No abstract

No abstract

Background Nursing home residents were impacted disproportionately by the coronavirus because of their vulnerabilities. Although many studies concentrated on risk factors associated with mortality of hospitalized patients, there were limited studies epitomizing them from skilled nursing facilities to hospitals. The study aims to identify inpatients’ characteristics on demographics, hospital admission types, insurance types, and chronic diseases associated with mortality among our cohort patients in Texas. Methods Individuals above 50 years, diagnosed with Covid-19, and admitted from skilled nursing facilities were included in the retrospective cohort study. Pearson’s Chi-Square and Mann-Whitney tests were applied to measure four major perspectives between survivors and non-survivors. Then, a binary logistic regression was employed to determine the association between independent variables and mortality. Results A total of 218 patients were included in the study, of which 54 (24.8%) died during hospitalization. According to the univariate analysis, expired patients were more likely to be emergency admission (p = 0.001), elective admission (p = 0.02), Medicaid as primary payment (p = 0.034), heart disease (p = 0.027), CKD (p = 0.03), and hypertension (p = 0.002). The binary logistic regression revealed that hypertension (OR = 3.176, 95% CI: 1.200-8.409, p = 0.02) and Medicaid (OR = 2.637, 95% CI: 1.287-5.405, p = 0.008) as primary payment had significantly high odds of mortality. Conclusion Hypertension and Medicaid as primary payment are the strongest predictive factors associated with mortality and suggest that hospitals in Texas distribute critical care and resources while prevent and treat them to increase survival rates.

Background Elderly patients are a vulnerable group during the Covid-19 pandemic, especially those with cancer. Our study aims to identify how Covid-19 impacts elderly inpatients with kidney cancer and determine risk factors associated with increased mortality. Methods Our retrospective cohort study utilized the PUDF dataset and included inpatients over 60-year-old, diagnosed with kidney cancer, and hospitalized within 30-day. Person’s Chi-Square was used to measure the differences between survivors and non-survivors, and the Mann-Whitney test was for non-normality distribution for continuous variables. Then, a binary logistic regression was employed to identify the association between independent variables and mortality. Results Five hundred and twenty-two patients were included in the study, of which 7 (1.4%) died during hospitalization. According to the univariate analysis and Mann-Whitney test, expired patients were more likely to experience older age (p = 0.005), longer length of stay (p = 0.009), ICU (p = 0.012), HMO Medicare Risk (p = 0.005), Covid-19 (p < 0.001), paralysis (p < 0.001), and higher illness severity (p < 0.001). The binary logistic regression revealed that older age (OR = 1.120, 95% CI: 1.004-1.249, p = 0.042) and the SOI (OR = 4.635, 95% CI: 1.339-16.052, p = 0.016) had significantly high odds of mortality. Conclusion The retrospective cohort study reveals that although Covid-19 was not a predictive factor associated with increased mortality, there was a statistically significant difference between the survivor and non-survivor groups. Further studies need to assess its association with kidney cancer or other various types of cancer.

Background Nursing homes were impacted disproportionately by the coronavirus because of their resident’s vulnerabilities and settings. Even many previous studies illustrated factors related to nursing home residents’ Covid-19 infections, there’s no such study epitomizing those factors systematically, while some factors were controversial in different studies. The article aims to summarize major types of factors and provide crucially influential implications for nursing homes to prevent and manage their resident infections. Methods All articles published between 01 January 2020 - 15 January 2021 in English version were searched through three electronic databases (PubMed, Web of Science, and Scopus). Two authors screened and evaluated a total of 121 studies independently based on selection and extraction criteria. Results Seventeen identified studies were included in the research, which involved five major types of factors (nursing home’s residence, nursing home, staff, resident, and others). Conclusion nursing home’s county infection rate, size, and staff residence were the strongest significant factors in many studies. Per-capital income, symptom-based screening and testing, and asymptomatic individuals have impacted resident’s infections variously since the beginning of the pandemic. Nursing home’s star rating and a total count of fines became factors when considered its locations. Other factors, including nursing home’s type, historical health deficiencies, staffing level, and staff working different facilities, etc., were also significant factors. The value of factors suggests healthcare systems reflect appropriate measures and allocate more resources to nursing homes in high prevalence counties on the basis of universal allocation.