Associations Between Fear of COVID-19, Mental Health, and Preventive Behaviours Across Pregnant Women and Husbands: An Actor-Partner Interdependence Modelling.

The present cross-sectional study examined the actor-partner interdependence effect of fear of COVID-19 among Iranian pregnant women and their husbands and its association with their mental health and preventive behaviours during the first wave of the COVID-19 pandemic in 2020. A total of 290 pregnant women and their husbands (N = 580) were randomly selected from a list of pregnant women in the Iranian Integrated Health System and were invited to respond to psychometric scales assessing fear of COVID-19, depression, anxiety, suicidal intention, mental quality of life, and COVID-19 preventive behaviours. The findings demonstrated significant dyadic relationships between husbands and their pregnant wives' fear of COVID-19, mental health, and preventive behaviours. Pregnant wives' actor effect of fear of COVID-19 was significantly associated with depression, suicidal intention, mental quality of life, and COVID-19 preventive behaviours but not anxiety. Moreover, a husband actor effect of fear of COVID-19 was significantly associated with depression, anxiety, suicidal intention, mental quality of life, and COVID-19 preventive behaviours. Additionally, there were significant partner effects observed for both the pregnant wives and their husbands concerning all outcomes. The present study used a cross-sectional design and so is unable to determine the mechanism or causal ordering of the effects. Also, the data are mainly based on self-reported measures which have some limitations due to its potential for social desirability and recall biases. Based on the findings, couples may benefit from psychoeducation that focuses on the effect of mental health problems on pregnant women and the foetus.

Co-morbidity associated with development of severe COVID-19 before vaccine availability: a retrospective cohort study in the first pandemic year among the middle-aged and elderly in Jönköping county, Sweden.

BACKGROUND: In preparation of future pandemics, it is important to recognise population-level determinants associated with development of severe illness before efficient vaccines and evidence-based therapeutic measures are available. The aim of this study was to identify pre-pandemic diagnoses recorded in a middle-aged and elderly population that were associated with development of severe COVID-19 during the first pandemic year. METHODS: A cohort study design was used. Severe COVID-19 was defined as a course of illness that resulted in hospital admission or death. A retrospective analysis was performed that comprised all individuals aged 39 years and older (N = 189,951) living in Jönköping County, Sweden. All diagnosed morbidity recorded in contacts with health care during the pre-pandemic year 2019 was used to identify which diagnoses that were associated with development of severe COVID-19 in the first pandemic year 2020. The analyses were performed separately for each diagnosis using binary logistic regression with adjustment for sex and age. RESULTS: Severe COVID-19 was suffered by 0.67% (N = 1,280) of the middle-aged and elderly population in the first pandemic year. Individuals previously diagnosed with dementia, cerebral palsy, kidney failure, type 2 diabetes mellitus, hypertension, and obesity were at higher risk of developing severe COVID-19. For patients with Type 2 diabetes mellitus, the odds ratio (OR) was 2.18 (95% confidence interval, 1.92-2.48). Type 1 diabetes mellitus was not associated with increased risk. CONCLUSION: Diagnoses suggesting service provision at long-term healthcare facilities and co-morbidity with components of the metabolic syndrome were associated with an increased risk of developing severe COVID-19 in a middle-aged and elderly population before vaccines were available.

Effectiveness of the BNT162b2 mRNA Vaccine Compared with Hybrid Immunity in Populations Prioritized and Non-Prioritized for COVID-19 Vaccination in 2021-2022: A Naturalistic Case-Control Study in Sweden.

The term hybrid immunity is used to denote the immunological status of vaccinated individuals with a history of natural infection. Reports of new SARS-CoV-2 variants of concern motivate continuous rethought and renewal of COVID-19 vaccination programs. We used a naturalistic case-control study design to compare the effectiveness of the BNT162b2 mRNA vaccine to hybrid immunity 180 days post-vaccination in prioritized and non-prioritized populations vaccinated before 31 July 2021 in three Swedish counties (total population 1,760,000). Subjects with a positive SARS-CoV-2 test recorded within 6 months before vaccination (n = 36,247; 6%) were matched to vaccinated-only controls. In the prioritized population exposed to the SARS-CoV-2 Alpha and Delta variants post-vaccination, the odds ratio (OR) for breakthrough infection was 2.2 (95% CI, 1.6-2.8; p < 0.001) in the vaccinated-only group compared with the hybrid immunity group, while in the later vaccinated non-prioritized population, the OR decreased from 4.3 (95% CI, 2.2-8.6; p < 0.001) during circulation of the Delta variant to 1.9 (95% CI, 1.7-2.1; p < 0.001) with the introduction of the Omicron variant (B.1.617.2). We conclude that hybrid immunity provides gains in protection, but that the benefits are smaller for risk groups and with circulation of the Omicron variant and its sublineages.

Computational models predicting the early development of the COVID-19 pandemic in Sweden: systematic review, data synthesis, and secondary validation of accuracy.

