Association of nirmatrelvir-ritonavir with post-acute sequelae and mortality in patients admitted to hospital with COVID-19: a retrospective cohort study.

BACKGROUND: Studies have established the short-term efficacy of nirmatrelvir-ritonavir in managing COVID-19, yet its effect on post-COVID-19 condition, especially in patients admitted to hospital, remains understudied. This study aimed to examine the effect of nirmatrelvir-ritonavir on post-COVID-19 condition among patients admitted to hospital in Hong Kong. METHODS: This retrospective cohort study used real-world, territory-wide inpatient records, vaccination records, and confirmed COVID-19 case data from the Hong Kong Hospital Authority and Department of Health, The Government of the Hong Kong Special Administrative Region. Patients aged 18 years and older who tested positive for SARS-CoV-2 between March 11, 2022, and Oct 10, 2023, and who were admitted to hospital with COVID-19 were included. The treatment group included patients prescribed nirmatrelvir-ritonavir within 5 days of symptom onset, excluding those prescribed molnupiravir within 21 days, and the control group had no exposure to either nirmatrelvir-ritonavir or molnupiravir. The outcomes were post-acute inpatient death and 13 sequelae (congestive heart failure, atrial fibrillation, coronary artery disease, deep vein thrombosis, chronic pulmonary disease, acute respiratory distress syndrome, interstitial lung disease, seizure, anxiety, post-traumatic stress disorder, end-stage renal disease, acute kidney injury, and pancreatitis). These outcomes were evaluated starting at 21 days after the positive RT-PCR date in each respective cohort constructed for the outcome. Standardised mortality ratio weights were applied to balance covariates, and Cox proportional hazards regression was used to investigate the relationship between nirmatrelvir-ritonavir and outcomes. FINDINGS: 136 973 patients were screened for inclusion, among whom 50 055 were eligible and included in the analysis (24 873 [49·7%] were female and 25 182 [50·3%] were male). 15 242 patients were prescribed nirmatrelvir-ritonavir during acute COVID-19 and 23 756 patients were included in the control group; 11 057 patients did not meet our definition for the exposed and unexposed groups. Patients were followed up for a median of 393 days (IQR 317-489). In the nirmatrelvir-ritonavir group compared with the control group, there was a significantly lower hazard of post-acute inpatient death (hazard ratio 0·62 [95% CI 0·57-0·68]; p<0·0001), congestive heart failure (0·70 [0·58-0·85]; p=0·0002), atrial fibrillation (0·63 [0·52-0·76]; p<0·0001), coronary artery disease (0·71 [0·59-0·85]; p=0·0002), chronic pulmonary disease (0·68 [0·54-0·86]; p=0·0011), acute respiratory distress syndrome (0·71 [0·58-0·86]; p=0·0007), interstitial lung disease (0·17 [0·04-0·75]; p=0·020), and end-stage renal disease (0·37 [0·18-0·74]; p=0·0049). There was no evidence indicating difference between the groups in deep vein thrombosis, seizure, anxiety, post-traumatic stress disorder, acute kidney injury, and pancreatitis. INTERPRETATION: This study showed extended benefits of nirmatrelvir-ritonavir for reducing the risk of post-acute inpatient death as well as cardiovascular and respiratory complications among patients admitted to hospital with COVID-19. Further research is essential to uncover the underlying mechanisms responsible for these observed negative associations and to devise effective strategies for preventing the onset of post-acute sequelae. FUNDING: Health and Medical Research Fund, Research Grants Council theme-based research schemes, and Research Grants Council Collaborative Research Fund.

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.

Projection of dengue fever transmissibility under climate change in South and Southeast Asian countries.

