Determinants of time to viral clearance among SARS-CoV-2 infected individuals at Millennium COVID-19 care center in Ethiopia: A prospective observational study

Background: Understanding the COVID-19 disease course in terms of viral shedding is important to assist in providing a tailored isolation and treatment practice. Therefore, the current study aimed to estimate time to viral clearance and identify determinants among SARS-CoV-2 infected individuals admitted to Millennium COVID-19 Care Center in Ethiopia. Methods: A Prospective observational study was conducted among 360 randomly selected SARS-CoV-2 infected individuals who were on follow up from 2nd June to 5th July 2020. Kaplan Meier plots, median survival times, and Log-rank test were used to describe the data and compare survival distribution between groups. Association between time to viral clearance and determinants was assessed using the Cox proportional hazard survival model, where hazard ratio, P-value, and 95% CI for hazard ratio were used for testing significance Results: The Median time to viral clearance was 16 days. The log-rank test shows that having moderate and severe disease, one or more symptoms at presentation, and presenting with respiratory and constitutional symptoms seems to extend the time needed to achieve viral clearance. The Final Cox regression result shows that the rate of achieving viral clearance among symptomatic patients was 44% lower than patients who were asymptomatic (AHR=0.560, 95% CI=0.322-0.975, p-value=0.040). Conclusions: Presence of symptoms was found to be associated with delayed viral clearance implying that symptomatic patients are more likely to be infectious and therefore, attention should be paid to the practices regarding isolation and treatment of COVID-19 patients.

COVID-19 disease severity and associated factors among Ethiopian patients: A study of the millennium COVID-19 care center.

BACKGROUND: The COVID-19 pandemic started a little later in Ethiopia than the rest of the world and most of the initial cases were reported to have a milder disease course and a favorable outcome. This changed as the disease spread into the population and the more vulnerable began to develop severe disease. Understanding the risk factors for severe disease in Ethiopia was needed to provide optimal health care services in a resource limited setting. OBJECTIVE: The study assessed COVID-19 patients admitted to Millennium COVID-19 Care Center in Ethiopia for characteristics associated with COVID-19 disease severity. METHODS: A cross-sectional study was conducted from June to August 2020 among 686 randomly selected patients. Chi-square test was used to detect the presence of a statistically significant difference in the characteristics of the patients based on disease severity (Mild vs Moderate vs Severe). A multinomial logistic regression model was used to identify factors associated with COVID-19 disease severity where Adjusted Odds ratio (AOR), 95% CIs for AOR and P-values were used for significance testing. RESULTS: Having moderate as compared with mild disease was significantly associated with having hypertension (AOR = 2.30, 95%CI = 1.27,4.18), diabetes mellitus (AOR = 2.61, 95%CI = 1.31,5.19for diabetes mellitus), fever (AOR = 6.12, 95%CI = 2.94,12.72) and headache (AOR = 2.69, 95%CI = 1.39,5.22). Similarly, having severe disease as compared with mild disease was associated with age group (AOR = 4.43, 95%CI = 2.49,7.85 for 40-59 years and AOR = 18.07, 95%CI = 9.29,35.14for ≥ 60 years), sex (AOR = 1.84, 95%CI = 1.12,3.03), hypertension (AOR = 1.97, 95%CI = 1.08,3.59), diabetes mellitus (AOR = 3.93, 95%CI = 1.96,7.85), fever (AOR = 13.22, 95%CI = 6.11, 28.60) and headache (AOR = 4.82, 95%CI = 2.32, 9.98). In addition, risk factors of severe disease as compared with moderate disease were found to be significantly associated with age group (AOR = 4.87, 95%CI = 2.85, 8.32 for 40-59 years and AOR = 18.91, 95%CI = 9.84,36.331 for ≥ 60 years), fever (AOR = 2.16, 95%CI = 1.29,3.63) and headache (AOR = 1.79, 95%CI = 1.03, 3.11). CONCLUSIONS: Significant factors associated with severe COVID-19 in Ethiopia are being older than 60 years old, male, a diagnosis of hypertension, diabetes mellitus, and the presence of fever and headache. This is consistent with severity indicators identified by WHO and suggests the initial finding of milder disease in Ethiopia may have been because the first people to get COVID-19 in the country were the relatively younger with fewer health problems.

