On the nature of over-dispersion in motor vehicle crash prediction models

Statistical modeling of traffic crashes has been of interest to researchers for decades. Over the most recent decade many crash models have accounted for extra-variation in crash counts—variation over and above that accounted for by the Poisson density. The extra-variation – or dispersion – is theorized to capture unaccounted for variation in crashes across sites. The majority of studies have assumed fixed dispersion parameters in over-dispersed crash models—tantamount to assuming that unaccounted for variation is proportional to the expected crash count. Miaou and Lord [Miaou, S.P., Lord, D., 2003. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes methods. Transport. Res. Rec. 1840, 31–40] challenged the fixed dispersion parameter assumption, and examined various dispersion parameter relationships when modeling urban signalized intersection accidents in Toronto. They suggested that further work is needed to determine the appropriateness of the findings for rural as well as other intersection types, to corroborate their findings, and to explore alternative dispersion functions. This study builds upon the work of Miaou and Lord, with exploration of additional dispersion functions, the use of an independent data set, and presents an opportunity to corroborate their findings. Data from Georgia are used in this study. A Bayesian modeling approach with non-informative priors is adopted, using sampling-based estimation via Markov Chain Monte Carlo (MCMC) and the Gibbs sampler. A total of eight model specifications were developed; four of them employed traffic flows as explanatory factors in mean structure while the remainder of them included geometric factors in addition to major and minor road traffic flows. The models were compared and contrasted using the significance of coefficients, standard deviance, chi-square goodness-of-fit, and deviance information criteria (DIC) statistics. The findings indicate that the modeling of the dispersion parameter, which essentially explains the extra-variance structure, depends greatly on how the mean structure is modeled. In the presence of a well-defined mean function, the extra-variance structure generally becomes insignificant, i.e. the variance structure is a simple function of the mean. It appears that extra-variation is a function of covariates when the mean structure (expected crash count) is poorly specified and suffers from omitted variables. In contrast, when sufficient explanatory variables are used to model the mean (expected crash count), extra-Poisson variation is not significantly related to these variables. If these results are generalizable, they suggest that model specification may be improved by testing extra-variation functions for significance. They also suggest that known influences of expected crash counts are likely to be different than factors that might help to explain unaccounted for variation in crashes across sites

Diarrhoea risk factors in enterally tube fed critically ill patients : a retrospective audit

Objective: Diarrhoea in the enterally tube fed (ETF) intensive care unit (ICU) patient is a multifactorial problem. Diarrhoeal aetiologies in this patient cohort remain debatable; however, the consequences of diarrhoea have been well established and include electrolyte imbalance, dehydration, bacterial translocation, peri anal wound contamination and sleep deprivation. This study examined the incidence of diarrhoea and explored factors contributing to the development of diarrhoea in the ETF, critically ill, adult patient. ---------- Method: After institutional ethical review and approval, a single centre medical chart audit was undertaken to examine the incidence of diarrhoea in ETF, critically ill patients. Retrospective, non-probability sequential sampling was used of all emergency admission adult ICU patients who met the inclusion/exclusion criteria. ---------- Results: Fifty patients were audited. Faecal frequency, consistency and quantity were considered important criteria in defining ETF diarrhoea. The incidence of diarrhoea was 78%. Total patient diarrhoea days (r = 0.422; p = 0.02) and total diarrhoea frequency (r = 0.313; p = 0.027) increased when the patient was ETF for longer periods of time. Increased severity of illness, peripheral oxygen saturation (Sp02), glucose control, albumin and white cell count were found to be statistically significant factors for the development of diarrhoea. ---------- Conclusion: Diarrhoea in ETF critically ill patients is multi-factorial. The early identification of diarrhoea risk factors and the development of a diarrhoea risk management algorithm is recommended.

Using data mining on road asset management data in analysing road crashes

Road safety is a major concern worldwide. Road safety will improve as road conditions and their effects on crashes are continually investigated. This paper proposes to use the capability of data mining to include the greater set of road variables for all available crashes with skid resistance values across the Queensland state main road network in order to understand the relationships among crash, traffic and road variables. This paper presents a data mining based methodology for the road asset management data to find out the various road properties that contribute unduly to crashes. The models demonstrate high levels of accuracy in predicting crashes in roads when various road properties are included. This paper presents the findings of these models to show the relationships among skid resistance, crashes, crash characteristics and other road characteristics such as seal type, seal age, road type, texture depth, lane count, pavement width, rutting, speed limit, traffic rates intersections, traffic signage and road design and so on.

