The Queensland study of melanoma : Environmental and genetic associations (Q-MEGA); Study design, baseline characteristics, and repeatability of phenotype and sun exposure measures

Cutaneous malignant melanoma (CMM) is a major health issue in Queensland, Australia, which has the world’s highest incidence. Recent molecular and epidemiologic studies suggest that CMM arises through multiple etiological pathways involving gene-environment interactions. Understanding the potential mechanisms leading to CMM requires larger studies than those previously conducted. This article describes the design and baseline characteristics of Q-MEGA, the Queensland Study of Melanoma: Environmental and Genetic Associations, which followed up 4 population-based samples of CMM patients in Queensland, including children, adolescents, men aged over 50, and a large sample of adult cases and their families, including twins. Q-MEGA aims to investigate the roles of genetic and environmental factors, and their interaction, in the etiology of melanoma. Three thousand, four hundred and seventy-one participants took part in the follow-up study and were administered a computer-assisted telephone interview in 2002-2005. Updated data on environmental and phenotypic risk factors, and 2777 blood samples were collected from interviewed participants as well as a subset of relatives. This study provides a large and well-described population-based sample of CMM cases with follow-up data. Characteristics of the cases and repeatability of sun exposure and phenotype measures between the baseline and the follow-up surveys, from 6 to 17 years later, are also described.

Development of self-efficacy and expectancy measures for benzodiazepines

This study aimed to develop and assess the reliability and validity of a pair of self-report questionnaires to measure self-efficacy and expectancy associated with benzodiazepine use, the Benzodiazepine Refusal Self- Efficacy Questionnaire (BRSEQ) and the Benzodiazepine Expectancy Questionnaire (BEQ). Internal structure of the questionnaireswas established by principal component analysis (PCA) in a sample of 155 respondents, and verified by confirmatory factor analyses (CFA) in a second independent sample (n=139) using structural equation modeling. The PCA of the BRSEQ resulted in a 16-item, 4-factor scale, and the BEQ formed an 18-item, 2-factor scale. Both scales were internally reliable. CFA confirmed these internal structures and reduced the questionnaires to a 14-item self-efficacy scale and a 12-item expectancy scale. Lower self-efficacy and higher expectancy were moderately associated with higher scores on the SDS-B. The scales provide reliable measures for assessing benzodiazepine self-efficacy and expectancies. Future research will examine the utility of the scales in prospective prediction of benzodiazepine cessation.

Cognitive assessment in closed head injury : stability, validity and parallel forms for two neuropsychological measures of recovery

Investigated the psychometric properties of the original and alternate sets of the Trail Making Test (TMT) and the Controlled Oral Word Association Test (COWAT; A. L. Benton and D. Hamsher, 1978) in 50 orthopedic and 15 closed head injured (1 yr after trauma) patients (aged 15–59 yrs). Although the alternate forms of both measures proved to be stable and consistent with each other in both groups, only the parallel sets of TMT reliably discriminated the clinical group from controls. Practice effects in the head injured were significant only for Trail B of TMT. Factor analysis of the control group's results identified Verbal Knowledge as a major contributor to performance on COWAT, whereas TMT was more dependent on Rapid Visual Search and Visuomotor Sequencing.

Interestingness Measures for Multi-Level Association Rules

Association rule mining is one technique that is widely used when querying databases, especially those that are transactional, in order to obtain useful associations or correlations among sets of items. Much work has been done focusing on efficiency, effectiveness and redundancy. There has also been a focusing on the quality of rules from single level datasets with many interestingness measures proposed. However, with multi-level datasets now being common there is a lack of interestingness measures developed for multi-level and cross-level rules. Single level measures do not take into account the hierarchy found in a multi-level dataset. This leaves the Support-Confidence approach,which does not consider the hierarchy anyway and has other drawbacks, as one of the few measures available. In this paper we propose two approaches which measure multi-level association rules to help evaluate their interestingness. These measures of diversity and peculiarity can be used to help identify those rules from multi-level datasets that are potentially useful.

Comparing subjective and objective measures of health : Evidence from hypertension for the income/health gradient

Economists rely heavily on self-reported measures to examine the relationship between income and health. We directly compare survey responses of a self-reported measure of health that is commonly used in nationally representative surveys with objective measures of the same health condition. We focus on hypertension. We find no evidence of an income/health greadient using self-reported hypertension but a sizeable gradient when using objectively measured hypertension. We also find that the probability of a false negative reporting is significantly income graded. Our results suggest that using commonly available self-reported chronic health measures might underestimate true income-related inequalities in health.

XML Schema Element Similarity Measures: A Schema Matching Context

In this paper, we classify, review, and experimentally compare major methods that are exploited in the definition, adoption, and utilization of element similarity measures in the context of XML schema matching. We aim at presenting a unified view which is useful when developing a new element similarity measure, when implementing an XML schema matching component, when using an XML schema matching system, and when comparing XML schema matching systems.

Robustness in health research : do differences in health measures, techniques, and time frame matter?

Survey-based health research is in a boom phase following an increased amount of health spending in OECD countries and the interest in ageing. A general characteristic of survey-based health research is its diversity. Different studies are based on different health questions in different datasets; they use different statistical techniques; they differ in whether they approach health from an ordinal or cardinal perspective; and they differ in whether they measure short-term or long-term effects. The question in this paper is simple: do these differences matter for the findings? We investigate the effects of life-style choices (drinking, smoking, exercise) and income on six measures of health in the US Health and Retirement Study (HRS) between 1992 and 2002: (1) self-assessed general health status, (2) problems with undertaking daily tasks and chores, (3) mental health indicators, (4) BMI, (5) the presence of serious long-term health conditions, and (6) mortality. We compare ordinal models with cardinal models; we compare models with fixed effects to models without fixed-effects; and we compare short-term effects to long-term effects. We find considerable variation in the impact of different determinants on our chosen health outcome measures; we find that it matters whether ordinality or cardinality is assumed; we find substantial differences between estimates that account for fixed effects versus those that do not; and we find that short-run and long-run effects differ greatly. All this implies that health is an even more complicated notion than hitherto thought, defying generalizations from one measure to the others or one methodology to another.