Does the healthy vaccinee bias rule them all? Association of COVID-19 vaccination status and all-cause mortality from an analysis of data from 2.2 million individual health records.

OBJECTIVES: We investigated the validity of claims of the healthy vaccinee effect (HVE) in COVID-vaccine studies by analyzing associations between all-cause mortality (ACM) and COVID-19 vaccination status. METHODS: Approximately 2.2 million individual records from two Czech health insurance companies were retrospectively analyzed. Each age group was stratified according to the vaccination status (unvaccinated vs. individuals less than 4 weeks vs. more than 4 weeks from Doses 1, 2, 3, and 4 or more doses of vaccine). ACMs in these groups were computed and compared. RESULTS: Consistently over datasets and age categories, ACM was substantially lower in the vaccinated than unvaccinated groups regardless of the presence or absence of a wave of COVID-19 deaths. Moreover, the ACMs in groups more than 4 weeks from Doses 1, 2, or 3 were consistently several times higher than in those less than 4 weeks from the respective dose. HVE appears to be the only plausible explanation for this, which is further corroborated by a created mathematical model. CONCLUSIONS: In view of the presence of HVE, the baseline difference in the frailty of vaccinated and unvaccinated populations in periods without COVID-19 must be taken into account when estimating COVID-19 vaccine effectiveness from observational data.

Crop growth dynamics: Fast automatic analysis of LiDAR images in field-plot experiments by specialized software ALFA.

Repeated measurements of crop height to observe plant growth dynamics in real field conditions represent a challenging task. Although there are ways to collect data using sensors on UAV systems, proper data processing and analysis are the key to reliable results. As there is need for specialized software solutions for agricultural research and breeding purposes, we present here a fast algorithm ALFA for the processing of UAV LiDAR derived point-clouds to extract the information on crop height at many individual cereal field-plots at multiple time points. Seven scanning flights were performed over 3 blocks of experimental barley field plots between April and June 2021. Resulting point-clouds were processed by the new algorithm ALFA. The software converts point-cloud data into a digital image and extracts the traits of interest-the median crop height at individual field plots. The entire analysis of 144 field plots of dimension 80 x 33 meters measured at 7 time points (approx. 100 million LiDAR points) takes about 3 minutes at a standard PC. The Root Mean Square Deviation of the software-computed crop height from the manual measurement is 5.7 cm. Logistic growth model is fitted to the measured data by means of nonlinear regression. Three different ways of crop-height data visualization are provided by the software to enable further analysis of the variability in growth parameters. We show that the presented software solution is a fast and reliable tool for automatic extraction of plant height from LiDAR images of individual field-plots. We offer this tool freely to the scientific community for non-commercial use.