Predictors of re-epithelialization in pediatric burn

INTRODUCTION An important treatment goal for burn wounds is to promote early wound closure. This study identifies factors associated with delayed re-epithelialization following pediatric burn. METHODS Data were collected from August 2011 to August 2012, at a pediatric tertiary burn center. A total of 106 burn wounds were analyzed from 77 participants aged 4-12 years. Percentage of wound re-epithelialization at each dressing change was calculated using Visitrak. Mixed effect regression analysis was performed to identify the demographic factors, wound and clinical characteristics associated with delayed re-epithelialization. RESULTS Burn depth determined by laser Doppler imaging, ethnicity, pain scores, total body surface area (TBSA), mechanism of injury and days taken to present to the burn center were significant predictors of delayed re-epithelialization, accounting for 69% of variance. Flame burns delayed re-epithelialization by 39% compared to all other mechanisms (p=0.003). When initial presentation to the burn center was on day 5, burns took an average of 42% longer to re-epithelialize, compared to those who presented on day 2 post burn (p<0.000). Re-epithelialization was delayed by 14% when pain scores were reported as 10 (on the FPS-R), compared to 4 on the first dressing change (p=0.015) for children who did not receive specialized preparation/distraction intervention. A larger TBSA was also a predictor of delayed re-epithelialization (p=0.030). Darker skin complexion re-epithelialized 25% faster than lighter skin complexion (p=0.001). CONCLUSIONS Burn depth, mechanism of injury and TBSA are always considered when developing the treatment and surgical management plan for patients with burns. This study identifies other factors influencing re-epithelialization, which can be controlled by the treating team, such as effective pain management and rapid referral to a specialized burn center, to achieve optimal outcomes.

Spatial Patterns of Malaria Reported Deaths in Yunnan Province, China

Malaria has been a heavy social and health burden in the remote and poor areas in southern China. Analyses of malaria epidemic patterns can uncover important features of malaria transmission. This study identified spatial clusters, seasonal patterns, and geographic variations of malaria deaths at a county level in Yunnan, China, during 1991–2010. A discrete Poisson model was used to identify purely spatial clusters of malaria deaths. Logistic regression analysis was performed to detect changes in geographic patterns. The results show that malaria mortality had declined in Yunnan over the study period and the most likely spatial clusters (relative risk [RR] = 23.03–32.06, P < 0.001) of malaria deaths were identified in western Yunnan along the China–Myanmar border. The highest risk of malaria deaths occurred in autumn (RR = 58.91, P < 0.001) and summer (RR = 31.91, P < 0.001). The results suggested that the geographic distribution of malaria deaths was significantly changed with longitude, which indicated there was decreased mortality of malaria in eastern areas over the last two decades, although there was no significant change in latitude during the same period. Public health interventions should target populations in western Yunnan along border areas, especially focusing on floating populations crossing international borders.

Traversability estimation for a planetary rover via experimental kernel learning in a Gaussian process framework

A critical requirement for safe autonomous navigation of a planetary rover is the ability to accurately estimate the traversability of the terrain. This work considers the problem of predicting the attitude and configuration angles of the platform from terrain representations that are often incomplete due to occlusions and sensor limitations. Using Gaussian Processes (GP) and exteroceptive data as training input, we can provide a continuous and complete representation of terrain traversability, with uncertainty in the output estimates. In this paper, we propose a novel method that focuses on exploiting the explicit correlation in vehicle attitude and configuration during operation by learning a kernel function from vehicle experience to perform GP regression. We provide an extensive experimental validation of the proposed method on a planetary rover. We show significant improvement in the accuracy of our estimation compared with results obtained using standard kernels (Squared Exponential and Neural Network), and compared to traversability estimation made over terrain models built using state-of-the-art GP techniques.

Physical activity patterns and correlates among adults from a developing country : the Sri Lanka Diabetes and Cardiovascular Study

