PanDa Game: Optimized Privacy-Preserving Publishing of Individual-Level Pandemic Data Based on a Game Theoretic Model.

Sharing individual-level pandemic data is essential for accelerating the understanding of a disease. For example, COVID-19 data have been widely collected to support public health surveillance and research. In the United States, these data are typically de-identified before publication to protect the privacy of the corresponding individuals. However, current data publishing approaches for this type of data, such as those adopted by the U.S. Centers for Disease Control and Prevention (CDC), have not flexed over time to account for the dynamic nature of infection rates. Thus, the policies generated by these strategies have the potential to both raise privacy risks or overprotect the data and impair the data utility (or usability). To optimize the tradeoff between privacy risk and data utility, we introduce a game theoretic model that adaptively generates policies for the publication of individual-level COVID-19 data according to infection dynamics. We model the data publishing process as a two-player Stackelberg game between a data publisher and a data recipient and then search for the best strategy for the publisher. In this game, we consider 1) the average performance of predicting future case counts, and 2) the mutual information between the original data and the released data. We use COVID-19 case data from Vanderbilt University Medical Center from March 2020 to December 2021 to demonstrate the effectiveness of the new model. The results indicate that the game theoretic model outperforms all state-of-the-art baseline approaches, including those adopted by CDC, while maintaining low privacy risk. We further perform an extensive sensitivity analyses to show that our findings are robust to order-of-magnitude parameter fluctuations.

Supporting COVID-19 Disparity Investigations with Dynamically Adjusting Case Reporting Policies

Data access limitations have stifled COVID-19 disparity investigations in the United States. Though federal and state legislation permits publicly disseminating de-identified data, methods for de-identification, including a recently proposed dynamic policy approach to pandemic data sharing, remain unproved in their ability to support pandemic disparity studies. Thus, in this paper, we evaluate how such an approach enables timely, accurate, and fair disparity detection, with respect to potential adversaries with varying prior knowledge about the population. We show that, when considering reasonably enabled adversaries, dynamic policies support up to three times earlier disparity detection in partially synthetic data than data sharing policies derived from two current, public datasets. Using real-world COVID-19 data, we also show how granular date information, which dynamic policies were designed to share, improves disparity characterization. Our results highlight the potential of the dynamic policy approach to publish data that supports disparity investigations in current and future pandemics.

Examining Rural and Urban Sentiment Difference in COVID-19-Related Topics on Twitter: Word Embedding-Based Retrospective Study.

BACKGROUND: By the end of 2022, more than 100 million people were infected with COVID-19 in the United States, and the cumulative death rate in rural areas (383.5/100,000) was much higher than in urban areas (280.1/100,000). As the pandemic spread, people used social media platforms to express their opinions and concerns about COVID-19-related topics. OBJECTIVE: This study aimed to (1) identify the primary COVID-19-related topics in the contiguous United States communicated over Twitter and (2) compare the sentiments urban and rural users expressed about these topics. METHODS: We collected tweets containing geolocation data from May 2020 to January 2022 in the contiguous United States. We relied on the tweets' geolocations to determine if their authors were in an urban or rural setting. We trained multiple word2vec models with several corpora of tweets based on geospatial and timing information. Using a word2vec model built on all tweets, we identified hashtags relevant to COVID-19 and performed hashtag clustering to obtain related topics. We then ran an inference analysis for urban and rural sentiments with respect to the topics based on the similarity between topic hashtags and opinion adjectives in the corresponding urban and rural word2vec models. Finally, we analyzed the temporal trend in sentiments using monthly word2vec models. RESULTS: We created a corpus of 407 million tweets, 350 million (86%) of which were posted by users in urban areas, while 18 million (4.4%) were posted by users in rural areas. There were 2666 hashtags related to COVID-19, which clustered into 20 topics. Rural users expressed stronger negative sentiments than urban users about COVID-19 prevention strategies and vaccination (P<.001). Moreover, there was a clear political divide in the perception of politicians by urban and rural users; these users communicated stronger negative sentiments about Republican and Democratic politicians, respectively (P<.001). Regarding misinformation and conspiracy theories, urban users exhibited stronger negative sentiments about the "covidiots" and "China virus" topics, while rural users exhibited stronger negative sentiments about the "Dr. Fauci" and "plandemic" topics. Finally, we observed that urban users' sentiments about the economy appeared to transition from negative to positive in late 2021, which was in line with the US economic recovery. CONCLUSIONS: This study demonstrates there is a statistically significant difference in the sentiments of urban and rural Twitter users regarding a wide range of COVID-19-related topics. This suggests that social media can be relied upon to monitor public sentiment during pandemics in disparate types of regions. This may assist in the geographically targeted deployment of epidemic prevention and management efforts.

