria were used to predict AKI and RRT during admission. Presence of Chronic Kidney Disease (CKD) and baseline serum creatinine were added to test improvement in model fit. RESULTS: Of 5,980 individuals, 829 (13.9%) developed an AKI during admission, and 149 (18.0%) of those with AKI received RRT. Proteinuria and hematuria degrees significantly increased with AKI severity (P < 0.001 for both). Any degree of proteinuria and hematuria was associated with an increased risk of AKI and RRT. In predictive models for AKI, presence of CKD improved the area under the curve (AUC) (95% confidence interval) to 0.73 (0.71, 0.75), P < 0.001, and adding baseline creatinine improved the AUC to 0.85 (0.83, 0.86), P < 0.001, when compared to the base model AUC using only proteinuria and hematuria, AUC = 0.64 (0.62, 0.67). In RRT models, CKD status improved the AUC to 0.78 (0.75, 0.82), P < 0.001, and baseline creatinine improved the AUC to 0.84 (0.80, 0.88), P < 0.001, compared to the base model, AUC = 0.72 (0.68, 0.76). There was no significant improvement in model discrimination when both CKD and baseline serum creatinine were included. CONCLUSIONS: Proteinuria and hematuria values on dipstick urinalysis can be utilized to predict AKI and RRT in hospitalized patients with COVID-19. We derived formulas using these two readily available values to help prognosticate kidney outcomes in these patients. Furthermore, the incorporation of CKD or baseline creatinine increases the accuracy of these formulas.

Early Crowdfunding Response to the COVID-19 Pandemic: Cross-sectional Study.

BACKGROUND: As the number of COVID-19 cases increased precipitously in the United States, policy makers and health officials marshalled their pandemic responses. As the economic impacts multiplied, anecdotal reports noted the increased use of web-based crowdfunding to defray these costs. OBJECTIVE: We examined the web-based crowdfunding response in the early stage of the COVID-19 pandemic in the United States to understand the incidence of initiation of COVID-19-related campaigns and compare them to non-COVID-19-related campaigns. METHODS: On May 16, 2020, we extracted all available data available on US campaigns that contained narratives and were created between January 1 and May 10, 2020, on GoFundMe. We identified the subset of COVID-19-related campaigns using keywords relevant to the COVID-19 pandemic. We explored the incidence of COVID-19-related campaigns by geography, by category, and over time, and we compared the characteristics of the campaigns to those of non-COVID-19-related campaigns after March 11, when the pandemic was declared. We then used a natural language processing algorithm to cluster campaigns by narrative content using overlapping keywords. RESULTS: We found that there was a substantial increase in overall GoFundMe web-based crowdfunding campaigns in March, largely attributable to COVID-19-related campaigns. However, as the COVID-19 pandemic persisted and progressed, the number of campaigns per COVID-19 case declined more than tenfold across all states. The states with the earliest disease burden had the fewest campaigns per case, indicating a lack of a case-dependent response. COVID-19-related campaigns raised more money, had a longer narrative description, and were more likely to be shared on Facebook than other campaigns in the study period. CONCLUSIONS: Web-based crowdfunding appears to be a stopgap for only a minority of campaigners. The novelty of an emergency likely impacts both campaign initiation and crowdfunding success, as it reflects the affective response of a community. Crowdfunding activity likely serves as an early signal for emerging needs and societal sentiment for communities in acute distress that could be used by governments and aid organizations to guide disaster relief and policy.

Rolling Up the Sleeve: Equitable, Efficient, and Safe COVID-19 Mass Immunization for Academic Medical Center Employees.