OBJECTIVE: To evaluate patterns of physical activity (PA), the prevalence of physical inactivity and the relationships between PA and sociodemographic, clinical and biochemical parameters among Sri Lankan adults. DESIGN: Descriptive cross-sectional study. SETTING: Nationally representative population-based survey conducted in Sri Lanka. SUBJECTS: Data on PA and associated details were obtained from 5000 adults. PA was assessed using the International Physical Activity Questionnaire (short-form). A binary logistic regression analysis was performed using the dichotomous variable ‘health-enhancing PA’ (05‘active’, 15‘inactive’). RESULTS: Sample size was 4485. Mean age was 46.1 (SD 15.1) years, 39.5% were males. The mean weekly total MET (metabolic equivalents of task) minutes of PA among the study population was 4703 (SD 4369). Males (5464 (SD 5452)) had a significantly higher weekly total MET minutes than females (4205 (SD 3394); P,0.001). Rural adults (5175 (SD 4583)) were significantly more active than urban adults (2956 (SD 2847); P<0.001). Tamils had the highest mean weekly total MET minutes among ethnicities. Those with tertiary education had lowest mean weekly total MET minutes. In all adults 60.0% were in the ‘highly active’ category, while only 11.0% were ‘inactive’ (males 14.6%, females 8.7%; P<0.001). Of the ‘highly active’ adults, 85.8% were residing in rural areas. Results of the binary logistic regression analysis indicated that female gender (OR52?1), age .70 years (OR53.8), urban living (OR52.5), Muslim ethnicity (OR52.7), tertiary education (OR53.6), obesity (OR51.8), diabetes (OR51.6), hypertension (OR51.2) and metabolic syndrome (OR51.3) were all associated with significantly increased odds of being physically ‘inactive’. CONCLUSIONS: The majority of Sri Lankan adults were ‘highly active’ physically. Female gender, older age, urban living, Muslim ethnicity and tertiary education were all significant predictors of physical inactivity. Physical inactivity was associated with obesity, diabetes, hypertension and metabolic syndrome.

Prognostic impact of vascular and lymphovascular invasion in early lung cancer

Background The prognostic significance of vascular and lymphatic invasion in non-small-cell lung cancer is under continuous debate. We analyzed the effect of tumor aggressiveness (lymphatic and/or vessel invasion) on survival and relapse in stage I and II non-small-cell lung cancer. Methods We retrospectively analyzed prospectively collected data of 457 patients with stage I and II non-small-cell lung cancer from 1998 to 2008. Specimens were analyzed for intratumoral vascular invasion and lymphovascular space invasion. Overall survival and disease-free survival were estimated using the Kaplan-Meier method, and differences were determined by the logrank test. Cox regression analysis was performed to identify independent risk factors. Results: The incidence of intratumoral vascular invasion was 23.4%, and this correlated significantly with grade of differentiation, visceral pleural involvement, lymphovascular space invasion, and N status. The incidence of lymphovascular space invasion was 5.5%, and this correlated significantly with grade of differentiation, lymph nodes involved, and intratumoral vascular invasion. On multivariate analyses, intratumoral vascular invasion proved to be an significant independent risk factor for overall survival but not for disease-free survival. Lymphovascular space invasion was associated significantly with early tumor recurrence but not with overall survival. Conclusions: Vascular and lymphatic invasion can serve as independent prognostic factors in completely resected nonsmall- cell lung cancer. Intratumoral vascular invasion and lymphovascular space invasion in early stage non-small-cell lung cancer are important factors in overall survival and early tumor recurrence. Further large scale studies with more recent patient cohorts and refined histological techniques are warranted.

Choroidal thickness in myopic and non-myopic children assessed with enhanced depth imaging optical coherence tomography

Purpose To examine choroidal thickness (ChT) and its topographical variation across the posterior pole in myopic and non-myopic children. Methods One hundred and four children aged 10-15 years of age (mean age 13.1 ± 1.4 years) had ChT measured using enhanced depth imaging optical coherence tomography (OCT). Forty one children were myopic (mean spherical equivalent -2.4 ± 1.5 D) and 63 non-myopic (mean +0.3 ± 0.3 D). Two series of 6 radial OCT line scans centred on the fovea were assessed for each child. Subfoveal ChT and ChT across a series of parafoveal zones over the central 6mm of the posterior pole were determined through manual image segmentation. Results Subfoveal ChT was significantly thinner in myopes (mean 303 ± 79 µm) compared to non-myopes (mean 359 ± 77 µm) (p<0.0001). Multiple regression analysis revealed both refractive error (r = 0.39, p<0.001) and age (r = 0.21, p = 0.02) were positively associated with subfoveal ChT. ChT also exhibited significant topographical variations, with the choroid being thicker in more central regions. The thinnest choroid was typically observed in nasal (mean 286 ± 77 µm) and inferior-nasal (306 ± 79 µm) locations, and the thickest in superior (346 ± 79 µm) and superior-temporal (341 ± 74 µm) locations. The difference in ChT between myopic and non-myopic children was significantly greater in central foveal regions compared to more peripheral regions (>3 mm diameter) (p<0.001). Conclusions Myopic children have significantly thinner choroids compared to non-myopic children of similar age, particularly in central foveal regions. The magnitude of difference in choroidal thickness associated with myopia appears greater than would be predicted by a simple passive choroidal thinning with axial elongation.

