Christopher E. Davies

Studies on encapsidated viroid-like RNA I. Characterization of velvet tobacco mottle virus

Velvet tobacco mottle virus (VTMoV) isolated from Nicotiana velutina growing wild in arid Central Australia was transmitted by inoculation to a limited number of plant species of which N. clevelandii was the most convenient experimental host. The virus was also transmitted from field-grown plants toN. velutina and N. clevelandii by the mirid Cyropeltis nicotianae. VTMoV preparations purified by clarification with organic solvents and differential centrifugation contained polyhedral particles about 30 nm in diameter sedimenting as a single component at about 115 S. The particles were shown to be located in the nucleus, cytoplasm, and vacuoles of infected plant cells. Virus dissociated in the presence of mercaptoethanol and sodium dodecyl sulfate (SDS) separated into one major and two minor polypeptides with estimated molecular weights of 33,000, 36,000 and 31,000, respectively. Single-stranded RNA isolated from VTMoV by extraction with phenol was separated into five components with apparent molecular weights of 1.5 x 10(6), 0.63 x 10(6), 0.25 x 10(6), 0.16 x 10(6), and 0.12 x 10(6) referred to as RNAs 1, 1a, 1b, 2, and 3, respectively. It appears that RNAs 1a and 1b are breakdown products of RNA 1, as shown elsewhere, and electron microscopic examination of the other species showed that whereas RNAs 1 and 3 are linear molecules, RNA 2 is circular. The similarity of RNAs 2 and 3 to the RNA of viroids is discussed. VTMoV has been compared with several RNA plant viruses with small polyhedral particles. Only solanum nodiflorum mottle virus appears to share some of its unique features and the two have been shown to be antigenically related.

Growth trajectories in early childhood, their relationship with antenatal and postnatal factors, and development of obesity by age 9 years: results from an Australian birth cohort study

Background:In an era where around one in four children in the United Kingdom, the United States, and Australia are overweight or obese, the development of obesity in early life needs to be better understood. We aimed to identify groups of children with distinct trajectories of growth in infancy and early childhood, to examine any association between these trajectories and body size at age 9, and to assess the relative influence of antenatal and postnatal exposures on growth trajectories.Design:Prospective Australian birth cohort study.Subjects and Methods:In total, 557 children with serial height and weight measurements from birth to 9 years were included in the study. Latent class growth models were used to derive distinct groups of growth trajectories from birth to age 3½ years. Multivariable logistic regression models were used to explore antenatal and postnatal predictors of growth trajectory groups, and multivariable linear and logistic regression models were used to examine the relationships between growth trajectory groups and body size at age 9 years.Results:We identified four discrete growth trajectories from birth to age 3½ years, characterised as low, intermediate, high, or accelerating growth. Relative to the intermediate growth group, the low group had reduced z-body mass index (BMI) (−0.75 s.d.; 95% confidence interval (CI) −1.02, −0.47), and the high and accelerating groups were associated with increased body size at age 9 years (high: z-BMI 0.70 s.d.; 95% CI 0.49, 0.62; accelerating: z-BMI 1.64 s.d.; 95% CI 1.16, 2.11). Of the antenatal and postnatal exposures considered, the most important differentiating factor was maternal obesity in early pregnancy, associated with a near quadrupling of risk of membership of the accelerating growth trajectory group compared with the intermediate growth group (odds ratio (OR) 3.72; 95% CI 1.15, 12.05).Conclusions:Efforts to prevent childhood obesity may need to be embedded within population-wide strategies that also pay attention to healthy weight for women in their reproductive years.

