Are urinary porphyrins a valid diagnostic biomarker of autism spectrum disorder?

A fundamental challenge to the timely diagnosis of Autism Spectrum Disorder (ASD) is the reliance on the observation of a set of aberrant behavior. Consequently, the diagnostic process requires that the child reach an age where the behaviors would typically be exhibited. The identification of a reliable biological marker (biomarker) could be of considerable benefit to the diagnostic process. As a diagnostic biomarker, porphyrins present an attractive prospect as previous studies have reported consistent findings of children with ASD showing significant elevations in porphyrin levels in contrast to controls. Furthermore, there is some evidence that ASD severity may be associated with porphyrins, which would be a valuable characteristic of any ASD biomarker. Importantly, for practical use, porphyrins can be tested non-invasively via a sample of urine. The present study sought to investigate whether porphyrin profiles can reliably be used to (a) differentiate ASD cases from healthy controls; and (b) predict ASD severity. The study compared the porphyrin levels of three groups of children aged 2-6 years: Group 1-children diagnosed with ASD (n = 70); Group 2-healthy, normally developing siblings of children diagnosed with ASD (n = 36); and Group 3-healthy, normally developing children with no known blood relative diagnosed with ASD (n = 54). The results of logistic regression analyses failed to find support for the hypotheses that porphyrin levels could be used as a valid tool to detect ASD cases or predict severity. Autism Res 2014, 7: 535-542. © 2014 International Society for Autism Research, Wiley Periodicals, Inc.

Depression and suicidal behavior in adolescents : a multi-informant and multi-methods approach to diagnostic classification

Informant discrepancies have been reported between parent and adolescent measures of depressive disorders and suicidality. We aimed to examine the concordance between adolescent and parent ratings of depressive disorder using both clinical interview and questionnaire measures and assess multi-informant and multi-method approaches to classification.

Interventions to reduce medication errors in pediatric intensive care

To systematically examine the research literature to identify which interventions reduce medication errors in pediatric intensive care units.

Medication errors in ED: do patient characteristics and the environment influence the nature and frequency of medication errors?

Background: Medication safety is of increasing importance and understanding the nature and frequency of medication errors in the Emergency Department (ED) will assist in tailoring interventions which will make patient care safer. The challenge with the literature to date is the wide variability in the frequency of errors reported and the reliance on incident reporting practices of busy ED staff. Methods: A prospective, exploratory descriptive design using point prevalence surveys was used to establish the frequency of observed medication errors in the ED. In addition, data related to contextual factors such as ED patients, staffing and workload were also collected during the point prevalence surveys to enable the analysis of relationships between the frequency and nature of specific error types and patient and ED characteristics at the time of data collection. Results: A total of 172 patients were included in the study: 125 of whom patients had a medication chart. The prevalence of medication errors in the ED studied was 41.2% for failure to apply patient ID bands, 12.2% for failure to document allergy status and 38.4% for errors of omission. The proportion of older patients in the ED did not affect the frequency of medication errors. There was a relationship between high numbers of ATS 1, 2 and 3 patients (indicating high levels of clinical urgency) and increased rates of failure to document allergy status. Medication errors were affected by ED occupancy, when cubicles in the ED were over 50% occupied, medication errors occurred more frequently. ED staffing affects the frequency of medication errors, there was an increase in failure to apply ID bands and errors of omission when there were unfilled nursing deficits and lower levels of senior medical staff were associated with increased errors of omission. Conclusions: Medication errors related to patient identification, allergy status and medication omissions occur more frequently in the ED when the ED is busy, has sicker patients and when the staffing is not at the minimum required staffing levels.

Medication errors in hospitalised children

This study aims to explore the characteristics of reported medication errors occurring among children in an Australian children's hospital, and to examine the types, causes and contributing factors of medication errors.