BACKGROUND: Novel coronavirus disease 2019 (COVID-19) vaccine administration has faced distribution barriers across the United States. We sought to delineate our vaccine delivery experience in the first week of vaccine availability, and our effort to prioritize employees based on risk with a goal of providing an efficient infrastructure to optimize speed and efficiency of vaccine delivery while minimizing risk of infection during the immunization process. OBJECTIVE: This article aims to evaluate an employee prioritization/invitation/scheduling system, leveraging an integrated electronic health record patient portal framework for employee COVID-19 immunizations at an academic medical center. METHODS: We conducted an observational cross-sectional study during January 2021 at a single urban academic center. All employees who met COVID-19 allocation vaccine criteria for phase 1a.1 to 1a.4 were included. We implemented a prioritization/invitation/scheduling framework and evaluated time from invitation to scheduling as a proxy for vaccine interest and arrival to vaccine administration to measure operational throughput. RESULTS: We allotted vaccines for 13,753 employees but only 10,662 employees with an active patient portal account received an invitation. Of those with an active account, 6,483 (61%) scheduled an appointment and 6,251 (59%) were immunized in the first 7 days. About 66% of invited providers were vaccinated in the first 7 days. In contrast, only 41% of invited facility/food service employees received the first dose of the vaccine in the first 7 days (p < 0.001). At the vaccination site, employees waited 5.6 minutes (interquartile range [IQR]: 3.9-8.3) from arrival to vaccination. CONCLUSION: We developed a system of early COVID-19 vaccine prioritization and administration in our health care system. We saw strong early acceptance in those with proximal exposure to COVID-19 but noticed significant difference in the willingness of different employee groups to receive the vaccine.

Characteristics and Outcomes of Infectious Diseases Electronic COVID-19 Consultations at a Multisite Academic Health System.

Objective The need for clinicians to access Infectious Diseases (ID) consultants for clinical decision-making support increased during the Coronavirus Disease 2019 (COVID-19) pandemic. Traditional ID consultations with face-to-face (FTF) patient assessments are not always possible or practical during a pandemic and involve added exposure risk and personal protective equipment (PPE) use. Electronic consultations (e-consults) may provide an alternative and improve access to ID specialists during the pandemic. Methods We implemented ID e-consult platforms designed to answer clinical questions related to COVID-19 at three academic clinical institutions in Dallas, Texas. We conducted a retrospective review of all COVID-19 ID e-consults between March 16, 2020 and May 15, 2020 evaluating characteristics and outcomes of e-consults among the clinical sites. Results We completed 198 COVID-19 ID e-consults at participating institutions. The most common e-consult indications were for 63 (32%) repeat testing, 61 (31%) initial testing, 65 (33%) treatment options, and 61 (31%) Infection Prevention (IP). Based on the e-consult recommendation, 53 (27%) of patients were initially tested for COVID-19, 45 (23%) were re-tested, 44 (22%) of patients had PPE precautions initiated, and 37 (19%) had PPE precautions removed. The median time to consult completion was four hours and 8 (4%) consults were converted to standard FTF consults. Conclusion E-consult services can provide safe and timely access to ID specialists during the COVID-19 pandemic, minimizing the risk of infection to the patient and health care workers, while preserving PPE and testing supplies.

Association of Healthcare Access With Intensive Care Unit Utilization and Mortality in Patients of Hispanic Ethnicity Hospitalized With COVID-19.

BACKGROUND: Racial and ethnic minority groups in the United States experience a disproportionate burden of COVID-19 deaths. OBJECTIVE: To evaluate whether outcome differences between Hispanic and non-Hispanic COVID-19 hospitalized patients exist and, if so, to identify the main malleable contributing factors. DESIGN, SETTING, PARTICIPANTS: Retrospective, cross-sectional, observational study of 6097 adult COVID-19 patients hospitalized within a single large healthcare system from March to November 2020. EXPOSURES: Self-reported ethnicity and primary language. MAIN OUTCOMES AND MEASURES: Clinical outcomes included intensive care unit (ICU) utilization and in-hospital death. We used age-adjusted odds ratios (OR) and multivariable analysis to evaluate the associations between ethnicity/language groups and outcomes. RESULTS: 32.1% of patients were Hispanic, 38.6% of whom reported a non-English primary language. Hispanic patients were less likely to be insured, have a primary care provider, and have accessed the healthcare system prior to the COVID-19 admission. After adjusting for age, Hispanic inpatients experienced higher ICU utilization (non-English-speaking: OR, 1.75; 95% CI, 1.47-2.08; English-speaking: OR, 1.13; 95% CI, 0.95-1.33) and higher mortality (non-English-speaking: OR, 1.43; 95% CI, 1.10-1.86; English-speaking: OR, 1.53; 95% CI, 1.19-1.98) compared to non-Hispanic inpatients. There were no observed treatment disparities among ethnic groups. After adjusting for age, Hispanic inpatients had elevated disease severity at admission (non-English-speaking: OR, 2.27; 95% CI, 1.89-2.72; English-speaking: OR, 1.33; 95% CI, 1.10- 1.61). In multivariable analysis, the associations between ethnicity/language and clinical outcomes decreased after considering baseline disease severity (P < .001). CONCLUSION: The associations between ethnicity and clinical outcomes can be explained by elevated disease severity at admission and limited access to healthcare for Hispanic patients, especially non-English-speaking Hispanics.

