OBJECTIVES: The purpose of this research was to study possible abnormalities in the beat to beat complexity of heart rate dynamics in patients with a previous myocardial infarction. BACKGROUND: Analysis of approximate entropy of time series data provides information on the complexity of both deterministic and random processes. It has been proposed that regularity or loss of complexity of RR interval dynamics may be related to pathologic states, but this hypothesis has not been well tested in cardiovascular disorders. METHODS: Approximate entropy and conventional time and frequency domain measures of RR interval variability were compared between 40 healthy subjects with no evidence of heart disease and 40 patients with coronary artery disease and a previous Q wave myocardial infarction. The groups were matched with respect to age, and cardiac medication was discontinued in the patients with coronary artery disease before the 24-h electrocardiographic recordings. RESULTS: Approximate entropy was significantly higher in the postinfarction patients (1.21 +/- 0.18 [mean +/- SD]) than in the healthy subjects (1.05 +/- 0.11, p
Successful academic performance during adolescence is a key predictor of lifetime achievement, including occupational and social success. The present study investigated the important transition from primary to secondary schooling during early adolescence, when academic performance among youth often declines. The goal of the study was to understand how risk factors, specifically lower family resources and male gender, threaten academic success following this "critical transition" in schooling. The study involved a longitudinal examination of the predictors of academic performance in grades 7-8 among 127 (56 % girls) French-speaking Quebec (Canada) adolescents from lower-income backgrounds. As hypothesized based on transition theory, hierarchical regression analyses showed that supportive parenting and specific academic, social and behavioral competencies (including spelling ability, social skills, and lower levels of attention problems) predicted success across this transition among at-risk youth. Multiple-mediation procedures demonstrated that the set of compensatory factors fully mediated the negative impact of lower family resources on academic success in grades 7-8. Unique mediators (social skills, spelling ability, supportive parenting) also were identified. In addition, the "gender gap" in performance across the transition could be attributed statistically to differences between boys and girls in specific competencies observed prior to the transition, as well as differential parenting (i.e., support from mother) towards girls and boys. The present results contribute to our understanding of the processes by which established risk factors, such as low family income and gender impact development and academic performance during early adolescence. These "transitional" processes and subsequent academic performance may have consequences across adolescence and beyond, with an impact on lifetime patterns of achievement and occupational success.
It has been well known that ignoring measurement error may result in substantially biased estimates in many contexts including linear and nonlinear regressions. For survival data with measurement error in covariates there has been extensive discussion in the literature with the focus being on the Cox proportional hazards models. However, the impact of measurement error on accelerated failure time (AFT) models has received little attention, though AFT models are very useful in survival data analysis. In this paper, we discuss AFT models with error-prone covariates and study the bias induced by the naive approach of ignoring measurement error in covariates. To adjust for such a bias, we describe a simulation and extrapolation method. This method is appealing because it is simple to implement and it does not require modelling the true but error-prone covariate process that is often not observable. Asymptotic normality for the resulting estimators is established. Simulation studies are carried out to evaluate the performance of the proposed method as well as the impact of ignoring measurement error in covariates. The proposed method is applied to analyse a data set arising from the Busselton Health study (Australian J. Public Health 1994; 18:129-135).
A number of options for treatment are available to young drug users, but little is known about the youth who actually attempt to access such services. Here we identify characteristics of a cohort of street-involved youth and highlight commonly encountered barriers.
From September 2005 to July 2007, data were collected from the At-Risk Youth Study (ARYS), a prospective cohort of 529 drug users aged 14-26 living in Vancouver, Canada. Participants who attempted to access any addiction services in the 6 months prior to enrollment were compared in univariate analyses and multiple logistic regression modeling of socio-demographic and drug-related factors.
Factors positively associated with attempting to access services included Aboriginal ethnicity (adjusted odds ratio [AOR]=1.66 [1.05-2.62]), high school education (AOR=1.66 [1.09-2.55]), mental illness (AOR=2.25 [1.50-3.38]), non-injection crack use (AOR=2.93 [1.76-4.89]), and spending >$50 on drugs per day (AOR=2.13 [1.41-3.22]). Among those who experienced difficulty-accessing services, the most commonly identified barrier was excessively long waiting lists. In a subgroup analysis comparing those who tried to access services but were unsuccessful to those who were successful, risk factors positively associated with failure included drug bingeing (odds ratio [OR]=2.86 [1.22-6.76]) and homelessness (OR=3.86 [1.11-13.4]).
In light of accumulating evidence that drug use among street youth is associated with risky health-related behaviors, improving access to treatment and other addiction services should remain an important public health priority.
