Lack of evidence for effects of lockdowns on stillbirth rates during the SARS-CoV-2 pandemic in Bavaria: analysis of the Bavarian perinatal survey from 2010 to 2020.

BACKGROUND: Internationally, potential effects of national SARS-CoV-2-related lockdowns on stillbirth rates have been reported, but data for Germany, including risk factors for fetal pregnancy outcome, are lacking. The aim of this study is to compare the stillbirth rates during the two first lockdown periods in 2020 with previous years from 2010 to 2019 in a large Bavarian cohort. METHODS: This study is a secondary analysis of the Bavarian perinatal data from 2010 to 2020, including 349,245 births. Univariate and multivariable regression analyses were performed to investigate the effect of two Bavarian lockdowns on the stillbirth rate in 2020 compared to the corresponding periods from 2010 to 2019. RESULTS: During the first lockdown, the stillbirth rate was significantly higher compared to the reference period (4.04 vs. 3.03 stillbirths per 1000 births; P = 0.03). After adjustment for seasonal and long-term trends, this effect can no longer be observed (P = 0.2). During the second lockdown, the stillbirth rate did not differ in univariate (3.46 vs. 2.93 stillbirths per 1000 births; P = 0.22) as well as in multivariable analyses (P = 0.68), compared to the years 2010 to 2019. CONCLUSION: After adjustment for known long-term effects, in this study we did not find evidence that the two Bavarian lockdowns had an effect on the rate of stillbirths.

Limited Effects of SARS-CoV-2 Pandemic-related Lockdowns and Reduced Population Mobility on Preterm Birth Rates: A Secondary Analysis of Bavarian Obstetric Quality Parameters from 2010 to 2020 Limitierte Effekte von SARS-CoV-2-bedingten Lockdowns und dem Rückgang der Bevölkerungsmobilität auf die Frühgeburtenrate: eine Sekundäranalyse der Bayerischen geburtshilflichen Qualitätsparameter von 2010 bis 2020

Introduction International studies on preterm birth rates during COVID-19 lockdowns report different results. This study examines preterm birth rates during lockdown periods and the impact of the mobility changes of the population in Bavaria, Germany. Material and Methods This is a secondary analysis of centrally collected data on preterm births in Bavaria from 2010 to 2020. Preterm births (< 37 weeks) in singleton and twin pregnancies during two lockdowns were compared with corresponding periods in 2010 – 2019. Fisherʼs exact test was used to compare raw prevalence between groups. Potential effects of two fixed lockdown periods and of variable changes in population mobility on preterm birth rates in 2020 were examined using additive logistic regression models, adjusting for long-term and seasonal trends. Results Unadjusted preterm birth rates in 2020 were significantly lower for singleton pregnancies during the two lockdown periods (Lockdown 1: 5.71% vs. 6.41%;OR 0.88;p < 0.001;Lockdown 2: 5.71% vs. 6.60%;OR = 0.86;p < 0.001). However, these effects could not be confirmed after adjusting for long-term trends (Lockdown 1: adj. OR = 0.99;p = 0.73;Lockdown 2: adj. OR = 0.96;p = 0.24). For twin pregnancies, differences during lockdown were less marked (Lockdown 1: 52.99% vs. 56.26%;OR = 0.88;p = 0.15;Lockdown 2: 58.06% vs. 58.91%;OR = 0.97;p = 0.70). Reduced population mobility had no significant impact on preterm birth rates in singleton pregnancies (p = 0.14) but did have an impact on twin pregnancies (p = 0.02). Conclusions Reduced preterm birth rates during both lockdown periods in 2020 were observed for singleton and twin pregnancies. However, these effects are reduced when adjusting for long-term and seasonal trends. Reduced population mobility was associated with lower preterm birth rates in twin pregnancies.

Limited Effects of SARS-CoV-2 Pandemic-related Lockdowns and Reduced Population Mobility on Preterm Birth Rates: A Secondary Analysis of Bavarian Obstetric Quality Parameters from 2010 to 2020.

