Visualising a state-wide patient data collection : a case study to expand the audience for healthcare data

This paper describes the application of existing and novel adaptations of visualisation techniques to routinely collected health data. The aim of this case study is to examine the capacity for visualisation approaches to quickly and e ectively inform clinical, policy, and scal decision making to improve healthcare provision. We demonstrate the use of interactive graphics, fluctuation plots, mosaic plots, time plots, heatmaps, and disease maps to visualise patient admission, transfer, in-hospital mortality, morbidity coding, execution of diagnosis and treatment guidelines, and the temporal and spatial variations of diseases. The relative e ectiveness of these techniques and associated challenges are discussed.

A nonparametric Bayesian Poisson Gamma model for count data

We propose a nonparametric Bayesian, linear Poisson gamma model for count data and use it for dictionary learning. A key property of this model is that it captures the parts-based representation similar to nonnegative matrix factorization. We present an auxiliary variable Gibbs sampler, which turns the intractable inference into a tractable one. Combining this inference procedure with the slice sampler of Indian buffet process, we show that our model can learn the number of factors automatically. Using synthetic and real-world datasets, we show that the proposed model outperforms other state-of-the-art nonparametric factor models.

An Iranian migrant mother's production of selves through her family photograph collection

In researching migrant identities, visual methodology offers much promise and yet there is a marked lack of recent research that makes use of visual methods. Previous studies of migrant identities have privileged verbal representations of identities. Gillian Rose's (2010) work with mothers and their family photographs in the United Kingdom, in which she describes the role of family photographs in both producing social subject positions and in maintaining family togetherness across distances, is a useful model for research into the construction and negotiation of migrant identities.

The literature on family photography suggests that it is usually the woman/mother who takes on the role of making the family photograph album; of narrating the family's story (Rose, 2010; Holland, 2009; 1991; Chambers, 2003). The family photograph collection, together with the participant's interwoven verbal interpretation, is a particularly relevant data source for use in identity research as there is the potential for key themes of place, mobilities and space to be explored at new depth and from a feminist perspective.

This paper will report on an ethics approved study of one Iranian migrant mother and her family photograph collection, focusing on her representation of the identities and subjectivities of herself and her children through photographs taken following migration to Australia. The paper will consider the participant's family photographs as both visual objects and visual-verbal narratives produced within historically and culturally situated discourses. It will explore how photographs and oral interpretations cohere to enable migrant mothers to re/produce selves. The paper will examine the production of subject positions specifically in relation to place, mobilities and space.

The research is situated within critical visual ethnography and is informed by a reflexive feminist approach. The meaning making of the photographs under study will be explored at the sites of production, image, and audience. The combined visual-verbal methods used in this study aim to provide a new contribution to the literature on migrant identities and form the basis of a scaled up research design of a larger cohort of mothers.

Hipsync screendance collection 1993–2012

An exhibition on 3 screens of 26 screendance works created by Dianne Reid between 1993-2012. Two paintings by visual artist/dancer Melinda Smithwere also hung in the gallery space with the screen works.

Measurement of immunoglobulin A in saliva by particle-enhanced nephelometric immunoassay: sample collection, limits of quantitation, precision, stability and reference range

Background: Total immunoglobulin A in saliva (s-IgA) is normally assayed using an enzyme-linked immunosorbent assay. We have investigated methodological issues relating to the use of particle-enhanced nephelometric immunoassay (PENIA)
to measure s-IgA in whole unstimulated saliva and determine its reference range.

Methods: Whole unstimulated resting saliva was collected to determine sample stability (temperature, time, effect of a protease inhibitor), limit of quantitation (LOQ), assay precision and analytical variation. The reference range for 134 healthy adults was determined.

Results: Linearity was excellent (4–10.3 mg L21, P, 0.001; R2 ¼ 0.997) and without significant bias (mean of 20.7%). The lowest intra- and inter-analytical coefficients of variation were 1.8% and 7.5% and LOQ was 1.4 mg L21. The concentration of s-IgA is stable at room temperature for up to 6 h, at 48C for 48 h, at 248C for two weeks and at 2808C for up to 1.3 yr. There is no evidence that a protease inhibitor increases the stability or that repeated freeze–thawing cycles degrade sample quality. The reference ranges for s-IgA concentration, s-IgA secretion, s-IgA:albumin and s-IgA:osmolality were 15.9–414.5 mg L21, 7.2–234.9 mg min21, 0.4–19 and 0.6–8.9, respectively.

Conclusion: Automated PENIA assay of s-IgA is precise and accurate. High stability of collected saliva samples and the ease and speed of the assay make this an ideal method for use in athletic and military training situations. The convenience of measuring albumin and IgA on the same analytical platform adds to the practicability of the test.

In situ osmometry : validation and effect of sample collection technique

Purpose: To compare tear film osmolarity measurements between in situ and vapor pressure osmometers. Repeatability of in situ measurements and the effect of sample collection techniques on tear film osmolarity were also evaluated.

Methods: Osmolarity was measured in one randomly determined eye of 52 healthy participants using the in situ (TearLab Corporation, San Diego, CA) and the vapor pressure (Vapro 5520; Wescor, Inc., Logan, UT) osmometers. In a subset of 20 participants, tear osmolarity was measured twice on-eye with the in situ osmometer and was additionally determined on a sample of nonstimulated collected tears (3 µL) with both instruments.

Results: Mean (SD) tear film osmolarity with the in situ osmometer was 299.2 (10.3) mOsmol/L compared with 298.4 (10) mmol/kg with the vapor pressure osmometer, which correlated moderately (r = 0.5, P < 0.05). Limits of agreement between the two instruments were -19.7 to +20.5 mOsmol/L. Using collected tears, measurements with the vapor pressure osmometer were marginally higher (mean [SD], 303.0 [11.0] vs 299.3 [8.0] mOsmol/L; P > 0.05) but correlated well with those using the in situ osmometer (r = 0.9, P < 0.05). The mean (SD) osmolarity of on-eye tears was 5.0 (6.6) mOsmol/L higher than that of collected tears, when both measurements were conducted with the in situ osmometer. This was a consistent effect because the measurements correlated well (r = 0.65, P < 0.05).The in situ osmometer showed good repeatability with a coefficient of repeatability of 9.4 mOsmol/L (r = 0.8, P < 0.05).

Conclusions: Correlation between the two instruments was better when compared on collected tear samples. Tear film osmolarity measurement is influenced by the sample collection technique with the osmolarity of on-eye tears being higher than that of collected tears. This highlights the importance of measuring tear film osmolarity directly on-eye. The in situ osmometer has good repeatability for conducting this measurement.

Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach

This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference.