RESUMO
Background: Understanding the usefulness of additional COVID-19 vaccine doses-particularly given varying disease incidence-is needed to support public health policy. We characterize the benefits of COVID-19 booster doses using number needed to vaccinate (NNV) to prevent one COVID-19-associated hospitalization or emergency department encounter. Methods: We conducted a retrospective cohort study of immunocompetent adults at five health systems in four U.S. states during SARS-CoV-2 Omicron BA.1 predominance (December 2021-February 2022). Included patients completed a primary mRNA COVID-19 vaccine series and were either eligible to or received a booster dose. NNV were estimated using hazard ratios for each outcome (hospitalization and emergency department encounters), with results stratified by three 25-day periods and site. Findings: 1,285,032 patients contributed 938 hospitalizations and 2076 emergency department encounters. 555,729 (43.2%) patients were aged 18-49 years, 363,299 (28.3%) 50-64 years, and 366,004 (28.5%) ≥65 years. Most patients were female (n = 765,728, 59.6%), White (n = 990,224, 77.1%), and non-Hispanic (n = 1,063,964, 82.8%). 37.2% of patients received a booster and 62.8% received only two doses. Median estimated NNV to prevent one hospitalization was 205 (range 44-615) and NNV was lower across study periods for adults aged ≥65 years (110, 46, and 88, respectively) and those with underlying medical conditions (163, 69, and 131, respectively). Median estimated NNV to prevent one emergency department encounter was 156 (range 75-592). Interpretation: The number of patients needed to receive a booster dose was highly dependent on local disease incidence, outcome severity, and patient risk factors for moderate-to-severe disease. Funding: Funding was provided by the Centers for Disease Control and Prevention though contract 75D30120C07986 to Westat, Inc. and contract 75D30120C07765 to Kaiser Foundation Hospitals.
RESUMO
Synthetic magnetic resonance (MR) imaging is an approach suggested in the literature to predict MR images at different design parameter settings from at least three observed MR scans. However, performance is poor when no regularization is used in the estimation and otherwise computationally impractical to implement for 3-D imaging methods. We propose a method which accounts for spatial context in MR images by the imposition of a Gaussian Markov random field (MRF) structure on a transformation of the spin-lattice relaxation time, the spin-spin relaxation time and the proton density at each voxel. The MRF structure is specified through a matrix normal distribution. We also model the observed magnitude images using the more accurate but computationally challenging Rice distribution. A one-step-late expectation-maximization approach is adopted to make our approach computationally practical. We evaluate predictive performance in generating synthetic MR images in a clinical setting: our results indicate that our suggested approach is not only computationally feasible to implement but also shows excellent performance.