Introduction International studies on preterm birth rates during COVID-19 lockdowns report different results. This study examines preterm birth rates during lockdown periods and the impact of the mobility changes of the population in Bavaria, Germany. Material and Methods This is a secondary analysis of centrally collected data on preterm births in Bavaria from 2010 to 2020. Preterm births (< 37 weeks) in singleton and twin pregnancies during two lockdowns were compared with corresponding periods in 2010 - 2019. Fisher's exact test was used to compare raw prevalence between groups. Potential effects of two fixed lockdown periods and of variable changes in population mobility on preterm birth rates in 2020 were examined using additive logistic regression models, adjusting for long-term and seasonal trends. Results Unadjusted preterm birth rates in 2020 were significantly lower for singleton pregnancies during the two lockdown periods (Lockdown 1: 5.71% vs. 6.41%; OR 0.88; p < 0.001; Lockdown 2: 5.71% vs. 6.60%; OR = 0.86; p < 0.001). However, these effects could not be confirmed after adjusting for long-term trends (Lockdown 1: adj. OR = 0.99; p = 0.73; Lockdown 2: adj. OR = 0.96; p = 0.24). For twin pregnancies, differences during lockdown were less marked (Lockdown 1: 52.99% vs. 56.26%; OR = 0.88; p = 0.15; Lockdown 2: 58.06% vs. 58.91%; OR = 0.97; p = 0.70). Reduced population mobility had no significant impact on preterm birth rates in singleton pregnancies (p = 0.14) but did have an impact on twin pregnancies (p = 0.02). Conclusions Reduced preterm birth rates during both lockdown periods in 2020 were observed for singleton and twin pregnancies. However, these effects are reduced when adjusting for long-term and seasonal trends. Reduced population mobility was associated with lower preterm birth rates in twin pregnancies.

Estimating the course of the COVID-19 pandemic in Germany via spline-based hierarchical modelling of death counts.

We consider a retrospective modelling approach for estimating effective reproduction numbers based on death counts during the first year of the COVID-19 pandemic in Germany. The proposed Bayesian hierarchical model incorporates splines to estimate reproduction numbers flexibly over time while adjusting for varying effective infection fatality rates. The approach also provides estimates of dark figures regarding undetected infections. Results for Germany illustrate that our estimates based on death counts are often similar to classical estimates based on confirmed cases; however, considering death counts allows to disentangle effects of adapted testing policies from transmission dynamics. In particular, during the second wave of infections, classical estimates suggest a flattening infection curve following the "lockdown light" in November 2020, while our results indicate that infections continued to rise until the "second lockdown" in December 2020. This observation is associated with more stringent testing criteria introduced concurrently with the "lockdown light", which is reflected in subsequently increasing dark figures of infections estimated by our model. In light of progressive vaccinations, shifting the focus from modelling confirmed cases to reported deaths with the possibility to incorporate effective infection fatality rates might be of increasing relevance for the future surveillance of the pandemic.

Changes in characteristics and outcomes of critically ill COVID-19 patients in Tyrol (Austria) over 1 year.

BACKGROUND: Widely varying mortality rates of critically ill Coronavirus disease 19 (COVID-19) patients in the world highlighted the need for local surveillance of baseline characteristics, treatment strategies and outcome. We compared two periods of the COVID-19 pandemic to identify important differences in characteristics and therapeutic measures and their influence on the outcome of critically ill COVID-19 patients. METHODS: This multicenter prospective register study included all patients with a SARS-CoV­2 infection confirmed by polymerase chain reaction, who were treated in 1 of the 12 intensive care units (ICU) from 8 hospitals in Tyrol, Austria during 2 defined periods (1 February 2020 until 17 July: first wave and 18 July 2020 until 22 February 2021: second wave) of the COVID-19 pandemic. RESULTS: Overall, 508 patients were analyzed. The majority (n = 401) presented during the second wave, where the median age was significantly higher (64 years, IQR 54-74 years vs. 72 years, IQR 62-78 years, p < 0.001). Invasive mechanical ventilation was less frequent during the second period (50.5% vs 67.3%, p = 0.003), as was the use of vasopressors (50.3% vs. 69.2%, p = 0.001) and renal replacement therapy (12.0% vs. 19.6%, p = 0.061), which resulted in shorter ICU length of stay (10 days, IQR 5-18 days vs. 18 days, IQR 5-31 days, p < 0.001). Nonetheless, ICU mortality did not change (28.9% vs. 21.5%, p = 0.159) and hospital mortality even increased (22.4% vs. 33.4%, p = 0.039) in the second period. Age, frailty and the number of comorbidities were significant predictors of hospital mortality in a multivariate logistic regression analysis of the overall cohort. CONCLUSION: Advanced treatment strategies and learning effects over time resulted in reduced rates of mechanical ventilation and vasopressor use in the second wave associated with shorter ICU length of stay. Despite these improvements, age appears to be a dominant factor for hospital mortality in critically ill COVID-19 patients.