Characteristics and outcome profile of hospitalized African patients with COVID-19: The Ethiopian context.

BACKGROUND: The COVID-19 pandemic seems to have a different picture in Africa; the first case was identified in the continent after it had already caused a significant loss to the rest of the world and the reported number of cases and mortality rate has been low. Understanding the characteristics and outcome of the pandemic in the African setup is therefore crucial. AIM: To assess the characteristics and outcome of Patients with COVID-19 and to identify determinants of the disease outcome among patients admitted to Millennium COVID-19 Care Center in Ethiopia. METHODS: A prospective cohort study was conducted among 1345 consecutively admitted RT-PCR confirmed Patients with COVID-19 from July to September, 2020. Frequency tables, KM plots, median survival times and Log-rank test were used to describe the data and compare survival distribution between groups. Cox proportional hazard survival model was used to identify determinants of time to clinical recovery and the independent variables, where adjusted hazard ratio, P-value and 95% CI for adjusted hazard ratio were used for testing significance and interpretation of results. Binary logistic regression model was used to assess the presence of a statistically significant association between disease outcome and the independent variables, where adjusted odds ratio, P-value and 95% CI for adjusted odds ratio were used for testing significance and interpretation of results. RESULTS: Among the study population, 71 (5.3%) died, 72 (5.4%) were transferred and the rest 1202 (89.4%) were clinically improved. The median time to clinical recovery was 14 days. On the multivariable Cox proportional hazard model; temperature (AHR = 1.135, 95% CI = 1.011, 1.274, p-value = 0.032), COVID-19 severity (AHR = 0.660, 95% CI = 0.501, 0.869, p-value = 0.003), and cough (AHR = 0.705, 95% CI = 0.519, 0.959, p-value = 0.026) were found to be significant determinants of time to clinical recovery. On the binary logistic regression, the following factors were found to be significantly associated with disease outcome; SPO2 (AOR = 0.302, 95% CI = 0.193, 0.474, p-value = 0.0001), shortness of breath (AOR = 0.354, 95% CI = 0.213, 0.590, p-value = 0.0001) and diabetes mellitus (AOR = 0.549, 95% CI = 0.337, 0.894, p-value = 0.016). CONCLUSIONS: The average duration of time to clinical recovery was 14 days and 89.4% of the patients achieved clinical recovery. The mortality rate of the studied population is lower than reports from other countries including those in Africa. Having severe COVID-19 disease severity and presenting with cough were found to be associated with delayed clinical recovery of the disease. On the other hand, being hyperthermic is associated with shorter disease duration (faster time to clinical recovery). In addition, lower oxygen saturation, subjective complaint of shortness of breath and being diabetic were associated with unfavorable disease outcome. Therefore, patients with these factors should be followed cautiously for a better outcome.

Duration of Supplemental Oxygen Requirement and Predictors in Severe COVID-19 Patients in Ethiopia: A Survival Analysis.

