Self-Reported Reactogenicity After Different COVID-19 Vaccination Regimens­An Analysis of Registry-Based Data.

BACKGROUND: In Rhineland-Palatinate, most COVID-19 vaccinations are centrally recorded by the Rhineland-Palatinate Division of Vaccine Documentation, which includes self-reported vaccination reactions (SRVR) and their level of perceived intensity. We analyzed the occurrence of SBIR reported between 12/2020 and 12/2021 in relation to the different vaccination regimens involving BioNTech/Pfizer (BNT) and Moderna (m1273) mRNA vaccines and AstraZeneca (ChAd) and Johnson & Johnson (Ad26) viral vector vaccines. METHODS: Using sex-specific logistic regression models, we analyzed the occurrence of all local and systemic SBIR, as well as the occurrence of only local and systemic SBIR that were selfrated as "severe" by the vaccinated persons, in relation to the vaccine of the first vaccination and the vaccination regimen of the second vaccination (BNT/BNT, ChAd/ChAd, m1273/m1273, ChAd/BNT, ChAd/m1273). Vaccination with BNT or the BNT/BNT regimen formed the reference category for the estimated odds ratios (OR) with respective 95% confidence intervals. RESULTS: Of all those vaccinated, 40.7% provided valid information on SBIR after the first vaccination and 33.8% after the second vaccination. As a result, 887 052 individuals were included in the analyses. Their median age was 60 years, and 58% were women. The most common vaccination regimen was BNT/BNT (67.3%). The most common SBIR were pain at the injection site and fatigue. Self-reported reactogenicity after the first vaccination was lowest for BNT. Self-reported systemic reactogenicity was notably higher after vaccination with a vector vaccine. After the second vaccination, self-reported reactogenicity was lowest after a ChAd/ChAd regimen and highest after an m1273 second vaccination. CONCLUSION: With overall acceptable tolerability, differences in self-reported reactogenicity were evident depending on the particular COVID-19 vaccines and vaccination regimens in question.

[Explaining the age-adjusted excess mortality with COVID-19-attributed deaths from January 2020 to July 2021]. / Erklärbarkeit der altersadjustierten Übersterblichkeit mit den COVID-19-attribuierten Sterbefällen von Januar 2020 bis Juli 2021.

BACKGROUND: Estimating COVID-19 mortality is impeded by uncertainties in cause of death coding. In contrast, age-adjusted excess all-cause mortality is a robust indicator of how the COVID-19 pandemic impacts public health. However, in addition to COVID-19 deaths, excess mortality potentially also reflects indirect negative effects of public health measures aiming to contain the pandemic. OBJECTIVES: The study examines whether excess mortality in Germany between January 2020 and July 2021 is consistent with fatalities attributed to COVID-19 or may be partially due to indirect effects of public health measures. METHODS: Excess mortality trends for the period from January 2020 to July 2021 were checked for consistency with deaths attributed to COVID-19 in both the German federal states and districts of Rhineland-Palatinate. The expected monthly mortality rates were predicted based on data from 2015-2019, taking into account the population demographics, air temperature, seasonal influenza activity, and cyclic and long-term time trends RESULTS: COVID-19-attributed mortality was included in the 95% prediction uncertainty intervals for excess mortality in 232 of 304 (76.3%) month-state combinations and in 607 of 684 (88.7%) month-district combinations. The Spearman rank correlation between excess mortality and COVID-19-attributed mortality across federal states was 0.42 (95% confidence interval [0.31; 0.53]) and 0.21 (95% confidence interval [0.13; 0.29]) across districts. CONCLUSIONS: The good agreement of spatiotemporal excess mortality patterns with COVID-19 attributed mortality is consistent with the assumption that indirect adverse effects from public health interventions to contain the COVID-19 pandemic did not substantially contribute to excess mortality in Germany between January 2020 and July 2021.

Association of COVID-19 mortality with COVID-19 vaccination rates in Rhineland-Palatinate (Germany) from calendar week 1 to 20 in the year 2021: a registry-based analysis.

