Assessment of the severity of Ebola virus disease in Sierra Leone in 2014–2015

The current Ebola virus disease (EVD) epidemic in West Africa is unprecedented in scale, and Sierra Leone is the most severely affected country. The case fatality risk (CFR) and hospitalization fatality risk (HFR) were used to characterize the severity of infections in confirmed and probable EVD cases in Sierra Leone. Proportional hazards regression models were used to investigate factors associated with the risk of death in EVD cases. In total, there were 17 318 EVD cases reported in Sierra Leone from 23 May 2014 to 31 January 2015. Of the probable and confirmed EVD cases with a reported final outcome, a total of 2536 deaths and 886 recoveries were reported. CFR and HFR estimates were 74·2% [95% credibility interval (CrI) 72·6–75·5] and 68·9% (95% CrI 66·2–71·6), respectively. Risks of death were higher in the youngest (0–4 years) and oldest (≥60 years) age groups, and in the calendar month of October 2014. Sex and occupational status did not significantly affect the mortality of EVD. The CFR and HFR estimates of EVD were very high in Sierra Leone.

The validity of the GENEActiv wrist-worn accelerometer for measuring adult sedentary time in free living

OBJECTIVES Based on self-reported measures, sedentary time has been associated with chronic disease and mortality. This study examined the validity of the wrist-worn GENEactiv accelerometer for measuring sedentary time (i.e. sitting and lying) by posture classification, during waking hours in free living adults. DESIGN Fifty-seven participants (age=18-55 years 52% male) were recruited using convenience sampling from a large metropolitan Australian university. METHODS Participants wore a GENEActiv accelerometer on their non-dominant wrist and an activPAL device attached to their right thigh for 24-h (00:00 to 23:59:59). Pearson's Correlation Coefficient was used to examine the convergent validity of the GENEActiv and the activPAL for estimating total sedentary time during waking hours. Agreement was illustrated using Bland and Altman plots, and intra-individual agreement for posture was assessed with the Kappa statistic. RESULTS Estimates of average total sedentary time over 24-h were 623 (SD 103) min/day from the GENEActiv, and 626 (SD 123) min/day from the activPAL, with an Intraclass Correlation Coefficient of 0.80 (95% confidence intervals 0.68-0.88). Bland and Altman plots showed slight underestimation of mean total sedentary time for GENEActiv relative to activPAL (mean difference: -3.44min/day), with moderate limits of agreement (-144 to 137min/day). Mean Kappa for posture was 0.53 (SD 0.12), indicating moderate agreement for this sample at the individual level. CONCLUSIONS The estimation of sedentary time by posture classification of the wrist-worn GENEActiv accelerometer was comparable to the activPAL. The GENEActiv may provide an alternative, easy to wear device based measure for descriptive estimates of sedentary time in population samples

Rethinking Early Childhood Education and Care: Implications for Research and Evaluation

Research in disadvantaged populations demonstrates that the effect of Early Childhood Education and Care (ECEC) programs can reach into adulthood and influence a wide range of achievement and social well-being outcomes. In English-speaking developed economies, these findings have sparked new perceptions of the role ECEC programs play in both the public and private sphere. Programs that achieve improved learning and social well-being for children are seen as an investment for both individuals and society. Yet, the empirical understanding of what programs best deliver positive outcomes across the diversity of social contexts is limited. A key research task is to identify the forms of ECEC that are most effective in delivering enduring and broad positive outcomes for all children. This article explores changing policy conceptualizations of ECEC, the outcome goals of ECEC, and directions for research in identifying quality in ECEC programs.

Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.

Head motion and Inattention/Hyperactivity share common genetic influences: Implications for fMRI studies of ADHD

