The Influence of Comorbidities, General Health Status, and Self-Care Self-Efficacy on COVID-19 Symptoms During the Omicron Wave.

Background The emergence of the less virulent COVID-19 strains such as Omicron and its subvariants shifted the paradigm of COVID-19 treatment from inpatient treatment to regular outpatient care. The individual health determinants affecting COVID-19 disease severity among vulnerable adults treated in outpatient settings are an under-researched area. Methods This study conducted in an outpatient COVID-19 antibody infusion center employed a cross-sectional survey design to explore the impact of comorbidities, general health status, and self-care self-efficacy on COVID-19 symptom severity. We recruited 120 COVID-19-positive participants over 40 years of age, of which 117 completed the study with 87 providing complete data. After the screening and consenting process, the participants completed the following surveys in a secure REDCap survey software (Vanderbilt University, Nashville, USA) on an iPad (Apple Inc., Cupertino, USA): 1) sociodemographic questionnaire, 2) Charlson Comorbidity Index (CCI) to capture comorbidities, 3) Medical Outcomes Study Short-Form (SF-12) to assess general health including physical (PCS) and mental (MCS) health subscales, 4) Self-Care Self-Efficacy Scale (SCSES) to measure self-care self-efficacy, and 5) the COVID-19 Symptom rating scale (COVID-19 SRS). Statistical analysis used were Chi-square and Pearson correlations.  Results As evidenced by CCI, the top five comorbidities were hypertension (42%), diabetes mellitus (31%), pulmonary disease (19%), depression (14%), and solid tumors (11%). Age was statistically significantly correlated to comorbidity burden (p<0.0001). Severe COVID-19 symptoms reported were fatigue, myalgia, cough, runny nose, and sore throat. The general health status measure (SF-12) subscales showed that the patient's mental component summary (MCS) was more statistically significant to COVID-19 symptom severity than the physical component summary (PCS). The MCS demonstrated a statistically significant correlation with fatigue and myalgia (p<0.0001), headache and breathing difficulties (p<0.001), nausea/vomiting (p<0.01), and abdominal pain/diarrhea (p<0.05). The PCS showed a lesser statistically significant correlation with fatigue, myalgia, headaches (p<0.01), fever/chills, cough, congestion/runny nose, night sweats, breathing difficulties, nausea/vomiting, and abdominal pain/diarrhea (p<0.05). Interestingly, the 'loss of smell' which is the hallmark symptom of COVID-19 was the only symptom that showed a statically significant correlation with the Charlson Comorbidity Index (p<0.05), and it did not show any association with either mental (SF-12 MCS) or physical (SF-12 PCS) health status. The SF-12 MCS also showed a statistically significant correlation with a diagnosis of depression (p< 0.01), validating it as a true measure of mental health among vulnerable adults. The SCSES was not correlated with any of the COVID-19 symptoms. Conclusions The patient's general health status, especially mental health was more statistically significant to COVID-19 symptoms. The COVID-19 hallmark symptom of 'loss of smell' was the only symptom that showed statistical significance with comorbidities. Within the limitations of a cross-sectional survey design and convenient sampling methods, this study calls to tailor general health status, especially mental health, and cumulative comorbidity burden to risk assessment/risk stratification of COVID-19 care.

Coronavirus Disease 2019, Diabetes, and Inflammation: A Systemic Review.

People with cardiometabolic diseases [namely type 2 diabetes (T2D), obesity, or metabolic syndrome] are more susceptible to coronavirus disease 2019 (COVID-19) infection and endure more severe illness and poorer outcomes. Hyperinflammation has been suggested as a common pathway for both diseases. To examine the role of inflammatory biomarkers shared between COVID-19 and cardiometabolic diseases, we reviewed and evaluated published data using PubMed, SCOPUS, and World Health Organization COVID-19 databases for English articles from December 2019 to February 2022. Of 248 identified articles, 50 were selected and included. We found that people with diabetes or obesity have (i) increased risk of COVID-19 infection; (ii) increased risk of hospitalization (those with diabetes have a higher risk of intensive care unit admissions) and death; and (iii) heightened inflammatory and stress responses (hyperinflammation) to COVID-19, which worsen their prognosis. In addition, COVID-19-infected patients have a higher risk of developing T2D, especially if they have other comorbidities. Treatments controlling blood glucose levels and or ameliorating the inflammatory response may be valuable for improving clinical outcomes in these patient populations. In conclusion, it is critical for health care providers to clinically evaluate hyperinflammatory states to drive clinical decisions for COVID-19 patients.

Virtual learning experiences in population health nursing course during the COVID-19 pandemic.

AIM: To discuss the virtual learning strategies used in population health nursing course during the coronavirus disease 2019 (COVID-19) pandemic. BACKGROUND: The School of Nursing faculty in a South Central University in the United States quickly combined innovation with digital resources and transitioned a course in population health during the COVID-19 pandemic. Nursing faculty were challenged to develop student nursing objectives in assessment, planning, intervention and evaluation of vulnerable populations in the community through a virtual environment. REFLECTIONS OF POPULATION HEALTH NURSING CLINICAL EDUCATION: The experiences of five clinical groups are described, covering adults with disabilities, older people, patients with COVID-19 and youth populations. DISCUSSION: The course objectives were met through use of a digital environment. Collaborative interventions were designed and implemented with community stakeholders while maintaining social distancing policies. Successes included increased frequency of communication and learning opportunities for students and the community, and student satisfaction. Barriers to student learning were not related to the digital learning environment, although the older adults required modifications to use electronic devices. CONCLUSION: Virtual classrooms are a viable platform to teach population health nursing and to benefit vulnerable populations. IMPLICATIONS FOR NURSING PRACTICE: Virtual learning offers benefits within academia and the community. Technology offers the possibility to improve mental health among older people and enhance knowledge among the general population. Students are better able to connect with clinical faculty and stakeholders through digital platforms. IMPLICATIONS FOR NURSING POLICY: Nurses play a vital role in improving population health and can collaborate with community stakeholders to implement innovative and sustainable solutions to nursing education, practices and policy. Digital platforms can enhance the involvement of students through these collaborations during and after the pandemic.