Vector-borne infectious disease such as dengue fever (DF) has spread rapidly due to more suitable living environments. Considering the limited studies investigating the disease spread under climate change in South and Southeast Asia, this study aimed to project the DF transmission potential in 30 locations across four South and Southeast Asian countries. In this study, weekly DF incidence data, daily mean temperature, and rainfall data in 30 locations in Singapore, Sri Lanka, Malaysia, and Thailand from 2012 to 2020 were collected. The effects of temperature and rainfall on the time-varying reproduction number (Rt) of DF transmission were examined using generalized additive models. Projections of location-specific Rt from 2030s to 2090s were determined using projected temperature and rainfall under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585), and the peak DF transmissibility and epidemic duration in the future were estimated. According to the results, the projected changes in the peak Rt and epidemic duration varied across locations, and the most significant change was observed under middle-to-high greenhouse gas emission scenarios. Under SSP585, the country-specific peak Rt was projected to decrease from 1.63 (95% confidence interval: 1.39-1.91), 2.60 (1.89-3.57), and 1.41 (1.22-1.64) in 2030s to 1.22 (0.98-1.51), 2.09 (1.26-3.47), and 1.37 (0.83-2.27) in 2090s in Singapore, Thailand, and Malaysia, respectively. Yet, the peak Rt in Sri Lanka changed slightly from 2030s to 2090s under SSP585. The epidemic duration in Singapore and Malaysia was projected to decline under SSP585. In conclusion, the change of peak DF transmission potential and disease outbreak duration would vary across locations, particularly under middle-to-high greenhouse gas emission scenarios. Interventions should be considered to slow down global warming as well as the potential increase in DF transmissibility in some locations of South and Southeast Asia.

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.

Relationship between temperature variability and daily hospitalisations in Hong Kong over two decades.

Background: Studies have highlighted the impacts of temperature variability (TV) on mortality from respiratory diseases and cardiovascular diseases, with inconsistent results specifically in subtropical urban areas than temperate ones. We aimed to fully determine TV-associated health risks over a spectrum of diseases and various subgroups in a subtropical setting. Methods: Using inpatient data from all public hospitals in Hong Kong from 1999 to 2019, we examined the TV-hospitalisation associations by causes, ages, and seasons by fitting a quasi-Poisson regression. We presented the results as estimated percentage changes of hospitalisations per interquartile range (IQR) of TV. Results: TVs in exposure days from 0-5 days (TV0-5) to 0-7 days (TV0-7) had detrimental effects on hospitalisation risks in Hong Kong. The overall population was significantly affected over TV0-5 to TV0-7 in endocrine, nutritional and metabolic (from 0.53% to 0.58%), respiratory system (from 0.38% to 0.53%), and circulatory systems diseases (from 0.47% to 0.56%). While we found no association with seasonal disparities, we did observe notable disparities by age, highlighting older adults' vulnerability to TVs. For example, people aged ≥65 years experienced the highest change of 0.88% (95% CI = 0.34%, 1.41%) in hospitalizations for injury and poisoning per IQR increase in TV0-4. Conclusions: Our population-based study highlighted that TV-related health burden, usually regarded as minimal compared to other environmental factors, should receive more attention and be addressed in future relevant health policies, especially for vulnerable populations during the cold seasons.

Impact of climate change on dengue fever epidemics in South and Southeast Asian settings: A modelling study.

The potential for dengue fever epidemic due to climate change remains uncertain in tropical areas. This study aims to assess the impact of climate change on dengue fever transmission in four South and Southeast Asian settings. We collected weekly data of dengue fever incidence, daily mean temperature and rainfall from 2012 to 2020 in Singapore, Colombo, Selangor, and Chiang Mai. Projections for temperature and rainfall were drawn for three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585) scenarios. Using a disease transmission model, we projected the dengue fever epidemics until 2090s and determined the changes in annual peak incidence, peak time, epidemic size, and outbreak duration. A total of 684,639 dengue fever cases were reported in the four locations between 2012 and 2020. The projected change in dengue fever transmission would be most significant under the SSP585 scenario. In comparison to the 2030s, the peak incidence would rise by 1.29 times in Singapore, 2.25 times in Colombo, 1.36 times in Selangor, and >10 times in Chiang Mai in the 2090s under SSP585. Additionally, the peak time was projected to be earlier in Singapore, Colombo, and Selangor, but be later in Chiang Mai under the SSP585 scenario. Even in a milder emission scenario of SSP126, the epidemic size was projected to increase by 5.94%, 10.81%, 12.95%, and 69.60% from the 2030s-2090s in Singapore, Colombo, Selangor, and Chiang Mai, respectively. The outbreak durations in the four settings were projected to be prolonged over this century under SSP126 and SSP245, while a slight decrease is expected in 2090s under SSP585. The results indicate that climate change is expected to increase the risk of dengue fever transmission in tropical areas of South and Southeast Asia. Limiting greenhouse gas emissions could be crucial in reducing the transmission of dengue fever in the future.

