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The influenza A (H1N1) 2009 monovalent vaccination programme was the largest mass vaccination initiative in recent US history. Commensurate with the size and scope of the vaccination programme, a project to monitor vaccine adverse events was undertaken, the most comprehensive safety surveillance agenda in the USA to date. The adverse event monitoring project identified an increased risk of Guillain-Barré syndrome after vaccination; however, some individual variability in results was noted. Guillain-Barré syndrome is a rare but serious health disorder in which a person's own immune system damages their nerve cells, causing muscle weakness, sometimes paralysis, and infrequently death. We did a meta-analysis of data from the adverse event monitoring project to ascertain whether influenza A (H1N1) 2009 monovalent inactivated vaccines used in the USA increased the risk of Guillain-Barré syndrome. Data were obtained from six adverse event monitoring systems. About 23 million vaccinated people were included in the analysis. The primary analysis entailed calculation of incidence rate ratios and attributable risks of excess cases of Guillain-Barré syndrome per million vaccinations. We used a self-controlled risk-interval design. Influenza A (H1N1) 2009 monovalent inactivated vaccines were associated with a small increased risk of Guillain-Barré syndrome (incidence rate ratio 2·35, 95% CI 1·42–4·01, p=0·0003). This finding translated to about 1·6 excess cases of Guillain-Barré syndrome per million people vaccinated. The modest risk of Guillain-Barré syndrome attributed to vaccination is consistent with previous estimates of the disorder after seasonal influenza vaccination. A risk of this small magnitude would be difficult to capture during routine seasonal influenza vaccine programmes, which have extensive, but comparatively less, safety monitoring. In view of the morbidity and mortality caused by 2009 H1N1 influenza and the effectiveness of the vaccine, clinicians, policy makers, and those eligible for vaccination should be assured that the benefits of inactivated pandemic vaccines greatly outweigh the risks. US Federal Government.

Nicholas Croucher and colleagues report whole-genome sequencing of 616 asymptomatically carried Streptococcus pneumoniae , a major cause of pneumonia, bacteremia and meningitis. They examine the impact of the introduction of the pneumococcal conjugate vaccine PCV7 on pneumococcal population dynamics. Whole-genome sequencing of 616 asymptomatically carried Streptococcus pneumoniae isolates was used to study the impact of the 7-valent pneumococcal conjugate vaccine. Comparison of closely related isolates showed the role of transformation in facilitating capsule switching to non-vaccine serotypes and the emergence of drug resistance. However, such recombination was found to occur at significantly different rates across the species, and the evolution of the population was primarily driven by changes in the frequency of distinct genotypes extant before the introduction of the vaccine. These alterations resulted in little overall effect on accessory genome composition at the population level, contrasting with the decrease in pneumococcal disease rates after the vaccine's introduction.

On December 11, 2020, the Food and Drug Administration (FDA) issued an Emergency Use Authorization (EUA) for the Pfizer-BioNTech COVID-19 (BNT162b2) vaccine (Pfizer, Inc; Philadelphia, Pennsylvania), a lipid nanoparticle-formulated, nucleoside-modified mRNA vaccine encoding the prefusion spike glycoprotein of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) (1). Vaccination with the Pfizer-BioNTech COVID-19 vaccine consists of 2 doses (30 μg, 0.3 mL each) administered intramuscularly, 3 weeks apart. On December 12, 2020, the Advisory Committee on Immunization Practices (ACIP) issued an interim recommendation* for use of the Pfizer-BioNTech COVID-19 vaccine in persons aged ≥16 years for the prevention of COVID-19. To guide its deliberations regarding the vaccine, ACIP employed the Evidence to Recommendation (EtR) Framework, using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. The recommendation for the Pfizer-BioNTech COVID-19 vaccine should be implemented in conjunction with ACIP's interim recommendation for allocating initial supplies of COVID-19 vaccines (2). The ACIP recommendation for the use of the Pfizer-BioNTech COVID-19 vaccine under EUA is interim and will be updated as additional information becomes available.

As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data. In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS- CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values. The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses.

