Publishing and sharing sensitive data

Sensitive data has often been excluded from discussions about data publication and sharing. It was believed that sharing sensitive data is not ethical or that it is too difficult to do safely. This opinion has changed with greater understanding and use of methods to ‘de-sensitise’ (i.e., confidentialise) data; that is, modify the data to remove information so that participants or subjects are no longer identifiable, and the capacity to grant ‘conditional access’ to data. Requirements of publishers and funding bodies for researchers to publish and share their data have also seen sensitive data sharing increase. This guide outlines best practice for the publication and sharing of sensitive research data in the Australian context. The Guide follows the sequence of steps that are necessary for publishing and sharing sensitive data, as outlined in the ‘Publishing and Sharing Sensitive Data Decision Tree’. It provides the detail and context to the steps in this Decision Tree. References for further reading are provided for those that are interested. By following the sections below, and steps within, you will be able to make clear, lawful, and ethical decisions about sharing your data safely. It can be done in most cases! How the Guide interacts with your institutional policies This Guide is not intended to override institutional policies on data management or publication. Most researchers operate within the policies of their institution and/or funding arrangement and must, therefore, ensure their decisions about data publication align with these policies. This is particularly relevant for Intellectual Property, and sometimes, your classification of sensitive data (e.g., NSW Government Department of Environment & Heritage, Sensitive Data Species Policy) or selection of data repository. The Guide indicates the steps at which you should check your institutional policies

‘Ecstasy’ and the use of sleep medications in a general community sample: a four-year follow-up

Aims: Animal models show that a single dose of MDMA (‘ecstasy’) can result in long-term disruption of sleep. We evaluated the relationship between ecstasy consumption and the use of sleep medications in humans after controlling for key factors. Design: The Personality and Total Health Through Life project uses a longitudinal cohort with follow-up every four years. This study reports data from waves two and three. Setting: Participants were recruited from the electoral roll in the Australian Capital Territory and Queanbeyan, New South Wales, Australia. Participants: Participants were aged 20-24 years at wave one (1999-2000). Measures: The study collected self-reported data on ecstasy, meth/amphetamine, cannabis, alcohol, tobacco and use of sleeping medications (pharmaceutical or other substances). Depression was categorised with the Brief Patient Health Questionnaire (BPHQ). Other psychosocial measures included lifetime traumas. We used generalised estimating equations to model outcomes. Results: Ecstasy data were available from 2128 people at wave two and 1977 at wave three: sleeping medication use was reported by 227 (10.7%) respondents at wave two and 239 (12.1%) at wave three. Increased odds ratios (OR) for sleeping medication use was found for those with depression (OR=1.88, (95% confidence interval (CI) 1.39, 2.53), women (OR=1.44, 95% CI 1.13, 1.84), and increased by 19% for each lifetime trauma. Ecstasy use was not a significant predictor, but >monthly versus never meth/amphetamine use increased the odds (OR=3.03, 95% CI 1.30, 7.03). Conclusion: The use of ecstasy was not associated with the use of sleeping medications controlling for other risk factors.The PATH study was supported by an NHMRC Program Grant 179805 and NHMRC Project Grant 157125. The sponsors had no role in the design, conduct or reporting of the research. None of the authors have connections (direct or indirect) with the tobacco, alcohol, pharmaceutical or gaming industries or any body substantially funded by one of these organisations

Health-y Sharing of Human Data

Presentation given at Li Ka ShIng Library on 30 September 2015</p

Relationship quality and levels of depression and anxiety in a large population-based survey

PURPOSE: There is substantial literature suggesting that the mental health benefits of marriage (compared to being single) are greater for those in 'good quality' relationships in comparison to those in 'poor quality' relationships. However, little of this research utilises large population-based surveys. Large surveys in psychiatric epidemiology have focused almost exclusively on the association between marital status and mental health. The current study explores some of the reasons for this gap in the literature, and adopts a large, representative community-based sample to investigate whether associations between relationship status and levels of depression and anxiety are moderated by relationship quality. METHODS: Participants were from Wave 3 of the PATH Survey, a longitudinal community survey assessing the health and well-being of residents of the Canberra region, Australia (n=3820). Relationship quality was measured using the 7 item Dyadic Adjustment Scale (DAS-7), and levels of depression and anxiety were measured using the Goldberg Scales. RESULTS: Both cross-sectional and prospective analyses showed that associations between relationship status and mental health were moderated by relationship quality for both men and women, such that only good quality relationships bestowed mental health benefits over remaining single. For women, being in a poor quality relationship was associated with greater levels of anxiety than being single. CONCLUSIONS: Epidemiological studies need to measure relationship quality to qualify the effect of relationship status on mental health.NHMRC (National Health and Medical Research Council of Australia

Mental health affects future employment as job loss affects mental health: findings from a longitudinal population study