The effects of house moves during early childhood on child mental health at age 9 years

BackgroundResidential mobility is common in families with young children; however, its impact on the social development of children is unclear. We examined associations between the number, timing and type of house moves in childhood and child behaviour problems using data from an ongoing longitudinal study.MethodsComplete data on residential mobility and child behaviour was available for 403 families. Three aspects of mobility were considered: (a) number of house moves from birth to <2 years, 2 to <5 years and 5 to 9 years; (b) lifetime number of house moves; and (c) moves associated with different housing trajectories characterized by changes in housing tenure. The primary outcomes were internalizing and externalizing behaviour problems at 9 years derived from Achenbach’s Child Behaviour Checklist. Linear regression analyses were used to investigate the effect of the housing variables on internalizing and externalizing behaviour problem scores with adjustment for a range of sociodemographic and household covariates.ResultsMoving house ≥2 times before 2 years of age was associated with an increased internalizing behaviour score at age 9 years. This association remained after adjustment for sociodemographic and household factors. There was no association between increased residential mobility in other time periods and internalizing behaviour, or mobility in any period and externalizing behaviour. There was no effect of lifetime number of moves, or of an upwardly or downwardly mobile housing trajectory. However, a housing trajectory characterized by continuous rental occupancy was associated with an increased externalizing behaviour score.ConclusionsThese findings may suggest that there is a sensitive period, in the first few years of life, in which exposure to increased residential mobility has a detrimental effect on mental health in later childhood.

The validity of military screening for mental health problems: diagnostic accuracy of the PCL, K10 and AUDIT scales in an entire military population

Depression, alcohol use disorders and post‐traumatic stress disorder (PTSD) are serious issues among military personnel due to their impact on operational capability and individual well‐being. Several military forces screen for these disorders using scales including the Kessler Psychological Distress Scale (K10), Alcohol Use Disorders Identification Test (AUDIT), and Post‐traumatic Stress Disorder Checklist (PCL). However, it is unknown whether established cutoffs apply to military populations. This study is the first to test the diagnostic accuracy of these three scales in a population‐based military cohort.

The Australian Defence Force Mental Health Prevalence and Wellbeing Study: design and methods

Background The Australian Defence Force (ADF) Mental Health Prevalence and Wellbeing Study (MHPWS) is the first study of mental disorder prevalence in an entire military population. Objective The MHPWS aims to establish mental disorder prevalence, refine current ADF mental health screening methods, and identify specific occupational factors that influence mental health. This paper describes the design, sampling strategies, and methodology used in this study. Method At Phase 1, approximately half of all regular Navy, Army, and Air Force personnel (n=24,481) completed self-report questionnaires. At Phase 2, a stratified sub-sample (n=1,798) completed a structured diagnostic interview to detect mental disorder. Based on data from non-responders, data were weighted to represent the entire ADF population (n=50,049). Results One in five ADF members met criteria for a 12-month mental disorder (22%). The most common disorder category was anxiety disorders (14.8%), followed by affective (9.5%) and alcohol disorders (5.2%). At risk ADF sub-groups were Army personnel, and those in the lower ranks. Deployment status did not have an impact on mental disorder rates. Conclusion This study has important implications for mental health service delivery for Australian and international military personnel as well as contemporary veterans.

Growth in early life and the development of obesity by age 9 years: are there critical periods and a role for an early life stressor?

Objective:Rapid growth, possibly occurring in critical periods in early life, may be important for the development of obesity. It is unknown whether this is influenced by postnatal exposures such as age-relevant sources of stress. Frequent house moves may be one such stressor. We aimed to examine if there is a period of growth in early life critical for the development of child obesity by age 9 years and assess the role of house moves in modifying any relationships between early life growth and obesity at age 9 years.Design:Prospective Australian birth cohort study.Subjects:In all, 392 children with serial body size measurements from birth to age 9 years.Methods:Standardized body mass index (z-BMI) was available for six time points (spanning birth to 3½ years), and the total number of house moves between birth and 3½ years. The outcomes considered were z-BMI and % body fat (%BF) at age 9 years. Linear regression models were used to estimate the effects of serial measurements of z-BMI and number of house moves on the outcomes.Results:Life-course plots showed that z-BMI at 3½ years was a statistically significant predictor of z-BMI at 9 years (β=0.80; standard error (s.e.), 0.04), whereas z-BMI at 9 months (β=−1.13; s.e., 0.40) and 3½ years (β=4.82; s.e., 0.42) were significant predictors of %BF at age 9 years. There were statistically significant interactions between the number of house moves and change in z-BMI between 9 and 12 months, such that ⩾3 house moves in early life amplified the detrimental effects of earlier rapid growth on both body size and composition at age 9 years.Conclusion:In the absence of evidence for a single critical period, efforts to prevent overweight and obesity are required throughout childhood. In addition, modifiable postnatal stressors may exacerbate effects of early growth on obesity in later childhood.

