Does COVID-19 Infection Increase the Risk of Diabetes? Current Evidence.

PURPOSE OF REVIEW: Multiple studies report an increased incidence of diabetes following SARS-CoV-2 infection. Given the potential increased global burden of diabetes, understanding the effect of SARS-CoV-2 in the epidemiology of diabetes is important. Our aim was to review the evidence pertaining to the risk of incident diabetes after COVID-19 infection. RECENT FINDINGS: Incident diabetes risk increased by approximately 60% compared to patients without SARS-CoV-2 infection. Risk also increased compared to non-COVID-19 respiratory infections, suggesting SARS-CoV-2-mediated mechanisms rather than general morbidity after respiratory illness. Evidence is mixed regarding the association between SARS-CoV-2 infection and T1D. SARS-CoV-2 infection is associated with an elevated risk of T2D, but it is unclear whether the incident diabetes is persistent over time or differs in severity over time. SARS-CoV-2 infection is associated with an increased risk of incident diabetes. Future studies should evaluate vaccination, viral variant, and patient- and treatment-related factors that influence risk.

Clinical encounter heterogeneity and methods for resolving in networked EHR data: a study from N3C and RECOVER programs.

OBJECTIVE: Clinical encounter data are heterogeneous and vary greatly from institution to institution. These problems of variance affect interpretability and usability of clinical encounter data for analysis. These problems are magnified when multisite electronic health record (EHR) data are networked together. This article presents a novel, generalizable method for resolving encounter heterogeneity for analysis by combining related atomic encounters into composite "macrovisits." MATERIALS AND METHODS: Encounters were composed of data from 75 partner sites harmonized to a common data model as part of the NIH Researching COVID to Enhance Recovery Initiative, a project of the National Covid Cohort Collaborative. Summary statistics were computed for overall and site-level data to assess issues and identify modifications. Two algorithms were developed to refine atomic encounters into cleaner, analyzable longitudinal clinical visits. RESULTS: Atomic inpatient encounters data were found to be widely disparate between sites in terms of length-of-stay (LOS) and numbers of OMOP CDM measurements per encounter. After aggregating encounters to macrovisits, LOS and measurement variance decreased. A subsequent algorithm to identify hospitalized macrovisits further reduced data variability. DISCUSSION: Encounters are a complex and heterogeneous component of EHR data and native data issues are not addressed by existing methods. These types of complex and poorly studied issues contribute to the difficulty of deriving value from EHR data, and these types of foundational, large-scale explorations, and developments are necessary to realize the full potential of modern real-world data. CONCLUSION: This article presents method developments to manipulate and resolve EHR encounter data issues in a generalizable way as a foundation for future research and analysis.

Are fewer cases of diabetes mellitus diagnosed in the months after SARS-CoV-2 infection? A population-level view in the EHR-based RECOVER program.

Long-term sequelae of severe acute respiratory coronavirus-2 (SARS-CoV-2) infection may include increased incidence of diabetes. Here we describe the temporal relationship between new type 2 diabetes and SARS-CoV-2 infection in a nationwide database. We found that while the proportion of newly diagnosed type 2 diabetes increased during the acute period of SARS-CoV-2 infection, the mean proportion of new diabetes cases in the 6 months post-infection was about 83% lower than the 6 months preinfection. These results underscore the need for further investigation to understand the timing of new diabetes after COVID-19, etiology, screening, and treatment strategies.

Coding long COVID: characterizing a new disease through an ICD-10 lens.

BACKGROUND: Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the USA took nearly 2 years after patients had begun to describe their condition. Here, we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." METHODS: We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 33,782), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. RESULTS: We established the diagnoses most commonly co-occurring with U09.9 and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty and low unemployment. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. CONCLUSIONS: This work offers insight into potential subtypes and current practice patterns around long COVID and speaks to the existence of disparities in the diagnosis of patients with long COVID. This latter finding in particular requires further research and urgent remediation.

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

T-cell cellular stress and reticulocyte signatures, but not loss of naïve T lymphocytes, characterize severe COVID-19 in older adults.