Harnessing the Electronic Health Record and Computerized Provider Order Entry Data for Resource Management During the COVID-19 Pandemic: Development of a Decision Tree.

BACKGROUND: The COVID-19 pandemic has resulted in shortages of diagnostic tests, personal protective equipment, hospital beds, and other critical resources. OBJECTIVE: We sought to improve the management of scarce resources by leveraging electronic health record (EHR) functionality, computerized provider order entry, clinical decision support (CDS), and data analytics. METHODS: Due to the complex eligibility criteria for COVID-19 tests and the EHR implementation-related challenges of ordering these tests, care providers have faced obstacles in selecting the appropriate test modality. As test choice is dependent upon specific patient criteria, we built a decision tree within the EHR to automate the test selection process by using a branching series of questions that linked clinical criteria to the appropriate SARS-CoV-2 test and triggered an EHR flag for patients who met our institutional persons under investigation criteria. RESULTS: The percentage of tests that had to be canceled and reordered due to errors in selecting the correct testing modality was 3.8% (23/608) before CDS implementation and 1% (262/26,643) after CDS implementation (P<.001). Patients for whom multiple tests were ordered during a 24-hour period accounted for 0.8% (5/608) and 0.3% (76/26,643) of pre- and post-CDS implementation orders, respectively (P=.03). Nasopharyngeal molecular assay results were positive in 3.4% (826/24,170) of patients who were classified as asymptomatic and 10.9% (1421/13,074) of symptomatic patients (P<.001). Positive tests were more frequent among asymptomatic patients with a history of exposure to COVID-19 (36/283, 12.7%) than among asymptomatic patients without such a history (790/23,887, 3.3%; P<.001). CONCLUSIONS: The leveraging of EHRs and our CDS algorithm resulted in a decreased incidence of order entry errors and the appropriate flagging of persons under investigation. These interventions optimized reagent and personal protective equipment usage. Data regarding symptoms and COVID-19 exposure status that were collected by using the decision tree correlated with the likelihood of positive test results, suggesting that clinicians appropriately used the questions in the decision tree algorithm.

COVID-19 Mass Vaccination Resource Calculator.

BACKGROUND: Despite the recent emergency use authorization of two vaccines for the prevention of the 2019 novel coronavirus (COVID-19) disease, vaccination rates are lower than expected. Vaccination efforts may be hampered by supply, delivery, storage, patient prioritization, administration infrastructure or logistics problems. To address the last issue, our institution is sharing publically a calculator to optimize the management of staffing and facility resources in an outpatient mass vaccination effort. OBJECTIVE: By sharing our calculator locally and through this paper, we aim to help health organizations administering vaccines optimize resource allocation while maximizing efficiency. METHODS: Our calculator determines the maximum number of vaccinations that can be administered per hour, the number of check-in staff (clerks) needed, the number of vaccination staff (nurses) needed, and the required room capacity needed for the vaccination and the mandatory 15-minute observation period after inoculation. RESULTS: We provide a functional version of the calculator, allowing users to replicate the calculation for their own vaccine events. CONCLUSION: An efficient and organized vaccination program is critical to halting the spread of COVID-19. By sharing this calculator, it is our hope that other organizations may use it to facilitate rapid and efficient vaccination.

