Increased cardio-metabolic risk is associated with increased TV viewing time

Purpose: Television viewing time, independent of leisure-time physical activity, has cross-sectional relationships with the metabolic syndrome and its individual components. We examined whether baseline and five-year changes in self-reported television viewing time are associated with changes in continuous biomarkers of cardio-metabolic risk (waist circumference, triglycerides, high density lipoprotein cholesterol, systolic and diastolic blood pressure, fasting plasma glucose; and a clustered cardio-metabolic risk score) in Australian adults. Methods: AusDiab is a prospective, population-based cohort study with biological, behavioral, and demographic measures collected in 1999–2000 and 2004–2005. Non-institutionalized adults aged ≥ 25 years were measured at baseline (11,247; 55% of those completing an initial household interview); 6,400 took part in the five-year follow-up biomedical examination, and 3,846 met the inclusion criteria for this analysis. Multiple linear regression analysis was used and unstandardized B coefficients (95% CI) are provided. Results: Baseline television viewing time (10 hours/week unit) was not significantly associated with change in any of the biomarkers of cardio-metabolic risk. Increases in television viewing time over five years (10 hours/week unit) were associated with increases in: waist circumference (cm) (men: 0.43 (0.08, 0.78), P = 0.02; women: 0.68 (0.30, 1.05), P <0.001), diastolic blood pressure (mmHg) (women: 0.47 (0.02, 0.92), P = 0.04), and the clustered cardio-metabolic risk score (women: 0.03 (0.01, 0.05), P = 0.007). These associations were independent of baseline television viewing time and baseline and change in physical activity and other potential confounders. Conclusion: These findings indicate that an increase in television viewing time is associated with adverse cardio-metabolic biomarker changes. Further prospective studies using objective measures of several sedentary behaviors are required to confirm causality of the associations found.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

The short-term effect of air pollution on cardiovascular mortality in Tianjin, China : comparison of time series and case–crossover analyses

Background: Many studies have illustrated that ambient air pollution negatively impacts on health. However, little evidence is available for the effects of air pollution on cardiovascular mortality (CVM) in Tianjin, China. Also, no study has examined which strata length for the time-stratified case–crossover analysis gives estimates that most closely match the estimates from time series analysis. Objectives: The purpose of this study was to estimate the effects of air pollutants on CVM in Tianjin, China, and compare time-stratified case–crossover and time series analyses. Method: A time-stratified case–crossover and generalized additive model (time series) were applied to examine the impact of air pollution on CVM from 2005 to 2007. Four time-stratified case–crossover analyses were used by varying the stratum length (Calendar month, 28, 21 or 14 days). Jackknifing was used to compare the methods. Residual analysis was used to check whether the models fitted well. Results: Both case–crossover and time series analyses show that air pollutants (PM10, SO2 and NO2) were positively associated with CVM. The estimates from the time-stratified case–crossover varied greatly with changing strata length. The estimates from the time series analyses varied slightly with changing degrees of freedom per year for time. The residuals from the time series analyses had less autocorrelation than those from the case–crossover analyses indicating a better fit. Conclusion: Air pollution was associated with an increased risk of CVM in Tianjin, China. Time series analyses performed better than the time-stratified case–crossover analyses in terms of residual checking.

History-aware, real-time risk detection in business processes (Extended Version)

This paper proposes a novel approach for identifying risks in executable business processes and detecting them at run time. The approach considers risks in all phases of the business process management lifecycle, and is realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of faults to occur. Both historical and current execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a process automation suite to prompt the results to the user who may take remedial actions. The proposed architecture has been implemented in the YAWL system and its performance has been evaluated in practice.

The impact of nonlinear exposure risk relationships on seasonal time series data: modelling danish neonatal birth anthropometric data

Background Birth weight and length have seasonal fluctuations. Previous analyses of birth weight by latitude effects identified seemingly contradictory results, showing both 6 and 12 monthly periodicities in weight. The aims of this paper are twofold: (a) to explore seasonal patterns in a large, Danish Medical Birth Register, and (b) to explore models based on seasonal exposures and a non-linear exposure-risk relationship. Methods Birth weight and birth lengths on over 1.5 million Danish singleton, live births were examined for seasonality. We modelled seasonal patterns based on linear, U- and J-shaped exposure-risk relationships. We then added an extra layer of complexity by modelling weighted population-based exposure patterns. Results The Danish data showed clear seasonal fluctuations for both birth weight and birth length. A bimodal model best fits the data, however the amplitude of the 6 and 12 month peaks changed over time. In the modelling exercises, U- and J-shaped exposure-risk relationships generate time series with both 6 and 12 month periodicities. Changing the weightings of the population exposure risks result in unexpected properties. A J-shaped exposure-risk relationship with a diminishing population exposure over time fitted the observed seasonal pattern in the Danish birth weight data. Conclusion In keeping with many other studies, Danish birth anthropometric data show complex and shifting seasonal patterns. We speculate that annual periodicities with non-linear exposure-risk models may underlie these findings. Understanding the nature of seasonal fluctuations can help generate candidate exposures.