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This paper describes some of the main findings from two separate studies on accident prediction models for urban junctions and urban road links described in [Uheldsmodel for bygader-Del1: Modeller for 3-og 4-benede kryds. Notat 22, The Danish Road Directorate, 1995; Uheldsmodel for bygader- Del2: Modeller for straekninger. Notat 59, The Danish Road Directorate, 1998] (Greibe and Hemdorff, 1995, 1988). The main objective for the studies was to establish simple, practicable accident models that can predict the expected number of accidents at urban junctions and road links as accurately as possible. The models can be used to identify factors affecting road safety and in relation to 'black spot' identification and network safety analysis undertaken by local road authorities. The accident prediction models are based on data from 1036 junctions and 142 km road links in urban areas. Generalised linear modelling techniques were used to relate accident frequencies to explanatory variables. The estimated accident prediction models for road links were capable of describing more than 60% of the systematic variation ('percentage-explained' value) while the models for junctions had lower values. This indicates that modelling accidents for road links is less complicated than for junctions, probably due to a more uniform accident pattern and a simpler traffic flow exposure or due to lack of adequate explanatory variables for junctions. Explanatory variables describing road design and road geometry proved to be significant for road link models but less important in junction models. The most powerful variable for all models was motor vehicle traffic flow.
Recent research advocates the use of count models with random parameters as an alternative method for analyzing accident frequencies. In this paper a dataset composed of urban arterials in Vancouver, British Columbia, is considered where the 392 segments were clustered into 58 corridors. The main objective is to assess the corridor effects with alternate specifications. The proposed models were estimated in a Full Bayes context via Markov Chain Monte Carlo (MCMC) simulation and were compared in terms of their goodness of fit and inference. A variety of covariates were found to significantly influence accident frequencies. However, these covariates resulted in random parameters and thereby their effects on accident frequency were found to vary significantly across corridors. Further, a Poisson-lognormal (PLN) model with random parameters for each corridor provided the best fit. Apart from the improvement in goodness of fit, such an approach is useful in gaining new insights into how accident frequencies are influenced by the covariates, and in accounting for heterogeneity due to unobserved road geometrics, traffic characteristics, environmental factors and driver behavior. The inclusion of corridor effects in the mean function could also explain enough variation that some of the model covariates would be rendered non-significant and thereby affecting model inference.
Trials in which intact communities are the units of randomization are increasingly being used to evaluate interventions which are more naturally administered at the community level, or when there is a substantial risk of treatment contamination. In this article we focus on the planning of community intervention trials in which k communities (for example, medical practices, worksites, or villages) are to be randomly allocated to each of an intervention and a control group, and fixed cohorts of m individuals enrolled in each community prior to randomization. Formulas to determine k or m may be obtained by adjusting standard sample size formulas to account for the intracluster correlation coefficient rho. In the presence of individual-level attrition however, observed cohort sizes are likely to vary. We show that conventional approaches of accounting for potential attrition, such as dividing standard sample size formulas by the anticipated follow-up rate pi or using the average anticipated cohort size m pi, may, respectively, overestimate or underestimate the required sample size when cluster follow-up rates are highly variable, and m or rho are large. We present new sample size estimation formulas for the comparison of two means or two proportions, which appropriately account for variation among cluster follow-up rates. These formulas are derived by specifying a model for the binary missingness indicators under the population-averaged approach, assuming an exchangeable intracluster correlation coefficient, denoted by tau. To aid in the planning of future trials, we recommend that estimates for tau be reported in published community intervention trials.
We evaluated whether the association between low education and greater risk of dementia is explained by genetic influences, using three different types of analyses. The HARMONY study (Swedish for "health" (Hälsa), "genes" (ARv), "environment" (Miljö), "and" (Och), and "new" (NY)) includes members of the Swedish Twin Registry who were aged 65 and older and alive in 1998, and who were screened and clinically assessed for dementia. There were 394 cases with dementia and 7786 unrelated controls. Analyses included co-twin control, tests for association between education and a measured genotype, and bivariate twin modeling. Low education was a significant risk factor for dementia both in case-control analyses (odds ratio=1.77, 95% confidence interval 1.38 to 2.28) and co-twin control analyses with monozygotic twin pairs (odds ratio=3.17, 95% confidence interval 1.26 to 7.93). Apolipoprotein E genotype was not associated with education and did not account for the relationship between education and dementia. Bivariate twin modeling showed that the association between education and dementia was not mediated by genetic influences in common between education and dementia. The association was mediated by shared environmental influences that were related to both dementia and to education. Low education is confirmed as a risk factor for dementia. Findings from three different analytic approaches showed that genetic influences did not explain this association.
In medical practices that do not have rosters, only the number of patients who come to the practice can be enumerated: the number who might have visited if they had had a reason to do so remains unknown. The Quadratic Odds Estimator is a technique for estimating the total number of patients cared for by a primary care medical practice, including the non-visitors. A revised version of the model is shown to have an error of less than 1% in predicting the number of patients at risk of visiting a primary care medical practice. Aggregate and sex-specific estimates of total practice size are shown to be comparable to within 2%. The model estimates the prevalence of hypertension among the patients of two family practice resdencies as 18 and 11%. The rationale for employing unconventional regression weights and dual regressions is explained.