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IntroductionThe challenge of accounting for practice effects (PEs) when modeling cognitive change was amplified by the COVID-19 pandemic, which introduced period and mode effects that may bias the estimation of cognitive trajectory.MethodsIn three Kaiser Permanente Northern California prospective cohorts, we compared predicted cognitive trajectories and the association of grip strength with cognitive decline using three approaches: (1) no acknowledgment of PE, (2) inclusion of a wave indicator, and (3) constraining PE based on a preliminary model (APM) fit using a subset of the data.ResultsAPM-based correction for PEs based on balanced, pre-pandemic data, and with current age as the timescale produced the smallest discrepancy between within-person and between-person estimated age effects. Estimated associations between grip strength and cognitive decline were not sensitive to the approach used.DiscussionConstraining PEs based on a preliminary model is a flexible, pragmatic approach allowing for meaningful interpretation of cognitive change.HighlightsThe magnitude of practice effects (PEs) varied widely by study. When PEs were present, the three PE approaches resulted in divergent estimated age-related cognitive trajectories. Estimated age-related cognitive trajectories were sometimes implausible in models that did not account for PEs. The associations between grip strength and cognitive decline did not differ by the PE approach used. Constraining PEs based on estimates from a preliminary model allows for a meaningful interpretation of cognitive change.

• Death rate due to Omicron infection in Hong Kong was ever among the highest globally. • The pandemic increased participants' willingness on influenza vaccine. • Women and the elderly are less likely to have a positive impact. • Policy makers should rectify common misperceptions of vaccine at post COVID-19 phase. To assess the intention of influenza vaccination during the Omicron pandemic of COVID-19 via a structured cross-sectional survey. 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. 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. 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. [ABSTRACT FROM AUTHOR]

In the context of infectious disease transmission, high heterogeneity in individual infectiousness indicates that a few index cases can generate large numbers of secondary cases, a phenomenon commonly known as superspreading. The potential of disease superspreading can be characterized by describing the distribution of secondary cases (of each seed case) as a negative binomial (NB) distribution with the dispersion parameter, k. Based on the feature of NB distribution, there must be a proportion of individuals with individual reproduction number of almost 0, which appears restricted and unrealistic. To overcome this limitation, we generalized the compound structure of a Poisson rate and included an additional parameter, and divided the reproduction number into independent and additive fixed and variable components. Then, the secondary cases followed a Delaporte distribution. We demonstrated that the Delaporte distribution was important for understanding the characteristics of disease transmission, which generated new insights distinct from the NB model. By using real-world dataset, the Delaporte distribution provides improvements in describing the distributions of COVID-19 and SARS cases compared to the NB distribution. The model selection yielded increasing statistical power with larger sample sizes as well as conservative type I error in detecting the improvement in fitting with the likelihood ratio (LR) test. Numerical simulation revealed that the control strategy-making process may benefit from monitoring the transmission characteristics under the Delaporte framework. Our findings highlighted that for the COVID-19 pandemic, population-wide interventions may control disease transmission on a general scale before recommending the high-risk-specific control strategies. Author summary: Superspreading is one of the key transmission features of many infectious diseases and is considered a consequence of the heterogeneity in infectiousness of individual cases. To characterize the superspreading potential, we divided individual infectiousness into two independent and additive components, including a fixed baseline and a variable part. Such decomposition produced an improvement in the fit of the model explaining the distribution of real-world datasets of COVID-19 and SARS that can be captured by the classic statistical tests. Disease control strategies may be developed by monitoring the characteristics of superspreading. For the COVID-19 pandemic, population-wide interventions are suggested first to limit the transmission at a scale of general population, and then high-risk-specific control strategies are recommended subsequently to lower the risk of superspreading. [ABSTRACT FROM AUTHOR]

