A model for predicting the future incidence of coronary heart disease within percentiles of coronary heart disease risk

Background We present a method (The CHD Prevention Model) for modelling the incidence of fatal and nonfatal coronary heart disease (CHD) within various CHD risk percentiles of an adult population. The model provides a relatively simple tool for lifetime risk prediction for subgroups within a population. It allows an estimation of the absolute primary CHD risk in different populations and will help identify subgroups of the adult population where primary CHD prevention is most appropriate and cost-effective. Methods The CHD risk distribution within the Australian population was modelled, based on the prevalence of CHD risk, individual estimates of integrated CHD risk, and current CHD mortality rates. Predicted incidence of first fatal and nonfatal myocardial infarction within CHD risk strata of the Australian population was determined. Results Approximately 25% of CHD deaths were predicted to occur amongst those in the top 10 percentiles of integrated CHD risk, regardless of age group or gender. It was found that while all causes survival did not differ markedly between percentiles of CHD risk before the ages of around 50-60, event-free survival began visibly to differ about 5 years earlier. Conclusions The CHD Prevention Model provides a means of predicting future CHD incidence amongst various strata of integrated CHD risk within an adult population. It has significant application both in individual risk counselling and in the identification of subgroups of the population where drug therapy to reduce CHD risk is most cost-effective. J Cardiovasc Risk 8:31-37 (C) 2001 Lippincott Williams & Wilkins.

An assumption-driven case-specific model editor

A case sensitive intelligent model editor has been developed for constructing consistent lumped dynamic process models and for simplifying them using modelling assumptions. The approach is based on a systematic assumption-driven modelling procedure and on the syntax and semantics of process,models and the simplifying assumptions.

The moderating role of social support in Karasek's job strain model

This paper examines whether social support is a boundary-determining criterion in the job strain model of Karasek (1979). The particular focus is the extent to which different sources of social support, work overload and task control influence job satisfaction, depersonalization and supervisor assessments of work performance. Hypotheses are tested using prospective survey data from 80 clerical staff in a university setting. Results revealed 3-way interactions among levels of support (supervisor, co-worker, non-work), perceived task control and work overload on levels of work performance and employee adjustment (self-report). After controlling for levels of negative affect in all analyses, there was evidence that high levels of supervisor support mitigated against the negative effects of high strain jobs on levels of job satisfaction and reduced reported levels of depersonalization. Moreover, high levels of non-work support and co-worker support also mitigated against the negative effects of high strain jobs on levels of work performance. The results are discussed in terms of the importance of social support networks both at, and beyond, the work context.