Risk factors for mortality among hospitalized COVID-19 patients in Northern Ethiopia: A retrospective analysis.

BACKGROUND: COVID-19 is a deadly pandemic caused by an RNA virus that belongs to the family of CORONA virus. To counter the COVID-19 pandemic in resource limited settings, it is essential to identify the risk factors of COVID-19 mortality. This study was conducted to identify the social and clinical determinants of mortality in COVID-19 patients hospitalized in four treatment centers of Tigray, Northern Ethiopia. METHODS: We reviewed data from 6,637 COVID-19 positive cases that were reported from May 7, 2020 to October 28, 2020. Among these, 925 were admitted to the treatment centers because of their severity and retrospectively analyzed. The data were entered into STATA 16 version for analysis. The descriptive analysis such as median, interquartile range, frequency distribution and percentage were used. Binary logistic regression model was fitted to identify the potential risk factors of mortality of COVID-19 patients. The adjusted odds ratio (AOR) with 95% confidence interval was used to determine the magnitude of the association between the outcome and predictor variables. RESULTS: The median age of the patients was 30 years (IQR, 25-44) and about 70% were male patients. The patients in the non-survivor group were much older than those in the survivor group (median 57.5 years versus 30 years, p-value < 0.001). The overall case fatality rate was 6.1% (95% CI: 4.5% - 7.6%) and was increased to 40.3% (95% CI: 32.2% - 48.4%) among patients with critical and severe illness. The proportions of severe and critical illness in the non-survivor group were significantly higher than those in the survivor group (19.6% versus 5.1% for severe illness and 80.4% versus 4.5% for critical illness, all p-value < 0.001). One or more pre-existing comorbidities were present in 12.5% of the patients: cardiovascular diseases (42.2%), diabetes mellitus (25.0%) and respiratory diseases (16.4%) being the most common comorbidities. The comorbidity rate in the non-survivor group (44.6%) was higher than in the survivor group (10.5%). The results from the multivariable binary regression showed that the odds of mortality was higher for patients who had cardiovascular diseases (AOR = 2.49, 95% CI: 1.03-6.03), shortness of breath (AOR = 9.71, 95% CI: 4.73-19.93) and body weakness (AOR = 3.04, 95% CI: 1.50-6.18). Moreover, the estimated odds of mortality significantly increased with patient's age. CONCLUSIONS: Age, cardiovascular diseases, shortness of breath and body weakness were the predictors for mortality of COVID-19 patients. Knowledge of these could lead to better identification of high risk COVID-19 patients and thus allow prioritization to prevent mortality.

Utilizing the SEIPS model to guide hand hygiene interventions at a tertiary hospital in Ethiopia.

We aimed to apply the Systems Engineering Initiative for Patient Safety (SEIPS) model to increase effectiveness and sustainability of the World Health Organization's (WHOs) hand hygiene (HH) guidelines within healthcare systems. Our cross-sectional, mixed-methods study took place at Jimma University Medical Center (JUMC), a tertiary care hospital in Jimma, Ethiopia, between November 2018 and August 2020 and consisted of three phases: baseline assessment, intervention, and follow-up assessment. We conducted questionnaires addressing HH knowledge and attitudes, interviews to identify HH barriers and facilitators within the SEIPS framework, and observations at the WHO's 5 moments of HH amongst healthcare workers (HCWs) at JUMC. We then implemented HH interventions based on WHO guidelines and results from our baseline assessment. Follow-up HH observations were conducted months later during the Covid-19 pandemic. 250 HCWs completed questionnaires with an average knowledge score of 61.4% and attitude scores indicating agreement that HH promotes patient safety. Interview participants cited multiple barriers to HH including shortages and location of HH materials, inadequate training, minimal Infection Prevention Control team presence, and high workload. We found an overall baseline HH compliance rate of 9.4% and a follow-up compliance rate of 72.1%. Drastically higher follow-up compared to baseline compliance rates were likely impacted by our HH interventions and Covid-19. HCWs showed motivation for patient safety despite low HH knowledge. Utilizing the SEIPS model helped identify institution-specific barriers that informed targeted interventions beyond WHO guidelines aimed at increasing effectiveness and sustainability of HH efforts.

COVID-19 response in Ethiopia: Challenges and opportunities

Ethiopia implemented public health measures to curve COVID pandemics earlier than many countries. Airport screening, followed by partial closure of international flights and quarantine of all international travelers have slowed the trajectory of COVID-19 pandemics in its early phase. Early adoption of Public health measures including hand hygiene and use of facemask have also contributed to the slow trajectory seen in the early days of the pandemics. Unfortunately, early gains have been beset by slow scale-up of public health measures, recent lifting of the state of emergency and public fatigue. Hospitals are already at capacity and not equipped to handle even the lowest estimate the country expects at the peak of the pandemic. To mitigate the impact of the pandemics, Ethiopia must return to the basics of public health measures: increase testing, upscale contact tracing, social distancing and universal use of face mask quickly and across the country.

COVID-19 in Ethiopia in the first 180 days: Lessons learned and the way forward

Within just nine months of its official identification by the World Health Organization, coronavirus disease 2019 (COVID-19) has caused 34 million confirmed infections and about 1 million deaths worldwide. The collateral damage and spill over effects to all sectors has caused severe social disruption and an economic crisis that the world was unprepared for. Despite the relentless global effort, the pandemic remains a serious threat to lives and livelihood. As a result, all countries are faced with the daunting task of balancing outbreak prevention strategies against efforts to save their economies. Nevertheless, almost every country now has months of local evidences about the pandemic that will support contextualized and measured actions. The number of confirmed cases and deaths attributable to COVID-19 in Ethiopia has steadily increased since the first reported case on 13 March 2020. Although the country has so far avoided the feared catastrophe, the true burden of the problem may be far beyond what has been reported due to limited testing capacity. With the current trends of widespread community transmission, COVID-19 remains a serious public health threat in the country. In addition, multiple human-related and environmental factors, combined with relaxed COVID-19 mitigation strategies, have put the country at a high epidemic risk. Thus, proactive and balanced measures based on local evidence should be taken to prevent the country from slipping into a dire public health crisis.

Early estimates of COVID-19 infections in small, medium and large population clusters.

INTRODUCTION: Since its emergence in late December 2019, COVID-19 has rapidly developed into a pandemic in mid of March with many countries suffering heavy human loss and declaring emergency conditions to contain its spread. The impact of the disease, while it has been relatively low in the sub-Saharan Africa (SSA) as of May 2020, is feared to be potentially devastating given the less developed and fragmented healthcare system in the continent. In addition, most emergency measures practised may not be effective due to their limited affordability as well as the communal way people in SSA live in relative isolation in clusters of large as well as smaller population centres. METHODS: To address the acute need for estimates of the potential impacts of the disease once it sweeps through the African region, we developed a process-based model with key parameters obtained from recent studies, taking local context into consideration. We further used the model to estimate the number of infections within a year of sustained local transmissions under scenarios that cover different population sizes, urban status, effectiveness and coverage of social distancing, contact tracing and usage of cloth face mask. RESULTS: We showed that when implemented early, 50% coverage of contact tracing and face mask, with 33% effective social distancing policies can bringing the epidemic to a manageable level for all population sizes and settings we assessed. Relaxing of social distancing in urban settings from 33% to 25% could be matched by introduction and maintenance of face mask use at 43%. CONCLUSIONS: In SSA countries with limited healthcare workforce, hospital resources and intensive care units, a robust system of social distancing, contact tracing and face mask use could yield in outcomes that prevent several millions of infections and thousands of deaths across the continent.