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To evaluate the impact of a mobile-app-based central line-associated bloodstream infection (CLABSI) prevention program in nursing home residents with peripherally inserted central catheters (PICCs).
Design:
Pre-post prospective cohort study with baseline (September 2015–December 2016), phase-in (January 2017–April 2017), and intervention (May 2017–December 2018). Generalized linear mixed models compared intervention with baseline frequency of localized inflammation/infection, dressing peeling, and infection-related hospitalizations. Cox proportional hazards models compared days-to-removal of lines with localized inflammation/infection.
Setting:
Six nursing homes in Orange County, California.
Patients:
Adult nursing home residents with PICCs.
Intervention:
CLABSI prevention program consisting of an actionable scoring system for identifying insertion site infection/inflammation coupled with a mobile-app enabling photo-assessments and automated physician alerting for remote response.
Results:
We completed 8,131 assessments of 817 PICCs in 719 residents (baseline: 4,865 assessments, 422 PICCs, 385 residents; intervention: 4,264 assessments, 395 PICCs, 334 residents). The intervention was associated with 57% lower odds of peeling dressings (OR 0.43, 95% CI 0.28–0.64, P < .001), 73% lower local inflammation/infection (OR = 0.27, 95% CI: 0.13–0.56, P < .001), and 41% lower risk of infection-related hospitalizations (OR = 0.59, 95% CI: 0.42–0.83, P = .002). Physician mobile-app alerting and response enabled 62% lower risk of lines remaining in place after inflammation/infection was identified (HR 0.38, CI: 0.24–0.62, P < .001) and 95% faster removal of infected lines from mean (SD) 19 (20) to 1 (2) days.
Conclusions:
A mobile-app-based CLABSI prevention program decreased the frequency of inflamed/infected central line insertion sites, improved dressing integrity, increased speed of removal when inflammation/infection were found, and reduced infection-related hospitalization risk.
We compared the Institute for Clinical and Economic Review’s (ICER) ratings of comparative clinical effectiveness with the German Federal Joint Committee’s (G-BA) added benefit ratings, and explored what factors, including the evidence base, may explain disagreement between the two organizations.
Methods
Drugs were included if they were assessed by ICER under its 2020–2023 Value Assessment Framework and had a corresponding assessment by G-BA as of March 2023 for the same indication, patient population, and comparator drug. To compare assessments, we modified ICER’s proposed crosswalk between G-BA and ICER benefit ratings to account for G-BA’s extent and certainty ratings. We also determined whether each assessment pair was based on similar or dissimilar evidence. Assessment pairs exhibiting disagreement based on the modified crosswalk despite a similar evidence base were qualitatively analyzed to identify reasons for disagreement.
Results
We identified 15 assessment pairs and seven out of fifteen were based on similar evidence. G-BA and ICER assessments disagreed for each of these drugs. For 4/7 drugs, G-BA (but not ICER) determined the evidence was unsuitable for assessment: for 2/4 drugs, G-BA concluded the key trials did not appropriately assess the comparator therapy; for 1/4, G-BA did not accept results of a before-and-after study due to non-comparable study settings; for 1/4, G-BA determined follow-up in the key trial was too short. Among assessment pairs where both organizations assessed the evidence, reasons for disagreement included concerns about long-term safety, generalizability, and study design.
Conclusions
This study underscores the role of value judgments within assessments of clinical effectiveness. These judgments are not always transparently presented in assessment summaries. The lack of clarity regarding these value-based decisions underscores the need for improvements in transparency and communication, which are essential for promoting a more robust health technology assessment process and supporting transferability of assessments across jurisdictions.
The reliable change index has been used to evaluate the significance of individual change in health-related quality of life. We estimate reliable change for two measures (physical function and emotional distress) in the Patient-Reported Outcomes Measurement Information System (PROMIS®) 29-item health-related quality of life measure (PROMIS-29 v2.1). Using two waves of data collected 3 months apart in a longitudinal observational study of chronic low back pain and chronic neck pain patients receiving chiropractic care, and simulations, we compare estimates of reliable change from classical test theory fixed standard errors with item response theory standard errors from the graded response model. We find that unless true change in the PROMIS physical function and emotional distress scales is substantial, classical test theory estimates of significant individual change are much more optimistic than estimates of change based on item response theory.
The new software package OpenMx 2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly written CSOLNP. Entire new methodologies such as item factor analysis and state space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R $ mechanisms, and improved tab completion from within the R Graphical User Interface.
Persistent discrimination and identity threats contribute to adverse health outcomes in minoritized groups, mediated by both structural racism and physiological stress responses.
Objective:
This study aims to evaluate the feasibility of recruiting African American volunteers for a pilot study of race-based stress, the acceptability of a mindfulness intervention designed to reduce racism-induced stress, and to evaluate preliminary associations between race-based stress and clinical, psychosocial, and biological measures.
Methods:
A convenience sample of African Americans aged 18–50 from New York City’s Tri-state area underwent assessments for racial discrimination using the Everyday Discrimination Scale (EDS) and Race-Based Traumatic Stress Symptom Scale. Mental health was evaluated using validated clinical scales measuring depression, anxiety, stress, resilience, mindfulness, resilience, sleep, interpersonal connection, and coping. Biomarkers were assessed through clinical laboratory tests, allostatic load assessment, and blood gene expression analysis.