Estimating effective infection fatality rates during the course of the COVID-19 pandemic in Germany.

BACKGROUND: The infection fatality rate (IFR) of the Coronavirus Disease 2019 (COVID-19) is one of the most discussed figures in the context of this pandemic. In contrast to the case fatality rate (CFR), the IFR depends on the total number of infected individuals - not just on the number of confirmed cases. In order to estimate the IFR, several seroprevalence studies have been or are currently conducted. METHODS: Using German COVID-19 surveillance data and age-group specific IFR estimates from multiple international studies, this work investigates time-dependent variations in effective IFR over the course of the pandemic. Three different methods for estimating (effective) IFRs are presented: (a) population-averaged IFRs based on the assumption that the infection risk is independent of age and time, (b) effective IFRs based on the assumption that the age distribution of confirmed cases approximately reflects the age distribution of infected individuals, and (c) effective IFRs accounting for age- and time-dependent dark figures of infections. RESULTS: Effective IFRs in Germany are estimated to vary over time, as the age distributions of confirmed cases and estimated infections are changing during the course of the pandemic. In particular during the first and second waves of infections in spring and autumn/winter 2020, there has been a pronounced shift in the age distribution of confirmed cases towards older age groups, resulting in larger effective IFR estimates. The temporary increase in effective IFR during the first wave is estimated to be smaller but still remains when adjusting for age- and time-dependent dark figures. A comparison of effective IFRs with observed CFRs indicates that a substantial fraction of the time-dependent variability in observed mortality can be explained by changes in the age distribution of infections. Furthermore, a vanishing gap between effective IFRs and observed CFRs is apparent after the first infection wave, while an increasing gap can be observed during the second wave. CONCLUSIONS: The development of estimated effective IFR and observed CFR reflects the changing age distribution of infections over the course of the COVID-19 pandemic in Germany. Further research is warranted to obtain timely age-stratified IFR estimates, particularly in light of new variants of the virus.

Structured ICU resource management in a pandemic is associated with favorable outcome in critically ill COVID­19 patients.

INTRODUCTION: On February 25, 2020, the first 2 patients were tested positive for severe acute respiratory syndrome coronavirus­2 (SARS-CoV-2) in Tyrol, Austria. Rapid measures were taken to ensure adequate intensive care unit (ICU) preparedness for a surge of critically ill coronavirus disease-2019 (COVID-19) patients. METHODS: This cohort study included all COVID-19 patients admitted to an ICU with confirmed or strongly suspected COVID-19 in the State of Tyrol, Austria. Patients were recorded in the Tyrolean COVID-19 intensive care registry. Date of final follow-up was July 17, 2020. RESULTS: A total of 106 critically ill patients with COVID-19 were admitted to 1 of 13 ICUs in Tyrol from March 9 to July 17, 2020. Median age was 64 years (interquartile range, IQR 54-74 years) and the majority of patients were male (76 patients, 71.7%). Median simplified acute physiology score III (SAPS III) was 56 points (IQR 49-64 points). The median duration from appearance of first symptoms to ICU admission was 8 days (IQR 5-11 days). Invasive mechanical ventilation was required in 72 patients (67.9%) and 6 patients (5.6%) required extracorporeal membrane oxygenation treatment. Renal replacement therapy was necessary in 21 patients (19.8%). Median ICU length of stay (LOS) was 18 days (IQR 5-31 days), median hospital LOS was 27 days (IQR 13-49 days). The ICU mortality was 21.7% (23 patients), hospital mortality was 22.6%. There was no significant difference in ICU mortality in patients receiving invasive mechanical ventilation and in those not receiving it (18.1% vs. 29.4%, p = 0.284). As of July 17th, 2020, two patients are still hospitalized, one in an ICU, one on a general ward. CONCLUSION: Critically ill COVID-19 patients in Tyrol showed high severity of disease often requiring complex treatment with increased lengths of ICU and hospital stay. Nevertheless, the mortality was found to be remarkably low, which may be attributed to our adaptive surge response providing sufficient ICU resources.