Computational models for predicting the early course of the COVID-19 pandemic played a central role in policy-making at regional and national levels. We performed a systematic review, data synthesis, and secondary validation of studies that reported on prediction models addressing the early stages of the COVID-19 pandemic in Sweden. A literature search in January 2021 based on the search triangle model identified 1672 peer-reviewed articles, preprints and reports. After applying inclusion criteria 52 studies remained out of which 12 passed a Risk of Bias Opinion Tool. When comparing model predictions with actual outcomes only 4 studies exhibited an acceptable forecast (mean absolute percentage error, MAPE < 20%). Models that predicted disease incidence could not be assessed due to the lack of reliable data during 2020. Drawing conclusions about the accuracy of the models with acceptable methodological quality was challenging because some models were published before the time period for the prediction, while other models were published during the prediction period or even afterwards. We conclude that the forecasting models involving Sweden developed during the early stages of the COVID-19 pandemic in 2020 had limited accuracy. The knowledge attained in this study can be used to improve the preparedness for coming pandemics.

App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden.

The app-based COVID Symptom Study was launched in Sweden in April 2020 to contribute to real-time COVID-19 surveillance. We enrolled 143,531 study participants (≥18 years) who contributed 10.6 million daily symptom reports between April 29, 2020 and February 10, 2021. Here, we include data from 19,161 self-reported PCR tests to create a symptom-based model to estimate the individual probability of symptomatic COVID-19, with an AUC of 0.78 (95% CI 0.74-0.83) in an external dataset. These individual probabilities are employed to estimate daily regional COVID-19 prevalence, which are in turn used together with current hospital data to predict next week COVID-19 hospital admissions. We show that this hospital prediction model demonstrates a lower median absolute percentage error (MdAPE: 25.9%) across the five most populated regions in Sweden during the first pandemic wave than a model based on case notifications (MdAPE: 30.3%). During the second wave, the error rates are similar. When we apply the same model to an English dataset, not including local COVID-19 test data, we observe MdAPEs of 22.3% and 19.0% during the first and second pandemic waves, respectively, highlighting the transferability of the prediction model.

Nowcasting (Short-Term Forecasting) of COVID-19 Hospitalizations Using Syndromic Healthcare Data, Sweden, 2020.

We report on local nowcasting (short-term forecasting) of coronavirus disease (COVID-19) hospitalizations based on syndromic (symptom) data recorded in regular healthcare routines in Östergötland County (population ≈465,000), Sweden, early in the pandemic, when broad laboratory testing was unavailable. Daily nowcasts were supplied to the local healthcare management based on analyses of the time lag between telenursing calls with the chief complaints (cough by adult or fever by adult) and COVID-19 hospitalization. The complaint cough by adult showed satisfactory performance (Pearson correlation coefficient r>0.80; mean absolute percentage error <20%) in nowcasting the incidence of daily COVID-19 hospitalizations 14 days in advance until the incidence decreased to <1.5/100,000 population, whereas the corresponding performance for fever by adult was unsatisfactory. Our results support local nowcasting of hospitalizations on the basis of symptom data recorded in routine healthcare during the initial stage of a pandemic.

Cyberchondria, Fear of COVID-19, and Risk Perception Mediate the Association between Problematic Social Media Use and Intention to Get a COVID-19 Vaccine.

Vaccination is the most effective way to control the COVID-19 pandemic, but vaccination hesitancy threatens this effort worldwide. Consequently, there is a need to understand what influences individuals' intention to get a COVID-19 vaccine. Restriction of information gathering on societal developments to social media may influence attitudes towards COVID-19 vaccination through exposure to disinformation and imbalanced arguments. The present study examined the association between problematic social media use and intention to get the COVID-19 vaccine, taking into account the mediating roles of cyberchondria, fear of COVID-19, and COVID-19 risk perception. In a cross-sectional survey study, a total of 10,843 residents of Qazvin City, Iran completed measures on problematic social media use, fear of COVID-19, cyberchondria, COVID-19 risk perception, and intention to get a COVID-19 vaccine. The data were analyzed using structural equation modeling (SEM). The results showed that there was no direct association between problematic social media use and intention to get a COVID-19 vaccine. Nonetheless, cyberchondria, fear of COVID-19, and COVID-19 risk perception (each or serially) mediated associations between problematic social media use and intention to get a COVID-19 vaccine. These results add to the understanding of the role of problematic social media use in COVID-19 vaccine hesitancy, i.e., it is not the quantity of social media use per se that matters. This knowledge of the mediating roles of cyberchondria, fear of COVID-19, and COVID-19 risk perception can be used by public health experts and policymakers when planning educational interventions and other initiatives in COVID-19 vaccination programs.

Predicting regional COVID-19 hospital admissions in Sweden using mobility data.

The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3 weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3 weeks in advance.

Face mask use during the COVID-19 pandemic-the significance of culture and the symbolic meaning of behavior.

During public emergencies, a door can open on the fundamental elements upon which a society's social order is built. The Covid-19 pandemic has opened such a door in societies worldwide. We outline in this commentary some of these social elements and how they may have influenced face mask use during the early stages of the pandemic. The purpose is to expand the perspective on mechanisms that are relevant to consider in pandemic response planning. Our look at these fundamental elements showed that latent aspects of the dominant culture and various symbolic meanings of behaviors can reduce adherence with public health recommendations if they are overlooked in the strategic health plans. We conclude that when policymakers decide non-pharmacological interventions during pandemics, they should take into account fundamental attitudes and beliefs that may influence population behavior. This will require paying attention to variations in things like culture and symbolic meanings of behavior.