Priority populations' experiences of isolation, quarantine and distancing for COVID-19: protocol for a longitudinal cohort study (Optimise Study).

INTRODUCTION: Longitudinal studies can provide timely and accurate information to evaluate and inform COVID-19 control and mitigation strategies and future pandemic preparedness. The Optimise Study is a multidisciplinary research platform established in the Australian state of Victoria in September 2020 to collect epidemiological, social, psychological and behavioural data from priority populations. It aims to understand changing public attitudes, behaviours and experiences of COVID-19 and inform epidemic modelling and support responsive government policy. METHODS AND ANALYSIS: This protocol paper describes the data collection procedures for the Optimise Study, an ongoing longitudinal cohort of ~1000 Victorian adults and their social networks. Participants are recruited using snowball sampling with a set of seeds and two waves of snowball recruitment. Seeds are purposively selected from priority groups, including recent COVID-19 cases and close contacts and people at heightened risk of infection and/or adverse outcomes of COVID-19 infection and/or public health measures. Participants complete a schedule of monthly quantitative surveys and daily diaries for up to 24 months, plus additional surveys annually for up to 48 months. Cohort participants are recruited for qualitative interviews at key time points to enable in-depth exploration of people's lived experiences. Separately, community representatives are invited to participate in community engagement groups, which review and interpret research findings to inform policy and practice recommendations. ETHICS AND DISSEMINATION: The Optimise longitudinal cohort and qualitative interviews are approved by the Alfred Hospital Human Research Ethics Committee (# 333/20). The Optimise Study CEG is approved by the La Trobe University Human Ethics Committee (# HEC20532). All participants provide informed verbal consent to enter the cohort, with additional consent provided prior to any of the sub studies. Study findings will be disseminated through public website (https://optimisecovid.com.au/study-findings/) and through peer-reviewed publications. TRIAL REGISTRATION NUMBER: NCT05323799.

Multilevel network interventions: Goals, actions, and outcomes.

COVID-19 has resulted in dramatic and widespread social network interventions across the globe, with public health measures such as distancing and isolation key epidemiological responses to minimize transmission. Because these measures affect social interactions between people, the networked structure of daily lives is changed. Such largescale changes to social structures, present simultaneously across many different societies and touching many different people, give renewed significance to the conceptualization of social network interventions. As social network researchers, we need a framework for understanding and describing network interventions consistent with the COVID-19 experience, one that builds on past work but able to cast interventions across a broad societal framework. In this theoretical paper, we extend the conceptualization of social network interventions in these directions. We follow Valente (2012) with a tripartite categorization of interventions but add a multilevel dimension to capture hierarchical aspects that are a key feature of any society and implicit in any network. This multilevel dimension distinguishes goals, actions, and outcomes at different levels, from individuals to the whole of the society. We illustrate this extended taxonomy with a range of COVID-19 public health measures of different types and at multiple levels, and then show how past network intervention research in other domains can also be framed in this way. We discuss what counts as an effective network, an effective intervention, plausible causality, and careful selection and evaluation, as central to a full theory of network interventions.

Exponential random graph model parameter estimation for very large directed networks.

Exponential random graph models (ERGMs) are widely used for modeling social networks observed at one point in time. However the computational difficulty of ERGM parameter estimation has limited the practical application of this class of models to relatively small networks, up to a few thousand nodes at most, with usually only a few hundred nodes or fewer. In the case of undirected networks, snowball sampling can be used to find ERGM parameter estimates of larger networks via network samples, and recently published improvements in ERGM network distribution sampling and ERGM estimation algorithms have allowed ERGM parameter estimates of undirected networks with over one hundred thousand nodes to be made. However the implementations of these algorithms to date have been limited in their scalability, and also restricted to undirected networks. Here we describe an implementation of the recently published Equilibrium Expectation (EE) algorithm for ERGM parameter estimation of large directed networks. We test it on some simulated networks, and demonstrate its application to an online social network with over 1.6 million nodes.

Norm talk and human cooperation: Can we talk ourselves into cooperation?

Norm talk is verbal communication that explicitly states or implicitly implies a social norm. To investigate its ability to shape cultural dynamics, 2 types of norm talk were examined: injunction, which explicitly states what should be done, and gossip, which implies a norm by stating an action approved or disapproved of by the communicator. In 2 experiments, participants engaged in norm talk in repeated public goods games. Norm talk was found to help sustain cooperation relative to the control condition; immediately after every norm talk opportunity, cooperation spiked, followed by a gradual decline. Despite the macrolevel uniformity in their effects on cooperation, evidence suggests different microlevel mechanisms for the cooperation-enhancing effects of injunction and gossip. A 3rd study confirmed that both injunction and gossip sustain cooperation by making salient the norm of cooperation, but injunction also effects mutual verification of the communicated norm, whereas gossip emphasizes its reputational implications by linking cooperation to status conferral and noncooperation to reputational damage. A 4th experiment provided additional evidence that norm talk was superior to the promise of conditional cooperation in sustaining cooperation. Implications of the findings for cultural dynamics are discussed in terms of how feelings of shared morality, language-based interpersonal communication, and ritualization of norm communication contribute to social regulation. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Outliers and Influential Observations in Exponential Random Graph Models.