BACKGROUND: Workforce participation is a key feature of public mental health and social inclusion policies across the globe, and often a therapeutic goal in treatment settings. Understanding the reciprocal relationship between participation and mental health has been limited by inadequate research methods. This is the first study to simultaneously examine and contrast the relative effects of unemployment on mental health and mental health on employment status in a single general population sample. METHOD: Data were from working-age respondents (20 to 55 years at baseline) who completed nine waves of the Household, Income and Labour Dynamics in Australia (HILDA) Survey (N=7176). Cross-lagged path analyses were used to test the lagged and concurrent associations between unemployment and mental health over time, adjusting for sociodemographic characteristics. RESULTS: Mental health was shown to be both a consequence of and risk factor for unemployment. Thus, the poorer mental health observed amongst people who are not working is attributable to both the impact of unemployment and existing mental health problems. While the strength of these two effects was similar for women, the results for men suggested that the effect of unemployment on subsequent mental health was weaker than the effect of mental health on subsequent risk of unemployment. CONCLUSION: Disentangling the reciprocal links between mental health and workforce participation is central to the development and success of clinical goals and health and social policies that aim to promote either aspect. This study demonstrates that both effects are important and supports concurrent responses to prevent a cycle of disadvantage and entrenched social exclusion.SCO and LSL were funded by the Australian National University and fellowships from the Australian National Health and Medical Research Council. PB and JP were funded by fellowships from the Australian National Health and Medical Research Council. MK was funded by a fellowship from the Australian Research Council

Psychosocial job adversity and health in Australia: analysis of data from the HILDA Survey

Objective: This study examines measures of psychosocial job quality developed from the Household Income and Labour Dynamics in Australia (HILDA) Survey, and reports on associations with physical and mental health. Methods: The study used seven waves of data from the HILDA Survey with 5,548 employed respondents. Longitudinal random-intercept regression models assessed the association of time-varying and between-person measures of psychosocial job quality job adversity with physical and mental health. Results: Respondents' specific experience of psychosocial job adversity, except marketability, was associated with increased risk of mental health problems, whereas the association between psychosocial job adversity and physical health was largely driven by differences between people. Conclusions and Implications: Moving into jobs with different psychosocial quality is associated with changes in mental health. In contrast, individuals with poor physical health show an increased propensity to work in poor-quality jobs but it seems that changes in physical health are not as strongly tied to changes in job quality. Differences in the relationship between physical and mental health and psychosocial job quality have implications for the design of employment, health and social policy. The HILDA Survey is an important resource for policy development in Australia, and the availability of valid measures of psychosocial of job quality will enhance its use to better understand this important determinant and correlate of health

Threat history controls flexible escape behavior in mice.

In many instances, external sensory-evoked neuronal activity is used by the brain to select the most appropriate behavioral response. Predator-avoidance behaviors such as freezing and escape1,2 are of particular interest since these stimulus-evoked responses are behavioral manifestations of a decision-making process that is fundamental to survival.3,4 Over the lifespan of an individual, however, the threat value of agents in the environment is believed to undergo constant revision,5 and in some cases, repeated avoidance of certain stimuli may no longer be an optimal behavioral strategy.6 To begin to study this type of adaptive control of decision-making, we devised an experimental paradigm to probe the properties of threat escape in the laboratory mouse Mus musculus. First, we found that while robust escape to visual looming stimuli can be observed after 2 days of social isolation, mice can also rapidly learn that such stimuli are non-threatening. This learned suppression of escape (LSE) is extremely robust and can persist for weeks and is not a generalized adaptation, since flight responses to novel live prey and auditory threat stimuli in the same environmental context were maintained. We also show that LSE cannot be explained by trial number or a simple form of stimulus desensitization since it is dependent on threat-escape history. We propose that the action selection process mediating escape behavior is constantly updated by recent threat history and that LSE can be used as a robust model system to understand the neurophysiological mechanisms underlying experience-dependent decision-making

Natural Bacterial Communities Serve as Quantitative Geochemical Biosensors

Biological sensors can be engineered to measure a wide range of environmental conditions. Here we show that statistical analysis of DNA from natural microbial communities can be used to accurately identify environmental contaminants, including uranium and nitrate at a nuclear waste site. In addition to contamination, sequence data from the 16S rRNA gene alone can quantitatively predict a rich catalogue of 26 geochemical features collected from 93 wells with highly differing geochemistry characteristics. We extend this approach to identify sites contaminated with hydrocarbons from the Deepwater Horizon oil spill, finding that altered bacterial communities encode a memory of prior contamination, even after the contaminants themselves have been fully degraded. We show that the bacterial strains that are most useful for detecting oil and uranium are known to interact with these substrates, indicating that this statistical approach uncovers ecologically meaningful interactions consistent with previous experimental observations. Future efforts should focus on evaluating the geographical generalizability of these associations. Taken as a whole, these results indicate that ubiquitous, natural bacterial communities can be used as in situ environmental sensors that respond to and capture perturbations caused by human impacts. These in situ biosensors rely on environmental selection rather than directed engineering, and so this approach could be rapidly deployed and scaled as sequencing technology continues to become faster, simpler, and less expensive