Relationship between antidepressants and severity of SARS-CoV-2 Omicron infection: a retrospective cohort study using real-world data.

Background: Few studies have used real-world data to evaluate the impact of antidepressant use on the risk of developing severe outcomes after SARS-CoV-2 Omicron infection. Methods: This is a retrospective cohort study using propensity-score matching to examine the relationship between antidepressant use and COVID-19 severity. Inpatient and medication records of all adult COVID-19 patients in Hong Kong during the Omicron-predominated period were obtained. Severe clinical outcomes including intensive care unit admission and inpatient death after the first positive results of reverse transcription polymerase chain reaction as well as a composite outcome of both were studied. Cox proportional hazard models were applied to estimate the crude and adjusted hazard ratios (HR). Findings: Of 60,903 hospitalised COVID-19 patients admitted, 40,459 were included for matching, among which 3821 (9.4%) were prescribed antidepressants. The rates of intensive care unit admission, inpatient death, and the composite event were 3.9%, 25.5%, and 28.3% respectively in the unexposed group, 1.3%, 20.0%, and 21.1% respectively in the exposed group, with adjusted HR equal to 0.332 (95% CI, 0.245-0.449), 0.868 (95% CI, 0.800-0.942), and 0.786 (95% CI, 0.727-0.850) respectively. The result was generally consistent when stratified by selective serotonin reuptake inhibitors (SSRIs) and non-SSRIs. Antidepressants with functional inhibition of acid sphingomyelinase activity, specifically fluoxetine, were also negatively associated with the outcomes. The effect of antidepressants was more apparent in female and fully vaccinated COVID-19 patients. Interpretation: Antidepressant use was associated with a lower risk of severe COVID-19. The findings support the continuation of antidepressants in patients with COVID-19, and provide evidence for the treatment potential of antidepressants for severe COVID-19. Funding: This research was supported by Health and Medical Research Fund [grant numbers COVID190105, COVID19F03, INF-CUHK-1], Collaborative Research Fund of University Grants Committee [grant numbers C4139-20G], National Natural Science Foundation of China (NSFC) [71974165], and Group Research Scheme from The Chinese University of Hong Kong.

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

Background: While coronavirus 2019 (COVID-19) deaths were generally underestimated in many countries, Hong Kong may show a different trend of excess mortality due to stringent measures, especially for deaths related to respiratory diseases. Nevertheless, the Omicron outbreak in Hong Kong evolved into a territory-wide transmission, similar to other settings such as Singapore, South Korea, and recently, mainland China. We hypothesized that the excess mortality would differ substantially before and after the Omicron outbreak. Methods: We conducted a time-series analysis of daily deaths stratified by age, reported causes, and epidemic wave. We determined the excess mortality from the difference between observed and expected mortality from 23 January 2020 to 1 June 2022 by fitting mortality data from 2013 to 2019. Results: During the early phase of the pandemic, the estimated excess mortality was -19.92 (95% confidence interval (CI) = -29.09, -10.75) and -115.57 (95% CI = -161.34, -69.79) per 100 000 population overall and for the elderly, respectively. However, the overall excess mortality rate was 234.08 (95% CI = 224.66, 243.50) per 100 000 population overall and as high as 928.09 (95% CI = 885.14, 971.04) per 100 000 population for the elderly during the Omicron epidemic. We generally observed negative excess mortality rates of non-COVID-19 respiratory diseases before and after the Omicron outbreak. In contrast, increases in excess mortality were generally reported in non-respiratory diseases after the Omicron outbreak. Conclusions: Our results highlighted the averted mortality before 2022 among the elderly and patients with non-COVID-19 respiratory diseases, due to indirect benefits from stringent non-pharmaceutical interventions. The high excess mortality during the Omicron epidemic demonstrated a significant impact from the surge of COVID-19 infections in a SARS-CoV-2 infection-naive population, particularly evident in the elderly group.