Effectiveness of an electronic inpatient medication record in reducing medication errors in Singapore

This study examined the effectiveness of an inpatient electronic medication record system in reducing medication errors in Singaporean hospitals. This pre- and post-intervention study involving a control group was undertaken in two Singaporean acute care hospitals. In one hospital the inpatient electronic medication record system was implemented while in another hospital the paper-based medication record system was used. The mean incidence difference in medication errors of 0.06 between pre-intervention (0.72 per 1000 patient days) and post-intervention (0.78 per 1000 patient days) for the two hospitals was not statistically significant (95%, CI: [0.26, 0.20]). The mean incidence differences in medication errors relating to prescription, dispensing, and administration were also not statistically different. Common system failures involved a lack of medication knowledge by health professionals and a lack of a systematic approach in identifying correct dosages. There was no difference in the incidence of medication errors following the introduction of the electronic medication record system. More work is needed on how this system can reduce medication error rates and improve medication safety. © 2013 Wiley Publishing Asia Pty Ltd.

Analysis of operating environments: A diagnostic model for linking science, society and policy for sustainability

Through analysis of the dynamics between science and decision-making, we argue that diagnosing fit-for purpose approaches to linking science and decision-making may be possible. Such diagnosis should enable identification of appropriate processes, institutions, objects (e.g. tools, information products) and relationships that can facilitate outcomes. We begin the paper by unsettling the traditional constructions that science must distance itself from debates about values and what is at stake, and so from policy making. Then, drawing from mixed methods case studies in coastal South-eastern Australia, we describe how scientific research has had a bearing on decisions affecting society and the environment. These analyses suggest that the willingness and capacity of research organisations, programmes or projects to actively reflect on and participate in the evolution of the 'operating environment' for their research is integral to their ability to inform outcomes through science. © 2014 Elsevier Ltd.

Symptom screening scales for detecting major depressive disorder in children and adolescents: A systematic review and meta-analysis of reliability, validity and diagnostic utility.

Depression symptom screening scales are often used to determine a clinical diagnosis of major depressive disorder (MDD) in prevention research. The aim of this review is to systematically examine the reliability, validity and diagnostic utility of commonly used screening scales in depression prevention research among children and adolescents.

On the choice of test for a unit root when the errors are conditionally heteroskedastic

It is well known that in the context of the classical regression model with heteroskedastic errors, while ordinary least squares (OLS) is not efficient, the weighted least squares (WLS) and quasi-maximum likelihood (QML) estimators that utilize the information contained in the heteroskedasticity are. In the context of unit root testing with conditional heteroskedasticity, while intuition suggests that a similar result should apply, the relative performance of the tests associated with the OLS, WLS and QML estimators is not well understood. In particular, while QML has been shown to be able to generate more powerful tests than OLS, not much is known regarding the relative performance of the WLS-based test. By providing an in-depth comparison of the tests, the current paper fills this gap in the literature.

The asymptotic distribution of the CADF unit root test in the presence of heterogeneous AR(p) errors

The CADF test of Pesaran (J Appl Econ 22:265–312, 2007) are among the most popular univariate tests for cross-section correlated panels around. Yet, the existing asymptotic analysis of this test statistic is limited to a model in which the errors are assumed to follow a simple AR(1) structure with homogenous autoregressive coefficients. One reason for this is that the model involves an intricate identification issue, as both the serial and cross-section correlation structures of the errors are unobserved. The purpose of the current paper is to tackle this issue and in so doing extend the existing analysis to the case of AR((Formula presented.)) errors with possibly heterogeneous coefficients.

A comprehensive diagnostic framework for evaluating business intelligence and analytics effectiveness

Business intelligence and analytics (BIA) initiatives are costly, complex and experience high failure rates. Organizations require effective approaches to evaluate their BIA capabilities in order to develop strategies for their evolution. In this paper, we employ a design scienceparadigm to develop a comprehensive BIA effectiveness diagnostic (BIAED) framework that can be easily operationalized. We propose that a useful BIAED framework must assess the correct factors, should be deployed in the proper process context and acquire the appropriateinput from different constituencies within an organization. Drawing on the BIAED framework, we further develop an online diagnostic toolkit that includes a comprehensive survey instrument. We subsequently deploy the diagnostic mechanism within three large organizations in North America (involving over 1500 participants) and use the results toinform BIA strategy formulation. Feedback from participating organizations indicates that the BIA diagnostic toolkit provides insights that are essential inputs to strategy development. This work addresses a significant research gap in the area of BIA effectiveness assessment.