Sociodemographic determinants and clinical risk factors associated with COVID-19 severity: a cross-sectional analysis of over 200,000 patients in Tehran, Iran

Joint modeling of longitudinal and competing risks for assessing blood oxygen saturation and its association with survival outcomes in COVID-19 patients

BACKGROUND

The objective of the present study is to evaluate the association between longitudinal and survival outcomes in the presence of competing risk events. To illustrate the application of joint modeling in clinical research, we assessed the blood oxygen saturation (SPO2) and its association with survival outcomes in coronavirus disease (COVID-19).

MATERIALS AND METHODS

In this prospective cohort study, we followed 300 COVID-19 patients, who were diagnosed with severe COVID-19 in the Rohani Hospital in Babol, the north of Iran from October 22, 2020 to March 5, 2021, where death was the event of interest, surviving was the competing risk event and SPO2 was the longitudinal outcome. Joint modeling analyses were compared to separate analyses for these data.

RESULT

The estimation of the association parameter in the joint modeling verified the association between longitudinal outcome SPO2 with survival outcome of death (Hazard Ratio (HR) = 0.33, P = 0.001) and the competing risk outcome of surviving (HR = 4.18, P < 0.001). Based on the joint modeling, longitudinal outcome (SPO2) decreased in hypertension patients (β = -0.28, P = 0.581) and increased in those with a high level of SPO2 on admission (β = 0.75, P = 0.03). Also, in the survival submodel in the joint model, the risk of death survival outcome increased in patients with diabetes comorbidity (HR = 4.38, P = 0.026).

CONCLUSION

The association between longitudinal measurements of SPO2 and survival outcomes of COVID-19 confirms that SPO2 is an important indicator in this disease. Thus, the application of this joint model can provide useful clinical evidence in the different areas of medical sciences.

Modeling risk factors of degree of severity of Coronavirus disease 2019 (COVID-19) infection in inpatient patients at Dr. M. Djamil Padang

Background

RSUP Dr. M. Djamil was one of the COVID referral hospitals that treats patients with degrees of severity ranging from mild to critical. The high prevalence of COVID-19 differs from the severity of which it is necessary to know the risk factors as a preventive measure to minimize the higher risk. This study aims to determine the factors associated with the severity of COVID-19 in inpatients at Dr. M. Djamil Hospital, Padang, Indonesia.

Design and methods

A quantitative study with a cross-sectional design was conducted by reviewing the medical record data of COVID-19 inpatients from March 2020 to February 2021. Sampling using a simple random sampling technique with a total of 95 patients. The Prevalence Odds Ratio (POR) is a statistical measure used in epidemiology and medical research to assess the association between an exposure or risk factor and a particular outcome in a cross-sectional study.

Results

Based on research, there are 41 (43.2%) severe patients. There is a relationship between age with p-value 0.004 (POR 4.5; 95% CI; 1.48-12.1), cardiovascular disease with p-value 0.003 (POR 5.9; 95% CI 1.7-21.4), and respiratory disease with p-value 0.001 (POR 6.6; 95% CI; 2.1-20.8) against COVID-19 infection. Diabetes Mellitus is the confounding variable. Respiratory disease is the dominant factor associated with the severity of COVID-19. Respiratory disease has 6.6 POR or Prevalence Odds Ratio values which means that COVID-19 patients with respiratory disease 6.6 more severe than those who has not respiratory disease history.

Conclusions

Age, cardiovascular, and respiratory diseases are associated with the severity of COVID-19 infection in patients at Dr. M. Djamil Hospital, Padang, Indonesia. It is highly recommended to increase health promotion regarding risk factors for the severity of COVID-19 to the community to avoid a more severe outcome.

Polymerase chain reaction-positivity and predictors for SARS-CoV-2 infection among diagnosed cases' in North West Ethiopia

Background

The lack of sophisticated diagnosing equipment for polymerase chain reaction (PCR) during the incidence of variant types of COVID-19 underestimates the morbidity and mortality patterns of this pandemic. Thus, this study aimed to estimate seropositive and confirmatory predictors for COVID-19 suspected and tested cases through polymerase chain reaction (RT-PCR) in two diagnosing.