Using data mining to predict road crash count with a focus on skid resistance values

Road crashes cost world and Australian society a significant proportion of GDP, affecting productivity and causing significant suffering for communities and individuals. This paper presents a case study that generates data mining models that contribute to understanding of road crashes by allowing examination of the role of skid resistance (F60) and other road attributes in road crashes. Predictive data mining algorithms, primarily regression trees, were used to produce road segment crash count models from the road and traffic attributes of crash scenarios. The rules derived from the regression trees provide evidence of the significance of road attributes in contributing to crash, with a focus on the evaluation of skid resistance.

The three little pigs and the art of playfulness

A number of pictorial based texts for children use animals as models for displaying or approaching aspects of childhood. Although authors and illustrators utilise various tactics for including anthropomorphic animals in their books, those that are used as 'surrogate' children can be seen to focus in the main on issues of behaviour, socialisation and maturity - issues that reflect the everyday life of the growing child. This paper aims to explore three pictorial texts that specifically utilise the pig character as a child model, to facilitate for authors/illustrators the opportunity to deal with examples of childhood experience. The paper also tentatively examines how such roles might encourage a reassessment of other more stereotypical associations some audiences have historically/culturally formed about the pig.

Partial protection against chlamydial reproductive tract infection by a recombinant major outer membrane protein/CpG/cholera toxin intranasal vaccine in the guinea pig Chlamydia cavaie model

Chlamydia trachomatis is a major cause of sexually transmitted diseases worldwide. There currently is no vaccine to protect against chlamydial infection of the female reproductive tract. Vaccine development has predominantly involved using the murine model, however infection of female guinea pigs with Chlamydia caviae more closely resembles chlamydial infection of the human female reproductive tract, and presents a better model to assess potential human chlamydial vaccines. We immunised female guinea pigs intranasally with recombinant major outer membrane protein (r-MOMP) combined with CpG-10109 and cholera toxin adjuvants. Both systemic and mucosal immune responses were elicited in immunised animals. MOMP-specific IgG and IgA were present in the vaginal mucosae, and high levels of MOMP-specific IgG were detected in the serum of immunised animals. Antibodies from the vaginal mucosae were also shown to be capable of neutralising C. caviae in vitro. Following immunisation, animals were challenged intravaginally with a live C. caviae infection of 102 inclusion forming units. We observed a decrease in duration of infection and a significant (p<0.025) reduction in infection load in r-MOMP immunised animals, compared to animals immunised with adjuvant only. Importantly, we also observed a marked reduction in upper reproductive tract (URT) pathology in r-MOMP immunised animals. Intranasal immunisation of female guinea pigs with r-MOMP was able to provide partial protection against C. caviae infection, not only by reducing chlamydial burden but also URT pathology. This data demonstrates the value of using the guinea pig model to evaluate potential chlamydial vaccines for protection against infection and disease pathology caused by C. trachomatis in the female reproductive tract.

Quantification of particle emission characteristics and development of an emission model for use in transport microenvironments affected by traffic emissions

