Information Based Diagnostic for Genetic Variance Parameter Estimation in Multi-Environment Trials

Plant breeding programs evaluate varieties in series of field trials across years and locations, referred to as multi-environment trials (METs). These are an essential part of variety evaluation with the key aim of the statistical analysis of these datasets to accurately estimate the variety by environment (VE) effects. It has previously been thought that the number of varieties in common between environments, referred to as "variety connectivity," was a key driver of the reliability of genetic variance parameter estimation and that this in turn affected the reliability of predictions of VE effects. In this paper we have provided the link between the objectives of this work and those in model-based experimental design. We propose the use of the D -optimality criterion as a diagnostic to capture the information available for the residual maximum likelihood (REML) estimation of the genetic variance parameters. We demonstrate the methods for a dataset with pedigree information as well as evaluating the performance of the diagnostic using two simulation studies. This measure is shown to provide a superior diagnostic to the traditional connectivity type measure in the sense of better forecasting the uncertainty of genetic variance parameter estimates.

Usual intake of meat in Australians: secondary analysis of the 2011-12 National Nutrition and Physical Activity Survey using the NCI method

INTRODUCTION

Global public health recommendations advise limiting the intake of red and processed meats. There is a need for more comprehensive information on meat consumption in Australia, specifically usual intake of meat from a nationally representative sample. The aim of this study was to use the National Cancer Institute (NCI) method to examine usual meat intakes in the 2011-12 National Nutrition and Physical Activity Survey (NNPAS).

METHODS

This was a secondary analysis of the cross-sectional 2011-12 NNPAS, which contains observations for 12 153 respondents (9341 adults and 2812 children/adolescents). Usual consumption of all meat, red and processed meats was calculated using the NCI method. Consumption of meat was explored by age and gender groups, as well as by household type.

RESULTS

Amongst adults and children/adolescents, consumption of all meat was significantly higher in males (adults: 187.1 g day^-1 ; children/adolescents: 125.0 g day^-1 ) than females (adults: 125.5 g day^-1 ; children/adolescents: 95.4 g day^-1 ). Similar patterns were observed for red (males: adults 85.5 g day^-1 , children/adolescents 42.9 g day^-1 ; females: adults 57.1 g day^-1 , children/adolescents 34.9 g day^-1 ) and processed meat, although intakes of processed meats in children and adolescents (males: 26.5 g day^-1 ; females: 16.8 g day^-1 ) were found to be similar to those of adults aged ≥19 years (males: 28.3 g day^-1 ; females: 15.3 g day^-1 ). Patterns of meat consumption across household types appeared to differ between genders.

CONCLUSIONS

The present study suggests that Australians are likely to be exceeding population recommendations for meat intakes, with differing patterns observed across gender and household types. These findings highlight a need for targeted dietetic and population strategies aimed at promoting a healthy consumption of meats within the Australian population.

Alcohol and smoking consumption behaviours in older Australian adults: prevalence, period and socio-demographic differentials in the DYNOPTA sample

PURPOSE

Alcohol consumption and tobacco use are key risk factors for chronic disease and health burden across the adult lifespan. We estimate the prevalence of alcohol consumption and smoking by age and time period in adults from mid to old age.

METHODS

Participants (n = 50,652) were drawn from the Dynamic Analyses to Optimise Ageing (DYNOPTA) project and were compared with Australian National Health Survey data. Alcohol and smoking consumption DYNOPTA data were weighted to the estimated resident population of the sampling frame for each contributing study according to age and sex distributions within major statistical regions.

RESULTS

Comparisons in the rates of smoking and alcohol consumption between DYNOPTA and other national surveys were comparable. Males were more likely to be (RRR = 2.12) or have been smokers (RRR = 2.97), whilst females were more likely to be non-drinkers (RRR = 2.52). Period effects were also identified; higher prevalence rates in consumption of alcohol (RRR = 3.21) and smoking (RRR = 1.67) for those contributing studies from the early 1990's, in comparison with those studies from the latter half of the decade, were reported.

CONCLUSIONS

Over a decade, prevalence rates for high-risk consumption of alcohol and current smoking behaviour declined and suggest the possible impact of government health policy, with targeted-health policies, that included bans on public smoking, and a toughening of legislation against alcohol-related crime.

Understanding ageing in older Australians: the contribution of the Dynamic Analyses to Optimise Ageing (DYNOPTA) project to the evidence base and policy

AIM

To describe the Dynamic Analyses to Optimise Ageing (DYNOPTA) project and illustrate its contributions to understanding ageing through innovative methodology, and investigations on outcomes based on the project themes. DYNOPTA provides a platform and technical expertise that may be used to combine other national and international datasets.

METHODS

The DYNOPTA project has pooled and harmonised data from nine Australian longitudinal studies to create the largest available longitudinal dataset (n= 50652) on ageing in Australia.

RESULTS

A range of findings have resulted from the study to date, including methodological advances, prevalence rates of disease and disability, and mapping trajectories of ageing with and without increasing morbidity. DYNOPTA also forms the basis of a microsimulation model that will provide projections of future costs of disease and disability for the baby boomer cohort.

CONCLUSION

DYNOPTA contributes significantly to the Australian evidence base on ageing to inform key social and health policy domains.

Estimates of probable dementia prevalence from population-based surveys compared with dementia prevalence estimates based on meta-analyses

Background

National data on dementia prevalence are not always available, yet it may be possible to obtain estimates from large surveys that include dementia screening instruments. In Australia, many of the dementia prevalence estimates are based on European data collected between 15 and 50 years ago. We derived population-based estimates of probable dementia and possible cognitive impairment in Australian studies using the Mini-Mental State Examination (MMSE), and compared these to estimates of dementia prevalence from meta-analyses of European studies.

Methods

Data sources included a pooled dataset of Australian longitudinal studies (DYNOPTA), and two Australian Bureau of Statistics National Surveys of Mental Health and Wellbeing. National rates of probable dementia (MMSE < 24) and possible cognitive impairment (24-26) were estimated using combined sample weights.

Results

Estimates of probable dementia were higher in surveys than in meta-analyses for ages 65-84, but were similar at ages 85 and older. Surveys used weights to account for sample bias, but no adjustments were made in meta-analyses. Results from DYNOPTA and meta-analyses had a very similar pattern of increase with age. Contrary to trends from some meta-analyses, rates of probable dementia were not higher among women in the Australian surveys. Lower education was associated with higher prevalence of probable dementia. Data from investigator-led longitudinal studies designed to assess cognitive decline appeared more reliable than government health surveys.

Conclusions

This study shows that estimates of probable dementia based on MMSE in studies where cognitive decline and dementia are a focus, are a useful adjunct to clinical studies of dementia prevalence. Such information and may be used to inform projections of dementia prevalence and the concomitant burden of disease.