Detecting drug-drug interactions between therapies for COVID-19 and concomitant medications through the FDA adverse event reporting system.

Background: COVID-19 patients with underlying medical conditions are vulnerable to drug-drug interactions (DDI) due to the use of multiple medications. We conducted a discovery-driven data analysis to identify potential DDIs and associated adverse events (AEs) in COVID-19 patients from the FDA Adverse Event Reporting System (FAERS), a source of post-market drug safety. Materials and Methods: We investigated 18,589 COVID-19 AEs reported in the FAERS database between 2020 and 2021. We applied multivariate logistic regression to account for potential confounding factors, including age, gender, and the number of unique drug exposures. The significance of the DDIs was determined using both additive and multiplicative measures of interaction. We compared our findings with the Liverpool database and conducted a Monte Carlo simulation to validate the identified DDIs. Results: Out of 11,337 COVID-19 drug-Co-medication-AE combinations investigated, our methods identified 424 signals statistically significant, covering 176 drug-drug pairs, composed of 13 COVID-19 drugs and 60 co-medications. Out of the 176 drug-drug pairs, 20 were found to exist in the Liverpool database. The empirical p-value obtained based on 1,000 Monte Carlo simulations was less than 0.001. Remdesivir was discovered to interact with the largest number of concomitant drugs (41). Hydroxychloroquine was detected to be associated with most AEs (39). Furthermore, we identified 323 gender- and 254 age-specific DDI signals. Conclusion: The results, particularly those not found in the Liverpool database, suggest a subsequent need for further pharmacoepidemiology and/or pharmacology studies.

Concordance of SARS-CoV-2 Antibody Results during a Period of Low Prevalence.

Accurate, highly specific immunoassays for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are needed to evaluate seroprevalence. This study investigated the concordance of results across four immunoassays targeting different antigens for sera collected at the beginning of the SARS-CoV-2 pandemic in the United States. Specimens from All of Us participants contributed between January and March 2020 were tested using the Abbott Architect SARS-CoV-2 IgG (immunoglobulin G) assay (Abbott) and the EuroImmun SARS-CoV-2 enzyme-linked immunosorbent assay (ELISA) (EI). Participants with discordant results, participants with concordant positive results, and a subset of concordant negative results by Abbott and EI were also tested using the Roche Elecsys anti-SARS-CoV-2 (IgG) test (Roche) and the Ortho-Clinical Diagnostics Vitros anti-SARS-CoV-2 IgG test (Ortho). The agreement and 95% confidence intervals were estimated for paired assay combinations. SARS-CoV-2 antibody concentrations were quantified for specimens with at least two positive results across four immunoassays. Among the 24,079 participants, the percent agreement for the Abbott and EI assays was 98.8% (95% confidence interval, 98.7%, 99%). Of the 490 participants who were also tested by Ortho and Roche, the probability-weighted percentage of agreement (95% confidence interval) between Ortho and Roche was 98.4% (97.9%, 98.9%), that between EI and Ortho was 98.5% (92.9%, 99.9%), that between Abbott and Roche was 98.9% (90.3%, 100.0%), that between EI and Roche was 98.9% (98.6%, 100.0%), and that between Abbott and Ortho was 98.4% (91.2%, 100.0%). Among the 32 participants who were positive by at least 2 immunoassays, 21 had quantifiable anti-SARS-CoV-2 antibody concentrations by research assays. The results across immunoassays revealed concordance during a period of low prevalence. However, the frequency of false positivity during a period of low prevalence supports the use of two sequentially performed tests for unvaccinated individuals who are seropositive by the first test. IMPORTANCE What is the agreement of commercial SARS-CoV-2 immunoglobulin G (IgG) assays during a time of low coronavirus disease 2019 (COVID-19) prevalence and no vaccine availability? Serological tests produced concordant results in a time of low SARS-CoV-2 prevalence and no vaccine availability, driven largely by the proportion of samples that were negative by two immunoassays. The CDC recommends two sequential tests for positivity for future pandemic preparedness. In a subset analysis, quantified antinucleocapsid and antispike SARS-CoV-2 IgG antibodies do not suggest the need to specify the antigen targets of the sequential assays in the CDC's recommendation because false positivity varied as much between assays targeting the same antigen as it did between assays targeting different antigens.

Telehealth Uptake into Primary Care During the COVID-19 Pandemic.

