The power of vivid experience in hand hygiene compliance

Summary In recent years, explicit behavioural theories have been used insome research into hand hygiene behaviour. One of the most prominent ofthese has been the theory of planned behaviour (TPB). In this qualitativestudy aimed at increasing understanding of infection prevention practicein the acute care setting, TPB was identified as a suitable framework forthe emergence of new insights that have the potential to improve thepower of existing education and training. The theory emerging from the researchwas based on a finding that individual experience is of greater importthan formal education in explaining hand hygiene behaviour. Thisindicated that exposure to vivid vicarious experience is a potential meansto improving the power of existing training methods and increasing the propensityfor instilling sustainable adequate hand hygiene habits

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia

<p>Abstract</p> <p>Background</p> <p>The automated monitoring of routinely collected disease surveillance data has the potential to ensure that important changes in disease incidence are promptly recognised. However, few studies have established whether the signals produced by automated monitoring methods correspond with events considered by epidemiologists to be of public health importance. This study investigates the correspondence between retrospective epidemiological evaluation of notifications of Ross River virus (RRv) disease in Western Australia, and the signals produced by two cumulative sum (cusum)-based automated monitoring methods.</p> <p>Methods</p> <p>RRv disease case notification data between 1991 and 2004 were assessed retrospectively by two experienced epidemiologists, and the timing of identified outbreaks was compared with signals generated from two different types of cusum-based automated monitoring algorithms; the three Early Aberration Reporting System (EARS) cusum algorithms (C1, C2 and C3), and a negative binomial cusum.</p> <p>Results</p> <p>We found the negative binomial cusum to have a significantly greater area under the receiver operator characteristic curve when compared with the EARS algorithms, suggesting that the negative binomial cusum has a greater level of agreement with epidemiological opinion than the EARS algorithms with respect to the existence of outbreaks of RRv disease, particularly at low false alarm rates. However, the performance of individual EARS and negative binomial cusum algorithms were not significantly different when timeliness was also incorporated into the area under the curve analyses.</p> <p>Conclusion</p> <p>Our retrospective analysis of historical data suggests that, compared with the EARS algorithms, the negative binomial cusum provides greater sensitivity for the detection of outbreaks of RRv disease at low false alarm levels, and decreased timeliness early in the outbreak period. Prospective studies are required to investigate the potential usefulness of these algorithms in practice.</p

Differential effects of radiant and mechanically applied thermal stimuli on human C-tactile afferent firing patterns

International audienceC-tactile (CT) afferents respond to gentle tactile stimulation, but only a handful of studies in humans and animals have investigated whether their firing is modified by temperature. We describe the effects of radiant thermal stimuli, and of stationary and very slowly moving mechanothermal stimuli, on CT afferent responses. We find that CT afferents are primarily mechanoreceptors, as they fired little during radiant thermal stimuli, but they exhibited different patterns of firing during combined mechano-cool stimulation compared with warming. CTs fired optimally to gentle, very slowly moving, or stationary mechanothermal stimuli delivered at neutral temperature (~32°C, normal skin temperature), but they responded with fewer spikes (median 67% decrease) and at significantly lower rates (47% decrease) during warm (~42°C) tactile stimuli. During cool tactile stimuli (~18°C), their mean instantaneous firing frequency significantly decreased by 35%, but they often fired a barrage of afterdischarge spikes at a low frequency (~5 Hz) that outlasted the mechanical stimulus. These effects were observed under a variety of stimulus conditions, including during stationary and slowly moving touch (0.1 cm/s), and we complemented these tactile approaches using a combined electrical-thermal stimulation experiment where we found a suppression of spiking during warming. Overall, CT afferents are exquisitely sensitive to tactile events, and we show that their firing is modulated with touch temperatures above and below neutral skin temperature. Warm touch consistently decreased their propensity to fire, whereas cool touch produced lower firing rates but afterdischarge spiking

Pediatric hospital admissions in Indigenous children: a population-based study in remote Australia

Background: We analysed hospital admissions of a predominantly Aboriginal cohort of children in the remote Fitzroy Valley in Western Australia during the first 7 years of life. Methods: All children born between January 1, 2002 and December 31, 2003 and living in the Fitzroy Valley in 2009-2010 were eligible to participate in the Lililwan Project. Of 134 eligible children, 127 (95%) completed Stage 1 (interviews of caregivers and medical record review) in 2011 and comprised our cohort. Lifetime (0-7 years) hospital admission data were available and included the dates, and reasons for admission, and comorbidities. Conditions were coded using ICD-10-AM discharge codes. Results: Of the 127 children, 95.3% were Indigenous and 52.8% male. There were 314 admissions for 424 conditions in 89 (70.0%) of 127 children. The 89 children admitted had a median of five admissions (range 1-12). Hospitalization rates were similar for both genders (p = 0.4). Of the admissions, 108 (38.6%) were for 56 infants aged <12 months (median = 2.5, range = 1-8). Twelve of these admissions were in neonates (aged 0-28 days). Primary reasons for admission (0-7 years) were infections of the lower respiratory tract (27.4%), gastrointestinal system (22.7%), and upper respiratory tract (11.4%), injury (7.0%), and failure to thrive (5.4%). Comorbidities, particularly upper respiratory tract infections (18.1%), failure to thrive (13.6%), and anaemia (12.7%), were common. In infancy, primary cause for admission were infections of the lower respiratory tract (40.8%), gastrointestinal (25.9%) and upper respiratory tract (9.3%). Comorbidities included upper respiratory tract infections (33.3%), failure to thrive (18.5%) and anaemia (18.5%). Conclusion: In the Fitzroy Valley 70.0% of children were hospitalised at least once before age 7 years and over one third of admissions were in infants. Infections were the most common reason for admission in all age groups but comorbidities were common and may contribute to need for admission. Many hospitalizations were feasibly preventable. High admission rates reflect disadvantage, remote location and limited access to primary healthcare and outpatient services. Ongoing public health prevention initiatives including breast feeding, vaccination, healthy diet, hygiene and housing improvements are crucial, as is training of Aboriginal Health Workers to increase services in remote communities.The Lililwan project is supported by the National Health and Medical Research Council of Australia (NHMRC) (Elizabeth Elliott, Practitioner Fellowships 457,084 and 1,021,480, and project grant 1,024,474); the Australian Research Council (Jane Latimer, Future Fellowship 0130007); the Australian Government Departments of Health and Ageing (DoHA); and Families, Housing, Community Services and Indigenous Affairs (FaHCSIA); Save the Children Australia, the Foundation for Alcohol Research and Education and the University of Sydney Poche Institute (Philippa Dossetor, Poche Scholarship). Pro bono support has been provided by M&C Saatchi, Blake Dawson solicitors, and the Australian Human Rights Commission. Alexandra Martiniuk is funded by an NHMRC TRIP (Translating Research into Practice) Fellowship (2016–2018). Philippa Dossetor is supported by a parttime PhD scholarship through the Australian National University Medical School and the College of Biology, Medicine and the Environment