Methods

A facility-based descriptive cross-sectional study was employed among COVID-19 suspected cases from January 2, 2022, to June 9, 2022. The data were collected both using a structured interviewees and nasopharyngeal (NP) swabs. The nasal swab (NS) was analyzed in the laboratory for RNA detection of the virus using PCR. The collected data were entered into Epi Data version 4.2 and then exported to STATA (SE) version R-14 software for further analysis. multivariable logistic regression was used to assess the associated risk.

Results

A total of 285 suspected cases have participated in this study. The overall mean (±SD) age of the participants was 37.5 (±18.5) years. The majority, 174 (61.1%) of the tested groups were symptomatic when diagnosed. The positivity of RT-PCR for suspected and COVID-19 diagnosed cases were confirmed in 62/285 (21.75%). In multivariable analysis, they were aged 26-50 years (adjusted odds ratio [AOR] = 4.2, 95% confidence interval [CI] = 1.5-10.75), had comorbidity (AOR = 5.8; 95% CI = 2.1-12.2), and cigarette smokers (AOR = 13.5; 95% CI = 5.3-36.6) were significantly associated with developing COVID-19 infection.

Conclusion

More than two in every nine suspected cases were positive RT-PCR tests, and the infectivity of COVID-19 was significantly associated with age 25-50 years, comorbidities, and cigarette smoking. The deployment of high-quality diagnostic kits like RT-PCR is crucial for the early detection and risk stratification of suspected cases.

Leukopenia and leukocytosis as strong predictors of COVID-19 severity: A cross-sectional study of the hematologic abnormalities and COVID-19 severity in hospitalized patients

Background and Aims

Predicting severe disease is important in provocative decision-making for the management of patients with the coronavirus disease 2019 (COVID-19); However, there are still some controversies about the COVID-19's severity predicting factors. This study aimed to investigate the relationships between clinical and laboratory findings regarding COVID-19's severity in patients admitted to a tertiary hospital in Mashhad, Iran.

Methods

A cross-sectional study was conducted on patients with documented COVID-19 infection based on the reverse transcription-polymerase chain reaction test. Clinical symptoms, vital signs, and medical history of the patients were recorded from their medical records. Laboratory findings and computed tomography (CT) study findings were documented. Disease severity was defined based on CT scan findings.

Results

A total of 564 patients (58.8 ± 16.8 years old) were evaluated. The frequency of severe disease was 70.4%. There was a significant difference in heart rate (p = 0.0001), fever (p = 0.002), dyspnea (p = 0.0001), chest pain (p = 0.0001), diarrhea (p = 0.021), arthralgia (p = 0.0001), and chills (p = 0.044) as well as lymphopenia (p = 0.014), white blood cell count (p = 0.001), neutrophil count (p < 0.0001), lymphocyte count (p < 0.0001), and prothrombin time (p = 0.001) between disease severity groups. Predictors of severe COVID-19 were pulse rate (crude odds ratio [cOR] = 1.014, 95% confidence interval [CI] for cOR: 1.001, 1.027) and leukopenia (cOR = 3.910, 95% CI for cOR: 1.294, 11.809). Predictors for critical COVID-19 were pulse rate (cOR = 1.075, 95% CI for cOR: 1.046, 1.104), fever (cOR = 2.516, 95%CI for cOR: 1.020, 6.203), dyspnea (cOR = 4.190, 95% CI for cOR: 1.227, 14.306), and leukocytosis (cOR = 3.866, 95% CI for cOR: 1.815, 8.236).

Conclusions

Leukopenia and leukocytosis have the strongest correlation with the COVID-19 severity. These findings could be a valuable guild for clinicians in COVID-19 patient management in the inpatient setting.

Probabilistic survival modeling in health research: an assessment using cohort data from hospitalized patients with COVID-19 in a Latin American city

Probabilistic survival methods have been used in health research to analyze risk factors and adverse health outcomes associated with COVID-19. The aim of this study was to employ a probabilistic model selected among three distributions (exponential, Weibull, and lognormal) to investigate the time from hospitalization to death and determine the mortality risks among hospitalized patients with COVID-19. A retrospective cohort study was conducted for patients hospitalized due to COVID-19 within 30 days in Londrina, Brazil, between January 2021 and February 2022, registered in the database for severe acute respiratory infections (SIVEP-Gripe). Graphical and Akaike Information Criterion (AIC) methods were used to compare the efficiency of the three probabilistic models. The results from the final model were presented as hazard and event time ratios. Our study comprised of 7,684 individuals, with an overall case fatality rate of 32.78%. Data suggested that older age, male sex, severe comorbidity score, intensive care unit admission, and invasive ventilation significantly increased risks for in-hospital mortality. Our study highlights the conditions that confer higher risks for adverse clinical outcomes attributed to COVID-19. The step-by-step process for selecting appropriate probabilistic models may be extended to other investigations in health research to provide more reliable evidence on this topic.