Head motion (HM) is a well known confound in analyses of functional MRI (fMRI) data. Neuroimaging researchers therefore typically treat HM as a nuisance covariate in their analyses. Even so, it is possible that HM shares a common genetic influence with the trait of interest. Here we investigate the extent to which this relationship is due to shared genetic factors, using HM extracted from resting-state fMRI and maternal and self report measures of Inattention and Hyperactivity-Impulsivity from the Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour (SWAN) scales. Our sample consisted of healthy young adult twins (N = 627 (63% females) including 95 MZ and 144 DZ twin pairs, mean age 22, who had mother-reported SWAN; N = 725 (58% females) including 101 MZ and 156 DZ pairs, mean age 25, with self reported SWAN). This design enabled us to distinguish genetic from environmental factors in the association between head movement and ADHD scales. HM was moderately correlated with maternal reports of Inattention (r = 0.17, p-value = 7.4E-5) and Hyperactivity-Impulsivity (r = 0.16, p-value = 2.9E-4), and these associations were mainly due to pleiotropic genetic factors with genetic correlations [95% CIs] of rg = 0.24 [0.02, 0.43] and rg = 0.23 [0.07, 0.39]. Correlations between self-reports and HM were not significant, due largely to increased measurement error. These results indicate that treating HM as a nuisance covariate in neuroimaging studies of ADHD will likely reduce power to detect between-group effects, as the implicit assumption of independence between HM and Inattention or Hyperactivity-Impulsivity is not warranted. The implications of this finding are problematic for fMRI studies of ADHD, as failing to apply HM correction is known to increase the likelihood of false positives. We discuss two ways to circumvent this problem: censoring the motion contaminated frames of the RS-fMRI scan or explicitly modeling the relationship between HM and Inattention or Hyperactivity-Impulsivity

Psychological distress may affect nutrition indicators in Australian adults

- Objective The purpose of this research was to explore which demographic and health status variables moderated the relationship between psychological distress and three nutrition indicators: the consumption of fruits, vegetables and takeaway. - Method We analysed data from the 2009 Self-Reported Health Status Survey Report collected in the state of Queensland, Australia. Adults (N = 6881) reported several demographic and health status variables. Moderated logistic regression models were estimated separately for the three nutrition indicators, testing as moderators demographic (age, gender, educational attainment, household income, remoteness, and area-level socioeconomic status) and health status indicators (body mass index, high cholesterol, high blood pressure, and diabetes status). - Results Several significant interactions emerged between psychological distress, demographic (age, area-level socioeconomic status, and income level), and health status variables (body mass index, diabetes status) in predicting the nutrition indicators. Relationships between distress and the nutrition indicators were not significantly different by gender, remoteness, educational attainment, high cholesterol status, and high blood pressure status. - Conclusions The associations between psychological distress and several nutrition indicators differ amongst population subgroups. These findings suggest that in distressed adults, age, area-level socio-economic status, income level, body mass index, and diabetes status may serve as protective or risk factors through increasing or decreasing the likelihood of meeting nutritional guidelines. Public health interventions for improving dietary behaviours and nutrition may be more effective if they take into account the moderators identified in this study rather than using global interventions.

The law and ethics of ‘self-quantified’ health information: An Australian perspective

This exploratory article examines the phenomenon of the ‘Quantified Self’—until recently, a subculture of enthusiasts who aim to discover knowledge about themselves and their bodies through self-tracking, usually using wearable devices to do so—and its implications for laws concerned with regulating and protecting health information. Quantified Self techniques and the ‘wearable devices’ and software that facilitate them—in which large transnational technology corporations are now involved—often involve the gathering of what would be considered ‘health information’ according to legal definitions, yet may occur outside the provision of traditional health services (including ‘e-health’) and the regulatory frameworks that govern them. This article explores the legal and regulatory framework for self-quantified health information and wearable devices in Australia and determines the extent to which this framework addresses privacy and other concerns that these techniques engender, along with suggestions for reform.

Predicting crashes using traffic offences. A meta-analysis that examines potential bias between self-report and archival data

Background Traffic offences have been considered an important predictor of crash involvement, and have often been used as a proxy safety variable for crashes. However the association between crashes and offences has never been meta-analysed and the population effect size never established. Research is yet to determine the extent to which this relationship may be spuriously inflated through systematic measurement error, with obvious implications for researchers endeavouring to accurately identify salient factors predictive of crashes. Methodology and Principal Findings Studies yielding a correlation between crashes and traffic offences were collated and a meta-analysis of 144 effects drawn from 99 road safety studies conducted. Potential impact of factors such as age, time period, crash and offence rates, crash severity and data type, sourced from either self-report surveys or archival records, were considered and discussed. After weighting for sample size, an average correlation of r = .18 was observed over the mean time period of 3.2 years. Evidence emerged suggesting the strength of this correlation is decreasing over time. Stronger correlations between crashes and offences were generally found in studies involving younger drivers. Consistent with common method variance effects, a within country analysis found stronger effect sizes in self-reported data even controlling for crash mean. Significance The effectiveness of traffic offences as a proxy for crashes may be limited. Inclusion of elements such as independently validated crash and offence histories or accurate measures of exposure to the road would facilitate a better understanding of the factors that influence crash involvement.