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Due to concerns over increasing fluoroquinolone (FQ) resistance among gram-negative organisms, our stewardship program implemented a preauthorization use policy. The goal of this study was to assess the relationship between hospital FQ use and antibiotic resistance.
Large academic medical center.
We performed a retrospective analysis of FQ susceptibility of hospital isolates for 5 common gram-negative bacteria: Acinetobacter spp., Enterobacter cloacae, Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. Primary endpoint was the change of FQ susceptibility. A Poisson regression model was used to calculate the rate of change between the preintervention period (1998–2005) and the postimplementation period (2006–2016).
Large rates of decline of FQ susceptibility began in 1998, particularly among P. aeruginosa, Acinetobacter spp., and E. cloacae. Our FQ restriction policy improved FQ use from 173 days of therapy (DOT) per 1,000 patient days to <60 DOT per 1,000 patient days. Fluoroquinolone susceptibility increased for Acinetobacter spp. (rate ratio [RR], 1.038; 95% confidence interval [CI], 1.005–1.072), E. cloacae (RR, 1.028; 95% CI, 1.013–1.044), and P. aeruginosa (RR, 1.013; 95% CI, 1.006–1.020). No significant change in susceptibility was detected for K. pneumoniae (RR, 1.002; 95% CI, 0.996–1.008), and the susceptibility for E. coli continued to decline, although the decline was not as steep (RR, 0.981; 95% CI, 0.975–0.987).
A stewardship-driven FQ restriction program stopped overall declining FQ susceptibility rates for all species except E. coli. For 3 species (ie, Acinetobacter spp, E. cloacae, and P. aeruginosa), susceptibility rates improved after implementation, and this improvement has been sustained over a 10-year period.
Economic growth over the past three decades has greatly improved the nutrition and living standards of people in China. However, increasingly, the Chinese are becoming heavier. As many as a quarter of Chinese school-age urban boys are overweight or obese, yet a third of Chinese children remain underweight. Drawing on six national surveys of children's health conducted since 1979, the article reports on trends in nutritional status and regional disparities. It shows that the drivers behind the increase in mean body mass and in nutritional inequality are associated with rising household incomes and associated inequalities between provinces.
We investigated longitudinal relations among gaze following and face scanning in infancy and later language development. At 12 months, infants watched videos of a woman describing an object while their passive viewing was measured with an eye-tracker. We examined the relation between infants' face scanning behavior and their tendency to follow the speaker's attentional shift to the object she was describing. We also collected language outcome measures on the same infants at 18 and 24 months. Attention to the mouth and gaze following at 12 months both predicted later productive vocabulary. The results are discussed in terms of social engagement, which may account for both attentional distribution and language onset. We argue that an infant's inherent interest in engaging with others (in addition to creating more opportunities for communication) leads infants to attend to the most relevant information in a social scene and that this information facilitates language learning.
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.
Regression models are perhaps the most common form of data analysis used to evaluate alternative explanations for outcomes of interest to quantitatively oriented social scientists. In the past 50 years, a remarkable variety of regression models have been developed by statisticians. Accordingly, most major data analysis software packages allow for regression estimation of the relationships between interval and categorical variables, in cross sections and longitudinal panels, and in nested and multilevel patterns. In this chapter, however, we restrict our attention to ordinary least squares (OLS) regression, focusing mostly on the regression of an interval-scaled variable on a binary causal variable. As we will show, the issues are complicated enough for these models, and it is our knowledge of how least squares models work that allows us to explain this complexity. In addition, nearly all of the insight that can be gained from a deep examination of OLS models carries over to more complex regression models because the identification and heterogeneity issues that generate the complexity apply in analogous fashion to all regression-type models.
In this chapter, we present least squares regression from three different perspectives: (1) regression as a descriptive modeling tool, (2) regression as a parametric adjustment technique for estimating causal effects, and (3) regression as a matching estimator of causal effects. We give more attention to the third of these three perspectives on regression than is customary in methodological texts because this perspective allows one to understand the others from a counterfactual perspective.