Clinical determinants of oxygen saturation and length of hospitalisation of COVID-19 patients: A cross-sectional study in Indonesia

Aims

Indonesia was one of the countries with the highest COVID-19 positive cases. Understanding the length of hospitalisation is critical for anticipating bed demand and resource allocation, such as oxygen. This study aims to examine the determinants of oxygen saturation and the length of hospitalisation in Hermina Mekarsari Hospital, West Java, Indonesia.

Methods

This cross-sectional study uses medical records from June to August 2021. The inclusion criteria were: COVID-19 patients aged between18 and 65, fully conscious, and not using mechanical ventilation. Participants who passed away during hospital stay were excluded. We used demographic information, laboratory data, and the clinician's assessments of the patients admitted to the hospital. Linear regression was performed for oxygen saturation on day seven, while logistic regression analysis was conducted to predict the length of hospital stay.

Results

In total, 371 participants with an average age of 47.2 (standard deviation 15.8) years were included. Most participants were female (57.7%) and smoking (78.4%). The results indicated that decreasing oxygen saturation was reported in vomiting patients (β = 1.63, p-value = .001), hypertensive patients (β 1.18 with, p-value = .034), and patients with the increased respiratory rate (β = 0.28, p-value = .000). In the logistic regression, we found that respondents who experienced dyspnoea, headache, fever, an increasing number of D-Dimer and blood glucose, and those with diabetes mellitus were more likely to stay more than 14 days.

Conclusions

Oxygen saturation was influenced by vomiting, hypertension, and increasing respiratory rate. Length of hospitalisation of more than 14 days was influenced by dyspnoea, headache, fever, increased number of D-Dimer, blood glucose, and diabetes mellitus. Identifying the determinants of oxygen saturation and length of stay can inform health professionals in designing a suitable intervention to reduce mortality and length of stay among COVID-19 patients in Indonesia.

Electrolyte Imbalance Among Bangladeshi Patients With COVID-19

Introduction Infection with SARS-CoV-2 begins in the lower respiratory tract, but COVID-19 often involves the renal system, resulting in serum electrolyte imbalance. Monitoring serum electrolyte levels and parameters of liver and kidney function is essential to understand disease prognosis. Objectives This study aimed to determine the effect of imbalances in serum electrolytes and other parameters on COVID-19 severity. Material and method This retrospective study comprised 241 patients, ages 14 years and older, including 186 patients who were moderately affected and 55 who were categorized as severely affected by COVID-19. Serum electrolytes (sodium (Na⁺), potassium (K⁺), and chloride (Cl^-)) and biomarkers of kidney and liver function (creatinine and alanine aminotransferase (ALT)) were measured and correlated with disease severity. This research was conducted among admitted patients of Holy Family Red Crescent Medical College Hospital designated into two groups based on retrospective hospital records. Individuals with moderate illness had evidence of lower respiratory tract infection (cough, cold, breathless, etc.) during clinical assessment or imaging (chest X-ray and computed tomography (CT) scan of the lungs) and have an oxygen saturation by pulse oximetry (SpO₂) ≥ 94% on room air at sea level. The severely ill group involved individuals with SpO₂ ≤94% on room air at sea level and respiratory rate ≥ 30 breaths/minute, and critically ill patients are those who needed mechanical ventilation or required intensive care unit (ICU) care. This categorization was based on the Coronavirus Disease 2019 (COVID-19) Treatment Guidelines (https://www.covid19treatmentguidelines.nih.gov/about-the-guidelines/whats-new/). Results Average Na⁺ and creatinine increased by 2.30 parts (95% confidence interval (CI) = 0.20, 4.81, P = 0.041) and 0.35 units (95% CI = 0.03, 0.68, P = 0.043) in severe cases compared with moderate cases. Older participants had relatively Na⁺ lowered to -0.06 parts (95% CI = -0.12, -0.001, P = 0.045), significant Cl^- reduction by 0.09 units (95% CI = -0.14, -0.04, P = 0.001), and ALT by 0.47 units (95% CI = -0.88, -0.06, P = 0.024), whereas serum creatinine was increased by 0.01 parts (95% CI = 0.001, 0.02, P = 0.024). The creatinine and ALT of COVID-19 participants were significantly higher in males by 0.34 units and 23.2 units, respectively, compared with females. In severe COVID-19 cases compared with moderate cases, the risks of hypernatremia, elevated chloride levels, and elevated serum creatinine levels were increased by 2.83-fold (95% CI = 1.26, 6.36, P = 0.012), 5.37-fold (95% CI = 1.90, 15.3, P = 0.002), and 2.00-fold (95% CI = 1.08, 4.31, P = 0.039), respectively. Conclusion Serum electrolyte and biomarker levels can serve as good indicators of the condition and disease prognosis of patients with COVID-19. Our purpose in this study was to determine the association between serum electrolyte imbalance and disease severity. We collected data from ex post facto hospital records and did not intend to assess the mortality rate. Consequently, this study expects that the prompt diagnosis of electrolyte disparity or disturbance possibly minimizes COVID-19-related morbidity and mortality.

