Age-specific changes in BMI and BMI distribution among Australian adults using cross-sectional surveys from 1980 to 2008

Background: Research efforts have focused mainly on trends in obesity among populations, or changes in mean body mass index (BMI), without consideration of changes in BMI across the BMI spectrum. Examination of age-specific changes in BMI distribution may reveal patterns that are relevant to targeting of interventions.

Methods: Using a synthetic cohort approach (which matches members of cross-sectional surveys by birth year) we estimated population representative annual BMI change across two time periods (1980 to 1989 and 1995 to 2008) by age, sex, socioeconomic position and quantiles of BMI. Our study population was a total of 27 349 participants from four nationally representative Australian health surveys; Risk Factor Prevalence Study surveys (1980 and 1989), the 1995 National Nutrition Survey and the 2007/8 National Health Survey.

Results: We found greater mean BMI increases in younger people, in those already overweight and in those with lower education. For men, age-specific mean annual BMI change was very similar in the 1980s and the early 2000s (P=0.39), but there was a recent slowing down of annual BMI gain for older women in the 2000s compared with their same-age counterparts in the 1980s (P<0.05). BMI change was not uniform across the BMI distribution, with different patterns by age and sex in different periods. Young adults had much greater BMI gain at higher BMI quantiles, thus adding to the increased right skew in BMI, whereas BMI gain for older populations was more even across the BMI distribution.

Conclusions: The synthetic cohort technique provided useful information from serial cross-sectional survey data. The quantification of annual BMI change has contributed to an understanding of the epidemiology of obesity progression and identified key target groups for policy attention—young adults, those who are already overweight and those of lower socioeconomic status.

A suite of visual languages for model-driven development of statistical surveys and services

Objective: To provide statistician end users with a visual language environment for complex statistical survey design and implementation. Methods: We have developed, in conjunction with professional statisticians, the Statistical Design Language (SDL), an integrated suite of visual languages aimed at supporting the process of designing statistical surveys, and its support environment, SDLTool. SDL comprises five diagrammatic notations: survey diagrams, data diagrams, technique diagrams, task diagrams and process diagrams. SDLTool provides an integrated environment supporting design, coordination, execution, sharing and publication of complex statistical survey techniques as web services. SDLTool allows association of model components with survey artefacts, including data sets, metadata, and statistical package analysis scripts, with the ability to execute elements of the survey design model to implement survey analysis. Results: We describe three evaluations of SDL and SDLTool: use of the notation by expert statistician to design and execute surveys; useability evaluation of the environment; and assessment of several generated statistical analysis web services. Conclusion: We have shown the effectiveness of SDLTool for supporting statistical survey design and implementation. Practice implications: We have developed a more effective approach to supporting statisticians in their survey design work.

Accounting for uncertainty in volumes of seabed change measured with repeat multibeam sonar surveys

Seafloors of unconsolidated sediment are highly dynamic features; eroding or accumulating under the action of tides, waves and currents. Assessing which areas of the seafloor experienced change and measuring the corresponding volumes involved provide insights into these important active sedimentation processes. Computing the difference between Digital Elevation Models (DEMs) obtained from repeat Multibeam Echosounders (MBES) surveys has become a common technique to identify these areas, but the uncertainty in these datasets considerably affects the estimation of the volumes displaced. The two main techniques used to take into account uncertainty in volume estimations are the limitation of calculations to areas experiencing a change in depth beyond a chosen threshold, and the computation of volumetric confidence intervals. However, these techniques are still in their infancy and, as a result, are often crude, seldom used or poorly understood. In this article, we explored a number of possible methodological advances to address this issue, including: (1) using the uncertainty information provided by the MBES data processing algorithm CUBE, (2) adapting fluvial geomorphology techniques for volume calculations using spatially variable thresholds and (3) volumetric histograms. The nearshore seabed off Warrnambool harbour - located in the highly energetic southwest Victorian coast, Australia - was used as a test site. Four consecutive MBES surveys were carried out over a four-months period. The difference between consecutive DEMs revealed an area near the beach experiencing large sediment transfers - mostly erosion - and an area of reef experiencing increasing deposition from the advance of a nearby sediment sheet. The volumes of sediment displaced in these two areas were calculated using the techniques described above, both traditionally and using the suggested improvements. We compared the results and discussed the applicability of the new methodological improvements. We found that the spatially variable uncertainty derived from the CUBE algorithm provided the best results (i.e. smaller confidence intervals), but that similar results can be obtained using as a fixed uncertainty value derived from a reference area under a number of operational conditions.