Statistical modelling of district-level residential electricity use in NSW, Australia

Electricity network investment and asset management require accurate estimation of future demand in energy consumption within specified service areas. For this purpose, simple models are typically developed to predict future trends in electricity consumption using various methods and assumptions. This paper presents a statistical model to predict electricity consumption in the residential sector at the Census Collection District (CCD) level over the state of New South Wales, Australia, based on spatial building and household characteristics. Residential household demographic and building data from the Australian Bureau of Statistics (ABS) and actual electricity consumption data from electricity companies are merged for 74 % of the 12,000 CCDs in the state. Eighty percent of the merged dataset is randomly set aside to establish the model using regression analysis, and the remaining 20 % is used to independently test the accuracy of model prediction against actual consumption. In 90 % of the cases, the predicted consumption is shown to be within 5 kWh per dwelling per day from actual values, with an overall state accuracy of -1.15 %. Given a future scenario with a shift in climate zone and a growth in population, the model is used to identify the geographical or service areas that are most likely to have increased electricity consumption. Such geographical representation can be of great benefit when assessing alternatives to the centralised generation of energy; having such a model gives a quantifiable method to selecting the 'most' appropriate system when a review or upgrade of the network infrastructure is required.

Expanding Emergency Department capacity : a multisite study

Objectives: To i) identify predictors of admission, and ii) describe outcomes for patients who arrived via ambulance to three Australian public Emergency Departments (EDs), before and after the opening of 41 additional ED beds within the area. Methods: A retrospective, comparative, cohort study using deterministically linked health data collected between 3 September 2006 and 2 September 2008. Data included ambulance offload delay, time to see doctor, ED length of stay (ED LOS), admission requirement, access block, hospital length of stay and in-hospital mortality. Logistic regression analysis was undertaken to identify predictors of hospital admission. Results: One third of all 286,037 ED presentations were via ambulance (n= 79,196) and 40.3% required admission. After increasing emergency capacity, the only outcome measure to improve was in-hospital mortality. Ambulance offload delay, time to see doctor, ED length of stay (ED LOS), admission requirement, access block, hospital length of stay did not improve. Strong predictors of admission before and after increased capacity included: age over 65 years, Australian Triage Scale (ATS) category 1-3, diagnoses of circulatory or respiratory conditions and ED LOS > 4 hours. With additional capacity the odds ratios for these predictors increased for age >65 and ED LOS > 4 hours and decreased for triage category and ED diagnoses. Conclusions: Expanding ED capacity from 81 to 122 beds within a health service area impacted favourably on mortality outcomes but not on time-related service outcomes such as ambulance offload time, time to see doctor and ED LOS. To improve all service outcomes, when altering (increasing/decreasing) ED bed numbers, the whole healthcare system needs to be considered.

What aspects of demographic, personality, attitudes, and perceptions of law enforcement influence self-reported likelihood of drink driving?

The aim of the current study was to examine the associations between a number of individual factors (demographic factors (age and gender), personality factors, risk-taking propensity, attitudes towards drink driving, and perceived legitimacy of drink driving enforcement) and how they influence the self-reported likelihood of drink driving. The second aim of this study was to examine the potential of attitudes mediating the relationship between risk-taking and self-reported likelihood of drink driving. In total, 293 Queensland drivers volunteered to participate in an online survey that assessed their self-reported likelihood to drink drive in the next month, demographics, traffic-related demographics, personality factors, risk-taking propensity, attitudes towards drink driving, and perceived legitimacy of drink driving enforcement. An ordered logistic regression analysis was utilised to evaluate the first aim of the study; at the first step the demographic variables were entered; at step two the personality and risk-taking were entered; at the third step, the attitudes and perceptions of legitimacy variables were entered. Being a younger driver and having a high risk-taking propensity were related to self-reported likelihood of drink driving. However, when the attitudes variable was entered, these individual factors were no longer significant; with attitudes being the most important predictor of self-reported drink driving likelihood. A significant mediation model was found with the second aim of the study, such that attitudes mediated the relationship between risk-taking and self-reported likelihood of drink driving. Considerable effort and resources are utilised by traffic authorities to reducing drink driving on the Australian road network. Notwithstanding these efforts, some participants still had some positive attitudes towards drink driving and reported that they were likely to drink drive in the future. These findings suggest that more work is needed to address attitudes regarding the dangerousness of drink driving.

