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We examined Clostridioides difficile infection (CDI) prevention practices and their relationship with hospital-onset healthcare facility-associated CDI rates (CDI rates) in Veterans Affairs (VA) acute-care facilities.
From January 2017 to February 2017, we conducted an electronic survey of CDI prevention practices and hospital characteristics in the VA. We linked survey data with CDI rate data for the period January 2015 to December 2016. We stratified facilities according to whether their overall CDI rate per 10,000 bed days of care was above or below the national VA mean CDI rate. We examined whether specific CDI prevention practices were associated with an increased risk of a CDI rate above the national VA mean CDI rate.
All 126 facilities responded (100% response rate). Since implementing CDI prevention practices in July 2012, 60 of 123 facilities (49%) reported a decrease in CDI rates; 22 of 123 facilities (18%) reported an increase, and 41 of 123 (33%) reported no change. Facilities reporting an increase in the CDI rate (vs those reporting a decrease) after implementing prevention practices were 2.54 times more likely to have CDI rates that were above the national mean CDI rate. Whether a facility’s CDI rates were above or below the national mean CDI rate was not associated with self-reported cleaning practices, duration of contact precautions, availability of private rooms, or certification of infection preventionists in infection prevention.
We found considerable variation in CDI rates. We were unable to identify which particular CDI prevention practices (i.e., bundle components) were associated with lower CDI rates.
A review of recently published temporal data from Shuidonggou Locality 1 indicates that a 40–43 cal ka date for the inception of Initial Upper Paleolithic (IUP) blade-oriented technologies in East Asia is warranted. Comparison of the dates from Shuidonggou to other Asian IUP dates in Korea, Siberia, and Mongolia supports this assertion, indicating that the initial appearance of the IUP in East Asia generally corresponds in time to the fluorescence of the IUP in eastern Europe and western Asia. This conclusion preliminarily suggests that either a version of the IUP originated independently in East Asia just prior to 40 cal ka, or more likely, that an early, initial diffusion of the IUP into East Asia occurred ∼41 cal ka, a hypothesis consistent with current estimates for the evolution or arrival of modern humans in the region.
Ni-based fcc alloys are frequently used as critical structural materials in nuclear energy applications. Despite extensive studies, fundamental questions remain regarding point defect migration and solute segregation as a function of grain boundary character after irradiation. In this study, a coupled experimental and modeling approach is used to understand the response of grain boundary character in a model Ni–5Cr alloy after high temperature heavy-ion irradiation. Radiation-induced segregation and void denuded zones were experimentally examined as a function of grain boundary character, while a kinetic rate theory model with grain boundary character boundary conditions was used to theoretically model Cr depletion in the alloy system. The results highlight major variations in the radiation response between the coherent and incoherent twin grain boundaries, but show limited disparity in defect sink strength between random low- and high-angle grain boundary regimes.
In this chapter, we will lay the groundwork for our presentation of three strategies to estimate causal effects when simple conditioning on observed variables that lie along back-door paths will not suffice. These strategies will be taken up in Chapters 9, 10, and 11, where we will explain instrumental variable estimators, front-door identification with causal mechanisms, and conditioning estimators that use data on pretreatment values of the outcome variable. Under very specific assumptions, these three strategies will identify average causal effects of interest, even though selection is on the unobservables and treatment assignment is nonignorable.
In this chapter, we will first review the related concepts of nonignorable treatment assignment and selection on the unobservables, using the directed graphs presented in prior chapters. To deepen the understanding of these concepts, we will then demonstrate why the usage of additional posttreatment data on the outcome of interest is unlikely to aid in the point identification of the treatment effects of most central concern. One indirect goal of this demonstration is to convince the reader that oft-heard claims such as “I would be able to establish that this association is causal if I had longitudinal data” are nearly always untrue if the longed-for longitudinal data are additional measurements taken only after treatment exposure. Instead, longitudinal data are most useful, as we will later explain in detail in Chapter 11, when pretreatment measures are available for those who are subsequently exposed to the treatment.
In his 2009 book titled Causality: Models, Reasoning, and Inference, Judea Pearl lays out a powerful and extensive graphical theory of causality. Pearl's work provides a language and a framework for thinking about causality that differs from the potential outcome model presented in Chapter 2. Beyond the alternative terminology and notation, Pearl (2009, section 7.3) shows that the fundamental concepts underlying the potential outcome perspective and his causal graph perspective are equivalent, primarily because they both encode counterfactual causal states to define causality. Yet, each framework has value in elucidating different features of causal analysis, and we will explain these differences in this and subsequent chapters, aiming to convince the reader that these are complementary perspectives on the same fundamental issues.
Even though we have shown in the last chapter that the potential outcome model is simple and has great conceptual value, Pearl has shown that graphs nonetheless provide a direct and powerful way of thinking about full causal systems and the strategies that can be used to estimate the effects within them. Some of the advantage of the causal graph framework is precisely that it permits suppression of what could be a dizzying amount of notation to reference all patterns of potential outcomes for a system of causal relationships. In this sense, Pearl's perspective is a reaffirmation of the utility of graphical models in general, and its appeal to us is similar to the appeal of traditional path diagrams in an earlier era of social science research. Indeed, to readers familiar with path models, the directed graphs that we will present in this chapter will look familiar.
What role should the counterfactual approach to observational data analysis play in causal analysis in the social sciences? Some scholars see its elaboration as a justification for experimental methodology as an alternative to observational data analysis. We agree that by laying bare the challenges that confront causal analysis with observational data, the counterfactual approach does indirectly support experimentation as an alternative to observation. But, because experiments are often (perhaps usually) infeasible for most of the causal questions that practicing social scientists appear to want to answer, this implication, when considered apart from others, is understandably distressing.
We see the observational data analysis methods associated with the potential outcome model, motivated using directed graphs, as useful tools that can help to improve the investigation of causal relationships within the social sciences, especially when experiments are infeasible. Accordingly, we believe that the methods associated with the counterfactual approach complement and extend older approaches to causal analysis with observational data by shaping the goals of an analysis, requiring explicit consideration of individual-level heterogeneity of causal effects, encouraging a wider consideration of available identification strategies, and clarifying standards for credible interpretations.
In this chapter, we first shore up our presentation of the counterfactual approach by considering several critical perspectives on its utility. We weigh in with the arguments that we find most compelling, and it will not be surprising to the reader that we find these objections less serious than do those who have formulated them.
In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed.