Risk of COVID-19 breakthrough infection and hospitalization in individuals with comorbidities.

BACKGROUND: The successful development of multiple COVID-19 vaccines has led to a global vaccination effort to reduce severe COVID-19 infection and mortality. However, the effectiveness of the COVID-19 vaccines wane over time leading to breakthrough infections where vaccinated individuals experience a COVID-19 infection. Here we estimate the risks of breakthrough infection and subsequent hospitalization in individuals with common comorbidities who had completed an initial vaccination series. METHODS: Our study population included vaccinated patients between January 1, 2021 to March 31, 2022 who are present in the Truveta patient population. Models were developed to describe 1) time from completing primary vaccination series till breakthrough infection; and 2) if a patient was hospitalized within 14 days of breakthrough infection. We adjusted for age, race, ethnicity, sex, and year-month of vaccination. RESULTS: Of 1,218,630 patients in the Truveta Platform who had completed an initial vaccination sequence between January 1, 2021 and March 31, 2022, 2.85, 3.42, 2.75, and 2.88 percent of patients with CKD, chronic lung disease, diabetes, or are in an immunocompromised state experienced breakthrough infection, respectively, compared to 1.46 percent of the population without any of these four comorbidities. We found an increased risk of breakthrough infection and subsequent hospitalization in individuals with any of the four comorbidities when compared to individuals without these four comorbidities. CONCLUSIONS: Vaccinated individuals with any of the studied comorbidities experienced an increased risk of breakthrough COVID-19 infection and subsequent hospitalizations compared to the people without any of the studied comorbidities. Individuals with immunocompromising conditions and chronic lung disease were most at risk of breakthrough infection, while people with CKD were most at risk of hospitalization following breakthrough infection. Patients with multiple comorbidities have an even greater risk of breakthrough infection or hospitalization compared to patients with none of the studied comorbidities. Individuals with common comorbidities should remain vigilant against infection even if vaccinated.

Where Is the Digitally Silent Provider? Development and Validation of a Team-Centered Electronic Health Record Attribution Model for Supervising Residents.

PROBLEM: Providing trainees with data and benchmarks on their own patient populations is an Accreditation Council for Graduate Medical Education core residency requirement. Leveraging electronic health records (EHRs) for this purpose relies on correctly attributing patients to the trainees responsible for their care. EHR activity logs are useful for attributing interns to inpatients but not for attributing supervising residents, who often have no inpatient EHR usage obligations, and therefore may generate no digital "footprints" on a given patient-day from which to ascertain attribution. APPROACH: The authors developed and tested a novel team-centered binary logistic regression model leveraging EHR activity logs from July 1, 2018, to June 30, 2019, for pediatric hospital medicine (PHM) supervising residents at the University of California, San Francisco. Unlike patient-centered models that determine daily attribution according to the trainee generating the greatest relative activity in individual patients' charts, the team-centered approach predicts daily attribution based on the trainee generating EHR activity across the greatest proportion of a team's patients. To assess generalizability, the authors similarly modeled supervising resident attribution in adult hospital medicine (AHM) and orthopedic surgery (OS). OUTCOMES: For PHM, AHM, and OS, 1,100, 1,399, and 803 unique patient encounters and 29, 62, and 10 unique supervising residents were included, respectively. Team-centered models outperformed patient-centered models for the 3 specialties, with respective accuracies of 85.4% versus 72.4% (PHM), 88.7% versus 75.4% (AHM), and 69.3% versus 51.6% (OS; P < .001 for all). AHM and PHM models demonstrated relative generalizability to one another while OS did not. NEXT STEPS: Validation at other institutions will be essential to understanding the potential for generalizability of this approach. Accurately attributed data are likely to be trusted more by trainees, enabling programs to operationalize feedback for use cases including performance measurement, case mix assessment, and postdischarge opportunities for follow-up learning.

Measuring political and economic uncertainty: a supervised computational linguistic approach.

In this paper, we develop a computational linguistic approach based on supervised machine learning using the People's Daily to measure Chinese official relations and political uncertainty towards the US. In the first step, we create training samples by asking experts to manually annotate news articles. In the second step, we use supervised machine learning algorithms to adjust our single neural network and support vector machine classifiers to better fit our training data. Finally, we combine our two individual classifiers and a dictionary approach to automatically detect whether an article in the newspaper sample is relevant. Using all of the relevant textual data, we then apply the computational linguistic approach to generate state-of-the-art indices and show that our indices outperform similar current textual indicators in some situations, particularly in the financial market. Supplementary Information: The online version contains supplementary material available at 10.1007/s43546-022-00209-2.

Racial inequality in COVID-treatment and in-hospital length of stay in the US over time.