Damping structure selection in nonlinear ship manoeuvring models

This paper presents the application of a statistical method for model structure selection of lift-drag and viscous damping components in ship manoeuvring models. The damping model is posed as a family of linear stochastic models, which is postulated based on previous work in the literature. Then a nested test of hypothesis problem is considered. The testing reduces to a recursive comparison of two competing models, for which optimal tests in the Neyman sense exist. The method yields a preferred model structure and its initial parameter estimates. Alternatively, the method can give a reduced set of likely models. Using simulated data we study how the selection method performs when there is both uncorrelated and correlated noise in the measurements. The first case is related to instrumentation noise, whereas the second case is related to spurious wave-induced motion often present during sea trials. We then consider the model structure selection of a modern high-speed trimaran ferry from full scale trial data.

Parameter estimation of structural commodity price models

Commodity price modeling is normally approached in terms of structural time-series models, in which the different components (states) have a financial interpretation. The parameters of these models can be estimated using maximum likelihood. This approach results in a non-linear parameter estimation problem and thus a key issue is how to obtain reliable initial estimates. In this paper, we focus on the initial parameter estimation problem for the Schwartz-Smith two-factor model commonly used in asset valuation. We propose the use of a two-step method. The first step considers a univariate model based only on the spot price and uses a transfer function model to obtain initial estimates of the fundamental parameters. The second step uses the estimates obtained in the first step to initialize a re-parameterized state-space-innovations based estimator, which includes information related to future prices. The second step refines the estimates obtained in the first step and also gives estimates of the remaining parameters in the model. This paper is part tutorial in nature and gives an introduction to aspects of commodity price modeling and the associated parameter estimation problem.

Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm

Background Heatwaves could cause the population excess death numbers to be ranged from tens to thousands within a couple of weeks in a local area. An excess mortality due to a special event (e.g., a heatwave or an epidemic outbreak) is estimated by subtracting the mortality figure under ‘normal’ conditions from the historical daily mortality records. The calculation of the excess mortality is a scientific challenge because of the stochastic temporal pattern of the daily mortality data which is characterised by (a) the long-term changing mean levels (i.e., non-stationarity); (b) the non-linear temperature-mortality association. The Hilbert-Huang Transform (HHT) algorithm is a novel method originally developed for analysing the non-linear and non-stationary time series data in the field of signal processing, however, it has not been applied in public health research. This paper aimed to demonstrate the applicability and strength of the HHT algorithm in analysing health data. Methods Special R functions were developed to implement the HHT algorithm to decompose the daily mortality time series into trend and non-trend components in terms of the underlying physical mechanism. The excess mortality is calculated directly from the resulting non-trend component series. Results The Brisbane (Queensland, Australia) and the Chicago (United States) daily mortality time series data were utilized for calculating the excess mortality associated with heatwaves. The HHT algorithm estimated 62 excess deaths related to the February 2004 Brisbane heatwave. To calculate the excess mortality associated with the July 1995 Chicago heatwave, the HHT algorithm needed to handle the mode mixing issue. The HHT algorithm estimated 510 excess deaths for the 1995 Chicago heatwave event. To exemplify potential applications, the HHT decomposition results were used as the input data for a subsequent regression analysis, using the Brisbane data, to investigate the association between excess mortality and different risk factors. Conclusions The HHT algorithm is a novel and powerful analytical tool in time series data analysis. It has a real potential to have a wide range of applications in public health research because of its ability to decompose a nonlinear and non-stationary time series into trend and non-trend components consistently and efficiently.

Encouragement of physical activity for weight loss may lead to negative psychological outcomes among girls