BACKGROUND: With the rising number of new cases of COVID-19, understanding the oxygen requirement of severe patients assists in identifying at risk groups and in making an informed decision on building hospitals capacity in terms of oxygen facility arrangement. Therefore, the study aimed to estimate time to getting off supplemental oxygen therapy and identify predictors among COVID-19 patients admitted to Millennium COVID-19 Care Center in Ethiopia. METHODS: A prospective observational study was conducted among 244 consecutively admitted COVID-19 patients from July to September, 2020. Kaplan Meier plots, median survival times and Log-rank test were used to describe the data and compare survival distribution between groups. Cox proportional hazard survival model was used to identify determinants of time to getting off supplemental oxygen therapy, where hazard ratio (HR), P-value and 95%CI for HR were used for testing significance and interpretation of results. RESULTS: Median time to getting off supplemental oxygen therapy among the studied population was 6 days (IQR,4.3-20.0). Factors that affect time to getting off supplemental oxygen therapy were age group (AHR=0.52,95%CI=0.32,0.84, p-value=0.008 for ≥70 years) and shortness of breath (AHR=0.71,95%CI=0.52,0.96, p-value=0.026). CONCLUSION: Average duration of supplemental oxygen therapy requirement among COVID-19 patients was 6 days and being 70 years and older and having shortness of breath were found to be associated with prolonged duration of supplemental oxygen therapy requirement. This result can be used as a guide in planning institutional resource allocation and patient management to provide a well-equipped care to prevent complications and death from the disease.

Factors associated with development of symptomatic disease in Ethiopian COVID-19 patients: a case-control study.

BACKGROUND: Studies show that having some symptoms seems to be associated with more severe disease and poor prognosis. Therefore, knowing who is more susceptible to symptomatic COVID-19 disease is important to provide targeted preventive and management practice. The aim of the study was to assess factors associated with the development of symptomatic disease among COVID-19 patients admitted to Millennium COVID-19 Care Center in Ethiopia. METHODS: A case-control study was conducted from August to September 2020 among a randomly selected 730 COVID-19 patients (337 Asymptomatic and 393 Symptomatic patients). Chi-square test and independent t-test were used to detect the presence of a statistically significant difference in the characteristics of the cases (symptomatic) and controls (asymptomatic), where p-value of < 0.05 considered as having a statistically significant difference. Multivariable binary logistic regression was used to assess a statistically significant association between the independent variables and developing symptomatic COVID-19 where Adjusted Odds ratio (AOR), 95% CIs for AOR, and P-values were used for testing significance and interpretation of results. RESULTS: The result of the multivariable binary logistic regression shows that age group (AOR = 1.89, 95% CI = 1.25, 2.87, p-value = 0.002 for 30-39 years; AOR = 1.69, 95% CI = 1.06, 2.73, p-value = 0.028 for 40-49 years and AOR = 4.42, 95% CI = 2.75, 7.12, p-value = 0.0001 for ≥50 years), sex (AOR = 1.76, 95% CI = 1.26, 2.45, p-value = 0.001) and history of diabetes mellitus (AOR = 3.90, 95% CI = 1.92, 7.94, p-value = 0.0001) were found to be significant factors that determine the development of symptomatic disease in COVID-19 patients. CONCLUSIONS: Developing a symptomatic COVID-19 disease was found to be associated with exposures of old age, male sex, and being diabetic. Therefore, patients with the above factors should be given enough attention in the prevention and management process, including inpatient management, to pick symptoms earlier and to manage accordingly so that these patients can have a favorable treatment outcome.

Laboratory biomarkers of COVID-19 disease severity and outcome: Findings from a developing country.