Introduction: Demonstrated health inequalities persist in the United States. SARS-CoV-2 (COVID) has been no exception, with access to treatment and hospitalization differing across race or ethnic groups. Here, we aim to assess differences in treatment with remdesivir and hospital length of stay across the four waves of the pandemic. Materials and methods: Using a subset of the Truveta data, we examine the odds ratio (OR) of in-hospital remdesivir treatment and risk ratio (RR) of in-hospital length of stay between Black or African American (Black) to White patients. We adjusted for confounding factors, such as age, sex, and comorbidity status. Results: There were statistically significant lower rates of remdesivir treatment and longer in-hospital length of stay comparing Black patients to White patients early in the pandemic (OR for treatment: 0.88, 95% confidence interval [CI]: 0.80, 0.96; RR for length of stay: 1.17, CI: 1.06, 1.21). Rates became close to parity between groups as the pandemic progressed. Conclusion: While inpatient remdesivir treatment rates increased and length of stay decreased over the beginning course of the pandemic, there are still inequalities in patient care.

A call for social informatics.

As evidence of the associations between social factors and health outcomes continues to mount, capturing and acting on social determinants of health (SDOH) in clinical settings has never been more relevant. Many professional medical organizations have endorsed screening for SDOH, and the U.S. Office of the National Coordinator for Health Information Technology has recommended increased capacity of health information technology to integrate and support use of SDOH data in clinical settings. As these efforts begin their translation to practice, a new subfield of health informatics is emerging, focused on the application of information technologies to capture and apply social data in conjunction with health data to advance individual and population health. Developing this dedicated subfield of informatics-which we term social informatics-is important to drive research that informs how to approach the unique data, interoperability, execution, and ethical challenges involved in integrating social and medical care.

The impact of transitioning from availability of outside records within electronic health records to integration of local and outside records within electronic health records.

OBJECTIVE: While there has been a substantial increase in health information exchange, levels of outside records use by frontline providers are low. We assessed whether integration between outside data and local data results in increased viewing of outside records, overall and by encounter, provider, and patient type. MATERIALS AND METHODS: Using data from UCSF Health, we measured change in outside record views after integrating the list of local (UCSF) and outside (other health systems on Epic [Epic Systems, Verona, WI]) encounters on the Chart Review tab. Previously, providers only viewed records from outside encounters on a separate tab. We used an interrupted time series design (with outside record viewing event counts aggregated to the week level) to measure changes in the level and trend over a 1-year period. RESULTS: There was a large increase in the level of outside record views of 22 920 per week (P < .001). The change in trend went from a weekly increase of 116 (P < .05) to a decrease of 402 (P = .08), reflecting a small effect decay. There were increases in the level of views for all provider and encounter types: attendings (n = 3675), residents (n = 3277), and nurses (n = 914); and inpatient (n = 1676), emergency (n = 487), and outpatient (n = 7228) (P < .001 for all). Results persisted when adjusted for total encounter volume. DISCUSSION: While outside records were readily available before the encounter integration, the simple step of clicking on a separate tab appears to have depressed use. CONCLUSIONS: User interface designs that comingle local and outside data result in higher levels of viewing and should be more broadly pursued.

Common procedures in internal medicine: improving knowledge and minimizing complications.

Internal medicine physicians have long been trained with the skills, knowledge, and attitudes to become proficient at certain medical procedures. Specifically, the lumbar puncture, paracentesis, thoracentesis, and central venous catheter placement are common medical procedures encountered during residency. Despite recent changes that no longer require documented competency in procedure performance, many residents and their attending supervisors continue to perform these procedures on a regular basis. In private practice many internists care for patients requiring these procedures. This review will summarize basic steps followed in these 4 medical procedures and highlight methods to minimize associated complications.

Mobile healthcare: the opportunities and challenges.

Mobile healthcare is an important extension of electronic healthcare. It enables the caregivers to have an ubiquitous and uninterrupted access to patients' clinical data and the latest medical knowledge; concurrently, it allows patients with chronic conditions to remain under constant observation without needing to be physically present at the clinic. The critical challenges to a full-scale implementation include establishing interoperability among electronic health records, developing better display technologies and security controls for mobile devices and developing smart algorithms to detect clinically significant events before notifying caregivers. As a consequence of mobile healthcare, new opportunities for physician-patient joint decision-making and personalized healthcare are beginning to take shape. Accompanying them are the challenges of mindset adjustment, the empowerment of patients with medical knowledge in everyday language and ensuring the confidentiality of patient data.

Accurate multiplex polony sequencing of an evolved bacterial genome.

We describe a DNA sequencing technology in which a commonly available, inexpensive epifluorescence microscope is converted to rapid nonelectrophoretic DNA sequencing automation. We apply this technology to resequence an evolved strain of Escherichia coli at less than one error per million consensus bases. A cell-free, mate-paired library provided single DNA molecules that were amplified in parallel to 1-micrometer beads by emulsion polymerase chain reaction. Millions of beads were immobilized in a polyacrylamide gel and subjected to automated cycles of sequencing by ligation and four-color imaging. Cost per base was roughly one-ninth as much as that of conventional sequencing. Our protocols were implemented with off-the-shelf instrumentation and reagents.