Real-time risk monitoring in business processes : a sensor-based approach

This article proposes an approach for real-time monitoring of risks in executable business process models. The approach considers risks in all phases of the business process management lifecycle, from process design, where risks are defined on top of process models, through to process diagnosis, where risks are detected during process execution. The approach has been realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of negative process states (faults) to eventuate. Both historical and current process execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a business process management system to prompt the results to process administrators who may take remedial actions. The proposed architecture has been implemented on top of the YAWL system, and evaluated through performance measurements and usability tests with students. The results show that risk conditions can be computed efficiently and that the approach is perceived as useful by the participants in the tests.

Vitamin D status and skin cancer risk independent of time outdoors : 11-year prospective study in and Australian community

Vitamin D may have anti-skin cancer effects, but population-based evidence is lacking. We therefore assessed associations between vitamin D status and skin cancer risk in an Australian subtropical community. We analyzed prospective skin cancer incidence for 11 years following baseline assessment of serum 25(OH)-vitamin D in 1,191 adults (average age 54 years) and used multivariable logistic regression analysis to adjust risk estimates for age, sex, detailed assessments of usual time spent outdoors, phenotypic characteristics, and other possible confounders. Participants with serum 25(OH)-vitamin D concentrations above 75 nmol  l(-1) versus those below 75 nmol  l(-1) more often developed basal cell carcinoma (odds ratio (OR)=1.51 (95% confidence interval (CI): 1.10-2.07, P=0.01) and melanoma (OR=2.71 (95% CI: 0.98-7.48, P=0.05)). Squamous cell carcinoma incidence tended to be lower in persons with serum 25(OH)-vitamin D concentrations above 75 nmol  l(-1) compared with those below 75 nmol  l(-1) (OR=0.67 (95% CI: 0.44-1.03, P=0.07)). Vitamin D status was not associated with skin cancer incidence when participants were classified as above or below 50 nmol  l(-1) 25(OH)-vitamin D. Our findings do not indicate that the carcinogenicity of high sun exposure can be counteracted by high vitamin D status. High sun exposure is to be avoided as a means to achieve high vitamin D status.

Mothers vs fathers as learner driver supervisors : time commitment, driving activities and perceptions of risk

The learner licence, within a graduated driver licensing system, provides new drivers with the opportunity to learn to drive under the supervision of a more experienced driver. The Queensland graduated driver licensing system requires learner drivers to record a minimum of 100 logbook hours of supervised practice with the support of parents appearing critical to ensure that this is achieved. This paper examines differences between mothers and fathers who supervise learner drivers. Mothers and fathers from Queensland who had recently supervised their child while they learnt to drive completed an internet survey about their experiences. It appears that one strategy that parents use to provide practice hours is for the child to drive themselves or their parents to or from activities that they would have attended anyway in addition to undertaking special trips in the car for the purposes of practising. The results suggest that mothers, when compared with fathers, consider driving at all stages of licensure riskier and that mothers provided more hours of supervision than fathers. However, despite this, there are limited differences between how frequently mothers and fathers provide different driving experiences such as deliberately practising in suburban areas or with passengers in the car. This research fills a gap in the literature by providing important information about the way in which parents supervise their children while they are driving on a learner licence as well as identifying some of the differences and similarities between mothers and fathers.

Methodology for developing real-time motorway traffic risk identification models using individual-vehicle data

Most of existing motorway traffic safety studies using disaggregate traffic flow data aim at developing models for identifying real-time traffic risks by comparing pre-crash and non-crash conditions. One of serious shortcomings in those studies is that non-crash conditions are arbitrarily selected and hence, not representative, i.e. selected non-crash data might not be the right data comparable with pre-crash data; the non-crash/pre-crash ratio is arbitrarily decided and neglects the abundance of non-crash over pre-crash conditions; etc. Here, we present a methodology for developing a real-time MotorwaY Traffic Risk Identification Model (MyTRIM) using individual vehicle data, meteorological data, and crash data. Non-crash data are clustered into groups called traffic regimes. Thereafter, pre-crash data are classified into regimes to match with relevant non-crash data. Among totally eight traffic regimes obtained, four highly risky regimes were identified; three regime-based Risk Identification Models (RIM) with sufficient pre-crash data were developed. MyTRIM memorizes the latest risk evolution identified by RIM to predict near future risks. Traffic practitioners can decide MyTRIM’s memory size based on the trade-off between detection and false alarm rates. Decreasing the memory size from 5 to 1 precipitates the increase of detection rate from 65.0% to 100.0% and of false alarm rate from 0.21% to 3.68%. Moreover, critical factors in differentiating pre-crash and non-crash conditions are recognized and usable for developing preventive measures. MyTRIM can be used by practitioners in real-time as an independent tool to make online decision or integrated with existing traffic management systems.

