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Background: Adolescence is characterized by marked psychosocial, behavioural and biological changes and represents a critical life transition through which adult health and well-being are established. Substantial research confirms the role of psycho-social and environmental influences on this transition, but objective research examining the role of puberty hormones, testosterone in males and oestradiol in females (as biomarkers of puberty) on adolescent events is lacking. Neither has the tempo of puberty, the time from onset to completion of puberty within an individual been studied, nor the interaction between age of onset and tempo. This study has been designed to provide evidence on the relationship between reproductive hormones and the tempo of their rise to adult levels, and adolescent behaviour, health and wellbeing. Methods/Design: The ARCHER study is a multidisciplinary, prospective, longitudinal cohort study in 400 adolescents to be conducted in two centres in regional Australia in the State of New South Wales. The overall aim is to determine how changes over time in puberty hormones independently affect the study endpoints which describe universal and risk behaviours, mental health and physical status in adolescents. Recruitment will commence in school grades 5, 6 and 7 (10-12 years of age). Data collection includes participant and parent questionnaires, anthropometry, blood and urine collection and geocoding. Data analysis will include testing the reliability and validity of the chosen measures of puberty for subsequent statistical modeling to assess the impact over time of tempo and onset of puberty (and their interaction) and mean-level repeated measures analyses to explore for significant upward and downward shifts on target outcomes as a function of main effects. Discussion: The strengths of this study include enrollment starting in the earliest stages of puberty, the use of frequent urine samples in addition to annual blood samples to measure puberty hormones, and the simultaneous use of parental questionnaires. [ABSTRACT FROM AUTHOR]

Background: The Conditional Autoregressive (CAR) model is widely used in many small-area ecological studies to analyse outcomes measured at an areal level. There has been little evaluation of the influence of different neighbourhood weight matrix structures on the amount of smoothing performed by the CAR model. We examined this issue in detail. Methods: We created several neighbourhood weight matrices and applied them to a large dataset of births and birth defects in New South Wales (NSW), Australia within 198 Statistical Local Areas. Between the years 1995-2003, there were 17,595 geocoded birth defects and 770,638 geocoded birth records with available data. Spatio-temporal models were developed with data from 1995-2000 and their fit evaluated within the following time period: 2001-2003. Results: We were able to create four adjacency-based weight matrices, seven distance-based weight matrices and one matrix based on similarity in terms of a key covariate (i.e. maternal age). In terms of agreement between observed and predicted relative risks, categorised in epidemiologically relevant groups, generally the distance-based matrices performed better than the adjacency-based neighbourhoods. In terms of recovering the underlying risk structure, the weight-7 model (smoothing by maternal-age 'Covariate model') was able to correctly classify 35/47 high-risk areas (sensitivity 74%) with a specificity of 47%, and the 'Gravity' model had sensitivity and specificity values of 74% and 39% respectively. Conclusion: We found considerable differences in the smoothing properties of the CAR model, depending on the type of neighbours specified. This in turn had an effect on the models' ability to recover the observed risk in an area. Prior to risk mapping or ecological modelling, an exploratory analysis of the neighbourhood weight matrix to guide the choice of a suitable weight matrix is recommended. Alternatively, the weight matrix can be chosen a priori based on decision-theoretic considerations including loss, cost and inferential aims. [ABSTRACT FROM AUTHOR]