In this article, we develop and make available measures of public ideology in 2010 for the 50 American states, 435 congressional districts, and state legislative districts. We do this using the geospatial statistical technique of Bayesian universal kriging, which uses the locations of survey respondents, as well as population covariate values, to predict ideology for simulated citizens in districts across the country. In doing this, we improve on past research that uses the kriging technique for forecasting public opinion by incorporating Alaska and Hawaii, making the important distinction between ZIP codes and ZIP Code Tabulation Areas, and introducing more precise data from the 2010 Census. We show that our estimates of ideology at the state,
congressional district, and state legislative district levels appropriately predict the ideology of legislators elected from these districts, serving as an external validity check.

Generalized Linear Models: A Unified Approach provides an introduction to and overview of GLMs, with each chapter carefully laying the groundwork for the next. The Second Edition provides examples using real data from multiple fields in the social sciences such as psychology, education, economics, and political science, including data on voting intentions in the 2016 U.S. Republican presidential primaries. The Second Edition also strengthens material on the exponential family form, including a new discussion on multinomial distribution; adds more information on how to interpret results and make inferences in the chapter on estimation procedures; and has a new section on extensions to generalized linear models.   Software scripts, supporting documentation, data for the examples, and some extended mathematical derivations are available on the authors’ websites as well as through the \textttR package \textttGLMpack. All links are available at www.sagepub.com/gill2e.

Inpatient care for children with severe traumatic brain injury (sTBI) is expensive, with inpatient charges averaging over $70,000 per case (Hospital Inpatient, Children Only, National Statistics. Diagnoses– clinical classification software (CCS) principal diagnosis category 85 coma, stupor, and brain damage, and 233 intracranial injury. Diagnoses by Aggregate charges [https://hcupnet.ahrq.gov/#setup]). This ranks sTBI in the top quartile of pediatric conditions with the greatest inpatient costs (Hospital Inpatient, Children Only, National Statistics. Diagnoses– clinical classification software (CCS) principal diagnosis category 85 coma, stupor, and brain damage, and 233 intracranial injury. Diagnoses by Aggregate charges [https://hcupnet.ahrq.gov/#setup]). The Brain Trauma Foundation developed sTBI intensive care guidelines in 2003, with revisions in 2012 (Kochanek, Carney, et. al. PCCM 3:S1-S2, 2012). These guidelines have been widely disseminated, and are associated with improved health outcomes (Pineda, Leonard. et. al. LN 12:45-52, 2013), yet research on the cost of associated hospital care is limited. The objective of this study was to assess the costs of providing hospital care to sTBI patients through a guideline-based Pediatric Neurocritical Care Program (PNCP) implemented at St. Louis Children’s Hospital, a pediatric academic medical center in the Midwest United States.  

Objective: To evaluate the effectiveness of a physician-led rapid response team (RRT) program on morbidity and mortality following unplanned admission to the pediatric intensive care unit (PICU). Design: Before-after study. Setting: Single center quaternary referral PICU. Patients: All unplanned PICU admissions from the ward from 2005-2011. Interventions: The dataset was divided into pre- and post-RRT groups for comparison. Measurements and Main Results: A Cox proportional hazards model was used to identify the patient characteristics associated with mortality following unplanned PICU admission. Following RRT implementation, PRISM-III illness severity was reduced 28.1%, PICU length of stay (LOS) was less 19.8%, and mortality declined 22%. Relative risk of death following unplanned admission to the PICU after RRT implementation was 0.685. Conclusions: For children requiring unplanned admission to the PICU, RRT implementation is associated with reduced mortality, admission severity of illness and length of stay. RRT implementation led to more proximal capture and aggressive intervention in the trajectory of a decompensating pediatric ward patient.