Coronavirus Disease 2019 Mortality Research Articles

Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients

Abstract South Africa was the most affected country in Africa by the coronavirus disease 2019 (COVID-19) pandemic, where over 4 million confirmed cases of COVID-19 and over 102,000 deaths have been recorded since 2019. Aside from clinical methods, artificial intelligence (AI)-based solutions such as machine learning (ML) models have been employed in treating COVID-19 cases. However, limited application of AI for COVID-19 in Africa has been reported in the literature. This study aimed to investigate the performance and interpretability of several ML algorithms, including deep multilayer perceptron (Deep MLP), support vector machine (SVM) and Extreme gradient boosting trees (XGBoost) for predicting COVID-19 mortality risk with an emphasis on the effect of cross-validation (CV) and principal component analysis (PCA) on the results. For this purpose, a dataset with 154 features from 490 COVID-19 patients admitted into the intensive care unit (ICU) of Tygerberg Hospital in Cape Town, South Africa, during the first wave of COVID-19 in 2020 was retrospectively analysed. Our results show that Deep MLP had the best overall performance (F1 = 0.92; area under the curve (AUC) = 0.94) when CV and the synthetic minority oversampling technique (SMOTE) were applied without PCA. By using the Shapley Additive exPlanations (SHAP) model to interpret the mortality risk predictions, we identified the Length of stay (LOS) in the hospital, LOS in the ICU, Time to ICU from admission, days discharged alive or death, D-dimer (blood clotting factor), and blood pH as the six most critical variables for mortality risk prediction. Also, Age at admission, Pf ratio (PaO2/FiO2 ratio), troponin T (TropT), ferritin, ventilation, C-reactive protein (CRP), and symptoms of acute respiratory distress syndrome (ARDS) were associated with the severity and fatality of COVID-19 cases. The study reveals how ML could assist medical practitioners in making informed decisions on handling critically ill COVID-19 patients with comorbidities. It also offers insight into the combined effect of CV, PCA, and SMOTE on the performance of ML models for COVID-19 mortality risk prediction, which has been little explored.

Investigating the effectiveness of whole-virus, protein-based, and vector-based SARS-CoV-2 vaccines on the rates of COVID-19 infection, hospitalization, and mortality: a historical cohort study in Iran

BackgroundEach of the Coronavirus disease 2019 (COVID-19) vaccines has its characteristics that can affect their effectiveness in preventing hospitalization and patient mortality. The present study aimed to determine the effectiveness of COVID-19 vaccines, including whole-virus, protein-based, and vector-based on COVID-19 infection, hospitalization, and mortality.MethodsThe current cohort study was conducted using the data of all people who received at least two doses of each type of COVID-19 vaccine from March 2020 to August 2022 in Khorasan Rzavi province. Quantitative data were characterized using measures of central tendency and dispersion, while the risks of infection, hospitalization, and mortality were assessed for each type of vaccine. Logistic regression was employed to evaluate the effect of various vaccine types on infection rate, hospitalization, and mortality while adjusting for other independent variables.ResultsIn this study, over 2 million vaccinated individuals had received at least two doses of the COVID-19 vaccine. Whole-virus vaccines, which had an infection rate of 3%, a hospitalization rate of 1.4%, and a mortality rate of 0.2%, were the most effective vaccines. The study also identified several factors associated with an increased risk of COVID-19 infection, hospitalization, and mortality, including kidney disease, cardiovascular disease, respiratory issues, age, sex, diabetes, olfactory dysfunction, fever, chills, body mass index (BMI), and the type of vaccine.ConclusionThis study highlights the substantial impact of COVID-19 vaccination in reducing infection, hospitalization, and mortality rates in Iran. The results support the use of whole-virus and vector-based vaccines as more effective options for preventing COVID-19 outcomes. The study also emphasizes the importance of considering various factors, including medical history and side effects, when evaluating the effectiveness of different vaccine types. The findings of this study can inform public health policy and vaccination strategies in Iran and other countries.