Estimating the serial intervals of SARS-CoV-2 Omicron BA.4, BA.5, and BA.2.12.1 variants in Hong Kong.

Empirical evidence on the epidemiological characteristics of the emerged SARS-CoV-2 variants could shed light on the transmission potential of the virus and strategic outbreak control planning. In this study, by using contact tracing data collected during an Omicron-predominant epidemic phase in Hong Kong, we estimated the mean serial interval of SARS-CoV-2 Omicron BA.4, BA.5, and BA.2.12.1 variants at 2.8 days (95% credible interval [CrI]: 1.5, 6.7), 2.7 days (95% CrI: 2.1, 3.6), and 4.4 days (95% CrI: 2.6, 7.5), respectively, with adjustment for right truncation and sampling bias. The short serial interval for the current circulating variant indicated that outbreak mitigations through contact tracing and case isolation would be quite challenging.

Determining meteorologically-favorable zones for seasonal influenza activity in Hong Kong.

Investigations of simple and accurate meteorology classification systems for influenza epidemics, particularly in subtropical regions, are limited. To assist in preparing for potential upsurges in the demand on healthcare facilities during influenza seasons, our study aims to develop a set of meteorologically-favorable zones for epidemics of influenza A and B, defined as the intervals of meteorological variables with prediction performance optimized. We collected weekly detection rates of laboratory-confirmed influenza cases from four local major hospitals in Hong Kong between 2004 and 2019. Meteorological and air quality records for hospitals were collected from their closest monitoring stations. We employed classification and regression trees to identify zones that optimize the prediction performance of meteorological data in influenza epidemics, defined as a weekly rate > 50th percentile over a year. According to the results, a combination of temperature > 25.1℃ and relative humidity > 79% was favorable to epidemics in hot seasons, whereas either temperature < 16.4℃ or a combination of < 20.4℃ and relative humidity > 76% was favorable to epidemics in cold seasons. The area under the receiver operating characteristic curve (AUC) in model training achieved 0.80 (95% confidence interval [CI], 0.76-0.83) and was kept at 0.71 (95%CI, 0.65-0.77) in validation. The meteorologically-favorable zones for predicting influenza A or A and B epidemics together were similar, but the AUC for predicting influenza B epidemics was comparatively lower. In conclusion, we established meteorologically-favorable zones for influenza A and B epidemics with a satisfactory prediction performance, even though the influenza seasonality in this subtropical setting was weak and type-specific.

Relationship between meteorological variations, seasonal influenza, and hip fractures in the elderly: A modelling investigation using 22-year data.