PURPOSE To examine correlates and consequences of parents' encouragement of girls' physical activity (PA) for weight loss (ENCLOSS). METHODS Data were collected for 181 girls, mothers and fathers when girls were 9, 11, and 13 years old. Mothers and fathers completed a self-report questionnaire of ENCLOSS (e.g., “I have talked to my daughter about how to exercise to lose weight”). Correlates of ENCLOSS that were assessed include girls' Body Mass Index (BMI) z-score and parents' modeling of and logistic support for PA. Dependent variables assessed at age 13 include girls' self-reported and objectively-measured PA, enjoyment of physical activity, and weight concerns. Associations between ENCLOSS, girls' BMI, and parent's support for PA were assessed using spearman rank correlations. To examine links between ENCLOSS and the outcome variables, scores for ENCLOSS were divided into tertiles at each age. Three groups were created including girls who were in the highest tertile at each age (high ENCLOSS), girls who were in the lowest tertile at each age (low ENCLOSS), and girls who varied in their tertile ranking (mid ENCLOSS). Group differences in the outcome variables were assessed using regression analysis (referent group: low ENCLOSS), controlling for girls' BMI and the outcome variable at age 9. RESULTS Girls' with higher BMI had mothers and fathers who reported higher ENCLOSS (r = .61-. 69, p<. 0001). Parents'reports of ENCLOSS were not associated with modeling of or logistic support for PA. Girls in the high ENCLOSS group reported significantly lower enjoyment of PA and higher weight concerns at age 13, independent of covariates. No differences in PA were noted. CONCLUSION Parents who encourage their daughters to be active for weight loss do not model PA or facilitate girls' PA. Persistent encouragement of PA for weight loss may lead to low enjoyment of PA and higher weight concerns among adolescent girls.

Benthic metabolism as an indicator of stream ecosystem health

We tested direct and indirect measures of benthic metabolism as indicators of stream ecosystem health across a known agricultural land-use disturbance gradient in southeast Queensland, Australia. Gross primary production (GPP) and respiration (R24) in benthic chambers in cobble and sediment habitats, algal biomass (as chlorophyll a) from cobbles and sediment cores, algal biomass accrual on artificial substrates and stable carbon isotope ratios of aquatic plants and benthic sediments were measured at 53 stream sites, ranging from undisturbed subtropical rainforest to catchments where improved pasture and intensive cropping are major land-uses. Rates of benthic GPP and R24 varied by more than two orders of magnitude across the study gradient. Generalised linear regression modelling explained 80% or more of the variation in these two indicators when sediment and cobble substrate dominated sites were considered separately, and both catchment and reach scale descriptors of the disturbance gradient were important in explaining this variation. Model fits were poor for net daily benthic metabolism (NDM) and production to respiration ratio (P/R). Algal biomass accrual on artificial substrate and stable carbon isotope ratios of aquatic plants and benthic sediment were the best of the indirect indicators, with regression model R2 values of 50% or greater. Model fits were poor for algal biomass on natural substrates for cobble sites and all sites. None of these indirect measures of benthic metabolism was a good surrogate for measured GPP. Direct measures of benthic metabolism, GPP and R24, and several indirect measures were good indicators of stream ecosystem health and are recommended in assessing process-related responses to riparian and catchment land use change and the success of ecosystem rehabilitation actions.

Accurately defining the reference condition for summary biotic metrics: A comparison of four approaches

Protocols for bioassessment often relate changes in summary metrics that describe aspects of biotic assemblage structure and function to environmental stress. Biotic assessment using multimetric indices now forms the basis for setting regulatory standards for stream quality and a range of other goals related to water resource management in the USA and elsewhere. Biotic metrics are typically interpreted with reference to the expected natural state to evaluate whether a site is degraded. It is critical that natural variation in biotic metrics along environmental gradients is adequately accounted for, in order to quantify human disturbance-induced change. A common approach used in the IBI is to examine scatter plots of variation in a given metric along a single stream size surrogate and a fit a line (drawn by eye) to form the upper bound, and hence define the maximum likely value of a given metric in a site of a given environmental characteristic (termed the 'maximum species richness line' - MSRL). In this paper we examine whether the use of a single environmental descriptor and the MSRL is appropriate for defining the reference condition for a biotic metric (fish species richness) and for detecting human disturbance gradients in rivers of south-eastern Queensland, Australia. We compare the accuracy and precision of the MSRL approach based on single environmental predictors, with three regression-based prediction methods (Simple Linear Regression, Generalised Linear Modelling and Regression Tree modelling) that use (either singly or in combination) a set of landscape and local scale environmental variables as predictors of species richness. We compared the frequency of classification errors from each method against set biocriteria and contrast the ability of each method to accurately reflect human disturbance gradients at a large set of test sites. The results of this study suggest that the MSRL based upon variation in a single environmental descriptor could not accurately predict species richness at minimally disturbed sites when compared with SLR's based on equivalent environmental variables. Regression-based modelling incorporating multiple environmental variables as predictors more accurately explained natural variation in species richness than did simple models using single environmental predictors. Prediction error arising from the MSRL was substantially higher than for the regression methods and led to an increased frequency of Type I errors (incorrectly classing a site as disturbed). We suggest that problems with the MSRL arise from the inherent scoring procedure used and that it is limited to predicting variation in the dependent variable along a single environmental gradient.