Clinico-demographic factors affecting mortality in COVID-19 patients at a health care facility, Western Uttar Pradesh

ABSTRACT Background: Coronavirus disease 2019 (COVID-19) a global problem, has affected all aspects of health, that is, physical, social, and mental. Our study aimed to describe various social-demographic factors and existing comorbidities that affected mortality in COVID-19 admitted patients. Materials and Methods: This was a hospital-based retrospective study. The study included medical records of COVID-19 patients admitted from April 2021 to August 2021. Data was entered in Microsoft Excel 2013 and analyzed in STATA version 18.0. The Chi-square test, unpaired t-test, and Cox proportional hazard model were employed for analysis. Results: Of the total 1156 admitted patients, 103 hospitalized patients progressed to death (8.91%). Factors found to be significant with non-survivorship were age, residence, admission status, and coexisting comorbidities. In hazard analysis, geriatric patients had 4.358 times more hazard of death. Patients with diabetes and hypertension had two times higher hazard of death than patients without these comorbidities. Conclusion: Mortality of patients was substantially higher in patients’ ages above 60 years and patients with comorbidities. Therefore, close monitoring and priority treatment should be provided to elderly patients and patients with comorbidities.

Red blood cell distribution width-to-monocyte ratio for predicting 90-day mortality of COVID-19 in patients with chronic kidney disease during the Omicron period: a prospective single-center study

We aimed to test whether red blood cell distribution width (RDW) to monocyte percentage ratio (RMR) was associated with the acute-phase prognosis of coronavirus disease 2019 (COVID-19) in chronic kidney disease (CKD) patients. Prospective enrollment and 90-day follow-up of CKD patients with COVID-19 were conducted from December 1, 2022 to January 31, 2023. Demographics, clinical data, and laboratory and radiographic findings were collected, and multiple logistic regression, subgroup analysis, and receiver operating characteristic (ROC) curve analysis were performed. A total of 218 patients were enrolled, with a mean age of 59 years and 69.7% being male. The 90-day mortality rate was 24.8%. The lnRMR level was 5.18 (4.91-5.43) and emerged as an independent risk factor (OR: 3.01, 95% CI: 1.72-5.85). The lnRMR-mortality association was consistent across sex, age, CKD stage, COVID-19 vaccination, and comorbidity subgroups. The area under the ROC curve of lnRMR was 0.737 (95% CI: 0.655-0.819). Our findings indicate that lnRMR is a simple and practical predictor for identifying high-risk CKD patients during the acute phase of COVID-19.

The Association of County-Level Presidential Election Outcome and COVID-19 Mortality in Colorado, 2020-2022.

The Coronavirus disease 2019 (COVID-19) pandemic occurred during a time of political tension in the United States. County-level political environment may have been influential in COVID-19 outcomes. This study examined the association between county-level political environment and age-adjusted COVID-19 mortality rates from 2020 to 2022. Political environment was measured by the 2020 Presidential election results and compared with age-adjusted COVID-19 mortality rates by county in Colorado. Rate ratios (RR) and 95% confidence intervals (CI) were estimated using negative binomial regression incorporating a population offset term. Models adjusted for populational differences using the demographics percentile from Colorado's EnviroScreen Environmental Justice Tool. Age-adjusted county mortality rates ranged from 14.3 to 446.8.0 per 100000. 2021 COVID-19 mortality rates were nearly twice as high in counties voting for Donald Trump compared to those voting for Joseph Biden (adjusted RR=1.98, 95% CI: 1.59, 2.47). Results for 2020 and 2022 mortality models were also in the positive direction, though the confidence intervals crossed null values. These results build on a growing body of evidence that political environment may have been influential for COVID-19 mortality, helping to understand the drivers of health outcomes. Implications for the public health system as we shift into the endemic period of COVID-19 include motivation for collaborative work to restore and rebuild trust among and between stakeholders and the community, as well as increase health education given its' influence on both individual and community behaviors.

Metabolic Disturbances Associated with In-Hospital Complication and Mortality in Different Types of Pneumonia.

