Variation in the type and frequency of postoperative invasive Staphylococcus aureus infections according to type of surgical procedure.

OBJECTIVE: To determine the epidemiological characteristics of postoperative invasive Staphylococcus aureus infection following 4 types of major surgical procedures.design. Retrospective cohort study. SETTING: Eleven hospitals (9 community hospitals and 2 tertiary care hospitals) in North Carolina and Virginia. PATIENTS: Adults undergoing orthopedic, neurosurgical, cardiothoracic, and plastic surgical procedures. METHODS: We used previously validated, prospectively collected surgical surveillance data for surgical site infection and microbiological data for bloodstream infection. The study period was 2003 through 2006. We defined invasive S. aureus infection as either nonsuperficial incisional surgical site infection or bloodstream infection. Nonparametric bootstrapping was used to generate 95% confidence intervals (CIs). P values were generated using the Pearson chi2 test, Student t test, or Wilcoxon rank-sum test, as appropriate. RESULTS: In total, 81,267 patients underwent 96,455 procedures during the study period. The overall incidence of invasive S. aureus infection was 0.47 infections per 100 procedures (95% CI, 0.43-0.52); 227 (51%) of 446 infections were due to methicillin-resistant S.aureus. Invasive S. aureus infection was more common after cardiothoracic procedures (incidence, 0.79 infections per 100 procedures [95%CI, 0.62-0.97]) than after orthopedic procedures (0.37 infections per 100 procedures [95% CI, 0.32-0.42]), neurosurgical procedures (0.62 infections per 100 procedures [95% CI, 0.53-0.72]), or plastic surgical procedures (0.32 infections per 100 procedures [95% CI, 0.17-0.47]) (P < .001). Similarly, S. aureus bloodstream infection was most common after cardiothoracic procedures (incidence, 0.57 infections per 100 procedures [95% CI, 0.43-0.72]; P < .001, compared with other procedure types), comprising almost three-quarters of the invasive S. aureus infections after these procedures. The highest rate of surgical site infection was observed after neurosurgical procedures (incidence, 0.50 infections per 100 procedures [95% CI, 0.42-0.59]; P < .001, compared with other procedure types), comprising 80% of invasive S.aureus infections after these procedures. CONCLUSION: The frequency and type of postoperative invasive S. aureus infection varied significantly across procedure types. The highest risk procedures, such as cardiothoracic procedures, should be targeted for ongoing preventative interventions.

I'll have what she's having: Effects of social influence and body type on the food choices of others

This research examines how the body type of consumers affects the food consumption of other consumers around them. We find that consumers anchor on the quantities others around them select but that these portions are adjusted according to the body type of the other consumer. We find that people choose a larger portion following another consumer who first selects a large quantity but that this portion is significantly smaller if the other is obese than if she is thin. We also find that the adjustment is more pronounced for consumers who are low in appearance self-esteem and that it is attenuated under cognitive load. © 2009 by Journal of Consumer Research, Inc.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.