Vehicle emitted particles are of significant concern based on their potential to influence local air quality and human health. Transport microenvironments usually contain higher vehicle emission concentrations compared to other environments, and people spend a substantial amount of time in these microenvironments when commuting. Currently there is limited scientific knowledge on particle concentration, passenger exposure and the distribution of vehicle emissions in transport microenvironments, partially due to the fact that the instrumentation required to conduct such measurements is not available in many research centres. Information on passenger waiting time and location in such microenvironments has also not been investigated, which makes it difficult to evaluate a passenger’s spatial-temporal exposure to vehicle emissions. Furthermore, current emission models are incapable of rapidly predicting emission distribution, given the complexity of variations in emission rates that result from changes in driving conditions, as well as the time spent in driving condition within the transport microenvironment. In order to address these scientific gaps in knowledge, this work conducted, for the first time, a comprehensive statistical analysis of experimental data, along with multi-parameter assessment, exposure evaluation and comparison, and emission model development and application, in relation to traffic interrupted transport microenvironments. The work aimed to quantify and characterise particle emissions and human exposure in the transport microenvironments, with bus stations and a pedestrian crossing identified as suitable research locations representing a typical transport microenvironment. Firstly, two bus stations in Brisbane, Australia, with different designs, were selected to conduct measurements of particle number size distributions, particle number and PM2.5 concentrations during two different seasons. Simultaneous traffic and meteorological parameters were also monitored, aiming to quantify particle characteristics and investigate the impact of bus flow rate, station design and meteorological conditions on particle characteristics at stations. The results showed higher concentrations of PN20-30 at the station situated in an open area (open station), which is likely to be attributed to the lower average daily temperature compared to the station with a canyon structure (canyon station). During precipitation events, it was found that particle number concentration in the size range 25-250 nm decreased greatly, and that the average daily reduction in PM2.5 concentration on rainy days compared to fine days was 44.2 % and 22.6 % at the open and canyon station, respectively. The effect of ambient wind speeds on particle number concentrations was also examined, and no relationship was found between particle number concentration and wind speed for the entire measurement period. In addition, 33 pairs of average half-hourly PN7-3000 concentrations were calculated and identified at the two stations, during the same time of a day, and with the same ambient wind speeds and precipitation conditions. The results of a paired t-test showed that the average half-hourly PN7-3000 concentrations at the two stations were not significantly different at the 5% confidence level (t = 0.06, p = 0.96), which indicates that the different station designs were not a crucial factor for influencing PN7-3000 concentrations. A further assessment of passenger exposure to bus emissions on a platform was evaluated at another bus station in Brisbane, Australia. The sampling was conducted over seven weekdays to investigate spatial-temporal variations in size-fractionated particle number and PM2.5 concentrations, as well as human exposure on the platform. For the whole day, the average PN13-800 concentration was 1.3 x 104 and 1.0 x 104 particle/cm3 at the centre and end of the platform, respectively, of which PN50-100 accounted for the largest proportion to the total count. Furthermore, the contribution of exposure at the bus station to the overall daily exposure was assessed using two assumed scenarios of a school student and an office worker. It was found that, although the daily time fraction (the percentage of time spend at a location in a whole day) at the station was only 0.8 %, the daily exposure fractions (the percentage of exposures at a location accounting for the daily exposure) at the station were 2.7% and 2.8 % for exposure to PN13-800 and 2.7% and 3.5% for exposure to PM2.5 for the school student and the office worker, respectively. A new parameter, “exposure intensity” (the ratio of daily exposure fraction and the daily time fraction) was also defined and calculated at the station, with values of 3.3 and 3.4 for exposure to PN13-880, and 3.3 and 4.2 for exposure to PM2.5, for the school student and the office worker, respectively. In order to quantify the enhanced emissions at critical locations and define the emission distribution in further dispersion models for traffic interrupted transport microenvironments, a composite line source emission (CLSE) model was developed to specifically quantify exposure levels and describe the spatial variability of vehicle emissions in traffic interrupted microenvironments. This model took into account the complexity of vehicle movements in the queue, as well as different emission rates relevant to various driving conditions (cruise, decelerate, idle and accelerate), and it utilised multi-representative segments to capture the accurate emission distribution for real vehicle flow. This model does not only helped to quantify the enhanced emissions at critical locations, but it also helped to define the emission source distribution of the disrupted steady flow for further dispersion modelling. The model then was applied to estimate particle number emissions at a bidirectional bus station used by diesel and compressed natural gas fuelled buses. It was found that the acceleration distance was of critical importance when estimating particle number emission, since the highest emissions occurred in sections where most of the buses were accelerating and no significant increases were observed at locations where they idled. It was also shown that emissions at the front end of the platform were 43 times greater than at the rear of the platform. The CLSE model was also applied at a signalled pedestrian crossing, in order to assess increased particle number emissions from motor vehicles when forced to stop and accelerate from rest. The CLSE model was used to calculate the total emissions produced by a specific number and mix of light petrol cars and diesel passenger buses including 1 car travelling in 1 direction (/1 direction), 14 cars / 1 direction, 1 bus / 1 direction, 28 cars / 2 directions, 24 cars and 2 buses / 2 directions, and 20 cars and 4 buses / 2 directions. It was found that the total emissions produced during stopping on a red signal were significantly higher than when the traffic moved at a steady speed. Overall, total emissions due to the interruption of the traffic increased by a factor of 13, 11, 45, 11, 41, and 43 for the above 6 cases, respectively. In summary, this PhD thesis presents the results of a comprehensive study on particle number and mass concentration, together with particle size distribution, in a bus station transport microenvironment, influenced by bus flow rates, meteorological conditions and station design. Passenger spatial-temporal exposure to bus emitted particles was also assessed according to waiting time and location along the platform, as well as the contribution of exposure at the bus station to overall daily exposure. Due to the complexity of the interrupted traffic flow within the transport microenvironments, a unique CLSE model was also developed, which is capable of quantifying emission levels at critical locations within the transport microenvironment, for the purpose of evaluating passenger exposure and conducting simulations of vehicle emission dispersion. The application of the CLSE model at a pedestrian crossing also proved its applicability and simplicity for use in a real-world transport microenvironment.