Bakground: The mortality rate from community-acquired pneumonia (CAP) or coronavirus disease 19 (COVID-19) is high, especially in hospitalized patients. This study aimed to assess the disturbances of glucose and lipid metabolism with in-hospital complications and short-term outcomes for patients with pneumonia with different etiologies. Methods: This observational study comprised 398 patients divided as follows: 155 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia, 129 participants with viral CAP, and 114 with bacterial pneumonia. Results: Fasting plasma glucose (FPG) at admission and glycemic variation during hospitalization was linked with acute kidney injury (AKI) in bacterial CAP. Compared with a value <110 mg/dL for FPG at admission, levels between 110 and 126 mg/dL are associated with mortality in both COVID-19 (OR = 3.462, 95% CI: 1.275-9.398, p = 0.015) and bacterial CAP participants (OR = 0.254; 95% CI: 0.069-0.935, p = 0.039), while a value ≥126 mg/dL was linked with mortality only in patients with SARS-CoV-2 (OR = 3.577, 95% CI: 1.166-10.976, p = 0.026). No relation between lipid biomarkers and complications or in-hospital outcomes was observed in all three participant groups. Conclusions: Patients with bacterial CAP are more prone to developing AKI due to increased FBG at admission and glycemic variations during hospitalization, while elevated FBG values at admission are associated with mortality in both COVID-19 and bacterial CAP.

Mycobiome analyses of critically ill COVID-19 patients.

Coronavirus disease 2019 (COVID-19)-associated pulmonary aspergillosis (CAPA) is a life-threatening complication in patients with severe COVID-19. Previously, acute respiratory distress syndrome in patients with COVID-19 has been associated with lung fungal dysbiosis, evidenced by reduced microbial diversity and Candida colonization. Increased fungal burden in the lungs of critically ill COVID-19 patients is linked to prolonged mechanical ventilation and increased mortality. However, specific mycobiome signatures associated with severe COVID-19 in the context of survival and antifungal drug prophylaxis have not yet been determined, and such knowledge could have an important impact on treatment. To understand the composition of the respiratory mycobiome in critically ill COVID-19 patients with and without CAPA and the impact of antifungal use in patient outcome, we performed a multinational study of 39 COVID-19 patients in intensive care units (ICUs). Respiratory mycobiome was profiled using internal transcribed spacer 1 sequencing, and Aspergillus fumigatus burden was further validated using quantitative PCR. Fungal communities were investigated using alpha diversity, beta diversity, taxa predominance, and taxa abundances. Respiratory mycobiomes of COVID-19 patients were dominated by Candida and Aspergillus. There was no significant association with corticosteroid use or CAPA diagnosis and respiratory fungal communities. Increased A. fumigatus burden was associated with mortality and, the use of azoles at ICU admission was linked with an absence of A. fumigatus. Our findings suggest that mold-active antifungal treatment at ICU admission may be linked with reduced A. fumigatus-associated mortality in severe COVID-19. However, further studies are warranted on this topic.IMPORTANCEInvasive fungal infections are a serious complication affecting up to a third of patients with severe COVID-19. Nevertheless, our understanding of the fungal communities in the lungs during critically ill COVID-19 remains limited. Evidence suggests a higher fungal burden is associated with prolonged ventilation and higher mortality, although the particular organisms responsible for this link are unclear. Antifungal prophylaxis may be beneficial for reducing the burden of fungal co-infections in COVID-19 intensive care. However, the composition of the fungal microbiome in severe COVID-19 in relation to prophylactic antifungals, as well as how this is associated with survival outcomes, is yet to be studied. Our study provides insights into the lung fungal microbiome in severe COVID-19 and has found antifungal treatment to be associated with lower Aspergillus fumigatus burden and that higher levels of this pathogen are associated with mortality. Therefore, our study suggests mold-active antifungal prophylaxis may be beneficial in severe COVID-19.

D-Dimer Level on Admission as an Outcome Predictor of COVID-19 Patients in Tertiary Care Hospitals in Bangladesh