The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis

There are different area-based factors affecting the COVID-19 mortality rate in urban areas. This research aims to examine COVID-19 mortality rates and their geographical association with various socioeconomic and ecological determinants in 350 of Tehran's neighborhoods as a big city. All deaths related to COVID-19 are included from December 2019 to July 2021. Spatial techniques, such as Kulldorff's SatScan, geographically weighted regression (GWR), and multi-scale GWR (MGWR), were used to investigate the spatially varying correlations between COVID-19 mortality rates and predictors, including air pollutant factors, socioeconomic status, built environment factors, and public transportation infrastructure. The city's downtown and northern areas were found to be significantly clustered in terms of spatial and temporal high-risk areas for COVID-19 mortality. The MGWR regression model outperformed the OLS and GWR regression models with an adjusted R² of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO₂, PM₁₀, and O₃); as air pollution increased, so did mortality. Additionally, the aging and illiteracy rates of urban neighborhoods were positively associated with COVID-19 mortality rates. Our approach in this study could be implemented to study potential associations of area-based factors with other emerging infectious diseases worldwide.

Risk Factors of Severe COVID-19: A Review of Host, Viral and Environmental Factors

The clinical course and outcome of COVID-19 are highly variable, ranging from asymptomatic infections to severe disease and death. Understanding the risk factors of severe COVID-19 is relevant both in the clinical setting and at the epidemiological level. Here, we provide an overview of host, viral and environmental factors that have been shown or (in some cases) hypothesized to be associated with severe clinical outcomes. The factors considered in detail include the age and frailty, genetic polymorphisms, biological sex (and pregnancy), co- and superinfections, non-communicable comorbidities, immunological history, microbiota, and lifestyle of the patient; viral genetic variation and infecting dose; socioeconomic factors; and air pollution. For each category, we compile (sometimes conflicting) evidence for the association of the factor with COVID-19 outcomes (including the strength of the effect) and outline possible action mechanisms. We also discuss the complex interactions between the various risk factors.

Five consecutive epidemiological waves of COVID-19: a population-based cross-sectional study on characteristics, policies, and health outcome

BACKGROUND

This study was conducted with the intension of providing a more detailed view about the dynamics of COVID-19 pandemic. To this aim, characteristics, implemented public health measures, and health outcome of COVID-19 patients during five consecutive waves of the disease were assessed.

METHODS

This study was a population-based cross-sectional analysis of data on adult patients who were diagnosed with COVID-19 during five waves of the disease in Iran. Chi-squared test, One-way ANOVA, and Logistic Regression analysis were applied. A detailed literature review on implemented public health policies was performed by studying published documents and official websites responsible for conveying information about COVID-19.

RESULTS

Data on 328,410 adult patients was analyzed. Main findings indicated that the probability of dying with COVID-19 has increased as the pandemic wore on, showing its highest odd during the third wave (odds ratio: 1.34, CI: 1.283-1.395) and has gradually decreased during the next two waves. The same pattern was observed in the proportion of patients requiring ICU admission (P < 0.001). First wave presented mainly with respiratory symptoms, gastrointestinal complaints were added during the second wave, neurological manifestations with peripheral involvement replaced the gastrointestinal complaints during the third wave, and central nervous system manifestations were added during the fourth and fifth waves. A significant difference in mean age of patients was revealed between the five waves (P < 0.001). Moreover, results showed a significant difference between men and women infected with COVID-19, with men having higher rates of the disease at the beginning. However, as the pandemic progressed the proportion of women gradually increased, and ultimately more women were diagnosed with COVID-19 during the fifth wave. Our observations pointed to the probability that complete lockdowns were the key measures that helped to mitigate the virus spread during the first twenty months of the pandemic in the country.

