Cardiac myosin-specific autoimmune T cells contribute to immune-checkpoint-inhibitor-associated myocarditis

Immune checkpoint inhibitors (ICIs) are an effective therapy for various cancers; however, they can induce immune-related adverse events (irAEs) as a side effect. Myocarditis is an uncommon, but fatal, irAE caused after ICI treatments. Currently, the mechanism of ICI-associated myocarditis is unclear. Here, we show the development of myocarditis in A/J mice induced by anti-PD-1 monoclonal antibody (mAb) administration alone without tumor cell inoculation, immunization, or viral infection. Mice with myocarditis have increased cardiac infiltration, elevated cardiac troponin levels, and arrhythmia. Anti-PD-1 mAb treatment also causes irAEs in other organs. Autoimmune T cells recognizing cardiac myosin are activated and increased in mice with myocarditis. Notably, cardiac myosin-specific T cells are present in naive mice, showing a phenotype of antigen-experienced T cells. Collectively, we establish a clinically relevant mouse model for ICI-associated myocarditis and find a contribution of cardiac myosin-specific T cells to ICI-associated myocarditis development and pathogenesi

Designing a Study to Investigate Older Novice Drivers

DTNH2217D00031/693JJ920F000207Drivers 15 to 20 years old\u2014many of whom were novice drivers\u2014represented 8.5 percent of drivers involved in fatal crashes but only 5.1 percent of all licensed drivers in 2020. Graduated driver licensing (GDL) laws are the most effective behavioral countermeasure for young drivers. However, although an increasing proportion of young people are delaying licensure until 18 or older, few States currently apply the full GDL program to 18- to 20-year-old novice drivers, and little is known about the safety and driving habits of this group. In this project the research team developed a hypothetical naturalistic driving study to investigate research questions about the safety and driving exposure of younger (15.5 to 16.5 years old) and older (18 to 20 years old) novice drivers in the first year of unsupervised (independent) driving

Comparison of alternative risk adjustment measures for predictive modeling: high risk patient case finding using Taiwan's National Health Insurance claims

<p>Abstract</p> <p>Background</p> <p>Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models.</p> <p>Methods</p> <p>A random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, & Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented.</p> <p>Results</p> <p>Both diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status.</p> <p>Conclusions</p> <p>Predicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling.</p

American journal of hygiene.

Vols. 2-7 include Proceedings of the Society of Hygiene of the School of Hygiene and Public Health of Johns Hopkins University.Vols. 2-7 include Proceedings of the Society of Hygiene of the School of Hygiene and Public Health of Johns Hopkins University.Mode of access: Internet.Issued 1928-64 by the School of Hygiene and Public Health of Johns Hopkins University.Vols. 1-28, 1921-38. 1 v.; Vols. 29-60, 1939-54. 1 v

Diagnosis of Protozoa and worms parasitic in man. /

At head of title: The Johns Hopkins university. School of hygiene and public health."General literature hist": p. 4-5.Mode of access: Internet

Treatment outcomes among children younger than five years living with HIV in rural Zambia, 2008–2018: a cohort study

Abstract Background HIV testing and treatment guidelines for children in sub-Saharan Africa have evolved over time, such that children are now treated at younger ages. The objective of this study was to describe the treatment experience for immunologic, virologic, and growth outcomes among HIV-infected Zambian children younger than 5 years of age from 2008 to 2018. Methods Participants enrolled in a clinical cohort study in Macha, Zambia and initiating antiretroviral treatment before 5 years of age between 2008 and 2015 were included in the analysis and followed up to the end of 2018. Outcomes, including growth, CD4+ T-cell percentage, viral suppression, and mortality, were evaluated among all children using longitudinal and survival analyses. Comparisons by age at treatment initiation

Economic Profiling of Primary Care Physicians: Consistency among Risk-Adjusted Measures

OBJECTIVE: To investigate whether different risk-adjustment methodologies and economic profiling or “practice efficiency” metrics produce differences in practice efficiency rankings for a set of primary care physicians (PCPs). DATA SOURCE: Twelve months of claims records (inpatient, outpatient, professional, and pharmacy) for an independent practice association HMO. STUDY DESIGN: Patient risk scores obtained with six profiling risk-adjustment methodologies were used in conjunction with claims cost tabulations to measure practice efficiency of all primary care physicians who managed 25 or more members of an HMO. DATA COLLECTION: For each of the risk-adjustment methodologies, two measures of “efficiency” were constructed: the standardized cost difference between total observed (standardized actual) and total expected costs for patients managed by each PCP, and the ratio of the PCP's total observed to total expected costs (O/E ratio). Primary care physicians were ranked from most to least efficient according to each risk-adjusted measure, and level of agreement among measures was tested using weighted kappa. Separate rankings were constructed for pediatricians and for other primary care physicians. FINDINGS: Moderate to high levels of agreement were observed among the six risk-adjusted measures of practice efficiency. Agreement was greater among pediatrician rankings than among adult primary care physician rankings, and, with the standardized difference measure, greater for identifying the least efficient than the most efficient physicians. The O/E ratio was shown to be a biased measure of physician practice efficiency, disproportionately targeting smaller sized panels as outliers. CONCLUSIONS: Although we observed moderate consistency among different risk-adjusted PCP rankings, consistency of measures does not prove that practice efficiency rankings are valid, and health plans should be careful in how they use practice efficiency information. Indicators of practice efficiency should be based on the standardized cost difference, which controls for number of patients in a panel, instead of O/E ratio, which does not