Sudan virus disease super-spreading, Uganda, 2022.

BACKGROUND: On 20 September 2022, Uganda declared its fifth Sudan virus disease (SVD) outbreak, culminating in 142 confirmed and 22 probable cases. The reproductive rate (R) of this outbreak was 1.25. We described persons who were exposed to the virus, became infected, and they led to the infection of an unusually high number of cases during the outbreak. METHODS: In this descriptive cross-sectional study, we defined a super-spreader person (SSP) as any person with real-time polymerase chain reaction (RT-PCR) confirmed SVD linked to the infection of ≥ 13 other persons (10-fold the outbreak R). We reviewed illness narratives for SSPs collected through interviews. Whole-genome sequencing was used to support epidemiologic linkages between cases. RESULTS: Two SSPs (Patient A, a 33-year-old male, and Patient B, a 26-year-old male) were identified, and linked to the infection of one probable and 50 confirmed secondary cases. Both SSPs lived in the same parish and were likely infected by a single ill healthcare worker in early October while receiving healthcare. Both sought treatment at multiple health facilities, but neither was ever isolated at an Ebola Treatment Unit (ETU). In total, 18 secondary cases (17 confirmed, one probable), including three deaths (17%), were linked to Patient A; 33 secondary cases (all confirmed), including 14 (42%) deaths, were linked to Patient B. Secondary cases linked to Patient A included family members, neighbours, and contacts at health facilities, including healthcare workers. Those linked to Patient B included healthcare workers, friends, and family members who interacted with him throughout his illness, prayed over him while he was nearing death, or exhumed his body. Intensive community engagement and awareness-building were initiated based on narratives collected about patients A and B; 49 (96%) of the secondary cases were isolated in an ETU, a median of three days after onset. Only nine tertiary cases were linked to the 51 secondary cases. Sequencing suggested plausible direct transmission from the SSPs to 37 of 39 secondary cases with sequence data. CONCLUSION: Extended time in the community while ill, social interactions, cross-district travel for treatment, and religious practices contributed to SVD super-spreading. Intensive community engagement and awareness may have reduced the number of tertiary infections. Intensive follow-up of contacts of case-patients may help reduce the impact of super-spreading events.

Readiness of health facilities to manage individuals infected with COVID-19, Uganda, June 2021.

BACKGROUND: The COVID-19 pandemic overwhelmed the capacity of health facilities globally, emphasizing the need for readiness to respond to rapid increases in cases. The first wave of COVID-19 in Uganda peaked in late 2020 and demonstrated challenges with facility readiness to manage cases. The second wave began in May 2021. In June 2021, we assessed the readiness of health facilities in Uganda to manage the second wave of COVID-19. METHODS: Referral hospitals managed severe COVID-19 patients, while lower-level health facilities screened, isolated, and managed mild cases. We assessed 17 of 20 referral hospitals in Uganda and 71 of 3,107 lower-level health facilities, selected using multistage sampling. We interviewed health facility heads in person about case management, coordination and communication and reporting, and preparation for the surge of COVID-19 during first and the start of the second waves of COVID-19, inspected COVID-19 treatment units (CTUs) and other service delivery points. We used an observational checklist to evaluate capacity in infection prevention, medicines, personal protective equipment (PPE), and CTU surge capacity. We used the "ReadyScore" criteria to classify readiness levels as > 80% ('ready'), 40-80% ('work to do'), and < 40% ('not ready') and tailored the assessments to the health facility level. Scores for the lower-level health facilities were weighted to approximate representativeness for their health facility type in Uganda. RESULTS: The median (interquartile range (IQR)) readiness scores were: 39% (IQR: 30, 51%) for all health facilities, 63% (IQR: 56, 75%) for referral hospitals, and 32% (IQR: 24, 37%) for lower-level facilities. Of 17 referral facilities, two (12%) were 'ready' and 15 (88%) were in the "work to do" category. Fourteen (82%) had an inadequate supply of medicines, 12 (71%) lacked adequate supply of oxygen, and 11 (65%) lacked space to expand their CTU. Fifty-five (77%) lower-level health facilities were "not ready," and 16 (23%) were in the "work to do" category. Seventy (99%) lower-level health facilities lacked medicines, 65 (92%) lacked PPE, and 53 (73%) lacked an emergency plan for COVID-19. CONCLUSION: Few health facilities were ready to manage the second wave of COVID-19 in Uganda during June 2021. Significant gaps existed for essential medicines, PPE, oxygen, and space to expand CTUs. The Uganda Ministry of Health utilized our findings to set up additional COVID-19 wards in hospitals and deliver medicines and PPE to referral hospitals. Adequate readiness for future waves of COVID-19 requires additional support and action in Uganda.

Ownership and use of long-lasting insecticidal nets three months after a mass distribution campaign in Uganda, 2021.