Background: Worldwide, along with clinical scenario and imaging techniques, biochemical markers have been used in management of Coronavirus Disease-2019 (COVID-19). Different haematological, biochemical and immunological markers are being used to identify high risk group of patients early for prompt initiation of treatment. Coagulopathy is a mainstay of COVID-19 disease pathogenesis and D-dimer is one of the markers elevated markedly earlier, even when there is no significant clinical symptom of severity and imaging changes. However, there is no conclusive data regarding this issue in our country. Objective: This study aimed to evaluate the association of on admission D-dimer level with in-hospital outcome. Methodology: This prospective observational study was conducted in Bangabandhu Sheikh Mujib Medical University and Dhaka Medical College Hospital from June 2020 to May 2021. Ethical clearance was obtained from institutional review board (IRB) of BSMMU. Total 186 COVID-19 admitted patients were enrolled in accordance with inclusion &amp; exclusion criteria. A detailed history and thorough clinical examination were carried out in each patient. D–dimer level was detected by standard procedure on the first day of admission by SYSMEX CS-1600 analyser. Clinical and laboratory information were recorded with a semi-structured questionnaire. Severity of COVID-19 patients was categorized according to WHO classification. After collection of all the required data, analysis was done by SPSS 24.0. Results: Mean age of all patients was 58.60±12.30 years (range: 22-90 years) wherein maximum belonged to 51-70 years of age (62.3%) and male gender (71.5%). Maximum study patients were diabetic (57%) and hypertensive (57%) for average duration more than 10 years. Most of the study patients (71%) were in severe category of COVID-19, followed by moderate (25.8%), mild (2.7%) and critical (0.5%) category. Median D-dimer of all patients was 0.96 µg/mL. On admission D-dimer level was significantly higher among severe/critical COVID-19 patients compared to mild/moderate (1.11 vs 0.54µg/mL, p &lt;0.05). Twenty-eight (28) days mortality rate of admitted COVID-19 patients was 18.3%. Receiver operator curve analysis found highly significant cut-off value for D-dimer (1.62 µg/mL) with 70.6% sensitivity and 78.3% specificity in predicting 28-days hospital mortality of COVID-19 patients (AUC= 0.839, p value &lt;0.001). Besides, patients with D-dimer ≥1.62 µg/mL had significantly higher NLR (8.6 vs 4.6), CRP (107 vs 57mg/L) and serum LDH (512 vs 370 IU/L) level compared to patients with D-dimer &lt;1.62 µg/mL. Conclusion: On admission D-dimer was significantly associated with severity as well as 28-days mortality of COVID-19. Keyword: COVID-19, Corona virus, D-dimer.

Evaluation of joint external evaluation to COVID-19 and other infectious diseases mortality outcomes in 96 countries.

This study evaluated the effectiveness of Joint External Evaluation (JEE) scores with regard to coronavirus disease 2019 (COVID-19) and other infectious diseases performance in 96 countries. To propose a revised JEE tool, potential JEE indicators were also examined. JEE data from 2016-2019 were linked with outcomes such as COVID-19 fatality rates and infections, as well as mortality rates for other infectious diseases. We also examined potential indicators such as the Sustainable Development Goals (SDGs) and Universal Health Coverage index to propose enhancements to the JEE tool. Multiple regression analysis was used to assess these associations. The average JEE score was 2.70 (SD=0.92) in 96 countries. Detection capabilities received the highest average score (3.23), while the other areas (2.30) section received the lowest scores. However, the analysis revealed that the JEE tool had limited predictive accuracy for COVID-19 outcomes. By contrast, the JEE scores showed a negative association with the performance of other infectious diseases. Notably, SDGs 2 (zero hunger), 4 (quality education) and 8 (decent work and economic growth) were strongly associated with better COVID-19 outcomes. The JEE scores showed limited predictive value for COVID-19 mortality outcomes in 96 countries. The tool offers insights into health security, but needs revision to better handle future pandemics.

Single cell multiomics reveal divergent effects of DNMT3A and TET2 mutant clonal hematopoiesis in inflammatory response.

