Changes in all-cause and cause-specific excess mortality before and after the Omicron outbreak of COVID-19 in Hong Kong

Association between meteorological variations and the superspreading potential of SARS-CoV-2 infections

BACKGROUND

While many investigations examined the association between environmental covariates and COVID-19 incidence, none have examined their relationship with superspreading, a characteristic describing very few individuals disproportionally infecting a large number of people.

METHODS

Contact tracing data of all the laboratory-confirmed COVID-19 cases in Hong Kong from February 16, 2020 to April 30, 2021 were used to form the infection clusters for estimating the time-varying dispersion parameter (kt), a measure of superspreading potential. Generalized additive models with identity link function were used to examine the association between negative-log kt (larger means higher superspreading potential) and the environmental covariates, adjusted with mobility metrics that account for the effect of social distancing measures.

RESULTS

A total of 6,645 clusters covering 11,717 cases were reported over the study period. After centering at the median temperature, a lower ambient temperature at 10th percentile (18.2 °C) was significantly associated with a lower estimate of negative-log kt (adjusted expected change: -0.239 [95 % CI: -0.431 to -0.048]). While a U-shaped relationship between relative humidity and negative-log kt was observed, an inverted U-shaped relationship with actual vapour pressure was found. A higher total rainfall was significantly associated with lower estimates of negative-log kt.

CONCLUSIONS

This study demonstrated a link between meteorological factors and the superspreading potential of COVID-19. We speculated that cold weather and rainy days reduced the social activities of individuals minimizing the interaction with others and the risk of spreading the diseases in high-risk facilities or large clusters, while the extremities of relative humidity may favor the stability and survival of the SARS-CoV-2 virus.

Estimated Excess Deaths Due to COVID-19 Among the Urban Population of Mainland China, December 2022 to January 2023

BACKGROUND

Mainland China experienced a major surge in SARS-CoV-2 infections in December 2022-January 2023, but its impact on mortality was unclear given the underreporting of coronavirus disease 2019 deaths.

METHODS

Using obituary data from the Chinese Academy of Engineering (CAE), we estimated the excess death rate among senior CAE members by taking the difference between the observed rate of all-cause death in December 2022-January 2023 and the expected rate for the same months in 2017-2022, by age groups. We used this to extrapolate an estimate of the number of excess deaths in December 2022-January 2023 among urban dwellers in Mainland China.

RESULTS

In December 2022-January 2023, we estimated excess death rates of 0.94 per 100 persons (95% confidence interval [CI] = -0.54, 3.16) in CAE members aged 80-84 years, 3.95 (95% CI = 0.50, 7.84) in 85-89 years, 10.35 (95% CI = 3.59, 17.71) in 90-94 years, and 16.88 (95% CI = 0.00, 34.62) in 95 years and older. Using our baseline assumptions, this extrapolated to 917,000 (95% CI = 425,000, 1.45 million) excess deaths among urban dwellers in Mainland China, much higher than the 81,000 in-hospital deaths officially reported from 9 December 2022 to 30 January 2023.

CONCLUSIONS

As in many jurisdictions, we estimate that the coronavirus disease 2019 pandemic had a much wider impact on mortality than what was officially documented in Mainland China.

Inferring Incidence of Unreported SARS-CoV-2 Infections Using Seroprevalence of Open Reading Frame 8 Antigen, Hong Kong

We tested seroprevalence of open reading frame 8 antigens to infer the number of unrecognized SARS-CoV-2 Omicron infections in Hong Kong during 2022. We estimate 33.6% of the population was infected, 72.1% asymptomatically. Surveillance and control activities during large-scale outbreaks should account for potentially substantial undercounts.

Impact of the surge of COVID-19 Omicron outbreak on the intention of seasonal influenza vaccination in Hong Kong: A cross-sectional study

OBJECTIVE

To assess the intention of influenza vaccination during the Omicron pandemic of COVID-19 via a structured cross-sectional survey.

METHODS

A cross-sectional study was conducted in 1,813 Hong Kong quota-sampled adults between March and September 2022, when Hong Kong was experiencing an outbreak of Omicron infections. Questions included self-reported medical and vaccination history, and perceptions and intention of influenza vaccine. A multiple logistic regression analysis was conducted to identify significant factors associated with the vaccination intention.

RESULTS

Of the 1,813 participants, 25.8% (95% CI: 23.8%-27.8%) perceived positive impact of COVID-19 pandemic on their influenza vaccine willingness, which was more than two times the proportion of those who feel less likely to take influenza vaccine (11.5%, 95% CI: 10.1%-13.1%). Compared with males, females were less likely to receive influenza vaccine for 2022-23 influenza seasons (OR = 0.71, 95% CI: 0.52-0.95, p = 0.023) and had less impact on their influenza vaccine willingness (OR = 0.76, 95% CI: 0.59-0.99, p = 0.043). Participants older than 60 years old were related to a less positive impact compared with the youngers (OR = 0.53, 95% CI: 0.30-0.93, p = 0.028). Participants with experience of influenza vaccine uptake also showed a higher intention of seasonal influenza vaccination.

CONCLUSION

The public intention of influenza vaccine has been raised in Hong Kong. With the identified subgroups (e.g., female and elderly) and reasons for being reluctant to the influenza vaccination, policy makers should rectify common misperceptions in order to increase influenza vaccination coverage at the post COVID-19 phase.