In children and younger adults up to 39 years of age, SARS-CoV-2 usually elicits mild symptoms that resemble the common cold. Disease severity increases with age starting at 30 and reaches astounding mortality rates that are ~330 fold higher in persons above 85 years of age compared to those 18-39 years old. To understand age-specific immune pathobiology of COVID-19, we have analyzed soluble mediators, cellular phenotypes, and transcriptome from over 80 COVID-19 patients of varying ages and disease severity, carefully controlling for age as a variable. We found that reticulocyte numbers and peripheral blood transcriptional signatures robustly correlated with disease severity. By contrast, decreased numbers and proportion of naïve T-cells, reported previously as a COVID-19 severity risk factor, were found to be general features of aging and not of COVID-19 severity, as they readily occurred in older participants experiencing only mild or no disease at all. Single-cell transcriptional signatures across age and severity groups showed that severe but not moderate/mild COVID-19 causes cell stress response in different T-cell populations, and some of that stress was unique to old severe participants, suggesting that in severe disease of older adults, these defenders of the organism may be disabled from performing immune protection. These findings shed new light on interactions between age and disease severity in COVID-19.

An integrated LSTM-HeteroRGNN model for interpretable opioid overdose risk prediction.

Opioid overdose (OD) has become a leading cause of accidental death in the United States, and overdose deaths reached a record high during the COVID-19 pandemic. Combating the opioid crisis requires targeting high-need populations by identifying individuals at risk of OD. While deep learning emerges as a powerful method for building predictive models using large scale electronic health records (EHR), it is challenged by the complex intrinsic relationships among EHR data. Further, its utility is limited by the lack of clinically meaningful explainability, which is necessary for making informed clinical or policy decisions using such models. In this paper, we present LIGHTED, an integrated deep learning model combining long short term memory (LSTM) and graph neural networks (GNN) to predict patients' OD risk. The LIGHTED model can incorporate the temporal effects of disease progression and the knowledge learned from interactions among clinical features. We evaluated the model using Cerner's Health Facts database with over 5 million patients. Our experiments demonstrated that the model outperforms traditional machine learning methods and other deep learning models. We also proposed a novel interpretability method by exploiting embeddings provided by GNNs to cluster patients and EHR features respectively, and conducted qualitative feature cluster analysis for clinical interpretations. Our study shows that LIGHTED can take advantage of longitudinal EHR data and the intrinsic graph structure of EHRs among patients to provide effective and interpretable OD risk predictions that may potentially improve clinical decision support.

Effect of SARS-CoV-2 Infection and Infection Severity on Longer-Term Glycemic Control and Weight in People With Type 2 Diabetes.

OBJECTIVE: To evaluate the association of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severity of infection with longer-term glycemic control and weight in people with type 2 diabetes (T2D) in the U.S. RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort study using longitudinal electronic health record data of patients with SARS-CoV-2 infection from the National COVID Cohort Collaborative (N3C). Patients were ≥18 years old with an ICD-10 diagnosis of T2D and at least one HbA1c and weight measurement prior to and after an index date of their first coronavirus disease 2019 (COVID-19) diagnosis or negative SARS-CoV-2 test. We used propensity scores to identify a matched cohort balanced on demographic characteristics, comorbidities, and medications used to treat diabetes. The primary outcome was the postindex average HbA1c and postindex average weight over a 1 year time period beginning 90 days after the index date among patients who did and did not have SARS-CoV-2 infection. Secondary outcomes were postindex average HbA1c and weight in patients who required hospitalization or mechanical ventilation. RESULTS: There was no significant difference in the postindex average HbA1c or weight in patients who had SARS-CoV-2 infection compared with control subjects. Mechanical ventilation was associated with a decrease in average HbA1c after COVID-19. CONCLUSIONS: In a multicenter cohort of patients in the U.S. with preexisting T2D, there was no significant change in longer-term average HbA1c or weight among patients who had COVID-19. Mechanical ventilation was associated with a decrease in HbA1c after COVID-19.

Building the Positive Emotion-Resilience-Coping Efficacy Model for COVID-19 Pandemic.