DNMT3A and TET2 are epigenetic regulator genes commonly mutated in age-related clonal hematopoiesis (CH). Despite having opposed epigenetic functions, these mutations are associated with increased all-cause mortality and a low risk for progression to hematological neoplasms. While individual impacts on the epigenome have been described using different model systems, the phenotypic complexity in humans remains to be elucidated. Here we make use of a natural inflammatory response occurring during coronavirus disease 2019 (COVID-19), to understand the association of these mutations with inflammatory morbidity (acute respiratory distress syndrome-ARDS) and mortality. We demonstrate the age-independent, negative impact of DNMT3A mutant CH on COVID-19-related ARDS and mortality. Using single cell (sc-) proteogenomics we show that DNMT3A mutations involve myeloid and lymphoid lineage cells. Using single cell multiomics sequencing, we identify cell-specific gene expression changes associated with DNMT3A mutations, along with significant epigenomic deregulation affecting enhancer accessibility, resulting in overexpression of IL32, a proinflammatory cytokine that can result in inflammasome activation in monocytes and macrophages. Finally, we show with single cell resolution that the loss of function of DNMT3A is directly associated with increased chromatin accessibility in mutant cells. Hence, we demonstrate the negative prognostic impact of DNMT3Amt CH on COVID-19 related ARDS and mortality. DNMT3Amt CH in the context of COVID-19, was associated with inflammatory transcriptional priming, resulting in overexpression of IL32. This overexpression was secondary to increased chromatic accessibility, specific to DNMT3Amt CH cells. DNMT3Amt CH can thus serve as a potential biomarker for adverse outcomes in COVID-19.

Correlation between rs7041 and rs4588 polymorphisms in vitamin D binding protein gene and COVID-19-related severity and mortality

BackgroundThe vitamin D binding protein (DBP) plays a critical role in both innate and adaptive immune systems, participating in several clinical conditions, including coronavirus disease 2019 infection severity, and mortality rate. The study aimed to investigate the correlation between rs7041 and rs4588 polymorphisms in the DBP gene and Coronavirus Disease-2019 (COVID-19) severity and mortality, in patients of Suez Canal University Hospitals in Ismailia, Egypt.MethodsA case-control study enrolled 220 individuals; 140 COVID-19 patients and 80 healthy controls. Serum 25(OH) vitamin D levels were determined by the enzyme-linked immunosorbent assay (ELISA), and rs7041 and rs4588 polymorphisms of the DBP gene were genotyped using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP).ResultsThe study found that both groups had vitamin D deficiency, which was considerably lower in the COVID-19 patients group compared to controls. Among COVID-19 patients, there was a significant difference in vitamin D levels according to the disease severity indicating that vitamin D levels can be used as predictors of COVID-19 severity. Negative significant correlations between genetic variants rs4588 CA genotype and genetic variants rs7041 TT genotype and COVID-19 prevalence (p = 0.006 and 0.009 respectively) were proved. No significant correlations between all the genetic variants of both rs4588 and rs7041 and COVID-19 severity (p > 0.05). Positive significant correlations between both genetic variants rs4588 CA genotype and genetic variants rs7041 TG genotype and COVID-19 mortality (p = 0.029 and 0.031 respectively).Conclusionvitamin D deficiency increased the severity of COVID-19. The DBP polymorphism correlated with vitamin COVID-19 prevalence and mortality.

CTLA-4 rs5742909 but not ADAM33 rs2280091 is a predictor factor for COVID-19 mortality.

Research has demonstrated the association between susceptibility to coronavirus disease 2019 (COVID-19) and single nucleotide polymorphisms (SNPs). On the other hand, the cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) serves as a pivotal inhibitory receptor with a substantial impact on the advancement of viral infections. Besides, the disintegrin and metalloproteinase33 (ADAM33) gene is associated with both asthma and heightened airway responsiveness. Hence, this investigation sought to elucidate the potential association between the CTLA-4 rs5742909 and ADAM33 rs2280091 SNPs and the fatality rate of COVID-19 across various variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Both SNPs were genotyped with the PCR-RFLP assay in 1734 improved and 1450 deceased individuals. Our obtained results revealed a significant association between the CTLA-4 rs5742909 C/T-T/T genotypes and increased mortality risk of COVID-19 in three different SARS-CoV-2 variants. The ADAM33 rs2280091 polymorphism demonstrated no significant association with COVID-19 mortality under various inheritance models. Nevertheless, subsequent adjustments for SARS-CoV-2 variants revealed a notable association between the GA genotype of ADAM33 rs2280091 and mortality rates specifically among individuals infected with the Delta variant. In summary, the prediction of COVID-19 severity could be facilitated through the utilization of the CTLA-4 rs5742909 marker. Conversely, in the case of ADAM33 rs2280091, such prognostication appears to be contingent upon the specific variants of the SARS-CoV-2 virus.

Differences in patient characteristics, treatments, and mortality of COVID-19 between 2022 and 2020-2021.