Results:
Twenty participants (12 females, 8 males) completed assessments after consent. Elevated EDS scores were associated with adverse lipid profiles, including higher cholesterol/high-density lipoprotein (HDL) ratios and lower HDL levels, as well as elevated inflammatory markers (NF-kB activity) and reduced antiviral response (interferon response factor). Those with high EDS reported poorer sleep, increased substance use, and lower resilience. Mindfulness was positively associated with coping and resilience but inversely to sleep disturbance. 90% showed interest in a mindfulness intervention targeting racism-induced stress.
Conclusions:
This study demonstrated an association between discrimination and adverse health effects among African Americans. These findings lay the groundwork for further research to explore the efficacy of mindfulness and other interventions on populations experiencing discrimination.
Introduction to Probability and Statistics for Data Science provides a solid course in the fundamental concepts, methods and theory of statistics for students in statistics, data science, biostatistics, engineering, and physical science programs. It teaches students to understand, use, and build on modern statistical techniques for complex problems. The authors develop the methods from both an intuitive and mathematical angle, illustrating with simple examples how and why the methods work. More complicated examples, many of which incorporate data and code in R, show how the method is used in practice. Through this guidance, students get the big picture about how statistics works and can be applied. This text covers more modern topics such as regression trees, large scale hypothesis testing, bootstrapping, MCMC, time series, and fewer theoretical topics like the Cramer-Rao lower bound and the Rao-Blackwell theorem. It features more than 250 high-quality figures, 180 of which involve actual data. Data and R are code available on our website so that students can reproduce the examples and do hands-on exercises.
This chapter treats love, desire and eroticism, arguing that eros and philotes serve as metapoetic structuring principles of epic narrative. It begins with a preliminary survey of the foundational texts, focusing on the scene of Helen at the loom as she weaves a tapestry of warriors in battle, essentially a figuration of the Iliad as an artistic product of sexual longing. The chapter then moves forward to consider how these same erotic structuring principles play out in imperial Greek epic, which absorbs Homer’s models through the filter of romantic fiction. Smith focuses on the first three books of Quintus of Smyrna’s Posthomerica – the events surrounding Penthesileia, Memnon, and the death of Achilles – reading them as flirtatious manipulations that intensify readerly anticipation, and then turns to Nonnus’ Dionysiaca, specifically the tendril imagery in the Ampelos episode and its sequel, the romance of Calamus and Carpus. These episodes serve as exemplars of the regenerative powers of epic desire.
What are the origins and effects of legal ambiguity in authoritarian regimes? Using a detailed case study of nationality rights in Jordan – which draws from interviews with 210 Jordanian political officials, judges, lawyers, activists, and citizens/residents – we develop a framework for understanding how legal ambiguity emerges, and how it matters, under authoritarianism. We first conceptualize four discrete forms in which legal ambiguity manifests: lexical ambiguity (in legal texts); substantive ambiguity (in status as law); conflictual ambiguity (between contradictory legal rules); and operational ambiguity (in enforcement processes). We then scrutinize the emergence and effects of legal ambiguity in Jordanian nationality policy by integrating historical process tracing, detailed interview evidence, and a content analysis of archival documents, laws, and court verdicts pertaining to nationality rights. Our findings contribute to scholarship on legal ambiguity, authoritarian legality, and discretionary state authority by showing that (1) crisis junctures make the emergence of legal ambiguity more likely; (2) legal ambiguity takes a variety of different forms that warrant conceptual disaggregation; and (3) different forms of legal ambiguity often have disparate effects on how authoritarian state power is organized and experienced in public life.
In Chapter 3 we learned how to do basic probability calculations and even put them to use solving some fairly complicated probability problems. In this chapter and the next two, we generalize how we do probability calculations, where we will transition from working with sets and events to working with random variables.
To do statistics you must first be able to “speak probability.” In this chapter we are going to concentrate on the basic ideas of probability. In probability, the mechanism that generates outcomes is assumed known and the problems focus on calculating the chance of observing particular types or sets of outcomes. Classical problems include flipping “fair” coins (where fair means that on one flip of the coin the chance it comes up heads is equal to the chance it comes up tails) and “fair” dice (where fair now means the chance of landing on any side of the die is equal to that of landing on any other side).
In Chapter 5 we learned about a number of discrete distributions. In this chapter we focus on continuous distributions, which are useful as models of various real-world events. By the end of this chapter you will know nine continuous and eight discrete distributions. There are many more continuous distributions, but these nine will suffice for our purposes. These continuous distributions are useful for modeling various types of processes and phenomena that are encountered in the real world.
Sampling joke: “If you don’t believe in random sampling, the next time you have a blood test, tell the doctor to take it all.” At the beginning of Chapter 7 we introduced the ideas of population vs. sample and parameter vs. statistic. We build on this in the current chapter. The key concept in this chapter is that if we were to take different samples from a distribution and compute some statistic, such as the sample mean, then we would get different results.
The last two chapters have covered the basic concepts of estimation. In Chapter 9 we studied the problem of giving a single number to estimate a parameter. In Chapter 10 we looked at ways to give an interval that we believe will include the true parameter. In many applications, we want to ask some very specific questions about the parameter(s).
We begin this chapter with a review of hypothesis testing from Chapter 12. A hypothesis is a statement about one or more parameters of a model. The null hypothesis is usually a specific statement that encapsulates “no effect.” For example, if we apply one of the two treatments, A or B, to volunteers we may be interested in testing whether the population mean outcomes are equal.
Up to this point we have been talking about what are often called frequentist methods, because a statistical method is based on properties of its long-run relative frequency. With this approach, the probability of an event is defined as the proportion of times the event occurs in the long run. Parameters, that is values that characterize a distribution, such as the mean and variance of a normal distribution, are considered fixed but unknown.