The teen years explained: a guide to healthy adolescent development

1. Physical Development -- 2. Cognitive Development -- 3. Emotional & Social Development -- 4. Forming an Identity -- 5. Sexuality -- 6. Spirituality & Religion -- 7. Profiles of Development -- 8. Conclusion -- Resources & Further Reading -- Referencesby Clea McNeely and Jayne Blanchard ; with a foreword by Nicole Yohalem, Karen PittmanAlso available via the World Wide Web.Includes bibliographical references (p. 94-102) and index.The Guide was made possible by funding from the Centers for Disease Control and Prevention (CDC) to the Center for Adolescent Health at the Johns Hopkins Bloomberg School of Public Health, a member of the Prevention Research Centers Program, CDC cooperative agreement 1-U48-DP-000040

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

Understanding community health volunteer incentive preferences in Kenya

In Kenya, community health services are implemented through community health units. Community health volunteers (CHVs) who serve these units are chosen by the community and trained by community health extension workers. This brief summarizes qualitative and quantitative findings from the Frontline Health project’s discrete choice experiment study in Kenya, which aimed to understand incentive preferences of CHVs with the aim of improving motivation, performance, and retention of CHVs

Preferred incentives for improving community health worker satisfaction and retention in Bangladesh

Community health workers (CHWs) are critical actors within community health systems, providing a range of reproductive, maternal, child, and primary health information, counseling, and services. To better understand factors that influence CHWs’ work motivation and satisfaction in Bangladesh, the Frontline Health project, implemented with collaborators from Johns Hopkins Bloomberg School of Public Health, and supported by the government of Bangladesh’s Directorate General of Health Services and the Directorate General of Family Planning, conducted a discrete choice experiment (DCE) study. This brief presents key qualitative findings from the DCE study in Bangladesh and implications for program and policy stakeholders

Identifying factors that support community health worker motivation, job satisfaction, and retention in Haiti

Community health workers (CHWs) play a critical role in Haiti’s health system, by connecting communities—particularly rural and hard-to-reach populations—to healthcare services. The Frontline Health project conducted a study to better understand the types of incentive structures that can best support the CHWs in Haiti and this brief summarizes those findings

Making New Mobility a Win for Public Health

69A3551747128Designing mobility interventions to improve public health requires a framework that formulates strategies for the deployment of mobility to maximize the potential for cross-cutting public health impacts. Researchers developed such a framework using a combination of epidemiology and simulation modeling. A case study is presented to demonstrate how a strategic dose of mobility could improve food access for the South Baltimore community of Cherry Hill

Seroprevalence and correlates of HIV, syphilis, and hepatitis B and C virus among intrapartum patients in Kabul, Afghanistan

BackgroundLittle current information is available for prevalence of vertically-transmitted infections among the Afghan population. The purpose of this study is to determine prevalence and correlates of human immunodeficiency virus (HIV), syphilis, and hepatitis B and C infection among obstetric patients and model hepatitis B vaccination approaches in Kabul, Afghanistan.MethodsThis cross-sectional study was conducted at three government maternity hospitals in Kabul, Afghanistan from June through September, 2006. Consecutively-enrolled participants completed an interviewer-administered survey and whole blood rapid testing with serum confirmation for antibodies to HIV, T. pallidum, and HCV, and HBsAg. Descriptive data and prevalence of infection were calculated, with logistic regression used to identify correlates of HBV infection. Modeling was performed to determine impact of current and birth dose vaccination strategies on HBV morbidity and mortality.ResultsAmong 4452 women, prevalence of HBsAg was 1.53% (95% CI: 1.18 - 1.94) and anti-HCV was 0.31% (95% CI: 0.17 - 0.53). No cases of HIV or syphilis were detected. In univariate analysis, HBsAg was associated with husband's level of education (OR = 1.13, 95% CI: 1.01 - 1.26). Modeling indicated that introduction of birth dose vaccination would not significantly reduce hepatitis-related morbidity or mortality for the measured HBsAg prevalence.ConclusionIntrapartum whole blood rapid testing for HIV, syphilis, HBV, and HCV was acceptable to patients in Afghanistan. Though HBsAg prevalence is relatively low, periodic assessments should be performed to determine birth dose vaccination recommendations for this setting

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