In 2021, vaccines against SARS-CoV-2 were developed, and the Omicron variant emerged. This study compared the characteristics, treatments, and mortality of patients with coronavirus disease 2019 (COVID-19) between 2022 and 2020-2021, using administrative claims data linked with vaccine records in a Japanese city. We identified patients underwent COVID-19 antigen or polymerase chain reaction tests and were diagnosed with COVID-19. Patient characteristics, treatments, and mortality were compared between 2022 and 2020-2021 among those diagnosed with COVID-19 and those who died. We identified 26,262 patients with COVID-19. The mortality in 2022 was lower than that in 2020-2021 (0.6% vs. 1.7%; P<0.01). Patients in 2022 were significantly less likely to receive oxygen therapy, high-flow nasal oxygenation, mechanical ventilation, steroid, or tocilizumab than those in 2020-2021. Among the deceased, the proportion of those aged ≥65 years was significantly higher in 2022 than in 2020-2021 (98.4% vs. 88.6%). On logistic regression, older age, male, and ≥3 comorbidities were associated with higher mortality, whereas ≥3 vaccinations were associated with lower mortality. Patients with COVID-19 in 2022 were less likely to require respiratory care or succumb to the disease. Older patients were more likely to die in 2022 than in 2020-2021.

Significant association between asthma and a lower risk of mortality among COVID-19 patients in Spain: A meta-analysis.

Various prevalences of asthma in coronavirus disease 2019 (COVID-19) have been reported in different regions, and the association between asthma and COVID-19 subsequent mortality has been in debate. Thus, this study aimed to investigate whether there was a significant association between asthma and COVID-19 mortality in Spain through a meta-analysis. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were strictly complied with conducting this study. The pooled odds ratio (OR) with a corresponding 95% confidence interval (CI) was calculated by a random-effects model. The I 2 statistics for heterogeneity, sensitivity analysis for robustness, Begg's test, and Egger's test for publication bias, along with subgroup analyses for confounding bias, were also performed to support the foundation of this study. The meta-analysis revealed that asthma was significantly associated with a lower risk of mortality among COVID-19 patients in Spain with a random-effects model (pooled OR = 0.78, 95% CI = 0.69-0.88, I 2 = 35%). Further subgroup analyses by male proportion and sample size also indicated that a statistically significant negative correlation did exist between asthma and COVID-19 mortality. Robustness and no publication on-bias were evidenced by sensitivity analysis, Egger's test, and Begg's test, respectively. In conclusion, patients with asthma were found to have a lower risk of mortality from COVID-19 in Spain, especially among elderly patients. In addition, asthmatic patients infected with COVID-19 may be at risk of death compared to non-asthmatic patients, which is not a cause for undue concern, thereby reducing the burden of medication.

Predictive modeling of COVID-19 mortality risk in chronic kidney disease patients using multiple machine learning algorithms

The coronavirus disease 2019 (COVID-19) has a significant impact on the global population, particularly on individuals with chronic kidney disease (CKD). COVID-19 patients with CKD will face a considerably higher risk of mortality than the general population. This study developed a predictive model for assessing mortality in COVID-19-affected CKD patients, providing personalized risk prediction to optimize clinical management and reduce mortality rates. We developed machine learning algorithms to analyze 219 patients’ clinical laboratory test data retrospectively. The performance of each model was assessed using a calibration curve, decision curve analysis, and receiver operating characteristic (ROC) curve. It was found that the LightGBM model showed the most satisfied performance, with an area under the ROC curve of 0.833, sensitivity of 0.952, and specificity of 0.714. Prealbumin, neutrophil percent, respiratory index in arterial blood, half-saturated pressure of oxygen, carbon dioxide in serum, glucose, neutrophil count, and uric acid were the top 8 significant variables in the prediction model. Validation by 46 patients demonstrated acceptable accuracy. This model can serve as a powerful tool for screening CKD patients at high risk of COVID-19-related mortality and providing decision support for clinical staff, enabling efficient allocation of resources, and facilitating timely and targeted management for those who need the relevant interference urgently.

