RESUMO
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.
Assuntos
Epidemias , Malária , Humanos , Uganda/epidemiologia , Surtos de Doenças , Malária/epidemiologiaRESUMO
BACKGROUND: Early detection of outbreaks requires robust surveillance and reporting at both community and health facility levels. Uganda implements Integrated Disease Surveillance and Response (IDSR) for priority diseases and uses the national District Health Information System (DHIS2) for reporting. However, investigations after the first case in the 2022 Uganda Sudan virus outbreak was confirmed on September 20, 2022 revealed many community deaths among persons with Ebola-like symptoms as far back as August. Most had sought care at private facilities. We explored possible gaps in surveillance that may have resulted in late detection of the Sudan virus disease (SVD) outbreak in Uganda. METHODS: Using a standardized tool, we evaluated core surveillance capacities at public and private health facilities at the hospital level and below in three sub-counties reporting the earliest SVD cases in the outbreak. Key informant interviews (KIIs) were conducted with 12 purposively-selected participants from the district local government. Focus group discussions (FGDs) were conducted with community members from six villages where early probable SVD cases were identified. KIIs and FGDs focused on experiences with SVD and Viral Hemorrhagic Fever (VHF) surveillance in the district. Thematic data analysis was used for qualitative data. RESULTS: Forty-six (85%) of 54 health facilities surveyed were privately-owned, among which 42 (91%) did not report to DHIS2 and 39 (85%) had no health worker trained on IDSR; both metrics were 100% in the eight public facilities. Weak community-based surveillance, poor private facility engagement, low suspicion index for VHF among health workers, inability of facilities to analyze and utilize surveillance data, lack of knowledge about to whom to report, funding constraints for surveillance activities, lack of IDSR training, and lack of all-cause mortality surveillance were identified as gaps potentially contributing to delayed outbreak detection. CONCLUSION: Both systemic and knowledge-related gaps in IDSR surveillance in SVD-affected districts contributed to the delayed detection of the 2022 Uganda SVD outbreak. Targeted interventions to address these gaps in both public and private facilities across Uganda could help avert similar situations in the future.
Assuntos
Surtos de Doenças , Humanos , Uganda/epidemiologia , Feminino , Masculino , Doença pelo Vírus Ebola/epidemiologia , Doença pelo Vírus Ebola/diagnóstico , Adulto , Sudão/epidemiologia , Vigilância da População/métodos , Febres Hemorrágicas Virais/epidemiologia , Febres Hemorrágicas Virais/diagnósticoRESUMO
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.
Assuntos
Surtos de Doenças , Humanos , Uganda/epidemiologia , Masculino , Estudos Transversais , Adulto , Feminino , Doença pelo Vírus Ebola/epidemiologia , Doença pelo Vírus Ebola/virologia , Sequenciamento Completo do Genoma , Ebolavirus/genética , Ebolavirus/isolamento & purificaçãoRESUMO
BACKGROUND: Contact tracing (CT) is critical for ebolavirus outbreak response. Ideally, all new cases after the index case should be previously-known contacts (PKC) before their onset, and spend minimal time ill in the community. We assessed the impact of CT during the 2022 Sudan Virus Disease (SVD) outbreak in Uganda. METHODS: We collated anonymized data from the SVD case and contacts database to obtain and analyze data on CT performance indicators, comparing confirmed cases that were PKC and were not PKC (NPKC) before onset. We assessed the effect of being PKC on the number of people infected using Poisson regression. RESULTS: There were 3844 contacts of 142 confirmed cases (mean: 22 contacts/case). Forty-seven (33%) confirmed cases were PKC. PKCs had fewer median days from onset to isolation (4 vs 6; P<0.007) and laboratory confirmation (4 vs 7; P<0.001) than NPKC. Being a PKC vs NPKC reduced risk of transmitting infection by 84% (IRR=0.16, 95% CI 0.08-0.32). CONCLUSION: Contact identification was sub-optimal during the outbreak. However, CT reduced the time SVD cases spent in the community before isolation and the number of persons infected in Uganda. Approaches to improve contact tracing, especially contact listing, may improve control in future outbreaks.