Background: Hong Kong is a densely populated city with a low incidence and mortality of coronavirus disease 2019 (COVID-19). The city imposed different levels of social distancing including, the closure of sports venues and restrictions on eateries. This inevitably affects the eating behaviour and physical activities of the population. We examined the changes in eating behavior and physical activities before and during the COVID-19 pandemic, and identified sociodemographic factors associated with the behavioral changes. Methods: This was a cross-sectional study via a random telephone survey of Chinese adults conducted in Hong Kong from May to June, 2020 - a period in which social distancing measures were being imposed. We measured the physical activity habits from four aspects and dietary consumption patterns from seven aspects before and during the pandemic based on the World Health Organization's guidelines and previous publications. Results: In total, 724 participants were recruited. Individuals were found to cook more frequently at home (p < 0.001) and order take-out (p < 0.001) during the COVID-19 pandemic. While no significant change in the frequency of fast food consumption was observed, we found significant increases in the frequency of eating fruits (p < 0.001) and vegetables (p = 0.004). The frequencies of walking, moderate-intensive sports, and high-intensity sports were significantly reduced (p < 0.001). We found that healthy lifestyle behaviors during the pandemic were negatively associated with participants' economic status. Conclusions: Social distancing measures likely provided an opportunity for individuals to stay home and thus eat healthier. However, in a prolonged period of social restrictions, a lower physical activity level poses a risk to public health. Public health officials are thus advised to monitor physical health on a population-wide basis. The findings highlighted the importance of interventions tailored to individuals who have prolonged home stays - particularly for individuals in the low economic group. [ABSTRACT FROM AUTHOR]

Keywords: SARS-CoV-2; Omicron variants; superspreading events; transmission heterogeneity EN SARS-CoV-2 Omicron variants superspreading events transmission heterogeneity 1 3 3 09/21/22 20220801 NES 220801 Although COVID-19 transmission dynamics are often heterogeneous, that is, only a small fraction of cases can generate unusual large outbreaks (i.e. superspreading), little is known about the superspreading potential of Omicron variant. We assessed the superspreading potential of COVID-19 outbreak seeded by the Omicron variants, using two observed transmission chains in Hong Kong. [Extracted from the article]

While many studies have focused on identifying the association between meteorological factors and the activity of COVID-19, we argue that the contribution of meteorological factors to a reduction of the risk of COVID-19 was minimal when the effects of control measures were taken into account. In this study, we assessed how much variability in COVID-19 activity is attributable to city-level socio-demographic characteristics, meteorological factors, and the control measures imposed. We obtained the daily incidence of COVID-19, city-level characteristics, and meteorological data from a total of 102 cities situated in 27 provinces/municipalities outside Hubei province in China from 1 January 2020 to 8 March 2020, which largely covers almost the first wave of the epidemic. Generalized linear mixed effect models were employed to examine the variance in the incidence of COVID-19 explained by different combinations of variables. According to the results, including the control measure effects in a model substantially raised the explained variance to 45%, which increased by >40% compared to the null model that did not include any covariates. On top of that, including temperature and relative humidity in the model could only result in < 1% increase in the explained variance even though the meteorological factors showed a statistically significant association with the incidence rate of COVID-19. In conclusion, we showed that very limited variability of the COVID-19 incidence was attributable to meteorological factors. Instead, the control measures could explain a larger proportion of variance. Author summary: COVID-19 has a great impact worldwide, especially in some rural settings where healthcare resources are not sufficient. While control measures in these area may be limited, scholars have been discussing the potential effects of meteorological factors on mitigating COVID-19 transmission. Unfortunately, the majority of literatures only looked at the association between COVID-19 and environmental factors in which their findings could mislead readers that certain environmental conditions could be 'protective'. In this study, we argue that the impact of the meteorological factors was very limited by using the incidence data from 102 Chinese cities in the first epidemic period when control measures have been taken into account. As what we expected, once the control measures have been incorporated in the modelling analysis, the meteorological factors could only explain < 1% increase in variability of COVID-19 while control measure explained the variance for more than 40% in total. Because of it, we suggest stringent control measures are necessary to control COVID-19 regardless the meteorological conditions of an area. Given that no vaccine is available to date, our investigation provides an additional evidence, as advocated by World Meteorological Organization rather than relying on changes in the natural environment for mitigation, active non-pharmaceutical interventions are necessary to curb the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]