Time to recovery from Covid-19 and its associated factors among patients hospitalized to the treatment center in South Central Ethiopia

Incidence of recovery rate and predictors among hospitalized COVID- 19 infected patients in Ethiopia; a systemic review and meta-analysis

BACKGROUND

Despite global efforts to mitigate COVID-19 infection through vaccination and therapeutic interventions, morbidity and mortality rates continued at variable rates. Although mortality risk and clinical features of COVID-19 are well-documented, recovery patterns and prognostic factors post-admission remain inconclusive, particularly in resource-limited settings like Ethiopia. This systematic review and meta-analysis (SRM) aimed to estimate the pooled incidence rate of recovery and predictors among hospitalized COVID-19 patients in Ethiopia.

METHODS

We searched (N = 1,191) articles using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline from PubMed/MEDLINE (N = 755), Scopus (N = 137), Web of Science (N = 84), Science Direct (N = 148), Cochran (N = 25), and Google Scholar searching (N = 42) from December 2019 to February 2024. The data were extracted using a Microsoft Excel spreadsheet and exported to Stata TM version 17.0 for further analysis. The Article quality was assessed using the Joanna Briggs Institute checklist. The pooled incidence rate of recovery was estimated using a weighted inverse variance random-effects meta-regression. Heterogeneity among studies was evaluated using the I2 statistic. Subgroup analyses and sensitivity tests were also conducted to explore publication bias. This file is registered in international Prospero with ID (CRD42024518569).

RESULT

Sixteen (N = 16) published studies with 7,676 hospitalized COVID-19 patients were included in the final report. The mean age of participants ranged from 29 (± 17) to 57.5 (± 3) years, with male patients constituting the largest proportion of participants, 4,491(58.5%). During recovery screening, 6,304(82.21%) cases were discharged as improved, 159 (2.1%) attriters, and 818 (10.6%) died during inpatient treatment. The pooled incidence of recovery, mortality, and attrition rates were found to be 82.32% (95% CI: 78.81-85.83; I2 = 94.8%), 14.3% (I2 = 98.45%), and 2.7% (I2 = 81.34%), respectively. Incidence of recovery rate varied across regions and epidemic phases, with the highest rate observed in Addis Ababa (89.94%, I2 = 78.33%) and the lowest reported in the Tigray region (59.7%, I2 = 0.0%). Across epidemic phases, the recovery rate was 88.05% (I2 = 29.56%) in Phase II, 84.09% (I2 = 97.57%) in Phase I, and 78.92% (I2 = 96.9%) in Phase III, respectively. Factors included being aged 15-30 years (pooled OR = 2.01), male sex (pooled OR = 1.46), no dyspnea (pooled OR = 2.4; I2 = 79%), and no baseline comorbidities (pooled OR = 1.15; I2 = 89.3%) were predictors for recovery. CONCLUSION AND RECOMMENDATION: In Ethiopia, more than eight out of ten hospitalized COVID-19 patients recovered after inpatient treatment. However, the incidence of recovery rates varied significantly across epidemic phases, study settings, and regions. Factors including younger age, male sex, no dyspnea (shortness of breathing), and no underlying comorbidity heightened recovery. It is highly recommended those inpatients cares should focus on high-risk groups (older adults) and implement standardized treatment protocols in each study setting. Regions with lower recovery rates need aid in logistical support and training for healthcare providers.

Time to recovery of COVID-19 patients and its predictors: a retrospective cohort study in HUCSH, Sidama, Ethiopia

INTRODUCTION

The 2019 COVID-19 pandemic had a global impact, leading to numerous deaths, long recovery times, and economic challenges worldwide, especially in countries with limited financial resources like Ethiopia. In Ethiopia, Hawassa lacks viral shedding information. Identifying predictors can help ease economic impact of illness.Therefore, this research aimed to examine the demographics, clinical features, and recovery time of COVID-19 patients, as well as determine predictive markers for severe adverse outcomes.