AIM: To identify laboratory biomarkers that predict disease severity and outcome among COVID-19 patients admitted to the Millennium COVID-19 Care Center in Ethiopia. METHODS: A retrospective cohort study was conducted among 429 COVID-19 patients who were on follow up from July to October 2020. Data was described using frequency tables. Robust Poisson regression model was used to identify predictors of COVID-19 severity where adjusted relative risk (ARR), P-value and 95 CI for ARR were used to test significance. Binary Logistic regression model was used to assess the presence of statistically significant association between the explanatory variables and COVID-19 outcome where adjusted odds ratio (AOR), P-value and 95%CI for AOR were used for testing significance. RESULTS: Among the 429 patients studied, 182 (42.4%) had Severe disease at admission and the rest 247 (57.6%) had Non-severe disease. Regarding disease outcome, 45 (10.5%) died and 384 (89.5%) were discharged alive. Age group (ARR = 1.779, 95%CI = 1.405-2.252, p-value <0.0001), Neutrophil to Lymphocyte ratio (NLR) (ARR = 4.769, 95%CI = 2.419-9.402 p-value <0.0001), Serum glutamic oxaloacetic transaminase (SGOT) (ARR = 1.358, 95%CI = 1.109-1.662 p-value = 0.003), Sodium (ARR = 1.321, 95%CI = 1.091-1.600 p-value = 0.004) and Potassium (ARR = 1.269, 95%CI = 1.059-1.521 p-value = 0.010) were found to be significant predictors of COVID-19 severity. The following factors were significantly associated with COVID-19 outcome; age group (AOR = 2.767, 95%CI = 1.099-6.067, p-value = 0.031), white blood cell count (WBC) (AOR = 4.253, 95%CI = 1.918-9.429, p-value = 0.0001) and sodium level (AOR = 3.435, 95%CI = 1.439-8.198, p-value = 0.005). CONCLUSIONS: Assessing and monitoring the laboratory markers of WBC, NLR, SGOT, sodium and potassium levels at the earliest stage of the disease could have a considerable role in halting disease progression and death.

Laboratory Biomarkers of COVID-19 Disease Severity and Outcome: Findings from a Developing Country

AimTo identify laboratory biomarkers that predict disease severity and outcome among COVID-19 patients admitted to the Millennium COVID-19 Care Center in Ethiopia. MethodsA retrospective cohort study was conducted among 429 RT-PCR confirmed COVID- 19 patients who were on follow up from July to October 2020 and with complete clinical and laboratory data. Data was described using frequency tables. Robust Poisson regression model was used to identify predictors of COVID-19 disease severity where adjusted relative risk (RR), P-value and 95% CI for RR were used to test significance and interpretation of results. Binary Logistic regression model was used to assess the presence of statistically significant association between the explanatory variables and COVID-19 disease outcome where adjusted odds ratio, P- value and 95% CI for adjusted odds ratio were used for testing significance and interpretation of results ResultsAmong the 429 patients studied, 182 (42.4%) had Severe disease at admission and the rest 247 (57.6%) had Non-severe disease (15.6% mild and 42.0% moderate). Regarding disease outcome, 45 (10.5%) died and 384 (89.5%) were discharged alive. Age group (ARR= 1.779, 95% CI= 1.405- 2.252, p-value < 0.0001), Neutrophil to Lymphocyte ratio (NLR) (ARR= 4.769, 95% CI= 2.419 - 9.402 p-value <0.0001), Serum glutamic oxaloacetic transaminase (SGOT) (ARR= 1.358, 95% CI= 1.109- 1.662 p-value=0.003), Sodium (ARR= 1.321, 95% CI= 1.091- 1.600 p-value=0.004) and Potassium (ARR= 1.269, 95% CI= 1.059-1.521 p-value=0.010) were found to be significant predictors of COVID-19 disease severity. The following factors were significantly associated with COVID-19 disease outcome; age group (AOR= 2.767, 95% CI= 1.099 - 6.067, p-value=0.031), white blood cell count (AOR= 4.253, 95% CI= 1.918 - 9.429, p-value=0.0001) and sodium level (AOR= 3.435, 95% CI= 1.439, 8.198, p-value=0.005). ConclusionsThe laboratory markers of NLR of above three, raised SGOT and deranged sodium and potassium levels (both hypo- and hyper-states) were found to be significant predictors of developing severe COVID-19 disease. In addition, deranged values of white blood cell count and sodium levels were significantly associated with worse outcome of the disease. Therefore, assessing and monitoring these laboratory markers at the earliest stage of the disease could have a considerable impact in halting disease progression and death.