Iron biogeochemistry and associated greenhouse gas evolution in a forested subtropical Australian coastal catchment : Poona Creek, Southeast Queensland

Iron (Fe) is the fourth most abundant element in the Earth’s crust. Excess Fe mobilization from terrestrial into aquatic systems is of concern for deterioration of water quality via biofouling and nuisance algal blooms in coastal and marine systems. Substantial Fe dissolution and transport involve alternate Fe(II) oxidation followed by Fe(III) reduction, with a diversity of Bacteria and Archaea acting as the key catalyst. Microbially-mediated Fe cycling is of global significance with regard to cycles of carbon (C), sulfur (S) and manganese (Mn). However, knowledge regarding microbial Fe cycling in circumneutral-pH habitats that prevail on Earth has been lacking until recently. In particular, little is known regarding microbial function in Fe cycling and associated Fe mobilization and greenhouse (CO2 and CH4, GHG) evolution in subtropical Australian coastal systems where microbial response to ambient variations such as seasonal flooding and land use changes is of concern. Using the plantation-forested Poona Creek catchment on the Fraser Coast of Southeast Queensland (SEQ), this research aimed to 1) study Fe cycling-associated bacterial populations in diverse terrestrial and aquatic habitats of a representative subtropical coastal circumneutral-pH (4–7) ecosystem; and 2) assess potential impacts of Pinus plantation forestry practices on microbially-mediated Fe mobilization, organic C mineralization and associated GHG evolution in coastal SEQ. A combination of wet-chemical extraction, undisturbed core microcosm, laboratory bacterial cultivation, microscopy and 16S rRNA-based molecular phylogenetic techniques were employed. The study area consisted primarily of loamy sands, with low organic C and dissolved nutrients. Total reactive Fe was abundant and evenly distributed within soil 0–30 cm profiles. Organic complexation primarily controlled Fe bioavailability and forms in well-drained plantation soils and water-logged, native riparian soils, whereas tidal flushing exerted a strong “seawater effect” in estuarine locations and formed a large proportion of inorganic Fe(III) complexes. There was a lack of Fe(II) sources across the catchment terrestrial system. Mature, first-rotation plantation clear-felling and second-rotation replanting significantly decreased organic matter and poorly crystalline Fe in well-drained soils, although variations in labile soil organic C fractions (dissolved organic C, DOC; and microbial biomass C, MBC) were minor. Both well-drained plantation soils and water-logged, native-vegetation soils were inhabited by a variety of cultivable, chemotrophic bacterial populations capable of C, Fe, S and Mn metabolism via lithotrophic or heterotrophic, (micro)aerobic or anaerobic pathways. Neutrophilic Fe(III)-reducing bacteria (FeRB) were most abundant, followed by aerobic, heterotrophic bacteria (heterotrophic plate count, HPC). Despite an abundance of FeRB, cultivable Fe(II)-oxidizing bacteria (FeOB) were absent in associated soils. A lack of links between cultivable Fe, S or Mn bacterial densities and relevant chemical measurements (except for HPC correlated with DOC) was likely due to complex biogeochemical interactions. Neither did variations in cultivable bacterial densities correlate with plantation forestry practices, despite total cultivable bacterial densities being significantly lower in estuarine soils when compared with well-drained plantation soils and water-logged, riparian native-vegetation soils. Given that bacterial Fe(III) reduction is the primary mechanism of Fe oxide dissolution in soils upon saturation, associated Fe mobilization involved several abiotic and biological processes. Abiotic oxidation of dissolved Fe(II) by Mn appeared to control Fe transport and inhibit Fe dissolution from mature, first-rotation plantation soils post-saturation. Such an effect was not observed in clear-felled and replanted soils associated with low SOM and potentially low Mn reactivity. Associated GHG evolution post-saturation mainly involved variable CO2 emissions, with low, but consistently increasing CH4 effluxes in mature, first-rotation plantation soil only. In comparison, water-logged soils in the riparian native-vegetation buffer zone functioned as an important GHG source, with high potentials for Fe mobilization and GHG, particularly CH4 emissions in riparian loam soils associated with high clay and crystalline Fe fractions. Active Fe–C cycling was unlikely to occur in lower-catchment estuarine soils associated with low cultivable bacterial densities and GHG effluxes. As a key component of bacterial Fe cycling, neutrophilic FeOB widely occurred in diverse aquatic, but not terrestrial, habitats of the catchment study area. Stalked and sheathed FeOB resembling Gallionella and Leptothrix were limited to microbial mat material deposited in surface fresh waters associated with a circumneutral-pH seep, and clay-rich soil within riparian buffer zones. Unicellular, Sideroxydans-related FeOB (96% sequence identity) were ubiquitous in surface and subsurface freshwater environments, with highest abundance in estuary-adjacent shallow coastal groundwater water associated with redox transition. The abundance of dissolved C and Fe in the groundwater-dependent system was associated with high numbers of cultivable anaerobic, heterotrophic FeRB, microaerophilic, putatively lithotrophic FeOB and aerobic, heterotrophic bacteria. This research represents the first study of microbial Fe cycling in diverse circumneutral-pH environments (terrestrial–aquatic, freshwater–estuarine, surface–subsurface) of a subtropical coastal ecosystem. It also represents the first study of its kind in the southern hemisphere. This work highlights the significance of bacterial Fe(III) reduction in terrestrial, and bacterial Fe(II) oxidation in aquatic catchment Fe cycling. Results indicate the risk of promotion of Fe mobilization due to plantation clear-felling and replanting, and GHG emissions associated with seasonal water-logging. Additional significant outcomes were also achieved. The first direct evidence for multiple biomineralization patterns of neutrophilic, microaerophilic, unicellular FeOB was presented. A putatively pure culture, which represents the first cultivable neutrophilic FeOB from the southern hemisphere, was obtained as representative FeOB ubiquitous in diverse catchment aquatic habitats.