Development of health risk-based metrics for defining a heatwave : a time series study in Brisbane, Australia

Background: This study attempted to develop health risk-based metrics for defining a heatwave in Brisbane, Australia. Methods: Poisson generalised additive model was performed to assess the impact of heatwaves on mortality and emergency hospital admissions (EHAs) in Brisbane. Results: In general, the higher the intensity and the longer the duration of a heatwave, the greater the health impacts. There was no apparent difference in EHAs risk during different periods of a warm season. However, there was a greater risk of mortality in the second half of a warm season than that in the first half. While elderly (>75 years)were particularly vulnerable to both the EHA and mortality effects of a heatwave, the risk for EHAs also significantly increased for two other age groups (0-64 years and 65-74 years) during severe heatwaves. Different patterns between cardiorespiratory mortality and EHAs were observed. Based on these findings, we propose the use of a teiered heat warning system based on the health risk of heatwave. Conclusions: Health risk-based metrics are a useful tool for the development of local heatwave definitions. thsi tool may have significant implications for the assessment of heatwave-related health consequences and development of heatwave response plans and implementation strategies.

Study of the structure and function of the sheath of tropically adapted bulls and risk factors for preputial eversion and preputial prolapse

Preputial prolapse is an obvious condition affecting bulls from many breeds. Unfortunately, the losses in production and welfare concerns associated with preputial prolapse can remain undetected for long periods of time in the extensive beef areas of northern Australia where the bulls are not inspected regularly. Thus, there is a critical need to identify the structural factors predisposing to preputial prolapse in young bulls so that they can be culled early. Despite there being no firm scientific evidence of an association between preputial eversion and preputial prolapse, it seems logical that the increased exposure of the sensitive prepuce as a consequence of preputial eversion may increase the risk of bulls developing preputial pathology, in particular preputial prolapse. This may be particularly relevant in Bos indicus bulls as they have a more pendulous sheath and thus eversion of the prepuce may be associated with a greater risk of injury to the prepuce compared to that in Bos taurus bulls. Further, studies of preputial eversion in Bos taurus bulls have concluded that there is an association between polledness and increased prevalence and severity (length of everted prepuce and duration of eversion) of preputial eversion due primarily to the absence or poor development of the caudal preputial muscles. No similar definitive work in Bos indicus bulls has been conducted and thus anatomical studies reported in this thesis were conducted to determine if a similar association occurred in Bos indicus bulls. A survey of a sample of large beef breeding herds in northern Australia found that preputial prolapse is a significant problem in Bos indicus and Bos indicus derived bulls and affected both young and older bulls. The importance of preputial prolapse confirmed the value of further research into the causes of this problem. A series of anatomical studies confirmed that preputial eversion in Bos indicus derived bulls was not more prevalent in polled bulls than horned bulls and was not associated with deficiency of the caudal preputial muscles as was established in Bos taurus bulls. An anatomical study of Bos indicus derived bulls with preputial prolapse found that preputial prolapse occurred in horned bulls of varying ages and these bulls did not have any evidence of deficiency in the caudal preputial muscles. However, preputial prolapse was observed in young polled bulls that had poorly developed or absent caudal preputial muscles. It was concluded that deficiency of the caudal preputial muscles in polled Bos indicus derived bulls may predispose to preputial prolapse at an early age, but no predisposing anatomical factors were found for horned Bos indicus derived bulls. In these studies, preputial eversion and preputial prolapse were found in horned Bos indicus derived bulls that did not have any preputial muscle deficiency and it was noted that preputial eversion was not related to the length of the prepuce. Further studies confirmed that preputial eversion was linearly and consistently associated with position of the glans penis within the sheath in Bos indicus derived bulls, and movement of the glans penis towards the preputial orifice consistently resulted in preputial eversion in these bulls. A method to objectively measure the relationship between movement of the glans penis within the sheath and preputial eversion was developed. Studies in humans have linked function of some abdominal muscles to function of the pelvic organs. This relationship was investigated in Bos indicus derived bulls to determine whether the function of specific abdominal muscles affected position of the penis in the sheath. Using the method developed to objectively measure the relationship between penis movement and preputial eversion, the abdominal muscles that potentially were associated with movement of the glans penis or preputial eversion were examined but no significant relationships were observed. In the anatomical study of Bos indicus derived bulls not affected with preputial prolapse a more pendulous sheath was associated with increased prevalence of preputial eversion. This relationship was confirmed for horned and polled bulls in the penis movement studies. Bos indicus derived bulls with more pendulous sheaths evert their prepuces more than bulls with less pendulous sheaths thus increasing the risk of damage to the prepuce either from the environment, other bulls, or from them inadvertently stepping on the everted prepuce when they get to their feet. Culling Bos indicus derived bulls with more pendulous sheaths should reduce the incidence of preputial eversion and possibly preputial prolapse. The anatomical study of Bos indicus derived bulls that did not have preputial prolapse demonstrates that there are herds of bulls where the polled bulls do not have any evidence of deficiency of the caudal preputial iv muscles. There is a need to develop a practical and cost effective test to identify polled Bos indicus bulls that have a deficiency in their caudal preputial muscles.