Ebola disease outbreak caused by the Sudan virus in Uganda, 2022: a descriptive epidemiological study.

BACKGROUND: Uganda has had seven Ebola disease outbreaks, between 2000 and 2022. On Sept 20, 2022, the Ministry of Health declared a Sudan virus disease outbreak in Mubende District, Central Uganda. We describe the epidemiological characteristics and transmission dynamics. METHODS: For this descriptive study, cases were classified as suspected, probable, or confirmed using Ministry of Health case definitions. We investigated all reported cases to obtain data on case-patient demographics, exposures, and signs and symptoms, and identified transmission chains. We conducted a descriptive epidemiological study and also calculated basic reproduction number (Ro) estimates. FINDINGS: Between Aug 8 and Nov 27, 2022, 164 cases (142 confirmed, 22 probable) were identified from nine (6%) of 146 districts. The median age was 29 years (IQR 20-38), 95 (58%) of 164 patients were male, and 77 (47%) patients died. Symptom onsets ranged from Aug 8 to Nov 27, 2022. The case fatality rate was highest in children younger than 10 years (17 [74%] of 23 patients). Fever (135 [84%] of 160 patients), vomiting (93 [58%] patients), weakness (89 [56%] patients), and diarrhoea (81 [51%] patients) were the most common symptoms; bleeding was uncommon (21 [13%] patients). Before outbreak identification, most case-patients (26 [60%] of 43 patients) sought care at private health facilities. The median incubation was 6 days (IQR 5-8), and median time from onset to death was 10 days (7-23). Most early cases represented health-care-associated transmission (43 [26%] of 164 patients); most later cases represented household transmission (109 [66%]). Overall Ro was 1·25. INTERPRETATION: Despite delayed detection, the 2022 Sudan virus disease outbreak was rapidly controlled, possibly thanks to a low Ro. Children (aged <10 years) were at the highest risk of death, highlighting the need for targeted interventions to improve their outcomes during Ebola disease outbreaks. Initial care-seeking occurred at facilities outside the government system, showing a need to ensure that private and public facilities receive training to identify possible Ebola disease cases during an outbreak. Health-care-associated transmission in private health facilities drove the early outbreak, suggesting gaps in infection prevention and control. FUNDING: None.

Prevalence and Factors Associated with transfusion-transmissible infections among blood donors in Arua regional blood bank, Uganda.

BACKGROUND: Blood transfusion services play a very key role in modern health care service delivery. About 118.5 million blood donations were collected globally in 2022. However, about 1.6 million units of blood are destroyed annually due to transfusion-transmissible infections (TTIs). There is a very high risk of TTIs through donated blood to recipients if safe transfusion practices are not observed. This study determined the prevalence and factors associated with TTIs among blood donors in Arua regional blood bank, Uganda. METHODS: This study was a retrospective cross-sectional design that involved a review of a random sample of 1370 blood donors registered between January 1st, 2018 and December 31st, 2019 at Arua regional blood bank, Uganda. Descriptive statistics were used to describe the characteristics of the blood donors. The binary logistic regression was used to determine the factors associated with TTIs. RESULTS: The majority of the blood donors were male (80.1%), and the median donor age was 23 years (IQR = 8 years). The overall prevalence of TTIs was found to be 13.8% (95%CI: 12.0-15.6%), with specific prevalences of 1.9% for HIV, 4.1% for HBV, 6.6% for HCV and 2.8% for treponema pallidum. Male sex (AOR = 2.10, 95%CI: 1.32-3.36, p-value = 0.002) and lapsed donor type compared to new donor type (AOR = 0.34, 95%CI: 0.13-0.87, p-value = 0.025) were found to be associated with TTIs. CONCLUSION: The prevalence of TTIs among blood donors of West Nile region, Uganda was found to be significantly high, which implies a high burden of TTIs in the general population. Hence, there is need to implement a more stringent donor screening process to ensure selection of risk-free donors, with extra emphasis on male and new blood donors. Additionally, sensitization of blood donors on risky behaviors and self-deferral will reduce the risk of donating infected blood to the recipients.

Time to care and factors influencing appropriate Sudan virus disease care among case patients in Uganda, September to November 2022.

OBJECTIVES: Early isolation and care for Ebola disease patients at Ebola Treatment Units (ETU) curb outbreak spread. We evaluated time to ETU entry and associated factors during the 2022 Sudan virus disease (SVD) outbreak in Uganda. METHODS: We included persons with RT-PCR-confirmed SVD with onset September 20-November 30, 2022. We categorized days from symptom onset to ETU entry ("delays") as short (≤2), moderate (3-5), and long (≥6); the latter two were "delayed isolation." We categorized symptom onset timing as "earlier" or "later," using October 15 as a cut-off. We assessed demographics, symptom onset timing, and awareness of contact status as predictors for delayed isolation. We explored reasons for early vs late isolation using key informant interviews. RESULTS: Among 118 case-patients, 25 (21%) had short, 43 (36%) moderate, and 50 (43%) long delays. Seventy-five (64%) had symptom onset later in the outbreak. Earlier symptom onset increased risk of delayed isolation (crude risk ratio = 1.8, 95% confidence interval (1.2-2.8]). Awareness of contact status and SVD symptoms, and belief that early treatment-seeking was lifesaving facilitated early care-seeking. Patients with long delays reported fear of ETUs and lack of transport as contributors. CONCLUSION: Delayed isolation was common early in the outbreak. Strong contact tracing and community engagement could expedite presentation to ETUs.

Evaluation of malaria outbreak detection methods, Uganda, 2022.

BACKGROUND: Malaria outbreaks are detected by applying the World Health Organization (WHO)-recommended thresholds (the less sensitive 75th percentile or mean + 2 standard deviations [2SD] for medium-to high-transmission areas, and the more sensitive cumulative sum [C-SUM] method for low and very low-transmission areas). During 2022, > 50% of districts in Uganda were in an epidemic mode according to the 75th percentile method used, resulting in a need to restrict national response to districts with the highest rates of complicated malaria. The three threshold approaches were evaluated to compare their outbreak-signaling outputs and help identify prioritization approaches and method appropriateness across Uganda. METHODS: The three methods were applied as well as adjusted approaches (85th percentile and C-SUM + 2SD) for all weeks in 2022 for 16 districts with good reporting rates ( ≥ 80%). Districts were selected from regions originally categorized as very low, low, medium, and high transmission; district thresholds were calculated based on 2017-2021 data and re-categorized them for this analysis. RESULTS: Using district-level data to categorize transmission levels resulted in re-categorization of 8/16 districts from their original transmission level categories. In all districts, more outbreak weeks were detected by the 75th percentile than the mean + 2SD method (p < 0.001). For all 9 very low or low-transmission districts, the number of outbreak weeks detected by C-SUM were similar to those detected by the 75th percentile. On adjustment of the 75th percentile method to the 85th percentile, there was no significant difference in the number of outbreak weeks detected for medium and low transmission districts. The number of outbreak weeks detected by C-SUM + 2SD was similar to those detected by the mean + 2SD method for all districts across all transmission intensities. CONCLUSION: District data may be more appropriate than regional data to categorize malaria transmission and choose epidemic threshold approaches. The 75th percentile method, meant for medium- to high-transmission areas, was as sensitive as C-SUM for low- and very low-transmission areas. For medium and high-transmission areas, more outbreak weeks were detected with the 75th percentile than the mean + 2SD method. Using the 75th percentile method for outbreak detection in all areas and the mean + 2SD for prioritization of medium- and high-transmission areas in response may be helpful.