COVID-19 in Hospitalized Ethiopian Children: Characteristics and Outcome Profile

BackgroundConsidering the number of people affected and the burden to the health care system due to the Coronavirus pandemic, there is still a gap in understanding the disease better leaving a space for new evidence to be filled by researchers. This scarcity of evidence is observed especially among children with the virus. Therefore, this study aimed to assess the characteristics and outcome profile of children with COVID-19 admitted to Millennium COVID-19 Care Center in Ethiopia. MethodsA prospective cohort study was conducted among 90 children with COVID-19 who were admitted from June 23 to September 17, 2020. Data was summarized using frequency tables, mean {+/-} standard deviation or median with Inter Quartile range values. A chi-square test/ Fischers exact test was used to compare disease severity between groups. ResultsThe median age of the participants was 15 years and 57 were females. The most common reported route of disease transmission was through close contact with a diagnosed person (41/90). Only three had a history of pre-existing comorbid illness. One-third (31/90) had one or more symptoms at diagnosis, the most common being cough (20/90). Among the 90 patients, 59 were asymptomatic, 14 had mild disease and the rest 17 had moderate disease. Based on the chi-square/ Fischers exact test result, no statistically significant difference was observed between the age groups and sex. ConclusionsPediatric patients seemed to have a milder disease presentation and a favorable outcome compared to other countries report and also the adult pattern observed in our country.

Characteristics and outcome profile of Hospitalized African COVID-19 patients: The Ethiopian Context

BackgroundThe COVID-19 pandemic seems to have a different picture in Africa; the first case was identified in the continent after it has already caused a significant loss to the rest of the world and the reported number of cases and mortality rate has been low. Understanding the characteristics and outcome of the pandemicin the African setup is therefore crucial. AimTo assess the characteristics and outcome of COVID-19 patients and to identify determinants of the disease outcome among patients admitted to Millennium COVID-19 Care Center in Ethiopia. MethodsA prospective cohort study was conducted among 1345 consecutively admitted RT-PCR confirmed COVID-19 patients from July to September, 2020. Frequency tables, KM plots, median survival times and Log-rank test were used to describe the data and compare survival distribution between groups. Cox proportional hazard survival model was used to identify determinants of time to clinical improvement and the independent variables, where adjusted hazard ratio, P-value and 95% CI for adjusted hazard ratio were used for testing significance and interpretation of results. Binary logistic regression model was used to assess the presence of a statistically significant association between disease outcome and the independent variables, where adjusted odds ratio, P-value and 95% CI for adjusted odds ratio were used for testing significance and interpretation of results ResultsAmong the study population, 71 (5.3%) died, 72 (5.4%) were transferred and the rest 1202 (89.4%) were clinically improved. The median time to clinical improvement was 14 days. On the multivariable Cox proportional hazard model; temperature (AHR= 1.135, 95% CI= 1.011, 1.274, p-value=0.032), COVID-19 severity (AHR= 0.660, 95% CI= 0.501, 0.869, p-value=0.003), and cough (AHR= 0.705, 95% CI= 0.519, 0.959, p-value=0.026) were found to be significant determinants of time to clinical improvement. On the binary logistic regression, the following factors were found to be significantly associated with disease outcome; SPO2 (AOR= 0.302, 95% CI= 0.193, 0.474, p-value=0.0001), shortness of breath (AOR= 0.354, 95% CI= 0.213, 0.590, p-value=0.0001) and diabetes mellitus (AOR= 0.549, 95% CI= 0.337, 0.894, p-value=0.016). ConclusionsThe average duration of time to clinical improvement was 14 days and 89.4 % of the patients achieved clinical improvement. The mortality rate of the studied population is lower than reports from other countries including those in Africa. Having severe COVID-19 disease severity and presenting with cough were found to be associated with delayed clinical improvement of the disease. On the other hand, being hyperthermic is associated with shorter disease duration (faster time to clinical improvement). In addition, lower oxygen saturation and subjective complaint of shortness of breath and being diabetic were associated with unfavorable disease outcome. Therefore, patients with these factors should be followed cautiously for a better outcome.