CONCLUSION

A changing pattern in demographic characteristics, clinical manifestations, and severity of the disease has been revealed as the pandemic unfolded. Reviewing COVID-19-related public health interventions highlighted the importance of immunization and early implementation of restrictive measures as effective strategies for reducing the acute burden of the disease.

Prognosis of Spontaneous Pneumothorax/Pneumomediastinum in Coronavirus Disease 2019: The CoBiF Score

Objectives: Pneumothorax and pneumomediastinum are associated with high mortality in invasively ventilated coronavirus disease 2019 (COVID-19) patients; however, the mortality rates among non-intubated patients remain unknown. We aimed to analyze the clinical features of COVID-19-associated pneumothorax/pneumomediastinum in non-intubated patients and identify risk factors for mortality. Methods: We searched PubMed Scopus and Embase from January 2020 to December 2021. We performed a pooled analysis of 151 patients with no invasive mechanical ventilation history from 17 case series and 87 case reports. Subsequently, we developed a novel scoring system to predict in-hospital mortality; the system was further validated in multinational cohorts from ten countries (n = 133). Results: Clinical scenarios included pneumothorax/pneumomediastinum at presentation (n = 68), pneumothorax/pneumomediastinum onset during hospitalization (n = 65), and pneumothorax/pneumomediastinum development after recent COVID-19 treatment (n = 18). Significant differences were not observed in clinical outcomes between patients with pneumomediastinum and pneumothorax (±pneumomediastinum). The overall mortality rate of pneumothorax/pneumomediastinum was 23.2%. Risk factor analysis revealed that comorbidities bilateral pneumothorax and fever at pneumothorax/pneumomediastinum presentation were predictors for mortality. In the new scoring system, i.e., the CoBiF system, the area under the curve which was used to assess the predictability of mortality was 0.887. External validation results were also promising (area under the curve: 0.709). Conclusions: The presence of comorbidity bilateral pneumothorax and fever on presentation are significantly associated with poor prognosis in COVID-19 patients with spontaneous pneumothorax/pneumomediastinum. The CoBiF score can predict mortality in clinical settings as well as simplify the identification and appropriate management of patients at high risk.

Post-COVID-19 complications in home and hospital-based care: A study from Dhaka city, Bangladesh

A cross-sectional survey was undertaken to understand the management patterns and post-COVID-19 complications among hospital and home-treated participants. Retrospective information was collected from four COVID-19 dedicated hospitals and four selected community settings. Using probability proportional sampling, 925 participants were selected. Data were collected using a semi-structured questionnaire. Bivariate and multivariate logistic regression analysis and the exact chi-square tests were utilized to analyze the association between the studied variables. A total of 659 participants responded (response rate 70.93%); 375 from hospitals and 284 from communities. About 80% of participants were mild cases, 75% were treated at home, and 65% of hospital-treated participants were referred after home treatment. Participants treated at home-to hospital and directly in the hospital had 1.64 and 3.38 times longer recovery time respectively than what home-based participants had. A significant increasing trend (p < 0.001) of co-morbidities was found among referred and hospital treated participants. Age, level of education, physical exercise, practicing preventive measures, exposure to sunlight, and intake of carbohydrate, additional liquid, food supplements, and avoidance of junk foods were significantly associated with place of treatment. Post-COVID-19 difficulties of all factors were statistically significant for home treatment participants, whilst only depression (p = 0.026), chest pain (p = 0.017), and digestive disorders (p = 0.047) were significant (p < 0.05) for hospital treated participants. The outcomes from this study provide insight into a range of post-COVID-19 difficulties relating to at home and in hospital treatment participants. There are clear differences in the complications experienced, many of which are statistically significant. The health care professionals, the community people and COVID-19 survivors will be benefitted from the study findings, and the policy level people may use the information for designing health education program on post COVID-19 complications.