BACKGROUND: Uganda conducted its third mass long-lasting insecticidal net (LLIN) distribution campaign in 2021. The target of the campaign was to ensure that 100% of households own at least one LLIN per two persons and to achieve 85% use of distributed LLINs. LLIN ownership, use and associated factors were assessed 3 months after the campaign. METHODS: A cross-sectional household survey was conducted in 14 districts from 13 to 30 April, 2021. Households were selected using multistage sampling. Each was asked about LLIN ownership, use, duration since received to the time of interview, and the presence of LLINs was visually verified. Outcomes were having at least one LLIN per two household members, and individual LLIN use. Modified Poisson regression was used to assess associations between exposures and outcomes. RESULTS: In total, 5529 households with 27,585 residents and 15,426 LLINs were included in the analysis. Overall, 95% of households owned ≥ 1 LLIN, 92% of the households owned ≥ 1 LLIN < 3 months old, 64% of households owned ≥ 1 LLIN per two persons in the household. Eighty-seven per cent could sleep under an LLIN if every LLIN in the household were used by two people, but only 69% slept under an LLIN the night before the survey. Factors associated with LLIN ownership included believing that LLINs are protective against malaria (aPR = 1.13; 95% CI 1.04-1.24). Reported use of mosquito repellents was negatively associated with ownership of LLINs (aPR = 0.96; 95% CI 0.95-0.98). The prevalence of LLIN use was 9% higher among persons who had LLINs 3-12 months old (aPR = 1.09; 95% CI 1.06-1.11) and 10% higher among those who had LLINs 13-24 months old (aPR = 1.10; 95% CI 1.06-1.14) than those who had LLINs < 3 months old. Of 3,859 LLINs identified in the households but not used for sleeping the previous night, 3250 (84%) were < 3 months old. Among these 3250, 41% were not used because owners were using old LLINs; 16% were not used because of lack of space for hanging them; 11% were not used because of fear of chemicals in the net; 5% were not used because of dislike of the smell of the nets; and, 27% were not used for other reasons. CONCLUSION: The substantial difference between the population that had access to LLINs and the population that slept under LLINs indicates that the National Malaria Control Programme (NMCP) may need to focus on addressing the main drivers or barriers to LLIN use. NMCP and/or other stakeholders could consider designing and conducting targeted behaviour change communication during subsequent mass distribution of LLINs after the mass distribution campaign to counter misconceptions about new LLINs.

Loss to follow-up among people living with HIV on tuberculosis preventive treatment at four regional referral hospitals, Uganda, 2019-2021.

Introduction: Tuberculosis (TB) remains the leading cause of death among people living with HIV (PLHIV). TB preventive treatment (TPT) can prevent active TB infection in PLHIV for several years after it is completed. During 2019-2021, the six-month course of TPT (using isoniazid) was the most readily available in Uganda; however, program data indicated a TPT program loss to follow-up (LTFU) rate of 12 % during this period. We evaluated factors associated with TPT LTFU among PLHIV in four regional referral hospitals (RRHs) in Uganda from 2019 to 2021. Methods: We abstracted program data from TPT registers on patient LTFU at Masaka, Mbale, Mubende, and Jinja RRHs. Additional data collected included client demographics, duration on HIV antiretroviral therapy (ART), year of TPT initiation, adherence, and point of entry. LTFU was defined as the failure to finish six consecutive months of isoniazid without stopping for more than two months at a time. We conducted bivariate analysis using the chi-square test for independence. Variables with p < 0.05 in bivariate analysis were included in a logistic regression model to establish independent factors associated with LTFU. Results: Overall, 24,206 clients were started on TPT in the four RRHs. Their median age was 40 years (range, 1-90 years), and 15,962 (66 %) were female. A total of 22,260 (92 %) had TPT adherence >95 %. Independent factors associated with LTFU included being on ART for <3 months (AOR: 3.1, 95 % CI: 2.1-4.5) and 20-24 years (AOR: 4.7, 95 % CI: 1.9-12) or 25-29 years (AOR: 3.3, 95 % CI: 1.3-8.2) compared to 15-19 years. Conclusions: PLHIV just starting ART and young adults had higher odds of being LTFU from TPT during 2019-2021 in the four RRHs. Close follow-up of PLHIV aged 20-29 years and those newly initiated on ART could improve TPT completion.

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.

Comparative epidemiologic analysis of COVID-19 patients during the first and second waves of COVID-19 in Uganda.

Introduction: Uganda was affected by two major waves of coronavirus disease 2019 (COVID-19). The first wave during late 2020 and the second wave in late April 2021. This study compared epidemiologic characteristics of hospitalized (HP) and non-hospitalized patients (NHP) with COVID-19 during the two waves of COVID-19 in Uganda. Methods: Wave 1 was defined as November-December 2020, and Wave 2 was defined as April-June 2021. In total, 800 patients were included in this study. Medical record data were collected for HP (200 for each wave). Contact information was retrieved for NHP who had polymerase-chain-reaction-confirmed COVID-19 (200 for each wave) from laboratory records; these patients were interviewed by telephone. Findings: A higher proportion of HP were male in Wave 1 compared with Wave 2 (73% vs 54%; P=0.0001). More HP had severe disease or died in Wave 2 compared with Wave 1 (65% vs 31%; P<0.0001). NHP in Wave 2 were younger than those in Wave 1, but this difference was not significant (mean age 29 vs 36 years; P=0.13). HP were significantly older than NHP in Wave 2 (mean age 48 vs 29 years; P<0.0001), but not Wave 1 (mean age 48 vs 43 years; P=0.31). Interpretation: Demographic and epidemiologic characteristics of HP and NHP differed between and within Waves 1 and 2 of COVID-19 in Uganda.