An approximate Bayesian computation approach for estimating parameters of complex environmental processes in a cellular automata

Modelling an environmental process involves creating a model structure and parameterising the model with appropriate values to accurately represent the process. Determining accurate parameter values for environmental systems can be challenging. Existing methods for parameter estimation typically make assumptions regarding the form of the Likelihood, and will often ignore any uncertainty around estimated values. This can be problematic, however, particularly in complex problems where Likelihoods may be intractable. In this paper we demonstrate an Approximate Bayesian Computational method for the estimation of parameters of a stochastic CA. We use as an example a CA constructed to simulate a range expansion such as might occur after a biological invasion, making parameter estimates using only count data such as could be gathered from field observations. We demonstrate ABC is a highly useful method for parameter estimation, with accurate estimates of parameters that are important for the management of invasive species such as the intrinsic rate of increase and the point in a landscape where a species has invaded. We also show that the method is capable of estimating the probability of long distance dispersal, a characteristic of biological invasions that is very influential in determining spread rates but has until now proved difficult to estimate accurately.

Modeling random effect and excess zeros in road traffic accident prediction

Poisson distribution has often been used for count like accident data. Negative Binomial (NB) distribution has been adopted in the count data to take care of the over-dispersion problem. However, Poisson and NB distributions are incapable of taking into account some unobserved heterogeneities due to spatial and temporal effects of accident data. To overcome this problem, Random Effect models have been developed. Again another challenge with existing traffic accident prediction models is the distribution of excess zero accident observations in some accident data. Although Zero-Inflated Poisson (ZIP) model is capable of handling the dual-state system in accident data with excess zero observations, it does not accommodate the within-location correlation and between-location correlation heterogeneities which are the basic motivations for the need of the Random Effect models. This paper proposes an effective way of fitting ZIP model with location specific random effects and for model calibration and assessment the Bayesian analysis is recommended.

The effectiveness of supervisory boards : an exploratory study of challenges in Dutch boardrooms

Triggered by highly publicized corporate scandals, changing societal expectations and the collapse of financial markets, the roles of boards of directors have changed significantly in safeguarding the interest of shareholders and other stakeholders. Yet relatively little is known about contemporary challenges non-executive directors face and whether their boards are well-equipped for their new tasks. Based on self-assessment reports by supervisory boards, a survey and interviews with supervisory board members, this paper investigates the challenges non-executive directors face in the Netherlands, particularly after a decade of corporate governance reform. Non-executive directors’ inadequate role in scrutinizing executive directors’ performance, information asymmetries and dysfunctional working relationships between executive and non-executive directors are among the greatest challenges indicated by non-executive directors on Dutch supervisory boards. The paper discusses several implications for scholars and practitioners and provides a unique insight in boardroom dynamics (word count: 138).