The world faces unprecedented challenges because of the Coronavirus Disease 2019 (COVID-19). Existing theories of human flourishing and coping efficacy are too broad and general to address COVID-19 unprecedented mental health challenges. This study examined two main objectives, first the associations between psychological outcomes (i.e., depression, anxiety, and stress) and psychological wellbeing of this phenomenon, and second, moderating and mediating factors emotions, resilience and coping self-efficacy. A nationwide survey was carried out on a Malaysian sample (n = 920). Participants completed an on-line survey that assessed psychological outcomes, psychological wellbeing, positive-negative emotions, resilience, and coping self-efficacy. The relationship between psychological states and psychological wellbeing was successfully mediated by coping self-efficacy (direct effects of -0.31 to -0.46 at p < 0.01) and resilience (direct effects of -0.06 to -0.26 at p < 0.01). Moreover, positive emotion significantly moderated depression (b = -0.02, p < 0.01) and anxiety (b = -0.14, p = 0.05) with psychological wellbeing. Findings highlighted the importance of these factors in developing a dedicated model to be built into the recovery plan to ameliorate the negative impact of COVID-19 on psychological wellbeing. Hence, the Positive Emotion-Resilience-Coping Efficacy Model was developed.

Living in a Region With a Low Level of COVID-19 Infection: Health Belief Toward COVID-19 Vaccination and Intention to Receive a COVID-19 Vaccine in Hong Kong Individuals.

INTRODUCTION: Vaccination is vital for controlling the COVID-19 pandemic. Individuals' vaccination intention is a good predictor of vaccine uptake and is influenced by individuals' health belief toward vaccination. Regions with different levels of pandemic severity may present varying effects. This study aimed to determine the influence of health belief on COVID-19 vaccination intention in a region with a low level of COVID-19 infection. METHODS: This cross-sectional telephone survey was conducted on a quota sample of 800 adults in Hong Kong before the commencement of the local COVID-19 vaccination program. The Health Belief Model Scale-COVID-19 was developed to assess health belief toward COVID-19 vaccination. The contribution of health belief on explaining intention to receive the COVID-19 vaccine was assessed using logistic regression. RESULTS: The subjects demonstrated moderate levels in all aspects of health belief. Only 28.9% of the subjects indicated an intention to receive a COVID-19 vaccine. After controlling for age, educational level, marital status, and high risk status, the logistic regression analysis indicated that perceived benefits of vaccination (OR = 1.615; CI 95%: 1.443-1.807; P < .001), perceived susceptibility to COVID-19 (OR = 1.130; CI 95%: 1.032-1.237; P = .008), cues to action toward vaccination (OR = 1.212; CI 95%: 1.108-1.326; P < .001), and perceived barriers to vaccination (OR = .696; CI 95%: .641-.756; P < .001) were associated with intention to receive a COVID-19 vaccine. CONCLUSION: Vaccination campaigns in regions with good control of the pandemic should promote the benefits of vaccination, emphasizing how it can help individuals regain a sense of normalcy in their daily lives and stop the spread of COVID-19. Although the COVID-19 pandemic affects countries worldwide, this study highlights the importance of adopting specific vaccination promotion strategies for regions with different levels of pandemic severity.

Glycemic Control and Clinical Outcomes in U.S. Patients With COVID-19: Data From the National COVID Cohort Collaborative (N3C) Database.

OBJECTIVE: The purpose of the study is to evaluate the relationship between HbA1c and severity of coronavirus disease 2019 (COVID-19) outcomes in patients with type 2 diabetes (T2D) with acute COVID-19 infection. RESEARCH DESIGN AND METHODS: We conducted a retrospective study using observational data from the National COVID Cohort Collaborative (N3C), a longitudinal, multicenter U.S. cohort of patients with COVID-19 infection. Patients were ≥18 years old with T2D and confirmed COVID-19 infection by laboratory testing or diagnosis code. The primary outcome was 30-day mortality following the date of COVID-19 diagnosis. Secondary outcomes included need for invasive ventilation or extracorporeal membrane oxygenation (ECMO), hospitalization within 7 days before or 30 days after COVID-19 diagnosis, and length of stay (LOS) for patients who were hospitalized. RESULTS: The study included 39,616 patients (50.9% female, 55.4% White, 26.4% Black or African American, and 16.1% Hispanic or Latino, with mean ± SD age 62.1 ± 13.9 years and mean ± SD HbA1c 7.6% ± 2.0). There was an increasing risk of hospitalization with incrementally higher HbA1c levels, but risk of death plateaued at HbA1c >8%, and risk of invasive ventilation or ECMO plateaued >9%. There was no significant difference in LOS across HbA1c levels. CONCLUSIONS: In a large, multicenter cohort of patients in the U.S. with T2D and COVID-19 infection, risk of hospitalization increased with incrementally higher HbA1c levels. Risk of death and invasive ventilation also increased but plateaued at different levels of glycemic control.