Performance of methods for estimating the effect of covariates on group membership probabilities in group-based trajectory models

One purpose of a longitudinal study is to gain insight of how characteristics at earlier points in time can impact on subsequent outcomes. Typically, the outcome variable varies over time and the data for each individual can be used to form a discrete path of measurements, that is a trajectory. Group-based trajectory modelling methods seek to identify subgroups of individuals within a population with trajectories that are more similar to each other than to trajectories in distinct groups. An approach to modelling the influence of covariates measured at earlier time points in the group-based setting is to consider models wherein these covariates affect the group membership probabilities. Models in which prior covariates impact the trajectories directly are also possible but are not considered here. In the present study, we compared six different methods for estimating the effect of covariates on the group membership probabilities, which have different approaches to account for the uncertainty in the group membership assignment. We found that when investigating the effect of one or several covariates on a group-based trajectory model, the full likelihood approach minimized the bias in the estimate of the covariate effect. In this ‘1-step’ approach, the estimation of the effect of covariates and the trajectory model are carried out simultaneously. Of the ‘3-step’ approaches, where the effect of the covariates is assessed subsequent to the estimation of the group-based trajectory model, only Vermunt’s improved 3 step resulted in bias estimates similar in size to the full likelihood approach. The remaining methods considered resulted in considerably higher bias in the covariate effect estimates and should not be used. In addition to the bias empirically demonstrated for the probability regression approach, we have shown analytically that it is biased in general.

The impact of covariance misspecification in group-based trajectory models for longitudinal data with non-stationary covariance structure:

One purpose of a longitudinal study is to gain a better understanding of how an outcome of interest changes among a given population over time. In what follows, a trajectory will be taken to mean the series of measurements of the outcome variable for an individual. Group-based trajectory modelling methods seek to identify subgroups of trajectories within a population, such that trajectories that are grouped together are more similar to each other than to trajectories in distinct groups. Group-based trajectory models generally assume a certain structure in the covariances between measurements, for example conditional independence, homogeneous variance between groups or stationary variance over time. Violations of these assumptions could be expected to result in poor model performance. We used simulation to investigate the effect of covariance misspecification on misclassification of trajectories in commonly used models under a range of scenarios. To do this we defined a measure of performance relative to the ideal Bayesian correct classification rate. We found that the more complex models generally performed better over a range of scenarios. In particular, incorrectly specified covariance matrices could significantly bias the results but using models with a correct but more complicated than necessary covariance matrix incurred little cost.

Screening for Depression and Psychological Distress in a Currently Serving Military Population: The Diagnostic Accuracy of the K10 and the PHQ9

This study is the first to examine the diagnostic accuracy of two depression screening scales—the Kessler Psychological Distress Scale (K10) and the Patient Health Questionnaire (PHQ)—in an entire regular-serving military population. Currently serving Australian Defence Force personnel (n = 24,481) completed the K10 and PHQ9. Then a targeted subsample (i.e., the analysis sample, n = 1,730) completed a diagnostic interview to identify DSM-IV 30-day disorder. Weighted results represented the entire population (N = 50,049). Both scales similarly showed a good ability to discriminate between personnel with and without depressive disorders. Optimal cutoffs (19 for K10, 6 for PHQ9) showed high sensitivity and good specificity, and were similar to though slightly lower than those recommended in civilian populations. Both scales appear to be valid screens for depressive disorder in the military, using the cutoffs identified. As both performed similarly, scale choice may depend on other factors (e.g., availability of norms).