METHODS

Study at Hawassa University Comprehensive Speciality Hospital COVID-19 quarantine and therapy facility in Ethiopia (Sep 24, 2020 - Nov 26, 2021) with 804 patients. Extracted clinical, epidemiological, demographic info from medical records. Researchers used statistical tests like T tests, Chi-square tests, and Fisher's exact tests to analyze relationships between variables. They also used a Cox PH model to identify risk factors for COVID-19 patient recovery time. Significance level was set at 0.05 for all analyses.

RESULTS

Out of 804 COVID-19 patients, 74% recovered at an average age of 44.8 years, with 64.1% being male. Severe and critical cases were 24.1% and 21.4% of the population, respectively, with only 16.0% of critical cases and 19.5% of severe cases recovering. Average length of stay was 12.3 days. 88.4% of recovered patients had symptoms, with chest pain (66.7%), cough (64.4%), shortness of breath (59.2%), and fever (57.1%) being common. Nearly half had comorbidities, with diabetes (15.9%) and hypertension (15.2%) prevalent. Male patients had higher recovery rates, while severe/critical patients had lower rates. Patients over 39 age category had lower recovery chance. Existence of at least one comorbidities, diabetes, fever, and hypertension impacted recovery. Fever with gender and shortness of breath affected recovery. Assumptions were met with no multicollinearity.

CONCLUSIONS

Recent studies found that about 95% of COVID-19 patients recover within 30 days, with a median of 12 days. Severe cases, elderly, and those with comorbidities may take longer to recover. By effectively managing hypertension and diabetes, individuals can improve their prognosis and facilitate a quicker recovery. Public health concerns persist regarding COVID-19, especially for comorbidities like diabetic and hypertension. Early detection of fever and treatment of hypertension may expedite recovery.

Integrate-and-fire model of disease transmission

We create an epidemiological susceptible-infected-susceptible model of disease transmission using integrate-and-fire nodes on a network, allowing memory of previous interactions and infections. Agents in the network sum infectious matter from their nearest neighbors at every time step, until they exceed their infection threshold, at which point they "fire" and become infected for as long as the recovery time. The model has memory of previous interactions by tracking the amount of infectious matter carried by agents as well as just binary infected or susceptible states, and the model has memory of previous infections by modeling immunity as increasing the infection threshold after recovery. Creating a simulation of the model on networks with a power-law degree distribution and homogeneous agent parameters, we find a single strain version of the model matches well with the England COVID-19 case data, with a root-mean-squared error of 0.014%. A simulation of a multistrain version of the model (where there is cross-strain immunity) matches well with the influenza strain A and strain B case numbers in Canada, with a root-mean-squared error of 0.002% and 0.0012%, respectively, though due to the coupling in the model, both strains peak in phase. Since the dynamics of the model successfully capture real-life transmission dynamics, we test interventions to study their effect on case numbers, with both quarantining and social gathering restrictions lowering the peak. Since the model has memory, the stricter the intervention, the higher the secondary peak when the restriction is removed, showing that interventions change only the shape of the curves and not the overall number infected in the population.

Time to recovery and determinant factors of COVID-19 patients under treatment in Sidama region, Ethiopia: A retrospective cohort study

Introduction

Evidence from several studies shows that the median recovery time among COVID-19 patients varies in different settings, and why that difference occurs is questionable. The current study was aimed to estimate the time to recovery and to identify determinant factors among COVID-19 patients admitted to treatment centers in the Sidama region, Ethiopia.

Methods

The secondary data was extracted from the Sidama Public Health Institute (SPHI), data management unit. A total of 1038 COVID-19 patients who were under treatment from 1, June 2020, to 30, June 2022, at different treatment centers in the region were included in the study. The Log-Logistic Accelerated Failure Time (AFT) model was employed to estimate the effects size of different covariates on recovery time of COVID-19 patients under treatment.

