Integrated Surveillance of Disparities in Vaccination Coverage and Morbidity during the COVID-19 Pandemic: A Cohort Study in Southeast Sweden.

We aimed to use the digital platform maintained by the local health service providers in Southeast Sweden for integrated monitoring of disparities in vaccination and morbidity during the COVID-19 pandemic. The monitoring was performed in the adult population of two counties (n = 657,926) between 1 February 2020 and 15 February 2022. The disparities monitored were relocated (internationally displaced), substance users, and suffering from a psychotic disorder. The outcomes monitored were COVID-19 vaccination, SARS-CoV-2 test results, and hospitalization with COVID-19. Relocated residents displayed an increased likelihood of remaining unvaccinated and a decreased likelihood of testing as well as increased risks of primary SARS-CoV-2 infection and hospitalization compared with the general population. Suffering from a major psychiatric disease was associated with an increased risk of remaining unvaccinated and an increased risk of hospitalization but a decreased risk of SARS-CoV-2 infection. From the digital monitoring, we concluded that the relocated minority received insufficient protection during the pandemic, suggesting the necessity for comprehensive promotion of overall social integration. Persons with major psychiatric diseases underused vaccination, while they benefitted from proactively provided testing, implying a need for active encouragement of vaccination. Further research is warranted on legal and ethical frameworks for digital monitoring in vaccination programs.

A cross-sectional study of anxiety and depression caseness in female competitive figure skaters in Sweden.

Objectives: Little is known about figure skaters' mental health. This study aimed to describe anxiety and depression caseness (defined as a screening condition qualifying for psychiatric examination) in competitive figure skaters and analyse factors associated with such caseness. Methods: A cross-sectional study was performed in April 2019 among all competitive figure skaters in the south-eastern region of Sweden (N=400). The primary outcomes were anxiety caseness, measured using the short-form Spielberger State-Trait Anxiety Inventory and depression caseness, measured using the WHO-5 index. Multivariable logistic regression models were employed to determine the association between anxiety caseness and explanatory factors. Results: In total, 36% (n=142) of the invited skaters participated. Only females (n=137), mean age 12.9 (SD 3.0) years) were selected for analysis. Of the participating skaters, 47% displayed anxiety caseness and 10% depression caseness. Overweight body image perception (OR 5.9; 95% CI 2.0 to 17.6; p=0.001) and older age (OR 1.2; 95% CI 1.1 to 1.4; p=0.005) were associated with anxiety caseness. Skaters reporting no caseness were younger than those reporting only anxiety caseness (mean age difference -1.9 years; 95% CI -3.1 to -0.7; p=0.001) or anxiety and depression caseness (OR -3.5 years; 95% CI -5.6 to -1.5 years; p<0.001). Conclusion: Anxiety caseness was associated with overweight body image perception and older age in female competitive figure skaters. Older skaters reported generally worse mental health. More research on the mental health of figure skaters is warranted, considering comorbidity and focusing on those needing further assessment and support.

Universal prevention through a digital health platform reduces injury incidence in youth athletics (track and field): a cluster randomised controlled trial.

OBJECTIVES: To examine whether universal prevention via a digital health platform can reduce the injury incidence in athletics athletes aged 12-15 years and if club size had an influence on the effect of the intervention. METHODS: This was a cluster randomised trial where young athletics athletes were randomised through their club following stratification by club size into intervention (11 clubs; 56 athletes) and control (10 clubs; 79 athletes) groups. The primary endpoint was time from baseline to the first self-reported injury. Intervention group parents and coaches were given access to a website with health information adapted to adolescent athletes and were encouraged to log in and explore its content during 16 weeks. The control group continued training as normal. Training exposure and injury data were self-reported by youths/parents every second week, that is, eight times. The primary endpoint data were analysed using the log-rank test. Cox proportional hazards regression was used to analyse the second study aim with intervention status and club size included in the explanatory models. RESULTS: The proportion of completed training reports was 85% (n=382) in the intervention group and 86% (n=545) in the control group. The injury incidence was significantly lower (HR=0.62; χ2=3.865; p=0.049) in the intervention group. The median time to first injury was 16 weeks in the intervention group and 8 weeks in the control group. An interaction effect between the intervention and stratification factor was observed with a difference in injury risk between athletes in the large clubs in the intervention group versus their peers in the control group (HR 0.491 (95% CI 0.242 to 0.998); p=0.049). CONCLUSIONS: A protective effect against injury through universal access to health information adapted for adolescent athletes was observed in youth athletics athletes. The efficacy of the intervention was stronger in large clubs. TRIAL REGISTRATION NUMBER: NCT03459313.

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.

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.

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.