ObjectiveMeasuring the incidence of healthcare-associated infections (HAI) is of increasing importance in current healthcare delivery systems. Administrative data algorithms, including (combinations of) diagnosis codes, are commonly used to determine the occurrence of HAI, either to support within-hospital surveillance programmes or as free-standing quality indicators. We conducted a systematic review evaluating the diagnostic accuracy of administrative data for the detection of HAI.MethodsSystematic search of Medline, Embase, CINAHL and Cochrane for relevant studies (1995–2013). Methodological quality assessment was performed using QUADAS-2 criteria; diagnostic accuracy estimates were stratified by HAI type and key study characteristics.Results57 studies were included, the majority aiming to detect surgical site or bloodstream infections. Study designs were very diverse regarding the specification of their administrative data algorithm (code selections, follow-up) and definitions of HAI presence. One-third of studies had important methodological limitations including differential or incomplete HAI ascertainment or lack of blinding of assessors. Observed sensitivity and positive predictive values of administrative data algorithms for HAI detection were very heterogeneous and generally modest at best, both for within-hospital algorithms and for formal quality indicators; accuracy was particularly poor for the identification of device-associated HAI such as central line associated bloodstream infections. The large heterogeneity in study designs across the included studies precluded formal calculation of summary diagnostic accuracy estimates in most instances.ConclusionsAdministrative data had limited and highly variable accuracy for the detection of HAI, and their judicious use for internal surveillance efforts and external quality assessment is recommended. If hospitals and policymakers choose to rely on administrative data for HAI surveillance, continued improvements to existing algorithms and their robust validation are imperative.

Background Like in many settings, implementation of evidence-based practices often fall short in pediatric intensive care units (PICU). Very few prior studies have applied implementation science frameworks to understand how best to improve practices in this unique environment. We used the relatively new integrated Promoting Action on Research Implementation in Health Services (iPARIHS) framework to assess practice improvement in the PICU and to explore the utility of the framework itself for that purpose. Methods We used the iPARIHS framework to guide development of a semi-structured interview tool to examine barriers, facilitators, and the process of change in the PICU. A framework approach to qualitative analysis, developed around iPARIHS constructs and subconstructs, helped identify patterns and themes in provider interviews. We assessed the utility of iPARIHS to inform PICU practice change. Results Fifty multi-professional providers working in 8 U.S. PICUs completed interviews. iPARIHS constructs shaped the development of a process model for change that consisted of phases that include planning, a decision to adopt change, implementation and facilitation, and sustainability; the PICU environment shaped each phase. Large, complex multi-professional teams, and high-stakes work at near-capacity impaired receptivity to change. While the unit leaders made decisions to pursue change, providers’ willingness to accept change was based on the evidence for the change, and provider’s experiences, beliefs, and capacity to integrate change into a demanding workflow. Limited analytic structures and resources frustrated attempts to monitor changes’ impacts. Variable provider engagement, time allocated to work on changes, and limited collaboration impacted facilitation. iPARIHS constructs were useful in exploring implementation; however, we identified inter-relation of subconstructs, unique concepts not captured by the framework, and a need for subconstructs to further describe facilitation. Conclusions The PICU environment significantly shaped the implementation. The described process model for implementation may be useful to guide efforts to integrate changes and select implementation strategies. iPARIHS was adequate to identify barriers and facilitators of change; however, further elaboration of subconstructs for facilitation would be helpful to operationalize the framework. Trial registration Not applicable, as no health care intervention was performed.