Results

Among the total number of patients in the study, 704 (67.82 %) were recovered, while 334(32.18 %) were censored. The median recovery time was 14 days (IQR: 10-18 days). Patients who were above 65 years old had nearly 1.17 times more prolonged recovery time as compared to patients who were below 25 years old, (OR = 1.168, p-value = 0.032, CI = 0.013,0.298). The log odds of recovery for patients who were in critical severity status at admission was 1.279 times more decelerated as compared to asymptomatic patients (P-value = 0.005, CI = 0.076, 0.417). The log odds of recovery for patients who had no history of headache was 1.107 times more accelerated as compared to patients who had a history of headache (OR = 1.107, p-value = 0.027, CI = 0.011, 0.192). The log odds of recovery for non-diabetic patients was 1.244 times more accelerated as compared to patients who were diabetic (OR = 1,244, p-value = 0.002, CI = 0.077, 0.360), holding other covariates constant in the model.

Conclusion

Age, critical severity status of infection, having symptoms of infection, having a history of headaches, and being diabetic had statistically significant effects on time to recovery among COVID-19 patients admitted to the treatment centers in the Sidama region.

Bridging the humanitarian-development divide in a protracted crisis: a case study of the use of a central plant to supply oxygen for COVID-19 case management in South Sudan

The rising demand for medicinal oxygen due to the COVID-19 pandemic exacerbated an underlying chronic shortage of the commodity in Africa. This situation is particularly dire in protracted crises where insecurity, dysfunctional health facilities, poor infrastructure and prohibitive costs hinder equitable access to the commodity. Against this backdrop, the Ministry of Health of South Sudan, with the guidance of its partners, procured and installed a pressure swing adsorption central oxygen supply plant to address the shortfall. The plant aimed to ensure a more sustainable and technologically appropriate medicinal oxygen supply system for the country and to bridge the humanitarian and development divide, which had always been challenging. This article discusses the key issues, challenges and lessons associated with the procurement and installation of this plant. The major challenges encountered during the procurement and installation of the plant were the time it took to procure and install in the face of urgent needs for medicinal oxygen and its short and long-term sustainability. Lessons learnt include the need for exhaustive and evidence-based considerations in deciding on which source of medicinal oxygen to deploy in protracted crisis settings. The successful installation and operationalization of the plant demonstrated that it is possible to bridge the humanitarian-development divide amidst the complexities of a protracted crisis and an ongoing pandemic. The Ministries of Health, with the support of its partners, should assess and document the impact of this and other similar central oxygen production plants in protracted crisis settings regarding their sustainability, cost, and effectiveness on medicinal oxygen supply. The Ministry of Health of South Sudan should expedite the finalization and operationalization of the longer-term public-private partnership and continue to monitor the quality of oxygen produced by this plant.

Time to recovery and its determinant factors among patients with COVID-19 in Assosa COVID-19 treatment center, Western Ethiopia

BACKGROUND

The Novel Coronavirus disease (COVID-19) pandemic has become a global threat. Determining the time to recovery from COVID-19 is intended to assist healthcare professionals in providing better care, and planning logistics. So, the study aimed to identify the factors that affect the time to recovery from COVID-19 for patients treated at Assosa COVID-19 treatment center, Benishangul Gumuz Regional State, Western Ethiopia.

METHODS

A retrospective study design was conducted on 334 randomly selected COVID-19 patients at Assosa COVID-19 treatment center from February 2021 to July 2021. The median survival time, Kaplan-Meier survival estimate, and Log-Rank test were used to describe the data and compare the survival time between groups. The study used the Cox PH model to analyze the time to the first recovery of COVID-19 patients, where hazard ratio, p-value, and 95% CI for hazard ratio were used for testing significance. Schoenfeld and Cox-Snell residuals were used for checking the model assumption.

RESULTS

The overall incidence rate was 13.79 per 100 (95% CI: 10.04, 18.95) person-days observations. The median time to recovery was 16 days. At the end of the follow-up, 77.2% of the patients had developed an event of recovery, and the rest 22.8% were censored. The mean age of patients was 45.22 years. Severe COVID-19 patients (AHR = 0.7876, 95% CI: 0.7090, 0.8748), presence of symptoms (AHR = 0.2814, 95% CI: 0.1340, 0.5914), comorbidity (AHR = 0.1627, 95% CI: 0.1396, 0.1897), ≥ 90 oxygen saturation (AHR = 3.2370, 95% CI: 2.161, 4.848), and being older age (AHR = 0.9840, 95% CI: 0.971, 0.9973) were found to have statistically significant association with the time to recovery from COVID-19.