Disease surveillance using a hidden Markov model

<p>Abstract</p> <p>Background</p> <p>Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data.</p> <p>Methods</p> <p>A Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS) algorithms and a negative binomial cusum.</p> <p>Results</p> <p>Algorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms.</p> <p>Conclusion</p> <p>Our findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.</p

Approaches to the evaluation of outbreak detection methods

BACKGROUND: An increasing number of methods are being developed for the early detection of infectious disease outbreaks which could be naturally occurring or as a result of bioterrorism; however, no standardised framework for examining the usefulness of various outbreak detection methods exists. To promote comparability between studies, it is essential that standardised methods are developed for the evaluation of outbreak detection methods. METHODS: This analysis aims to review approaches used to evaluate outbreak detection methods and provide a conceptual framework upon which recommendations for standardised evaluation methods can be based. We reviewed the recently published literature for reports which evaluated methods for the detection of infectious disease outbreaks in public health surveillance data. Evaluation methods identified in the recent literature were categorised according to the presence of common features to provide a conceptual basis within which to understand current approaches to evaluation. RESULTS: There was considerable variation in the approaches used for the evaluation of methods for the detection of outbreaks in public health surveillance data, and appeared to be no single approach of choice. Four main approaches were used to evaluate performance, and these were labelled the Descriptive, Derived, Epidemiological and Simulation approaches. Based on the approaches identified, we propose a basic framework for evaluation and recommend the use of multiple approaches to evaluation to enable a comprehensive and contextualised description of outbreak detection performance. CONCLUSION: The varied nature of performance evaluation demonstrated in this review supports the need for further development of evaluation methods to improve comparability between studies. Our findings indicate that no single approach can fulfil all evaluation requirements. We propose that the cornerstone approaches to evaluation identified provide key contributions to support internal and external validity and comparability of study findings, and suggest these be incorporated into future recommendations for performance assessment

RESEARCH Open Access

Involving consumers and the community in the development of a diagnostic instrument for fetal alcohol spectrum disorders in Australi

Association of fluorescent protein pairs and it's significant impact on fluorescence and energy transfer

Fluorescent proteins (FPs) are commonly used in pairs to monitor dynamic biomolecular events through changes in proximity via distance dependent processes such as Förster resonance energy transfer (FRET). The impact of FP association is assessed by predicting dimerization sites in silico and stabilizing the dimers by bio‐orthogonal covalent linkages. In each tested case dimerization changes inherent fluorescence, including FRET. GFP homodimers demonstrate synergistic behavior with the dimer being brighter than the sum of the monomers. The homodimer structure reveals the chromophores are close with favorable transition dipole alignments and a highly solvated interface. Heterodimerization (GFP with Venus) results in a complex with ≈87% FRET efficiency, significantly below the 99.7% efficiency predicted. A similar efficiency is observed when the wild‐type FPs are fused to a naturally occurring protein–protein interface system. GFP complexation with mCherry results in loss of mCherry fluorescence. Thus, simple assumptions used when monitoring interactions between proteins via FP FRET may not always hold true, especially under conditions whereby the protein–protein interactions promote FP interaction

Using GIS to create synthetic disease outbreaks

BACKGROUND: The ability to detect disease outbreaks in their early stages is a key component of efficient disease control and prevention. With the increased availability of electronic health-care data and spatio-temporal analysis techniques, there is great potential to develop algorithms to enable more effective disease surveillance. However, to ensure that the algorithms are effective they need to be evaluated. The objective of this research was to develop a transparent user-friendly method to simulate spatial-temporal disease outbreak data for outbreak detection algorithm evaluation. A state-transition model which simulates disease outbreaks in daily time steps using specified disease-specific parameters was developed to model the spread of infectious diseases transmitted by person-to-person contact. The software was developed using the MapBasic programming language for the MapInfo Professional geographic information system environment. RESULTS: The simulation model developed is a generalised and flexible model which utilises the underlying distribution of the population and incorporates patterns of disease spread that can be customised to represent a range of infectious diseases and geographic locations. This model provides a means to explore the ability of outbreak detection algorithms to detect a variety of events across a large number of stochastic replications where the influence of uncertainty can be controlled. The software also allows historical data which is free from known outbreaks to be combined with simulated outbreak data to produce files for algorithm performance assessment. CONCLUSION: This simulation model provides a flexible method to generate data which may be useful for the evaluation and comparison of outbreak detection algorithm performance