Managing Pandemics with Health Informatics: Successes and Challenges.

INTRODUCTION: The novel COVID-19 pandemic struck the world unprepared. This keynote outlines challenges and successes using data to inform providers, government officials, hospitals, and patients in a pandemic. METHODS: The authors outline the data required to manage a novel pandemic including their potential uses by governments, public health organizations, and individuals. RESULTS: An extensive discussion on data quality and on obstacles to collecting data is followed by examples of successes in clinical care, contact tracing, and forecasting. Generic local forecast model development is reviewed followed by ethical consideration around pandemic data. We leave the reader with thoughts on the next inevitable outbreak and lessons learned from the COVID-19 pandemic. CONCLUSION: COVID-19 must be a lesson for the future to direct us to better planning and preparing to manage the next pandemic with health informatics.

Evaluating short-term forecasting of COVID-19 cases among different epidemiological models under a Bayesian framework.

BACKGROUND: Forecasting of COVID-19 cases daily and weekly has been one of the challenges posed to governments and the health sector globally. To facilitate informed public health decisions, the concerned parties rely on short-term daily projections generated via predictive modeling. We calibrate stochastic variants of growth models and the standard susceptible-infectious-removed model into 1 Bayesian framework to evaluate and compare their short-term forecasts. RESULTS: We implement rolling-origin cross-validation to compare the short-term forecasting performance of the stochastic epidemiological models and an autoregressive moving average model across 20 countries that had the most confirmed COVID-19 cases as of August 22, 2020. CONCLUSION: None of the models proved to be a gold standard across all regions, while all outperformed the autoregressive moving average model in terms of the accuracy of forecast and interpretability.

Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter.

OBJECTIVE: Social distancing policies are key in curtailing severe acute respiratory coronavirus virus 2 (SARS-CoV-2) spread, but their effectiveness is heavily contingent on public understanding and collective adherence. We studied public perception of social distancing through organic, large-scale discussion on Twitter. DESIGN: Retrospective cross-sectional study. METHODS: Between March 27 and April 10, 2020, we retrieved English-only tweets matching two trending social distancing hashtags, #socialdistancing and #stayathome. We analyzed the tweets using natural language processing and machine-learning models, and we conducted a sentiment analysis to identify emotions and polarity. We evaluated the subjectivity of tweets and estimated the frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and associated sentiments. RESULTS: We studied a sample of 574,903 tweets. For both hashtags, polarity was positive (mean, 0.148; SD, 0.290); only 15% of tweets had negative polarity. Tweets were more likely to be objective (median, 0.40; IQR, 0-0.6) with ~30% of tweets labeled as completely objective (labeled as 0 in range from 0 to 1). Approximately half of tweets (50.4%) primarily expressed joy and one-fifth expressed fear and surprise. Each correlated well with topic clusters identified by frequency including leisure and community support (ie, joy), concerns about food insecurity and quarantine effects (ie, fear), and unpredictability of coronavirus disease 2019 (COVID-19) and its implications (ie, surprise). CONCLUSIONS: Considering the positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms, we concluded that Twitter users generally supported social distancing in the early stages of their implementation.

Ethics and informatics in the age of COVID-19: challenges and recommendations for public health organization and public policy.

The COVID-19 pandemic response in the United States has exposed significant gaps in information systems and processes that prevent timely clinical and public health decision-making. Specifically, the use of informatics to mitigate the spread of SARS-CoV-2, support COVID-19 care delivery, and accelerate knowledge discovery bring to the forefront issues of privacy, surveillance, limits of state powers, and interoperability between public health and clinical information systems. Using a consensus-building process, we critically analyze informatics-related ethical issues in light of the pandemic across 3 themes: (1) public health reporting and data sharing, (2) contact tracing and tracking, and (3) clinical scoring tools for critical care. We provide context and rationale for ethical considerations and recommendations that are actionable during the pandemic and conclude with recommendations calling for longer-term, broader change (beyond the pandemic) for public health organization and policy reform.