The Effect of Pets on Human Mental Health and Wellbeing during COVID-19 Lockdown in Malaysia.

The adverse impact of SARS-CoV-2 (COVID-19) on mental and physical health has been witnessed across the globe. Associated mental health and wellbeing issues include stress, social isolation, boredom, and anxiety. Research suggests human-animal interactions may improve the overall wellbeing of an individual. However, this has been less explored in Southeast Asian countries like Malaysia and the present study examined the effect of pets on the mental health and wellbeing of Malaysians during the lockdown, or movement control order (MCO), due to COVID-19 pandemic. A cross-sectional survey was carried out, with 448 Malaysian participants, who completed online assessments for psychological outcomes, psychological wellbeing, positive-negative emotions, resilience, and coping self-efficacy. Results indicate that pet owners reported significantly better coping self-efficacy, significantly more positive emotions, and better psychological wellbeing, but contrary to expectations, there was no differences on other measures. Among pet owners, cat owners reported more positive emotions and greater wellbeing than dog owners. The results show that that pets have some impact on improved psychological health of their owners and could be integrated into recovery frameworks for promoting mental health and wellbeing.

Reuse of face masks among adults in Hong Kong during the COVID-19 pandemic.

BACKGROUND: During the COVID-19 pandemic, over 99% of adults in Hong Kong use face masks in public. With the limited supply of face masks in the market and the uncertainty about the future development of COVID-19, reusing face masks is a legitimate way to reduce usage. Although this practice is not recommended, reusing face masks is common in Hong Kong. This study aimed to examine the practice of reusing face masks among adults in Hong Kong during the COVID-19 pandemic and its association with their health beliefs toward this health crisis. METHODS: A cross-sectional descriptive study was conducted. A quota sample of 1000 adults was recruited in Hong Kong in April 2020. Guided by the Health Belief Model, the subjects were invited to answer questions on their practice of reusing face masks and health beliefs toward COVID-19 through telephone interview. Their practice on reuse, storage, and decontamination of used face masks were summarized by descriptive statistics. The difference in health beliefs between the subjects who reused and did not reuse face masks was examined by conducting an independent t test. The association between health beliefs and reuse of face masks was determined by conducting a logistic regression analysis. RESULTS: One-third (n = 345, 35.4%) of the subjects reused face masks in an average of 2.5 days. Among them, 207 subjects stored and 115 subjects decontaminated their used face masks by using various methods. The subjects who reused face masks significantly perceived having inadequate face masks (t = 3.905; p <  0.001). Having a higher level of perception of having inadequate face masks increased the likelihood of reusing face masks (OR = 0.784; CI 95%: 0.659-0.934; p = 0.006). CONCLUSION: Despite having 90 face masks in stock, the adults who reused face masks significantly perceived that they had inadequate face masks. Concerted effort of health care professionals, community organizations, and the government will improve individuals' practice in use of face masks and alleviate their actual and perceived feeling of having inadequate face masks, which lead them to reuse.

Orthogonal SARS-CoV-2 Serological Assays Enable Surveillance of Low-Prevalence Communities and Reveal Durable Humoral Immunity.

We conducted a serological study to define correlates of immunity against SARS-CoV-2. Compared to those with mild coronavirus disease 2019 (COVID-19) cases, individuals with severe disease exhibited elevated virus-neutralizing titers and antibodies against the nucleocapsid (N) and the receptor binding domain (RBD) of the spike protein. Age and sex played lesser roles. All cases, including asymptomatic individuals, seroconverted by 2 weeks after PCR confirmation. Spike RBD and S2 and neutralizing antibodies remained detectable through 5-7 months after onset, whereas α-N titers diminished. Testing 5,882 members of the local community revealed only 1 sample with seroreactivity to both RBD and S2 that lacked neutralizing antibodies. This fidelity could not be achieved with either RBD or S2 alone. Thus, inclusion of multiple independent assays improved the accuracy of antibody tests in low-seroprevalence communities and revealed differences in antibody kinetics depending on the antigen. We conclude that neutralizing antibodies are stably produced for at least 5-7 months after SARS-CoV-2 infection.