Using the Triangle of Human Ecology for understanding self-rated depression: A quantitative study based on the HUNT 3 cohort.

AIMS: To test the Triangle of Human Ecology by examining associations between unipolar depression and different measures of human biological factors, health behaviour, and the physical environment. METHODS: Data originate from the third wave of the Nord-Trøndelag Health Study (2006-2008). The survey was based on a random sample of 50,000 Norwegians (response rate: 54%). Logistic regression was performed, using unipolar depression, measured with the Hospital Anxiety and Depression Scale score, as outcome variable and 38 explanatory variables. RESULTS: Biological factors including older age and male gender were associated with higher odds of depression, as were behavioural factors including drinking behaviour and having a neurotic personality. Reduced odds were associated with units of alcohol consumed, extrovert personality and physical activity. Social networks were an environmental factor with reduced odds at both personal and neighbourhood levels, as was warmer outdoor temperatures. CONCLUSIONS: Using the Triangle of Human Ecology provides a holistic insight into how behaviour, biology and the environment influence mental health.

The use of multilevel models for the prediction of road accident outcomes.

An important problem in road traffic accident research is the resolution of the magnitude by which individual accident characteristics affect the risk of fatality for each person involved. This article introduces the potential of a recently developed form of regression models, known as multilevel models, for quantifying the various influences on casualty outcomes. The application of multilevel models is illustrated by the analysis of the predictors of outcome amongst over 16,000 fatally and seriously injured casualties involved in accidents between 1985 and 1996 in Norway. Risk of fatality was found to be associated with casualty age and sex, as well as the type of vehicles involved, the characteristics of the impact, the attributes of the road section on which it took place, the time of day, and whether alcohol was suspected. After accounting for these factors, the multilevel analysis showed that 16% of unexplained variation in casualty outcomes was between accidents, whilst approximately 1% was associated with the area of Norway in which each incident occurred. The benefits of using multilevel models to analyse accident data are discussed along with the limitations of traditional regression modelling approaches.