Background Assessing factors associated with mortality among COVID-19 patients could guide in developing context relevant interventions to mitigate the risk. The study aimed to describe mortality and associated factors among COVID-19 patients admitted at six health facilities in Uganda. Methods We reviewed medical records of patients admitted with COVID-19 between January 1st 2021 and December 31st 2021 in six hospitals in Uganda. Using Stata version 17.0, Kaplan Meier and Cox regression analyses were performed to describe the time to death and estimate associations between various exposures and time to death. Finally, accelerated failure time (AFT) models with a lognormal distribution were used to estimate corresponding survival time ratios. Results Out of the 1040 study participants, 234 (22.5%: 95%CI 12.9 to 36.2%) died. The mortality rate was 30.7 deaths per 1000 person days, 95% CI (26.9 to 35.0). The median survival time was 33 days, IQR (9–82). Factors associated with time to COVID-19 death included; age ≥ 60 years [adjusted hazard ratio (aHR) = 2.4, 95% CI: [1.7, 3.4]], having malaria test at admission [aHR = 2.0, 95% CI:[1.0, 3.9]], a COVID-19 severity score of severe/critical [aHR = 6.7, 95% CI:[1.5, 29.1]] and admission to a public hospital [aHR = 0.4, 95% CI:[0.3, 0.6]]. The survival time of patients aged 60 years or more is estimated to be 63% shorter than that of patients aged less than 60 years [adjusted time ratio (aTR) 0.37, 95% CI 0.24, 0.56]. The survival time of patients admitted in public hospitals was 2.5 times that of patients admitted in private hospitals [aTR 2.5 to 95%CI 1.6, 3.9]. Finally, patients with a severe or critical COVID-19 severity score had 87% shorter survival time than those with a mild score [aTR 0.13, 95% CI 0.03, 0.56]. Conclusion In-hospital mortality among COVID-19 patients was high. Factors associated with shorter survival; age ≥ 60 years, a COVID-19 severity score of severe or critical, and having malaria at admission. We therefore recommend close monitoring of COVID-19 patients that are elderly and also screening for malaria in COVID-19 admitted patients.

Introduction Identification of factors predicting prolonged hospitalization of patients with coronavirus disease (COVID-19) guides the planning, care and flow of patients in the COVID-19 Treatment Units (CTUs). We determined the length of hospital stay and factors associated with prolonged hospitalization among patients with COVID-19 at six CTUs in Uganda. Methods We conducted a retrospective cohort study of patients admitted with COVID-19 between January and December 2021 in six CTUs in Uganda. We conducted generalized linear regression models of the binomial family with a log link and robust variance estimation to estimate risk ratios of selected exposure variables and prolonged hospitalization (defined as a hospital stay for 14 days or more). We also conducted negative binomial regression models with robust variance to estimate the rate ratios between selected exposures and hospitalization duration. Results Data from 968 participants were analyzed. The median length of hospitalization was 5 (range: 1–89) days. A total of 136/968 (14.1%: 95% confidence interval (CI): 11.9–16.4%) patients had prolonged hospitalization. Hospitalization in a public facility (adjusted risk ratio (ARR) = 2.49, 95% CI: 1.65–3.76), critical COVID-19 severity scores (ARR = 3.24: 95% CI: 1.01–10.42), and malaria co-infection (adjusted incident rate ratio (AIRR) = 0.67: 95% CI: 0.55–0.83) were associated with prolonged hospitalization. Conclusion One out of seven COVID-19 patients had prolonged hospitalization. Healthcare providers in public health facilities should watch out for unnecessary hospitalization. We encourage screening for possible co-morbidities such as malaria among patients admitted for COVID-19.

Objective The purpose of this study was to evaluate the accuracy and reliability of neoGuard in comparison to a conventional bedside monitor on patients in a low-resource clinical setting. Design This was a single-arm methods comparison study involving the use of a wearable vital signs monitor (neoGuardTM) versus a conventional bedside monitor (Edan iM8). Setting The study was conducted at Jinja Regional Referral Hospital, a tertiary care hospital situated in Eastern Uganda. Participants Thirty patients (10 male, 20 female) were enrolled from the adult recovery ward at JRRH. Participants were eligible for the study if they were at least 18 years of age, had 2 sets of normal vital sign measurements obtained 1 h apart, and were able and willing to provide informed consent. Main Outcome and Measures The primary outcome measures were (i) bias (mean deviation) and (ii) limits of agreement [95% CI]. Bland-Altman plots were generated to illustrate the level of agreement between the neoGuardTM technology and the Edan iM8 monitor. Results Bland-Altman analysis was performed for 24 participants; datasets from six participants were excluded due to missing or invalid measurements. Findings showed a moderate level of agreement for measurement of SpO2, PR, and RR, with >80% of subject means falling within the predefined acceptability limits. However, there was also notable variation in accuracy between subjects, with large standard deviations observed for measurement of all four parameters. While the level of agreement for measurement of temperature was low, this is partly explained by limitations in the comparison method.

In many resource-poor countries, CD4 count thresholds of eligibility for antiretroviral treatment (ART) were initially low (<200 cells/mm(3)) but are now being increased to improve patient survival and to reduce HIV transmission. There are few quantitative data on the effect of such increases on the demand for ART. The objective of this study was to measure HIV prevalence and the proportion of HIV-positives eligible for antiretroviral therapy at different CD4 cut-off levels among users of public health care services in Kampala, Uganda. We recruited 1200 adults from three primary care clinics in Kampala, including equal numbers of family planning (FP) clients, pregnant women, adult patients with any complaint, and persons seeking HIV counseling and testing. All participants were screened for HIV and those positive had a CD4 count done. HIV prevalence in all patients was 16.9% (203/1200). ART eligibility based on CD4 counts significantly increased from 36% at a 200 cells/mm(3) cut-off to 44% at 250 cells and to 57% at 350 cells cut-off (p for χ(2) trend<0.001). We concluded that changing cut-off levels to higher CD4 counts will significantly increase patient load in Kampala's primary care clinics, but a phased implementation should minimize negative effects on quality of care.