Changes in the incidence, clinical features and outcomes of tuberculosis during COVID-19 pandemic.

BACKGROUND: COVID-19 pandemic has disrupted tuberculosis (TB) services in many countries, but the impacts on sites of involvement, drug susceptibility, smear positivity and clinical outcomes, and clinical outcomes of co-infection with influenza and COVID-19 remain unclear. METHODS: Descriptive epidemiological study using episode-based and patient unique data of tuberculosis from Hospital Authority's territory-wide electronic medical record database, comparing baseline (January 2015-December 2019) and COVID-19 period (January 2020-December 2022), followed by univariate and multivariate analyses. Effects of co-infection with influenza and COVID-19 were investigated. RESULTS: The study included 10,473 episodes of laboratory-confirmed TB, with 6818 in baseline period and 3655 during COVID-19 period. During COVID-19 period, TB patients had a lower proportion of smear positivity (49.2 % vs 54.7 %, P < 0.001), and fewer cases of extrapulmonary TB (7.0 % vs 8.0 %, P = 0.078) and multidrug resistant TB (1.0 % vs 1.6 %, P = 0.020). Mortality was higher in TB patients with COVID-19 coinfection (OR 1.7, P = 0.003) and influenza coinfection (OR 2.6, P = 0.004). During COVID-19 period, there were higher rates of treatment delay (20.5 % vs 15.5 %, P < 0.001) and episodic death (15.1 % vs 13.3 %, P = 0.006). Factors associated with higher mortality included age ≥ 70 years (OR 7.24), treatment delay (OR 2.16), extrapulmonary TB (OR 2.13). smear positivity (OR 1.71) and Charlson comorbidity index score ≥ 3 (OR 1.37). Higher mortality was observed with co-infection by influenza (OR 1.18) and COVID-19 (OR 1.7). CONCLUSIONS: The epidemiology and outcomes of TB were changed during COVID-19 period. Mortality was higher during COVID-19 period and with co-infection by influenza and COVID-19.

Border Restriction as a Public Health Measureto Limit Outbreak of Coronavirus Disease 2019 (COVID-19)

BackgroundCoronavirus Disease 2019 (COVID-19) led to pandemic that affected almost all countries in the world. Many countries have implemented border restriction as a public health measure to limit local outbreak. However, there is inadequate scientific data to support such a practice, especially in the presence of an established local transmission of the disease. MethodA novel metapopulation Susceptible-Exposed-Infectious-Recovered (SEIR) model with inspected migration was applied to investigate the effect of border restriction between Hong Kong and mainland China on the epidemiological characteristics of COVID-19 in Hong Kong. Isolation facilities occupancy was also studied. ResultsAt R0 of 2{middle dot}2, the cumulative COVID-19 cases in Hong Kong can be reduced by 13{middle dot}99% (from 29,163 to 25,084) with complete border closure. At an in-patient mortality of 1{middle dot}4%, the number of deaths can be reduced from 408 to 351 (57 lives saved). However, border closure alone was insufficient to prevent full occupancy of isolation facilities in Hong Kong; effective public health measures to reduce local R0 to below 1{middle dot}6 was necessary. ConclusionAs a public health measure to tackle COVID-19, border restriction is effective in reducing cumulative cases and mortality. Article summaryO_ST_ABSStrengths and limitations of this studyC_ST_ABSO_LIA novel metapopulation SEIR model with inspected migration was developed to investigate the epidemiological characteristics of COVID-19 in Hong Kong, Guangdong and the rest of China (excluding Hubei) in the presence or absence of border restriction. C_LIO_LIThe presented model is also suitable for further analysis of other emerging infectious diseases. C_LIO_LIBorder restriction is an effective public health measure in reducing cumulative cases and mortality for COVID-19. C_LIO_LIInteraction was assumed to be well-mixed within patch. The spatial effect in disease transmission within each patch is ignored, which can have a non-trivial effect on the dynamic of infectious disease. C_LIO_LIThe proposed model is deterministic in nature which ignores the randomness in migration and in the interactions among people; a stochastic model would be more realistic especially early in the disease. C_LI