A simple statistical model for prediction of acute coronary syndrome in chest pain patients in the emergency department

BACKGROUND: Several models for prediction of acute coronary syndrome (ACS) among chest pain patients in the emergency department (ED) have been presented, but many models predict only the likelihood of acute myocardial infarction, or include a large number of variables, which make them less than optimal for implementation at a busy ED. We report here a simple statistical model for ACS prediction that could be used in routine care at a busy ED. METHODS: Multivariable analysis and logistic regression were used on data from 634 ED visits for chest pain. Only data immediately available at patient presentation were used. To make ACS prediction stable and the model useful for personnel inexperienced in electrocardiogram (ECG) reading, simple ECG data suitable for computerized reading were included. RESULTS: Besides ECG, eight variables were found to be important for ACS prediction, and included in the model: age, chest discomfort at presentation, symptom duration and previous hypertension, angina pectoris, AMI, congestive heart failure or PCI/CABG. At an ACS prevalence of 21% and a set sensitivity of 95%, the negative predictive value of the model was 96%. CONCLUSION: The present prediction model, combined with the clinical judgment of ED personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS

Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior

Renewable, ethical? Assessing the energy justice potential of renewable electricity

Energy justice is increasingly being used as a framework to conceptualize the impacts of energy decision making in more holistic ways and to consider the social implications in terms of existing ethical values. Similarly, renewable energy technologies are increasingly being promoted for their environmental and social benefits. However, little work has been done to systematically examine the extent to which, in what ways and in what contexts, renewable energy technologies can contribute to achieving energy justice. This paper assesses the potential of renewable electricity technologies to address energy justice in various global contexts via a systematic review of existing studies analyzed in terms of the principles and dimensions of energy justice. Based on publications including peer reviewed academic literature, books, and in some cases reports by government or international organizations, we assess renewable electricity technologies in both grid integrated and off-grid use contexts. We conduct our investigation through the rubric of the affirmative and prohibitive principles of energy justice and in terms of its temporal, geographic, socio-political, economic, and technological dimensions. Renewable electricity technology development has and continue to have different impacts in different social contexts, and by considering the different impacts explicitly across global contexts, including differences between rural and urban contexts, this paper contributes to identifying and understanding how, in what ways, and in what particular conditions and circumstances renewable electricity technologies may correspond with or work to promote energy justice

Reliability of triclosan measures in repeated urine samples from Norwegian pregnant women

Triclosan (TCS) is a synthetic antibacterial chemical that is used in personal care products and is measurable in urine. Urinary TCS has been associated with allergy in children in Norway and the United States. A reasonable degree of temporal reliability of TCS urinary concentrations has been reported among U.S. children as well as for Puerto Rican pregnant women. We examined the reliability of TCS measures in urine among Norwegian pregnant women. Triclosan was measured in spot urine samples collected in gestational weeks 17, 23, and 29 from 45 women in The Norwegian Mother and Child Cohort Study (MoBa) enrolled in 2007 and 2008. Spearman’s rank correlation coefficient (r(s)) and intraclass correlation coefficient (ICC) statistics were calculated. Fifty-six percent of the 45 women had a least one sample with a value above the method limit of detection (2.3 µg/L). The correlation coefficients were 0.61 for TCS concentrations at 17 and 23 weeks and 0.49 for concentrations at 17 and 29 weeks. For the three time points, the ICC was 0.49. The reliability of TCS concentrations in repeated urine samples from pregnant Norwegian women was reasonably good, suggesting a single urine sample can adequately represent TCS exposure during pregnancy