Telehealth is designed to provide health services through the use of electronic information and telecommunication technologies. It has quickly become an important tool to ensure continued care in response to the COVID-19 pandemic while mitigating the risk of viral exposure for patients and providers. This study compared the number of monthly telehealth visits in primary care settings at a large academic medical center from 2019 and 2020. To investigate what health conditions are suitable for telehealth visits, we report on the ten ICD-10 codes with the largest number of telehealth visits.

Telehealth Use in the COVID-19 Pandemic: A Retrospective Study of Prenatal Care.

Telehealth is an alternative care delivery model to in-person care. It uses electronic information and telecommunication technologies to provide remote clinical care to patients, especially those living in rural areas that lack sufficient access to health care services. Like other areas of care affected by the COVID-19 pandemic, the prevalence of telehealth has increased in prenatal care. This study reports on telehealth use in prenatal care at a large academic medical center in Middle Tennessee, USA. We examine the electronic health records of over 2500 women to characterize 1) the volume of prenatal visits participating in telehealth, 2) disparities in obstetric patients using telehealth, and 3) the impact of telehealth use on obstetric outcomes, including duration of intrapartum hospital stays, preterm birth, Cesarean rate, and newborn birthweight. Our results show that telehealth mainly was used in the second and third trimesters, especially for consulting services. In addition, we found that certain demographics correlated with lower telehealth utilization, including patients who were under 26 years old, were Black and/or Hispanic, were on a state-sponsored health insurance program, and those who lived in urban areas. Furthermore, no significant differences were found on preterm birth and Cesarean between the patients who used telehealth in their prenatal care and those who did not.

Digital Health Applications in Oncology: An Opportunity to Seize.

Digital health advances have transformed many clinical areas including psychiatric and cardiovascular care. However, digital health innovation is relatively nascent in cancer care, which represents the fastest growing area of health-care spending. Opportunities for digital health innovation in oncology include patient-facing technologies that improve patient experience, safety, and patient-clinician interactions; clinician-facing technologies that improve their ability to diagnose pathology and predict adverse events; and quality of care and research infrastructure to improve clinical workflows, documentation, decision support, and clinical trial monitoring. The COVID-19 pandemic and associated shifts of care to the home and community dramatically accelerated the integration of digital health technologies into virtually every aspect of oncology care. However, the pandemic has also exposed potential flaws in the digital health ecosystem, namely in clinical integration strategies; data access, quality, and security; and regulatory oversight and reimbursement for digital health technologies. Stemming from the proceedings of a 2020 workshop convened by the National Cancer Policy Forum of the National Academies of Sciences, Engineering, and Medicine, this article summarizes the current state of digital health technologies in medical practice and strategies to improve clinical utility and integration. These recommendations, with calls to action for clinicians, health systems, technology innovators, and policy makers, will facilitate efficient yet safe integration of digital health technologies into cancer care.

Antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in All of Us Research Program Participants, 2 January to 18 March 2020.

BACKGROUND: With limited severe acute respiratory syndrome coronavirus (SARS-CoV-2) testing capacity in the United States at the start of the epidemic (January-March 2020), testing was focused on symptomatic patients with a travel history throughout February, obscuring the picture of SARS-CoV-2 seeding and community transmission. We sought to identify individuals with SARS-CoV-2 antibodies in the early weeks of the US epidemic. METHODS: All of Us study participants in all 50 US states provided blood specimens during study visits from 2 January to 18 March 2020. Participants were considered seropositive if they tested positive for SARS-CoV-2 immunoglobulin G (IgG) antibodies with the Abbott Architect SARS-CoV-2 IgG enzyme-linked immunosorbent assay (ELISA) and the EUROIMMUN SARS-CoV-2 ELISA in a sequential testing algorithm. The sensitivity and specificity of these ELISAs and the net sensitivity and specificity of the sequential testing algorithm were estimated, along with 95% confidence intervals (CIs). RESULTS: The estimated sensitivities of the Abbott and EUROIMMUN assays were 100% (107 of 107 [95% CI: 96.6%-100%]) and 90.7% (97 of 107 [83.5%-95.4%]), respectively, and the estimated specificities were 99.5% (995 of 1000 [98.8%-99.8%]) and 99.7% (997 of 1000 [99.1%-99.9%]), respectively. The net sensitivity and specificity of our sequential testing algorithm were 90.7% (97 of 107 [95% CI: 83.5%-95.4%]) and 100.0% (1000 of 1000 [99.6%-100%]), respectively. Of the 24 079 study participants with blood specimens from 2 January to 18 March 2020, 9 were seropositive, 7 before the first confirmed case in the states of Illinois, Massachusetts, Wisconsin, Pennsylvania, and Mississippi. CONCLUSIONS: Our findings identified SARS-CoV-2 infections weeks before the first recognized cases in 5 US states.