Increased national critical care demands were associated with a higher mortality of intubated COVID-19 patients in Japan: a retrospective observational study

BackgroundThere was no study to investigate the association between the national surge of Coronavirus disease 2019 (COVID-19) patients and the mortality of mechanically ventilated COVID-19 patients. The aim of this study was to assess the association between mortality in mechanically ventilated COVID-19 patients and two distinct national COVID-19 surge indices: (1) the daily number of newly confirmed COVID-19 cases, representing overall medical demands and (2) the total number of critically ill COVID-19 patients, reflecting critical care demands.MethodsWe analyzed the patient data registered in a national database of mechanically ventilated COVID-19 patients between February 6, 2020, and May 16, 2023, combined with the data officially published by the Japanese government. Multivariable logistic regression analysis was performed to evaluate the association of these two indices with COVID-19 mortality. A generalized linear mixed effect model was used to examine the relationships between the variation in the impact of critical care demands across hospitals and the variation in baseline risk across hospitals.ResultsThe data of 8327 patients from 264 centers in Japan were analyzed. The overall mortality rate was 24% (1990/8327). The critical care demands, but not overall medical demands, were independently associated with the mortality (OR, 1.11; 95% CI 1.07–1.16; p < 0.001). This effect of critical care demands on the mortality was more pronounced in hospitals with higher baseline risk (r = 0.67).ConclusionsThe national critical care demands were independently associated with the mortality of COVID-19 patients requiring mechanical ventilation. This effect was more pronounced in hospitals with higher baseline risk.

Neighborhood Ecologies, Cases, and Deaths during the Beginning of the COVID-19 Pandemic: Lessons for Current and Future Epidemics?

The coronavirus disease 2019 (COVID-19) pandemic has disproportionately affected Black and Latinx communities. Ecologic analyses have shown that counties with a higher percentage of Latinx and Black people have worse COVID-19 outcome rates. Few ecologic analyses have been published at the neighborhood (census tract) level. We sought to determine whether certain sociodemographic neighborhood ecologies were associated with COVID-19 case and death rates in metropolitan Atlanta, Georgia. We used census data and principal-component analysis to identify unique neighborhood ecologies. We then estimated correlation coefficients to determine whether the neighborhood profiles produced by a principal-component analysis were correlated with COVID-19 case and death rates. We conducted geographically weighted regression models to assess how correlation coefficients varied spatially for neighborhood ecologies and COVID-19 outcomes. We identified two unique neighborhood profiles: (1) high percentage of residents, Hispanic ethnicity, without a high school diploma, without health insurance, living in crowded households, and lower percentage older than 65 years; and (2) high percentage of residents, Black race, living in poverty, unemployed, and households receiving Supplemental Nutrition Assistance Program benefits. Profile 1 was associated with COVID-19 case rate (Pearson r = 0.462, P < 0.001) and profile 2 was associated with COVID-19 death rate (Spearman r = 0.279, P < 0.001). Correlations between neighborhood profiles and COVID-19 outcomes varied spatially. Neighborhoods were differentially at risk of COVID-19 cases or deaths depending on their sociodemographic ecology at the beginning of the COVID-19 pandemic. Prevention methods and interventions may need to consider different social determinants of health when addressing potential cases and deaths during future emergent epidemics.

Difference in the epidemiological and clinical profiles of mortality between the first and second waves of COVID-19 pandemic in a tertiary care hospital of Andaman and Nicobar Islands.

The Andaman and Nicobar Islands reported the first coronavirus disease 2019 (COVID-19) case on March 26, 2020. This study aimed to describe the epidemiological and clinical profiles of COVID-19 mortality that occurred during the first and second waves of COVID-19 pandemic and to compare the differences between them. This was a retrospective cohort study carried out among COVID-19 patients admitted at GB Pant Hospital, Port Blair. Socio-demographic and clinical details were retrieved from the case records using structured proforma. Comparison of the study variables between the first and second waves was done by using Chi-square test using SPSS version 20. The first wave had total cases of 4994 and 62 deaths, and the second wave had 2734 cases and 67 deaths. The case fatality among COVID-19 cases during the first and second waves of the pandemic was 1.24% and 2.45%, respectively. There was no significant age and gender difference of patients between the two waves. Those who had co-morbidities during the first wave were 52 (83.8%), and those who had co-morbidities during the second wave were 45 (67.2%). The median duration of hospital stay in days during the first and second waves of the pandemic was 4 and 6 days, respectively. There was no significant difference in the age and gender distribution between the two waves; however, those who died during the second wave had a significantly higher percentage of co-morbidities. Therefore, health policy makers should develop a targeted approach to provide appropriate education, support, and awareness of patients with underlying diseases and allocate health care resources accordingly.

