A longitudinal environmental surveillance study for SARS-CoV-2 from the emergency department of a teaching hospital in Hong Kong.

BACKGROUND: Understanding factors associated with SARS-CoV-2 exposure risk in the hospital setting may help improve infection control measures for prevention. AIM: To monitor SARS-CoV-2 exposure risk among healthcare workers and to identify risk factors associated with SARS-CoV-2 detection. METHODS: Surface and air samples were collected longitudinally over 14 months spanning 2020-2022 at the Emergency Department (ED) of a teaching hospital in Hong Kong. SARS-CoV-2 viral RNA was detected by real-time reverse-transcription polymerase chain reaction. Ecological factors associated with SARS-CoV-2 detection were analysed by logistic regression. A sero-epidemiological study was conducted in January-April 2021 to monitor SARS-CoV-2 seroprevalence. A questionnaire was used to collect information on job nature and use of personal protective equipment (PPE) of the participants. FINDINGS: SARS-CoV-2 RNA was detected at low frequencies from surfaces (0.7%, N = 2562) and air samples (1.6%, N = 128). Crowding was identified as the main risk factor, as weekly ED attendance (OR = 1.002, P=0.04) and sampling after peak-hours of ED attendance (OR = 5.216, P=0.03) were associated with the detection of SARS-CoV-2 viral RNA from surfaces. The low exposure risk was corroborated by the zero seropositive rate among 281 participants by April 2021. CONCLUSION: Crowding may introduce SARS-CoV-2 into the ED through increased attendances. Multiple factors may have contributed to the low contamination of SARS-CoV-2 in the ED, including hospital infection control measures for screening ED attendees, high PPE compliance among healthcare workers, and various public health and social measures implemented to reduce community transmission in Hong Kong where a dynamic zero COVID-19 policy was adopted.

Prediction of upcoming global infection burden of influenza seasons after relaxation of public health and social measures during the COVID-19 pandemic: a modelling study

Background The transmission dynamics of influenza were affected by public health and social measures (PHSMs) implemented globally since early 2020 to mitigate the COVID-19 pandemic. We aimed to assess the effect of COVID-19 PHSMs on the transmissibility of influenza viruses and to predict upcoming influenza epidemics. Methods For this modelling study, we used surveillance data on influenza virus activity for 11 different locations and countries in 2017-22. We implemented a data-driven mechanistic predictive modelling framework to predict future influenza seasons on the basis of pre-COVID-19 dynamics and the effect of PHSMs during the COVID-19 pandemic. We simulated the potential excess burden of upcoming influenza epidemics in terms of fold rise in peak magnitude and epidemic size compared with pre-COVID-19 levels. We also examined how a proactive influenza vaccination programme could mitigate this effect. Findings We estimated that COVID-19 PHSMs reduced influenza transmissibility by a maximum of 17.3% (95% CI 13.3-21.4) to 40.6% (35.2-45.9) and attack rate by 5.1% (1.5-7.2) to 24.8% (20.8-27.5) in the 2019-20 influenza season. We estimated a 10-60% increase in the population susceptibility for influenza, which might lead to a maximum of 1-5-fold rise in peak magnitude and 1-4-fold rise in epidemic size for the upcoming 2022-23 influenza season across locations, with a significantly higher fold rise in Singapore and Taiwan. The infection burden could be mitigated by additional proactive one-off influenza vaccination programmes. Interpretation Our results suggest the potential for substantial increases in infection burden in upcoming influenza seasons across the globe. Strengthening influenza vaccination programmes is the best preventive measure to reduce the effect of influenza virus infections in the community. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.

Hospitalizations and mortality during the first year of the COVID-19 pandemic in Hong Kong, China: An observational study