CONCLUSION

The study concludes that severe COVID-19 patients, male patients, patients having comorbidity, older age, and patients having symptoms as poor prognostic factors of COVID-19 disease and also prolonged recovery time. Therefore, health providers in treatment centers should give strict follow-up and priority to older patients, severe COVID-19 patients, and patients having another co-morbid illness by focusing on respiratory difficulties and underlying pre-existing medical conditions to manage the disease severity and recover quickly.

Leveraging an epidemic-economic mathematical model to assess human responses to COVID-19 policies and disease progression

It is imperative that resources are channelled towards programs that are efficient and cost effective in combating the spread of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This study proposed and analyzed control strategies for that purpose. We developed a mathematical disease model within an optimal control framework that allows us to investigate the best approach for curbing COVID-19 epidemic. We address the following research question: what is the role of community compliance as a measure for COVID-19 control? Analyzing the impact of community compliance of recommended guidelines by health authorities-examples, social distancing, face mask use, and sanitizing-coupled with efforts by health authorities in areas of vaccine provision and effective quarantine-showed that the best intervention in addition to implementing vaccination programs and effective quarantine measures, is the active incorporation of individuals' collective behaviours, and that resources should also be directed towards community campaigns on the importance of face mask use, social distancing, and frequent sanitizing, and any other collective activities. We also demonstrated that collective behavioral response of individuals influences the disease dynamics; implying that recommended health policy should be contextualized.

Esophagogastroduodenoscopy in Patients With Dyspepsia: A Retrospective Study at a Tertiary Hospital

Background Recurrent upper abdominal pain or dyspepsia is one of the patients' most common chief complaints. This study correlates the symptoms of dyspepsia to esophagogastroduodenoscopy findings among Sudanese patients attending Soba University Hospital. Methods A retrospective observational study was conducted at Soba University Hospital from April 2019 to April 2020. Patients were selected according to inclusion and exclusion criteria. Each patient filled out a standardized data collection form with data on their demographics, symptoms, and endoscopic findings. A P-value of < 0.05 was considered statistically significant. Results The study included 142 patients, where 57.7% (n=82) were females, and 59.9% (n=85) of the study participants were of normal body mass index. In contrast, 57% of the study participants had a symptom duration of less than six months. Approximately 95.1% (n=135) suffered from heartburn, 80.3% (n=114) suffered from epigastric fullness, and 96.5% (n=137) suffered from epigastric pain. Seventy-eight point two percent (78.2%; n=111) who suffered from epigastric pain mentioned that it increased in intensity with food, 85.9% (n=121) who suffered from epigastric pain mentioned that it decreased in intensity with food, 54.2% (n=76) of the study participants suffered from regurgitation, 59.9% (n=85) of the study participants suffered from weight loss, 52.1% (n=73) of the study participants were using non-steroidal anti-inflammatory drugs (NSAIDs), 41.7% (n=59) of the study participants had normal esophagogastroduodenoscopy findings, followed by 35.9% (n=51) who had duodenitis or gastritis during the endoscopic assessment. Conclusion The study showed that esophagogastroduodenoscopy is not recommended in young patients without alarm symptoms who can be managed conservatively. However, every patient with alarming symptoms should have an esophagogastroduodenoscopy. Also, the study revealed that females and old-aged patients had higher rates of dyspeptic symptoms.