Evaluating the Mortality Risk Factors Among COVID-19 Patients with Diabetes Mellitus

Background: Management of Coronavirus disease 2019 (COVID-19) patients with underlying disorders is a challenging issue at any time. Objectives: The present survey investigated the mortality rate of COVID-19 patients with various underlying disorders. Methods: In the present cross-sectional study, patients with COVID-19 who were referred to the Razi Hospital in Ahvaz from Feb 2020 to Oct 2020 were evaluated. Non-probability sampling method was used for sample collection. All documented information, including length of hospitalization, comorbidities, survival, and clinical and laboratory findings, were collected. Results: In the present study, 500 diabetic patients with COVID-19 infection were included. The mean age of participants was 59.61 ± 14.88 years old. Of the subjects, 192 (38.4%) were female and 308 (61.6%) were male. DM solely was the most frequent underlying disorder (46.2%), followed by co-occurrence of DM and hypertension (HTN) (43%). The mortality rate was 15% and was more frequent in patients with simultaneous DM and HTN (66%) (P-value &lt; 0.001). Also, the mortality rate was significantly higher in males than females (72% vs 28%) (P-value = 0.042). Further analysis indicated that the mean of white blood cells (WBC), neutrophil count, blood urea nitrogen, glucose, creatinine, and aspartate aminotransferase were significantly higher in expired patients (P-value &lt; 0.05). Post Hoc analysis showed the highest pulse rate (PR) and respiratory rate (RR) in patients with DM, HTN, and chronic obstructive pulmonary disease (COPD) (P-value &lt; 0.001). Conclusions: The present investigation showed that comorbidities significantly increase the COVID-19 mortality rate. Our findings revealed that more attention is required for COVID-19 patients with HTN and DM. Due to discrepancies with recent investigations, further studies with more follow-up are recommended.

Disparities in infectious disease-related health care utilization between Medicaid-enrolled American Indians and non-Hispanic Whites-Lessons from the first 16 months of coronavirus disease 2019 and a decade of flu seasons.

To understand why American Indian and Alaskan Native (AIAN) populations have had exceptionally high COVID-19 mortality, we compare patterns of healthcare utilization and outcomes for two serious infectious respiratory diseases-Influenza-like-illness (ILI) and coronavirus disease 2019 (COVID-19)-between American Indian and Alaskan Native (AIAN) populations (as identified in Medicaid data) and non-Hispanic Whites over the 2009-2021 period. We select all people under the age of 65 years identified as non-Hispanic White or AIAN in the New York State Medicaid claims data between 2009 and 2021. We analyze data across 10 ILI cohorts (between September 2009 and August 2020) and 4 COVID-19 cohorts (March-June 2020, July-September 2020, October-December 2020, and January-June 2021). We examine mortality and utilization rates using logistic regressions, adjusting for demographic characteristics, prior chronic conditions, and geographic location (including residence near a reservation). We stratify the analysis by rural vs. nonrural counties. We use the New York State Medicaid claims data for the analysis. We find that even among Medicaid beneficiaries, who are similar in socioeconomic status and identical in health insurance coverage, AIAN populations have much lower rates of use of outpatient services and much higher rates of acute (inpatient and emergency room) service utilization for both ILI and COVID-19 than non-Hispanic Whites. Prior to COVID-19, demographic and health status-adjusted all-cause mortality rates, including from ILI, were lower among American Indians than among non-Hispanic Whites on New York State Medicaid, but this pattern reversed during the COVID-19 pandemic. Both findings are driven by nonrural counties. We did not observe significant differences in all-cause mortality and acute service utilization comparing AIAN to non-Hispanic Whites in rural areas. The utilization and mortality disparities we identify within the Medicaid population highlight the need to move beyond insurance in addressing poor health outcomes in the American Indian population.