Background: Hong Kong followed a strict COVID-19 elimination strategy in 2020. We estimated the impact of the COVID-19 pandemic responses on all-cause and cause-specific hospitalizations and deaths in 2020. Methods: Interrupted time-series analysis using negative binomial regression accounting for seasonality and long-term trend was used on weekly 2010–2020 data to estimate the change in hospitalization risk and excess mortality occurring both within and out of hospitals. Findings: In 2020, as compared to a 2010–2019 baseline, we observed an overall reduction in all-cause hospitalizations, and a concurrent increase in deaths. The overall hospitalization reduction (per 100,000 population) was 4809 (95% CI: 4692, 4926) in 2020, with respiratory diseases (632, 95% CI: 607, 658) and cardiovascular diseases (275, 95% CI: 264, 286) contributing most. The overall excess mortality (per 100,000 population) was 25 (95% CI: 23, 27) in 2020, mostly among individuals with pre-existing cardiovascular diseases (12, 95% CI: 11, 13). A reduction in excess in-hospital mortality (−10 per 100,000, 95% CI: −12, −8) was accompanied by an increase in excess out-of-hospital mortality (32, 95% CI: 29, 34). Interpretation: The COVID-19 pandemic might have caused indirect impact on population morbidity and mortality likely through changed healthcare seeking particularly in youngest and oldest individuals and those with cardiovascular diseases. Better healthcare planning is needed during public health emergencies with disruptions in healthcare services. Funding: Health and Medical Research Fund, Collaborative Research Fund, AIR@InnoHK and RGC Senior Research Fellow Scheme, Hong Kong. © 2022 The Authors

Local Surveillance of the COVID-19 Outbreak

Given the worldwide pandemic of the novel coronavirus disease 2019 (COVID-19) and its continuing threat brought by the emergence of virus variants, there are great demands for accurate surveillance and monitoring of outbreaks. A valuable metric for assessing the current risk posed by an outbreak is the time-varying reproduction number ((Formula presented.)). Several methods have been proposed to estimate (Formula presented.) using different types of data. We developed a new tool that integrated two commonly used approaches into a unified and user-friendly platform for the estimation of time-varying reproduction numbers. This tool allows users to perform simulations and yield real-time tracking of local epidemic of COVID-19 with an R package. Copyright © 2022 Liu, Xu, Bai, Xu, Lau, Cowling and Du.

RiskEstim: A Software Package to Quantify COVID-19 Importation Risk

We present an R package developed to quantify coronavirus disease 2019 (COVID-19) importation risk. Quantifying and visualizing the importation risk of COVID-19 from inbound travelers is urgent and imperative to trigger public health responses, especially in the early stages of the COVID-19 pandemic and emergence of new SARS-CoV-2 variants. We provide a general modeling framework to estimate COVID-19 importation risk using estimated pre-symptomatic prevalence of infection and air traffic data from the multi-origin places. We use Hong Kong as a case study to illustrate how our modeling framework can estimate the COVID-19 importation risk into Hong Kong from cities in Mainland China in real time. This R package can be used as a complementary component of the pandemic surveillance system to monitor spread in the next pandemic.

Changes in health-seeking behavior among adult and pediatric patients with seizures during the COVID-19 pandemic

Objective: To demonstrate any changes in seizure-related medical help-seeking behavior and admission outcomes during COVID-19. Background: The global outbreak of COVID-19 has imposed a huge challenge on healthcare systems globally. Infection control measures may have impacted patients' health-seeking behavior for non-respiratory conditions, particularly chronic diseases such as epilepsy. Design/Methods: All Accident & Emergency (A&E) attendances in Hong Kong for seizures in 2020 from January 23 to March 24 (adult) and April 22 (pediatric) were compared with parallel periods from 2015 to 2019. All data and admission outcomes were identified from a centralized territory-wide electronic database. Pre-existing time trend in control periods and changes during COVID-19 were analyzed by Poisson, negative and logistic regression models. Results: Among adults aged ≥ 18 years, seizure-related A&E attendances (adjusted relative risk, aRR 0.78, 95% CI=0.65 - 0.92, p=0.003) and admissions (aRR 0.70, 95% CI=0.60 - 0.80, p<0.001) decreased significantly during COVID-19. Ratio of ward admission per A&E attendance, intensive care utility and mortality rates remained stable. Among pediatric patients aged < 18 years, seizure-related attendances also decreased in 2020 (RR 0.38, 95% CI=0.25 - 0.59, p<0.001), with a disproportionate decrease in the 0-6 age group (RR 0.303, 95% CI=0.17 - 0.53, p<0.001). A drastic decline in upper respiratory infection-related A&E attendances (RR 0.208, 95% CI=0.14 - 0.31, p<0.001) was congruent to the time trends of seizure-related attendances in in the 0-6 age group. Conclusions: A significant reduction in emergency attendances for seizures during COVID-19 calls for appropriate measures to ensure healthcare services and education are provided to patients with epilepsy in a timely and effective manner during this global public health crisis. The congruent decrease in seizure- and URTI-related attendances among younger children suggests potential novel approaches focusing on infection control measures and immunization programs among younger children in prevention of febrile seizures and thus other related epileptic disorders in later life.