Vaccination is among the measures implemented by authorities to control the spread of the COVID-19 pandemic. However, real-world evidence of population-level effects of vaccination campaigns against COVID-19 are required to confirm that positive results from clinical trials translate into positive public health outcomes. Since the age group 80 + years is most at risk for severe COVID-19 disease progression, this group was prioritized during vaccine rollout in Germany. Based on comprehensive vaccination data from the German federal state of Rhineland-Palatinate for calendar week 1-20 in the year 2021, we calculated sex- and age-specific vaccination coverage. Furthermore, we calculated the proportion of weekly COVID-19 fatalities and reported SARS-CoV-2 infections formed by each age group. Vaccination coverage in the age group 80 + years increased to a level of 80% (men) and 75% (women). Increasing vaccination coverage coincided with a reduction in the age group's proportion of COVID-19 fatalities. In multivariable logistic regression, vaccination coverage was associated both with a reduction in an age-group's proportion of COVID-19 fatalities [odds ratio (OR) per 5 percentage points = 0.89, 95% confidence interval (CI) = 0.82-0.96, p = 0.0013] and of reported SARS-CoV-2 infections (OR per 5 percentage points = 0.82, 95% CI 0.76-0.88, p < 0.0001). The results are consistent with a protective effect afforded by the vaccination campaign against severe COVID-19 disease in the oldest age group.

Gender specific excess mortality in Italy during the COVID-19 pandemic accounting for age.

Since the beginning of the COVID-19 pandemic, data have been accumulated to examine excess mortality in the first half of 2020. Mortality in the preceding year or years is used to calculate the expected number of deaths, which is then compared with the actual number of deaths in 2020. We calculated weekly age- and sex-specific mortality rates for 93.1% of the Italian municipalities for the years 2015-2019 and for the first 26 weeks in 2020. We assumed the mortality experience during 2015-2019 as the reference period to calculate standardised mortality ratios. Furthermore, in order to compare the mortality experience of males and females, we calculated sex- and age- specific weekly direct standardised mortality rates and differences between the observed and expected number of deaths. We observed considerable changes in the demographics in the Italian population between the years 2015 and 2020, particularly among people 60 years and older and among males. The population is aging and the proportion of elderly males has increased, which was not reflected adequately in previous estimates of excess mortality. Standardized excess mortality results show that in Italy between the 8th and 26th weeks in 2020, there were 33,035 excess deaths, which is only 643 fewer deaths than the official COVID-19 death toll for this time period. A comparative increase in the mortality rates was observed in March among both sexes, but particularly for males. Comparisons with recently published data show considerably higher excess deaths, but these data were either not covering the complete country or did not account for age and sex. Neglecting the demographic changes in a region, even over a short time span, can result in biased estimates.

Epidemiological Measures in the Context of the COVID-19 Pandemic.

BACKGROUND: The various epidemiological indicators used to communicate the impact of COVID-19 have different strengths and limitations. METHODS: We conducted a selective literature review to identify the indicators used and to derive appropriate definitions. We calculated crude and age-adjusted indicators for selected countries. RESULTS: The proportion of deaths (case fatality proportion [CFP]; number of deaths/ total number of cases) is commonly used to estimate the severity of a disease. If the CFP is used for purposes of comparison, the existence of heterogeneity in the detection and registration of cases and deaths has to be taken into account. In the early phase of an epidemic, when case numbers rise rapidly, the CFP suffers from bias. For these reasons, variants have been proposed: the "confirmed CFP" (number of deaths/total number of confirmed cases), and the "delay-adjusted CFP," which considers the delay between infection with the disease and death from the disease. The indicator mortality (number of deaths/total population) has at first sight the advantage of being based on a defined denominator, the total population. During the outbreak of a disease, however, the cumulative deaths rise while the total population remains stable. The phase of the epidemic therefore has to be considered when using this indicator. In this context, R0 and R(t) are important indicators. R0 estimates the maximum rate of spread of a disease in a population, while R(t) describes the dynamics of the epidemic at a given time. Age-adjusted analysis of the CFP shows that the differences between countries decrease but do not dis - appear completely. If the test strategies depend on age or symptom severity, however, the bias cannot be entirely eliminated. CONCLUSION: Various indicators of the impact of the COVID-19 epidemic at population level are used in daily communication. Considering the relevance of the pandemic and the importance of relevant communications, however, the strengths and the limitations of each parameter must be considered carefully.