Socioeconomic disadvantage and changes in health risk behaviours in Australia: 1989-90 to 2001

Objective. Lower socioeconomic status (SES) is associated in industrialized countries with unhealthy lifestyle characteristics, such as smoking, physical inactivity and being overweight or obese. This paper examines changes over time in the association between SES and smoking status, physical activity and being overweight or obese in Australia. Methods. Data were taken from three successive national health surveys in Australia carried out in 1989-90 (n = 54 576), 1995 (n = 53 828) and 2001 (n = 26 863). Participants in these surveys were selected using a national probability sampling strategy, and aggregated data for geographical areas are used to determine the changing association between SES and lifestyle over time. Findings. Overall, men had less healthy lifestyles, In 2001 inverse SES trends for both men and women showed that those living in lower SES areas were more likely to smoke and to be sedentary and obese, There were some important socioeconomic changes over the period 1989-90 to 2001. The least socioeconomically disadvantaged areas had the largest decrease in the percentage of people smoking tobacco (24% decrease for men and 12% for women) and the largest decrease in the percentage of people reporting sedentary activity levels (25% decrease for men and 22% for women). While there has been a general increase in the percentage over time of those who are overweight or obese, there is a modest trend for being overweight to have increased (by about 16% only among females) among those living in areas of higher SES. Conclusion. Socioeconomic inequalities have been increasing for several key risk behaviours related to health; this suggests that T specific population-based prevention strategies intended to reduce health inequalities are needed.

Ontological clarity and comprehension in health data models

Conceptual modeling forms an important part of systems analysis. If this is done incorrectly or incompletely, there can be serious implications for the resultant system, specifically in terms of rework and useability. One approach to improving the conceptual modelling process is to evaluate how well the model represents reality. Emergence of the Bunge-Wand-Weber (BWW) ontological model introduced a platform to classify and compare the grammar of conceptual modelling languages. This work applies the BWW theory to a real world example in the health arena. The general practice computing group data model was developed using the Barker Entity Relationship Modelling technique. We describe an experiment, grounded in ontological theory, which evaluates how well the GPCG data model is understood by domain experts. The results show that with the exception of the use of entities to represent events, the raw model is better understood by domain experts

Leadership clarity and team innovation in health care

The relationships among leadership clarity (i.e., team members' consensual perceptions of clarity of and no conflict over leadership of their teams), team processes, and innovation were examined in health care contexts. The sample comprised 3447 respondents from 98 primary health care teams (PHCTs), 113 community mental health teams (CMHTs), and 72 breast cancer care teams (BCTs). The results revealed that leadership clarity is associated with clear team objectives, high levels of participation, commitment to excellence, and support for innovation. Team processes consistently predicted team innovation across all three samples. Team leadership predicted innovation in the latter two samples, and there was some evidence that team processes partly mediated this relationship. The results imply the need for theory that incorporates clarity and not just style of leadership. For health care teams in particular, and teams in general, the results suggest a need to ensure leadership is clear in teams when innovation is a desirable team performance outcome. © 2003 Elsevier Inc. All rights reserved.

Getting the most out of multidisciplinary teams: a multi-sample study of team innovation in health care

Driven by the assumption that multidisciplinarity contributes positively to team outcomes teams are often deliberately staffed such that they comprise multiple disciplines. However, the diversity literature suggests that multidisciplinarity may not always benefit a team. This study departs from the notion of a linear, positive effect of multidisciplinarity and tests its contingency on the quality of team processes. It was assumed that multidisciplinarity only contributes to team outcomes if the quality of team processes is high. This hypothesis was tested in two independent samples of health care workers (N = 66 and N = 95 teams), using team innovation as the outcome variable. Results support the hypothesis for the quality of innovation, rather than the number of innovations introduced by the teams.

Spatially clustered associations in health GIS

Overlaying maps using a desktop GIS is often the first step of a multivariate spatial analysis. The potential of this operation has increased considerably as data sources and Web services to manipulate them are becoming widely available via the Internet. Standards from the OGC enable such geospatial mashups to be seamless and user driven, involving discovery of thematic data. The user is naturally inclined to look for spatial clusters and correlation of outcomes. Using classical cluster detection scan methods to identify multivariate associations can be problematic in this context, because of a lack of control on or knowledge about background populations. For public health and epidemiological mapping, this limiting factor can be critical but often the focus is on spatial identification of risk factors associated with health or clinical status. Spatial entropy index HSu for the ScankOO analysis of the hypothetical dataset using a vicinity which is fixed by the number of points without distinction between their labels. (The size of the labels is proportional to the inverse of the index) In this article we point out that this association itself can ensure some control on underlying populations, and develop an exploratory scan statistic framework for multivariate associations. Inference using statistical map methodologies can be used to test the clustered associations. The approach is illustrated with a hypothetical data example and an epidemiological study on community MRSA. Scenarios of potential use for online mashups are introduced but full implementation is left for further research.