With the heavy negative health effect and economic burden of hip fractures in the elderly, the relationship of hip fractures with climate and seasonal influenza has not been quantified explicitly. In this study, we aim to make use of population-based data to evaluate the impact of meteorological factors and influenza activity on the hip fracture admissions for the elderly in Hong Kong from 1998 to 2019. Weekly numbers of admissions for the elderly due to hip fractures were used as the study outcome, and were matched with the meteorological factors included air temperature, relative humidity, solar radiation, and total rainfall. Strain-specific influenza-like illness-positive (ILI+) rates were employed as proxies for seasonal influenza activity. Quasi-Poisson generalized additive model in conjunction with distributed-lag non-linear model was used to elucidate the association of interest. According to the results, a total of 191,680 hip fracture admissions for the elderly aged ≥65 years were recorded over a 22-year span. The cumulative adjusted relative risks of hip fracture were 1.35 (95 % CI, 1.26-1.44) at the 5th percentile (15.05 °C) of air temperature, and 1.06 (95 % CI, 1.02-1.10) at the 95th percentile (20.91 MJ/m2) of solar radiation, with the reference value set to their respective medians. ILI+ rates were not associated with the risk of hip fracture. In the stratified analyses, a stronger association between cold condition and hip fracture was observed in males. Based on the results, strategies for preventing hip fractures with a focus on behaviors under unfavorable weather conditions should be targeted at individuals at risk.

Association between cold weather, influenza infection, and asthma exacerbation in adults in Hong Kong.

Despite a conspicuous exacerbation of asthma among patients hospitalized due to influenza infection, no study has attempted previously to elucidate the relationship between environmental factors, influenza activity, and asthma simultaneously in adults. In this study, we examined this relationship using population-based hospitalization records over 22 years. Daily numbers of hospitalizations due to asthma in adults of 41 public hospitals in Hong Kong during 1998-2019 were obtained. The data were matched with meteorological records and air pollutant concentrations. We used type-specific and all-type influenza-like illness plus (ILI+) rates as proxies for seasonal influenza activity. Quasi-Poisson generalized additive models together with distributed-lag non-linear models were used to examine the association. A total of 212,075 hospitalization episodes due to asthma were reported over 22 years. The cumulative adjusted relative risk (ARR) of asthma hospitalizations reached 1.15 (95 % confidence interval [CI], 1.12-1.18) when the ILI+ total rate increased from zero to 20.01 per 1000 consultations. Compared with the median temperature, a significantly increased risk of asthma hospitalization (cumulative ARR = 1.10, 95 % CI, 1.05-1.15) was observed at the 5th percentile of temperature (i.e., 14.6 °C). Of the air pollutants, oxidant gas was significantly associated with asthma, but only at its extreme level of concentrations. In conclusion, cold conditions and influenza activities are risk factors to asthma exacerbation in adult population. Influenza-related asthma exacerbation that appeared to be more common in the warm and hot season, is likely to be attributable to influenza A/H3N2. The heavy influence of both determinants on asthma activity implies that climate change may complicate the asthma burden.

Impact of extreme weather on dengue fever infection in four Asian countries: A modelling analysis.

The rapid spread of dengue fever (DF) infection has posed severe threats to global health. Environmental factors, such as weather conditions, are believed to regulate DF spread. While previous research reported inconsistent change of DF risk with varying weather conditions, few of them evaluated the impact of extreme weather conditions on DF infection risk. This study aims to examine the short-term associations between extreme temperatures, extreme rainfall, and DF infection risk in South and Southeast Asia. A total of 35 locations in Singapore, Malaysia, Sri Lanka, and Thailand were included, and weekly DF data, as well as the daily meteorological data from 2012 to 2020 were collected. A two-stage meta-analysis was used to estimate the overall effect of extreme weather conditions on the DF infection risk. Location-specific associations were obtained by the distributed lag nonlinear models. The DF infection risk appeared to increase within 1-3 weeks after extremely high temperature (e.g. lag week 2: RR = 1.074, 95 % CI: 1.022-1.129, p = 0.005). Compared with no rainfall, extreme rainfall was associated with a declined DF risk (RR = 0.748, 95 % CI: 0.620-0.903, p = 0.003), and most of the impact was across 0-3 weeks lag. In addition, the DF risk was found to be associated with more intensive extreme weathers (e.g. seven extreme rainfall days per week: RR = 0.338, 95 % CI: 0.120-0.947, p = 0.039). This study provides more evidence in support of the impact of extreme weather conditions on DF infection and suggests better preparation of DF control measures according to climate change.