Application of machine learning models based on decision trees in classifying the factors affecting mortality of COVID-19 patients in Hamadan, Iran

BACKGROUND

Due to the high mortality of COVID-19 patients, the use of a high-precision classification model of patient's mortality that is also interpretable, could help reduce mortality and take appropriate action urgently. In this study, the random forest method was used to select the effective features in COVID-19 mortality and the classification was performed using logistic model tree (LMT), classification and regression tree (CART), C4.5, and C5.0 tree based on important features.

METHODS

In this retrospective study, the data of 2470 COVID-19 patients admitted to hospitals in Hamadan, west Iran, were used, of which 75.02% recovered and 24.98% died. To classify, at first among the 25 demographic, clinical, and laboratory findings, features with a relative importance more than 6% were selected by random forest. Then LMT, C4.5, C5.0, and CART trees were developed and the accuracy of classification performance was evaluated with recall, accuracy, and F1-score criteria for training, test, and total datasets. At last, the best tree was developed and the receiver operating characteristic curve and area under the curve (AUC) value were reported.

RESULTS

The results of this study showed that among demographic and clinical features gender and age, and among laboratory findings blood urea nitrogen, partial thromboplastin time, serum glutamic-oxaloacetic transaminase, and erythrocyte sedimentation rate had more than 6% relative importance. Developing the trees using the above features revealed that the CART with the values of F1-score, Accuracy, and Recall, 0.8681, 0.7824, and 0.955, respectively, for the test dataset and 0.8667, 0.7834, and 0.9385, respectively, for the total dataset had the best performance. The AUC value obtained for the CART was 79.5%.

CONCLUSIONS

Finding a highly accurate and qualified model for interpreting the classification of a response that is considered clinically consequential is critical at all stages, including treatment and immediate decision making. In this study, the CART with its high accuracy for diagnosing and classifying mortality of COVID-19 patients as well as prioritizing important demographic, clinical, and laboratory findings in an interpretable format, risk factors for prognosis of COVID-19 patients mortality identify and enable immediate and appropriate decisions for health professionals and physicians.

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]training = 0.799, p = 4.2e-6; multi-class AUCvalidation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.

Significant association between HIV infection and increased risk of COVID-19 mortality: a meta-analysis based on adjusted effect estimates

To investigate the relationship between human immunodeficiency virus (HIV) infection and the risk of mortality among coronavirus disease 2019 (COVID-19) patients based on adjusted effect estimate by a quantitative meta-analysis. A random-effects model was used to estimate the pooled effect size (ES) with corresponding 95% confidence interval (CI). I² statistic, sensitivity analysis, Begg’s test, meta-regression and subgroup analyses were also conducted. This meta-analysis presented that HIV infection was associated with a significantly higher risk of COVID-19 mortality based on 40 studies reporting risk factors-adjusted effects with 131,907,981 cases (pooled ES 1.43, 95% CI 1.25–1.63). Subgroup analyses by male proportion and setting yielded consistent results on the significant association between HIV infection and the increased risk of COVID-19 mortality. Allowing for the existence of heterogeneity, further meta-regression and subgroup analyses were conducted to seek the possible source of heterogeneity. None of factors might be possible reasons for heterogeneity in the further analyses. Sensitivity analysis indicated the robustness of this meta-analysis. The Begg’s test manifested that there was no publication bias (P = 0.2734). Our findings demonstrated that HIV infection was independently associated with a significantly increased risk of mortality in COVID-19 patients. Further well-designed studies based on prospective study estimates are warranted to confirm our findings.

Low Oxygen Saturation of COVID-19 in Patient Case Fatalities, Limpopo Province, South Africa

On 1 August 2020, South Africa’s Minister of Health announced that more than half of a million coronavirus cases were confirmed in the country. South Africa was by far the hardest-hit country in Africa, accounting for half of all infections reported across the continent. The prevalence of underlying conditions such as fever and blood oxygen saturation (SpO2) has been known known to be a significant determinant in the hospitalisation of COVID-19 patients. Low oxygen saturation on admission was reported as a strong predictor of in-hospital mortality in COVID-19 patients. The study sought to assess the association between body temperature and other clinical risk factors with low SpO2 among COVID-19 inpatient case fatalities. A quantitative retrospective study was carried out in Limpopo Province, employing secondary data from the Limpopo Department of Health (LDoH) on COVID-19 inpatients case fatalities across all districts in the province. The chi-square test and Pearson’s correlation coefficient were used to assess the relationship between body temperature and clinical risk factors with SpO2 levels. The findings of this paper indicated that age (older age), chills, sore throat, anosmia, dysgeusia, myalgia/body aches, diarrhoea and HIV/AIDS were associated with low SpO2 in-hospital mortality in COVID-19 patients. Nasal prongs and a face mask with a reservoir for respiratory support cannula were commonly used patient interfaces to provide supplemental oxygen, with the use of only a high-flow nasal cannula (HFNC) being minimal (7%). The majority of COVID-19 inpatient fatalities had normal body temperature (<38 °C) and SpO2, with no correlation between the two variables. Considering temperature screening as a possible strategy to combat the spread of COVID-19 or suspicious COVID-19 cases appeared, then, to be a pointless exercise. This study aimed to recommend new clinical criteria for detecting COVID-19 cases.