Dynamically adjusting case reporting policy to maximize privacy and public health utility in the face of a pandemic.

OBJECTIVE: Supporting public health research and the public's situational awareness during a pandemic requires continuous dissemination of infectious disease surveillance data. Legislation, such as the Health Insurance Portability and Accountability Act of 1996 and recent state-level regulations, permits sharing deidentified person-level data; however, current deidentification approaches are limited. Namely, they are inefficient, relying on retrospective disclosure risk assessments, and do not flex with changes in infection rates or population demographics over time. In this paper, we introduce a framework to dynamically adapt deidentification for near-real time sharing of person-level surveillance data. MATERIALS AND METHODS: The framework leverages a simulation mechanism, capable of application at any geographic level, to forecast the reidentification risk of sharing the data under a wide range of generalization policies. The estimates inform weekly, prospective policy selection to maintain the proportion of records corresponding to a group size less than 11 (PK11) at or below 0.1. Fixing the policy at the start of each week facilitates timely dataset updates and supports sharing granular date information. We use August 2020 through October 2021 case data from Johns Hopkins University and the Centers for Disease Control and Prevention to demonstrate the framework's effectiveness in maintaining the PK11 threshold of 0.01. RESULTS: When sharing COVID-19 county-level case data across all US counties, the framework's approach meets the threshold for 96.2% of daily data releases, while a policy based on current deidentification techniques meets the threshold for 32.3%. CONCLUSION: Periodically adapting the data publication policies preserves privacy while enhancing public health utility through timely updates and sharing epidemiologically critical features.

Collaboration Structures in COVID-19 Critical Care: Retrospective Network Analysis Study.

BACKGROUND: Few intensive care unit (ICU) staffing studies have examined the collaboration structures of health care workers (HCWs). Knowledge about how HCWs are connected to the care of critically ill patients with COVID-19 is important for characterizing the relationships among team structures, care quality, and patient safety. OBJECTIVE: We aimed to discover differences in the teamwork structures of COVID-19 critical care by comparing HCW collaborations in the management of critically ill patients with and without COVID-19. METHODS: In this retrospective study, we used network analysis methods to analyze the electronic health records (EHRs) of 76 critically ill patients (with COVID-19: n=38; without COVID-19: n=38) who were admitted to a large academic medical center, and to learn about HCW collaboration. We used the EHRs of adult patients who were admitted to the COVID-19 ICU at the Vanderbilt University Medical Center (Nashville, Tennessee, United States) between March 17, 2020, and May 31, 2020. We matched each patient according to age, gender, and their length of stay. Patients without COVID-19 were admitted to the medical ICU between December 1, 2019, and February 29, 2020. We used two sociometrics-eigencentrality and betweenness-to quantify HCWs' statuses in networks. Eigencentrality characterizes the degree to which an HCW is a core person in collaboration structures. Betweenness centrality refers to whether an HCW lies on the path of other HCWs who are not directly connected. This sociometric was used to characterize HCWs' broad skill sets. We measured patient staffing intensity in terms of the number of HCWs who interacted with patients' EHRs. We assessed the statistical differences in the core and betweenness statuses of HCWs and the patient staffing intensities of COVID-19 and non-COVID-19 critical care, by using Mann-Whitney U tests and reporting 95% CIs. RESULTS: HCWs in COVID-19 critical care were more likely to frequently work with each other (eigencentrality: median 0.096) than those in non-COVID-19 critical care (eigencentrality: median 0.057; P<.001). Internal medicine physicians in COVID-19 critical care had higher core statuses than those in non-COVID-19 critical care (P=.001). Nurse practitioners in COVID-19 care had higher betweenness statuses than those in non-COVID-19 care (P<.001). Compared to HCWs in non-COVID-19 settings, the EHRs of critically ill patients with COVID-19 were used by a larger number of internal medicine nurse practitioners (P<.001), cardiovascular nurses (P<.001), and surgical ICU nurses (P=.002) and a smaller number of resident physicians (P<.001). CONCLUSIONS: Network analysis methodologies and data on EHR use provide a novel method for learning about differences in collaboration structures between COVID-19 and non-COVID-19 critical care. Health care organizations can use this information to learn about the novel changes that the COVID-19 pandemic has imposed on collaboration structures in urgent care.

SCOR: A secure international informatics infrastructure to investigate COVID-19.

Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale.