Evaluating the impact on health outcomes of an event that resulted in a delay in contact tracing of COVID-19 cases

ObjectiveIn September 2020, records of 15,861 SARS-CoV-2 cases failed to upload from the Second Generation Laboratory Surveillance System (SGSS) to the Contact Tracing Advisory Service (CTAS) tool, resulting in a delay in the contact tracing of these cases. This study used CTAS data to determine the impact of this delay on health outcomes: transmission events, hospitalisations, and mortality. Previously, a modelling study had suggested a substantial impact. DesignObservational study SettingEngland. PopulationIndividuals testing positive for SARS-CoV-2 and their reported contacts. Main outcome measuresSecondary attack rates (SARs), hospitalisations, and deaths amongst primary and secondary contacts were calculated, compared to all other concurrent, unaffected cases. SGSS records affected by the event were matched to CTAS records and successive contacts and cases were identified. ResultsThe initiation of contact tracing was delayed by 3 days on average in the primary cases in the delay group (6 days) compared to the control group (3 days). This was associated with lower completion of contact tracing of primary cases in the delay group: 80% (95%CI: 79-81%) in the delay group and 83% (95%CI: 83-84%) in the control group. There was some evidence to suggest an increase in transmission to non-household contacts amongst those affected by the delay. The SAR for non-household contacts was higher amongst secondary contacts in the delay group than the control group (delay group: 7.9%, 95%CI:6.4% to 9.2%; control group: 5.9%, 95%CI: 5.3% to 6.6%). There was no evidence of a difference between the delay and control groups in the odds of hospitalisation (crude odds ratio: 1.1 (95%CI: 0.9 to 1.2) or death (crude odds ratio: 0.7 (0.1 to 4.0)) amongst secondary contacts. ConclusionsThe delay in contact tracing had a limited impact on population health outcomes. Strengths and limitations of the studyO_LIShows empirical data on the health impact of an event leading to a delay in contact tracing so can test hypotheses generated by models of the potential impact of a delay in contact tracing C_LIO_LIEstimates the extent of further transmission and odds of increased mortality or hospitalisation in up to the third generation of cases affected by the event C_LIO_LIThe event acts as a natural experiment to describe the possible impact of contact tracing, comparing a group affected by chance by delayed contact tracing to a control group who experienced no delay C_LIO_LIContact tracing was not completed for all individuals, so the study might not capture all affected contacts or transmissions C_LI

A prospective study of risk factors associated with seroprevalence of SARS-CoV-2 antibodies in healthcare workers at a large UK teaching hospital

BackgroundThe COVID-19 pandemic continues to grow at an unprecedented rate. Healthcare workers (HCWs) are at higher risk of SARS-CoV-2 infection than the general population but risk factors for HCW infection are not well described. MethodsWe conducted a prospective sero-epidemiological study of HCWs at a UK teaching hospital using a SARS-CoV-2 immunoassay. Risk factors for seropositivity were analysed using multivariate logistic regression. Findings410/5,698 (7{middle dot}2%) staff tested positive for SARS-CoV-2 antibodies. Seroprevalence was higher in those working in designated COVID-19 areas compared with other areas (9{middle dot}47% versus 6{middle dot}16%) Healthcare assistants (aOR 2{middle dot}06 [95%CI 1{middle dot}14-3{middle dot}71]; p=0{middle dot}016) and domestic and portering staff (aOR 3{middle dot}45 [95% CI 1{middle dot}07-11{middle dot}42]; p=0{middle dot}039) had significantly higher seroprevalence than other staff groups after adjusting for age, sex, ethnicity and COVID-19 working location. Staff working in acute medicine and medical sub-specialities were also at higher risk (aOR 2{middle dot}07 [95% CI 1{middle dot}31-3{middle dot}25]; p<0{middle dot}002). Staff from Black, Asian and minority ethnic (BAME) backgrounds had an aOR of 1{middle dot}65 (95% CI 1{middle dot}32 - 2{middle dot}07; p<0{middle dot}001) compared to white staff; this increased risk was independent of COVID-19 area working. The only symptoms significantly associated with seropositivity in a multivariable model were loss of sense of taste or smell, fever and myalgia; 31% of staff testing positive reported no prior symptoms. InterpretationRisk of SARS-CoV-2 infection amongst HCWs is heterogeneous and influenced by COVID-19 working location, role, age and ethnicity. Increased risk amongst BAME staff cannot be accounted for solely by occupational factors. FundingWellcome Trust, Addenbrookes Charitable Trust, National Institute for Health Research, Academy of Medical Sciences, the Health Foundation and the NIHR Cambridge Biomedical Research Centre. Research in context Evidence before this studySpecific risk factors for SARS-CoV-2 infection in healthcare workers (HCWs) are not well defined. Additionally, it is not clear how population level risk factors influence occupational risk in defined demographic groups. Only by identifying these factors can we mitigate and reduce the risk of occupational SARS-CoV-2 infection. We performed a review of the evidence for HCW-specific risk factors for SARS-CoV-2 infection. We searched PubMed with the terms "SARS-CoV-2" OR "COVID-19" AND "Healthcare worker" OR "Healthcare Personnel" AND "Risk factor" to identify any studies published in any language between December 2019 and September 2020. The search identified 266 studies and included a meta-analysis and two observational studies assessing HCW cohort seroprevalence data. Seroprevalence and risk factors for HCW infections varied between studies, with contradictory findings. In the two serological studies, one identified a significant increased risk of seroprevalence in those working with COVID-19 patients (Eyre et al 2020), as well as associations with job role and department. The other study (Dimcheff et al 2020) found no significant association between seropositivity and any identified demographic or occupational factor. A meta-analysis of HCW (Gomez-Ochoa et al 2020) assessed >230,000 participants as a pooled analysis, including diagnoses by both RT-PCR and seropositivity for SARS-CoV-2 antibodies and found great heterogeneity in study design and reported contradictory findings. Of note, they report a seropositivity rate of 7% across all studies reporting SARS-CoV-2 antibodies in HCWs. Nurses were the most frequently affected healthcare personnel and staff working in non-emergency inpatient settings were the most frequently affected group. Our search found no prospective studies systematically evaluating HCW specific risk factors based entirely on seroprevalence data. Added value of this studyOur prospective cohort study of almost 6,000 HCWs at a large UK teaching hospital strengthens previous findings from UK-based cohorts in identifying an increased risk of SARS-CoV-2 exposure amongst HCWs. Specifically, factors associated with SARS-CoV-2 exposure include caring for confirmed COVID-19 cases and identifying as being within specific ethnic groups (BAME staff). We further delineated the risk amongst BAME staff and demonstrate that occupational factors alone do not account for all of the increased risk amongst this group. We demonstrate for the first time that healthcare assistants represent a key at-risk occupational group, and challenge previous findings of significantly higher risk amongst nursing staff. Seroprevalence in staff not working in areas with confirmed COVID-19 patients was only marginally higher than that of the general population within the same geographical region. This observation could suggest the increased risk amongst HCWs arises through occupational exposure to confirmed cases and could account for the overall higher seroprevalence in HCWs, rather than purely the presence of staff in healthcare facilities. Over 30% of seropositive staff had not reported symptoms consistent with COVID-19, and in those who did report symptoms, differentiating COVID-19 from other causes based on symptom data alone was unreliable. Implications of all the available evidenceInternational efforts to reduce the risk of SARS-CoV-2 infection amongst HCWs need to be prioritised. The risk of SARS-CoV-2 infection amongst HCWs is heterogenous but also follows demonstrable patterns. Potential mechanisms to reduce the risk for staff working in areas with confirmed COVID-19 patients include improved training in hand hygiene and personal protective equipment (PPE), better access to high quality PPE, and frequent asymptomatic testing. Wider asymptomatic testing in healthcare facilities has the potential to reduce spread of SARS-CoV-2 within hospitals, thereby reducing patient and staff risk and limiting spread between hospitals and into the wider community. The increased risk of COVID-19 amongst BAME staff cannot be explained by purely occupational factors; however, the increased risk amongst minority ethnic groups identified here was stark and necessitates further evaluation.

Nowcasting CoVID-19 Deaths in England by Age and Region

Understanding the trajectory of the daily numbers of deaths in people with CoVID-19 is essential to decisions on the response to the CoVID-19 pandemic. Estimating this trajectory from data on numbers of deaths is complicated by the delay between deaths occurring and their being reported to the authorities. In England, Public Health England receives death reports from a number of sources and the reporting delay is typically several days, but can be several weeks. Delayed reporting results in considerable uncertainty about the number of deaths that occurred on the most recent days. In this article, we estimate the number of deaths per day in each of five age strata within seven English regions. We use a Bayesian hierarchical model that involves a submodel for the number of deaths per day and a submodel for the reporting delay distribution. This model accounts for reporting-day effects and longer-term changes over time in the delay distribution. We show how the model can be fitted in a computationally efficient way when the delay distribution is same in multiple strata, e.g. over a wide range of ages.