Urban and sub-urban disparities in health outcomes among patients with COVID-19; a cross-sectional study of 234 418 patients in Iran

Background

It remains crucial to understand socio-demographic determinants of COVID-19 infection to improve access to care and recovery rates from the disease. This study aimed to investigate the urban and sub-urban disparities associated with COVID-19 in patients visiting healthcare facilities in the province of Tehran, Iran.

Methods

Data from 234 418 patients who were diagnosed with COVID-19 infection from March 2020 to March 2021 in the province of Tehran were used in this analysis. Descriptive statistics were used to describe the characteristics of the study population. Chi-Squared test was applied to examine the association of study variables with residing area. Independent samples t-test was performed to compare mean age of patients in urban and sub-urban areas. Multiple Logistic Regression model was applied to examine the association of study variables with disease outcome.

Results

Overall, most patients resided in the urban settings (73%). Mean age of patients was significantly lower in sub-urban areas compared to their counterparts in urban settings (49 ± 23.1 years versus 53 ± 21.1 years, P < 0.001). Positive PCR test results were more common in urban areas (48.5% versus 41.3%, P < 0.001). Yet, sub-urban settings had higher rates of positive chest CT scan reports (62.8% versus 53.4%, P < 0.001). After accounting for age and sex covariates, residing in urban areas was associated with higher likelihood of being admitted to an ICU (OR = 1.27, CI: 1.240-1.305). Yet, a greater vulnerability to fatal outcome of COVID-19 infection was shown in patients living in sub-urban areas (OR = 1.13, CI: 1.105-1.175).

Conclusions

This study revealed a clear disparity in the health outcome of patients infected with COVID-19 between urban and sub-urban areas.

Relationship Between Income Level and Hospitalization Rate in COVID-19 Cases; an Example of Social Factors Affecting Health

Introduction

Considering the population's socioeconomic status and clinical features is essential in planning and performing interventions related to disease control. The main purpose of this study was to investigate the relationship between income level and hospitalization rate of COVID-19 patients‌.

Methods

A cross-sectional study was performed on 198,944 hospitalized COVID-19 patients in Tehran province between March 2020 and March 2021. Data of hospitalized COVID-19 patients was obtained from the Hospital Intelligent Management System (HIM). The income data of patients were obtained from the Iranian Database on Targeted Subsidies belonging to the Ministry of Cooperatives, Labor, and Social Welfare. Data analyses were performed using SPSS software.

Results

About 2.5% of the inpatients were from the first decile, while 20.6% were from the tenth. The share of the lower three deciles of total hospitalization was about 11%, while the share of the upper three deciles was 50%. There was a big difference between the upper- and lower-income deciles regarding death rates. In the first decile, 30% of inpatients died, while the proportion was 10% in the tenth decile. There was a significant and positive relationship between income decline and hospitalization (r = 0.75; p = 0.02). Also, there was a significant and negative relationship between income decline and death rate (r = -0.90; p = 0.01).

Conclusion

Low-income groups use fewer inpatient services, are more prone to severe illness and death from COVID-19‌, and treatment in this group has a lower chance of success. Using a systemic approach to address socioeconomic factors in healthcare planning is crucial.

Obstructive lung diseases burden and COVID-19 in developing countries: a perspective

PURPOSE OF REVIEW

Asthma and chronic obstructive pulmonary disease (COPD) are widely prevalent disorders, and important contributors to morbidity and mortality, in both developing and developed countries. It is conjectured that these obstructive lung diseases may have had more deleterious effects in developing nations during the 2019 coronavirus disease (COVID-19) pandemic. We provide an evidence-based perspective on the relationship between asthma/COPD prevalence and COVID-19 burden, and the impact of comorbid asthma/COPD on selected COVID-19 outcomes and healthcare utilization, with special reference to developing countries.