Human papillomavirus prevalence, viral load and pre-cancerous lesions of the cervix in women initiating highly active antiretroviral therapy in South Africa : a cross-sectional study

Background Cervical cancer and infection with human immunodeficiency virus (HIV) are both important public health problems in South Africa (SA). The aim of this study was to determine the prevalence of cervical squamous intraepithelial lesions (SILs), high-risk human papillomavirus (HR-HPV), HPV viral load and HPV genotypes in HIV positive women initiating anti-retroviral (ARV) therapy. Methods A cross-sectional survey was conducted at an anti-retroviral (ARV) treatment clinic in Cape Town, SA in 2007. Cervical specimens were taken for cytological analysis and HPV testing. The Digene Hybrid Capture 2 (HC2) test was used to detect HR-HPV. Relative light units (RLU) were used as a measure of HPV viral load. HPV types were determined using the Roche Linear Array HPV Genotyping test. Crude associations with abnormal cytology were tested and multiple logistic regression was used to determine independent risk factors for abnormal cytology. Results The median age of the 109 participants was 31 years, the median CD4 count was 125/mm3, 66.3% had an abnormal Pap smear, the HR-HPV prevalence was 78.9% (Digene), the median HPV viral load was 181.1 RLU (HC2 positive samples only) and 78.4% had multiple genotypes. Among women with abnormal smears the most prevalent HR-HPV types were HPV types 16, 58 and 51, all with a prevalence of 28.5%. On univariate analysis HR-HPV, multiple HPV types and HPV viral load were significantly associated with the presence of low and high-grade SILs (LSIL/HSIL). The multivariate logistic regression showed that HPV viral load was associated with an increased odds of LSIL/HSIL, odds ratio of 10.7 (95% CI 2.0 – 57.7) for those that were HC2 positive and had a viral load of ≤ 181.1 RLU (the median HPV viral load), and 33.8 (95% CI 6.4 – 178.9) for those that were HC2 positive with a HPV viral load > 181.1 RLU. Conclusion Women initiating ARVs have a high prevalence of abnormal Pap smears and HR-HPV. Our results underscore the need for locally relevant, rigorous screening protocols for the increasing numbers of women accessing ARV therapy so that the benefits of ARVs are not partially offset by an excess risk in cervical cancer.

When the right to be counted doesn’t count : the politics and challenges of researching the health of asylum seekers

A fundamental prerequisite of population health research is the ability to establish an accurate denominator. This in turn requires that every individual in the study population is counted. However, this seemingly simple principle has become a point of conflict between researchers whose aim is to produce evidence of disparities in population health outcomes and governments whose policies promote(intentionally or not) inequalities that are the underlying causes of health disparities. Research into the health of asylum seekers is a case in point. There is a growing body of evidence documenting the adverse affects of recent changes in asylum-seeking legislation, including mandatory detention. However, much of this evidence has been dismissed by some governments as being unsound, biased and unscientific because, it is argued, evidence is derived from small samples or from case studies. Yet, it is the policies of governments that are the key barrier to the conduct of rigorous population health research on asylum seekers. In this paper, the authors discuss the challenges of counting asylum seekers and the limitations of data reported in some industrialized countries. They argue that the lack of accurate statistical data on asylum seekers has been an effective neo-conservative strategy for erasing the health inequalities in this vulnerable population, indeed a strategy that renders invisible this population. They describe some alternative strategies that may be used by researchers to obtain denominator data on hard-to-reach populations such as asylum seekers.

Realistic irrigation visualization in a surgical wound debridement simulator

Wound debridement refers to the removal of necrotic, devitalized, or contaminated tissue and/or foreign material to promote wound healing. Surgical debridement uses sharp instruments to cut dead tissue from a wound and it is the quickest and most efficient method of debridement. A wound debridement simulator [1,2] can ensure that a medical trainee is competent prior to performing a procedure on a genuine patient. Irrigation is performed at different stages of debridement in order to remove debris and reduce the bacteria count through rinsing the wound. This paper presents a novel approach for realistic irrigation visualization based on texture representations of debris. This approach applies image processing techniques to a series of images, which model the cleanliness of the wound. The active texture is generated and updated dynamically based on the irrigation state, location, and range. Presented results demonstrate that texture mapping and image processing techniques can provide effective and efficient solutions for irrigation visualization in the wound debridement simulator.