Sleep disturbance in post COVID-19 conditions: Prevalence and quality of life

Post COVID-19 conditions are complaints and symptoms in patients with a history of probable or confirmed COVID-19 after 3 months of the onset of COVID-19 and last at least 2 months. About 10-20% of people may experience post COVID-19 conditions, one of which is sleep disturbance. There is a wide range of prevalence of sleep disturbances from 6% to more than 70%. An online survey of the post COVID-19 conditions in various countries showed that 78.58% of subjects had sleep disturbances, including insomnia, sleep-disordered breathing, central disorders of hypersomnolence, circadian rhythm sleep-wake disorders, parasomnias, and sleep-related movement disorders. Sleep disturbance can be found starting from 2 weeks until 48 weeks or more after discharge or after having a negative COVID-19 test results. Women aged < 50 years old with severe COVID-19 infection reported a worse outcome. Several mechanisms may cause sleep disturbance in post COVID-19 condition, namely persistent viral infection and inflammation, immunity dysregulation, and mitochondrial dysfunction. Several studies discovered sleep disturbance was a major problem that affected different domains of QoL in post COVID-19 conditions. Significant correlation was found between several dimensions of SF-36 with moderate-to-severe insomnia in post COVID-19 conditions. Therefore, sleep disturbance is a major problem in post COVID-19 conditions and may affect patients' QoL, and the existence of sleep disturbance should be a concern in post COVID-19 conditions period. Further research is required to determine the prevalence based on agreed definition as well as methods to assess this condition and its impact on QoL.

Factors Affecting Confirmed COVID-19 Patient's Recovery Time at King Abdulaziz Medical City, Jeddah

INTRODUCTION

The COVID-19 pandemic has become a threat to the public's health, especially to the elderly and those with chronic conditions. It is capable of spreading from carriers who are both asymptomatic and symptomatic. Associated factors such as age, sex, severe symptoms of COVID-19 disease, and chronic disease have a significant impact on the recovery time of patients.

AIM

The study aimed to determine associated factors on recovery time in COVID-19 patients hospitalized at King Abdulaziz Medical city.

METHODS

A single-center retrospective study was utilized to recruit 1776 confirmed COVID-19 patients from 13 September to 24 October 2020 at King Abdulaziz Medical City (KAMC) in Jeddah.

RESULTS

The patients were categorized into three age groups: below 5 years, 5 to 65 years, and above 65 years. The number of male patients in each group was 49, 764, and 73, and the number of female patients in each group was 54, 754, and 82, respectively. Impact recovery time on female patients was 11.75 days; with male patients was 10.95 days. Symptoms such as sore throat, diarrhea, and fever in female patients declined the recovery time. On the other hand, symptoms such as runny nose, diarrhea, fever, and headache in male patients declined the recovery time.

DISCUSSION AND CONCLUSION

It was revealed that older aged COVID-19 patients, male sex, and some symptoms decline recovery time. The study findings show an independent predictor of particular symptoms and sign which delay the time of recovery in the COVID-19 patients enrolled in the study differently, for male and female patients. Thus, patients who are infected with COVID-19 should be monitored keenly to prevent a prolonged rate of recovery and should be eligible for priority management to enhance a good clinical outcome.

Predictors and time to recovery from COVID-19 among patients attended at the treatment centers in Ekiti State, South West, Nigeria

Introduction

time to clinical recovery from COVID-19 infection and associated factors has not been explored in Nigeria. This study was conducted to assess the predictors and time to recovery from COVID-19 among patients attended to at the treatment centers in Ekiti State, South West.

Methods

a facility-based retrospective cohort study was conducted between March 2020 to October 2021. Laboratory confirmed COVID-19 positive test result of 586 patients receiving treatment at the treatment centres in Ekiti were included. Data were extracted from COVID-19 intake forms and medical records of patients. Data were analysed using descriptive statistics and survival analysis methods including Cox proportional hazards regression model. Level of significance was set at 5%.

Results

the mean age of the patients was 43.46 (SD 0.74) years. Forty-seven percent (47%) of the patients were aged 25-44 years, fifty-one percent (51%) were males. The median recovery time of COVID-19 patients was 21 days (IQR: 14-23). Being a male-patient (95% CI 20.46-21.54), older age (95% CI 20.14-21.86), not admitted in the hospital (95% CI 22.74-23.26), and associated multiple co-morbidities (95% CI 17.65-28.35) were associated with delayed recovery time. Predictors of recovery time of patients from COVID-19 infection were admission status (aHR: 0.71, 95%CI 0.56-0.88; p=0.002) and symptoms on admission (aHR: 0.81, 95%CI 0.66-0.99; p=0.020).

Conclusion

patients with comorbidities, older and those not admitted were more likely to have a delayed clinical recovery from COVID-19. Knowledge of the predictors might help health professionals in risk stratification and better management of patients with COVID-19.