Since October 2012, the combined tetanus toxoid, reduced diphtheria toxoid, acellular pertussis vaccine (Tdap) has been recommended in the United States during every pregnancy. In this observational study from the Vaccine Safety Datalink, we describe receipt of Tdap during pregnancy among insured women with live births across seven health systems. Using a retrospective matched cohort, we evaluated risks for selected medically attended adverse events in pregnant women, occurring within 42 days of vaccination. Using a generalized estimating equation, we calculated adjusted incident rate ratios (AIRR). Our vaccine coverage cohort included 438,487 live births between January 1, 2007 and November 15, 2013. Across the coverage cohort, 14% received Tdap during pregnancy. By 2013, Tdap was administered during pregnancy in 41.7% of live births, primarily in the 3rd trimester. Our vaccine safety cohort included 53,885 vaccinated and 109,253 matched unvaccinated pregnant women. There was no increased risk for a composite outcome of medically attended acute adverse events within 3 days of vaccination. Similarly, across the safety cohort, over a 42 day window, incident neurologic events, thrombotic events, and new onset proteinuria did not differ by maternal receipt of Tdap. Among women receiving Tdap at 20 weeks gestation or later, as compared to their matched controls, there was no increased risk for gestational diabetes or cardiac events while venous thromboembolic events and thrombocytopenia were diagnosed within 42 days of vaccination at slightly decreased rates. Tdap coverage during pregnancy increased from 2007 through 2013, but was still below 50%. No acute maternal safety signals were detected in this large cohort.

On December 18, 2020, the Food and Drug Administration (FDA) issued an Emergency Use Authorization (EUA) for the Moderna COVID-19 (mRNA-1273) vaccine (ModernaTX, Inc; Cambridge, Massachusetts), a lipid nanoparticle-encapsulated, nucleoside-modified mRNA vaccine encoding the stabilized prefusion spike glycoprotein of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) (1). This vaccine is the second COVID-19 vaccine authorized under an EUA for the prevention of COVID-19 in the United States (2). Vaccination with the Moderna COVID-19 vaccine consists of 2 doses (100 μg, 0.5 mL each) administered intramuscularly, 1 month (4 weeks) apart. On December 19, 2020, the Advisory Committee on Immunization Practices (ACIP) issued an interim recommendation* for use of the Moderna COVID-19 vaccine in persons aged ≥18 years for the prevention of COVID-19. To guide its deliberations regarding the vaccine, ACIP employed the Evidence to Recommendation (EtR) Framework, using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Use of all COVID-19 vaccines authorized under an EUA, including the Moderna COVID-19 vaccine, should be implemented in conjunction with ACIP's interim recommendations for allocating initial supplies of COVID-19 vaccines (3). The ACIP recommendation for the use of the Moderna COVID-19 vaccine under EUA is interim and will be updated as additional information becomes available.

Vaccine safety is critical for the successful implementation of any vaccination program, especially during a pandemic. In February 1976, the Centers for Disease Control and Prevention confirmed a cluster of cases of severe influenza-like illness among Army recruits at Fort Dix, New Jersey. 1 A swine influenza A strain that resembled the 1918 pandemic influenza strain was identified, 2 and a vaccination program was subsequently initiated for the entire U.S. population. After more than 40 million persons were vaccinated, a small excess risk of Guillain–Barré syndrome was noted, with an attributable risk of approximately 1 case per 100,000 doses administered. Given these . . .

Multi-system inflammatory syndrome in children (MIS-C) is a severe post-acute sequela of SARS-CoV-2 infection in children, and there is a critical need to unfold its highly heterogeneous disease patterns. Our objective was to characterize the illness spectrum of MIS-C for improved recognition and management. We conducted a retrospective cohort study using data from March 1, 2020–September 30, 2022, in 8 pediatric medical centers from PEDSnet. We included 1139 children hospitalized with MIS-C and used their demographics, symptoms, conditions, laboratory values, and medications for analyses. We applied heterogeneity-adaptive latent class analyses and identified three latent classes. We further characterized the sociodemographic and clinical characteristics of the latent classes and evaluated their temporal patterns. Class 1 (47.9%) represented children with the most severe presentation, with more admission to the ICU, higher inflammatory markers, hypotension/shock/dehydration, cardiac involvement, acute kidney injury and respiratory involvement. Class 2 (23.3%) represented a moderate presentation, with 4–6 organ systems involved, and some overlapping features with acute COVID-19. Class 3 (28.8%) represented a mild presentation. Our results indicated that MIS-C has a spectrum of clinical severity ranging from mild to severe and the proportion of severe or critical MIS-C decreased over time.