RECENT FINDINGS

Developing countries with higher COPD (but not asthma) prevalence appear to have higher COVID-19 related mortality. Patients with asthma (but not COPD) in developing countries may be less likely to acquire COVID-19. Published literature suggests that the overall impact of comorbid asthma or COPD on adverse COVID-19 outcomes may be broadly similar between developed and developing nations.

SUMMARY

There is paucity of information on interaction between asthma/COPD and COVID-19 in developing countries. Limited data suggest minor differences between developed and developing nations. In view of inadequacies in healthcare preparedness and delivery in several developing countries, there is a need to generate quality evidence to assess impact of obstructive lung diseases and COVID-19 on each other.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 ) IgG-antibody seroprevalence among quarantined population, during the first wave of COVID-19 pandemic, In North West Ethiopia (from 30 April to 30 May 2020)

Objective:

The spread of Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2) in Ethiopia is below par understood and to date has been poorly characterized by a lower number of confirmed cases and deaths as compared with other regions of the Sub-Saharan African (SSA) countries. We aimed to investigate the seroprevalence of SARS-CoV-2 specific IgG antibodies, using the Abbott anti-nucleocapsid IgG chemiluminescent microparticle immunoassay, in two COVID-19 diagnosed and treatment centers of quarantined population during the first wave of the COVID-19 pandemic (since 30 April–30 May 2020).

Methods:

We analyzed data of 446 quarantined individuals during the first wave of COVID-19 pandemic. The data were collected using both interviewed and blood sample collection. Participants asked about demographic characteristics, COVID-19 infection symptoms, and its practice of preventive measures. Seroprevalence was determined using the severe acute respiratory syndrome coronavirus 2 IgG test.

Results:

The mean (± standard deviation) age of the respondent was 37.5 (±18.5) years. The estimated SARS-CoV-2 infection seroprevalence was found 4.7% (95% confidence interval: 3.1–6.2) with no significant difference on age and gender of participants. Severe acute respiratory syndrome coronavirus 2 antibody seroprevalence was significantly associated with individuals who have been worked by moving from home to work area (adjusted odds ratio = 7.8, 95% confidence interval: 4.2–14.3, p < 0.019), not wearing masks (adjusted odds ratio = 2.4, 95% confidence interval: 1.9–3.8, p < 0.02), and baseline comorbidity (adjusted odds ratio = 6.3, 95% confidence interval: 2.3–17.1, p < 0.01) as compared to their counter groups, respectively.

Conclusion:

Our study concluded that lower coronavirus disease 2019 seroprevalence, yet the large population in the community to be infected and insignificant proportion of seroprevalence, was observed between age and sex of respondents. Protective measures like contact tracing, face covering, and social distancing are therefore vital to demote the risk of community—strengthening factors should be continued as effect modification of anticipation for severe course of coronavirus disease 2019.

Assessing Predictive Factors of COVID-19 Outcomes: A Retrospective Cohort Study in the Metropolitan Region of São Paulo (Brazil)

Background and Objectives: The aim of this retrospective cohort study was to search individual, sociodemographic and environmental predictors of COVID-19 outcomes. Materials and Methods: A convenience sample of 1036 COVID-19 confirmed patients (3-99 years, mean 59 years; 482 females) who sought treatment at the emergency units of the public health system of Diadema (Brazil; March-October 2020) was included. Primary data were collected from medical records: sex, age, occupation/education, onset of symptoms, presence of chronic diseases/treatment and outcome (death and non-death). Secondary socioeconomic and environmental data were provided by the Department of Health. Results: The mean time spent between COVID-19 symptom onset and admission to the health system was 7.4 days. Principal component analysis summarized secondary sociodemographic data, and a Poisson regression model showed that the time between symptom onset and health system admission was higher for younger people and those from the least advantaged regions (availability of electricity, a sewage network, a water supply and garbage collection). A multiple logistic regression model showed an association of age (OR = 1.08; 1.05-1.1), diabetes (OR = 1.9; 1.1-3.4) and obesity (OR = 2.9; 1.1-7.6) with death outcome, while hypertension and sex showed no significant association. Conclusion: The identification of vulnerable groups may help the development of health strategies for the prevention and treatment of COVID-19.