Determinants of Developing Symptomatic Disease in Ethiopian COVID-19 Patients

BackgroundStudies show that having some symptoms seems to be associated with more severe disease and poor prognosis. Therefore, knowing who is more susceptible to symptomatic COVID-19 disease is important to provide targeted preventive and management practice. The aim of the study was to assess the determinants of having symptomatic disease among COVID-19 patients admitted to Millennium COVID-19 Care Center in Ethiopia. MethodsA case-control study was conducted from August to September 2020 among a randomly selected 765 COVID-19 patients (372 Asymptomatic and 393 Symptomatic patients). Chi-square test and independent t-test were used to detect the presence of a statistically significant difference in the characteristics of the cases (symptomatic) and controls (asymptomatic), where p-value of <0.05 considered as having a statistically significant difference. Multivariable binary logistic regression was used to assess a statistically significant association between the independent variables and developing symptomatic COVID-19 where Adjusted Odds ratio (AOR), 95% CIs for AOR, and P-values were used for testing significance and interpretation of results. ResultsThe result of the multivariable binary logistic regression shows that age group (AOR= 1.818, 95% CI= 1.210, 2.731, p-value=0.004 for 30-39 years; AOR= 1.611, 95% CI= 1.016, 2.554, p-value=0.043 for 40-49 years and AOR= 4.076, 95% CI= 2.582, 6.435, p-value=0.0001 for years and above), sex (AOR= 1.672, 95% CI= 1.216, 2.299, p-value=0.002) and history of diabetes mellitus (AOR= 2.406, 95% CI= 1.384, 4.181, p-value=0.002) were found to be significant factors that determine the development of symptomatic disease in COVID-19 patients. ConclusionsDeveloping a symptomatic COVID-19 disease was found to be determined by exposures of old age, male sex, and being diabetic. Therefore, patients with the above factors should be given enough attention in the prevention and management process, including inpatient management, to pick symptoms earlier and to manage accordingly so that these patients can have a favorable treatment outcome.

Optimizing data quality of pharmaceutical information systems in public health care in resource limited settings.

BACKGROUND: Robust pharmaceutical management information systems (PMIS) strengthen healthcare planning and delivery. Few nationwide studies in resource limited settings in Africa validate the data quality of PMIS in public healthcare. OBJECTIVE: To determine predictors and quality of data in a nationwide PMIS database in Namibia. METHODS: A population-level analysis of the quality of data i.e. completeness, accuracy and consistency in a nationwide PMIS database, 2007-2015. Data quality of the PMIS was determined by three domains, completeness, accuracy and consistency. Data completeness was determined by level of missing data in SPSSv25, with acceptable level set at <5%. Data accuracy was determined by proportion of PMIS indicators with extreme outliers. Data consistence was determined by patterns of missingness, i.e. random or systematic. Predictors of data quality were determined using logistic regression modelling. RESULTS: A total of 544 entries and 12 indicators were registered in the PMIS at 38 public health facilities. All the PMIS indicators had missing data and 50% (n = 6) had inaccurate data i.e. extreme values. The data for most PMIS indicators (75%, n = 12) were consistent with the pattern of missing completely at random (MCAR, i.e. missingness <5%). Incompleteness of PMIS data was highest for average number of prescriptions 6%, annual expenditure per capita for pharmaceuticals 5% and population per pharmacist's assistant 5%. The main predictors of poor quality of PMIS data were year of reporting of PMIS data (p = 0.035), level of health facility (p < 0.001), vital reference materials available at the pharmacy (p = 0.002), and pharmacists' posts filled (p = 0.013). CONCLUSIONS: The data quality of PMIS in public health care in Namibia is sub-optimal and widely varies by reporting period, level of health facility and region. The integration of data quality assurance systems is required to strengthen quality of PMIS data to optimize quality of PMIS data in public health care.