We discuss measuring and detecting influential observations and outliers in the context of exponential family random graph (ERG) models for social networks. We focus on the level of the nodes of the network and consider those nodes whose removal would result in changes to the model as extreme or "central" with respect to the structural features that "matter". We construe removal in terms of two case-deletion strategies: the tie-variables of an actor are assumed to be unobserved, or the node is removed resulting in the induced subgraph. We define the difference in inferred model resulting from case deletion from the perspective of information theory and difference in estimates, in both the natural and mean-value parameterisation, representing varying degrees of approximation. We arrive at several measures of influence and propose the use of two that do not require refitting of the model and lend themselves to routine application in the ERGM fitting procedure. MCMC p values are obtained for testing how extreme each node is with respect to the network structure. The influence measures are applied to two well-known data sets to illustrate the information they provide. From a network perspective, the proposed statistics offer an indication of which actors are most distinctive in the network structure, in terms of not abiding by the structural norms present across other actors.

Fast Maximum Likelihood Estimation via Equilibrium Expectation for Large Network Data.

A major line of contemporary research on complex networks is based on the development of statistical models that specify the local motifs associated with macro-structural properties observed in actual networks. This statistical approach becomes increasingly problematic as network size increases. In the context of current research on efficient estimation of models for large network data sets, we propose a fast algorithm for maximum likelihood estimation (MLE) that affords a significant increase in the size of networks amenable to direct empirical analysis. The algorithm we propose in this paper relies on properties of Markov chains at equilibrium, and for this reason it is called equilibrium expectation (EE). We demonstrate the performance of the EE algorithm in the context of exponential random graph models (ERGMs) a family of statistical models commonly used in empirical research based on network data observed at a single period in time. Thus far, the lack of efficient computational strategies has limited the empirical scope of ERGMs to relatively small networks with a few thousand nodes. The approach we propose allows a dramatic increase in the size of networks that may be analyzed using ERGMs. This is illustrated in an analysis of several biological networks and one social network with 104,103 nodes.

Diversity and Community Can Coexist.

We examine the (in)compatibility of diversity and sense of community by means of agent-based models based on the well-known Schelling model of residential segregation and Axelrod model of cultural dissemination. We find that diversity and highly clustered social networks, on the assumptions of social tie formation based on spatial proximity and homophily, are incompatible when agent features are immutable, and this holds even for multiple independent features. We include both mutable and immutable features into a model that integrates Schelling and Axelrod models, and we find that even for multiple independent features, diversity and highly clustered social networks can be incompatible on the assumptions of social tie formation based on spatial proximity and homophily. However, this incompatibility breaks down when cultural diversity can be sufficiently large, at which point diversity and clustering need not be negatively correlated. This implies that segregation based on immutable characteristics such as race can possibly be overcome by sufficient similarity on mutable characteristics based on culture, which are subject to a process of social influence, provided a sufficiently large "scope of cultural possibilities" exists.

A Simulation Study Comparing Epidemic Dynamics on Exponential Random Graph and Edge-Triangle Configuration Type Contact Network Models.

We compare two broad types of empirically grounded random network models in terms of their abilities to capture both network features and simulated Susceptible-Infected-Recovered (SIR) epidemic dynamics. The types of network models are exponential random graph models (ERGMs) and extensions of the configuration model. We use three kinds of empirical contact networks, chosen to provide both variety and realistic patterns of human contact: a highly clustered network, a bipartite network and a snowball sampled network of a "hidden population". In the case of the snowball sampled network we present a novel method for fitting an edge-triangle model. In our results, ERGMs consistently capture clustering as well or better than configuration-type models, but the latter models better capture the node degree distribution. Despite the additional computational requirements to fit ERGMs to empirical networks, the use of ERGMs provides only a slight improvement in the ability of the models to recreate epidemic features of the empirical network in simulated SIR epidemics. Generally, SIR epidemic results from using configuration-type models fall between those from a random network model (i.e., an Erdos-Rényi model) and an ERGM. The addition of subgraphs of size four to edge-triangle type models does improve agreement with the empirical network for smaller densities in clustered networks. Additional subgraphs do not make a noticeable difference in our example, although we would expect the ability to model cliques to be helpful for contact networks exhibiting household structure.

Social encounter profiles of greater Melbourne residents, by location--a telephone survey.

BACKGROUND: Models of infectious disease increasingly seek to incorporate heterogeneity of social interactions to more accurately characterise disease spread. We measured attributes of social encounters in two areas of Greater Melbourne, using a telephone survey. METHODS: A market research company conducted computer assisted telephone interviews (CATIs) of residents of the Boroondara and Hume local government areas (LGAs), which differ markedly in ethnic composition, age distribution and household socioeconomic status. Survey items included household demographic and socio-economic characteristics, locations visited during the preceding day, and social encounters involving two-way conversation or physical contact. Descriptive summary measures were reported and compared using weight adjusted Wald tests of group means. RESULTS: The overall response rate was 37.6%, higher in Boroondara [n = 650, (46%)] than Hume [n = 657 (32%)]. Survey conduct through the CATI format was challenging, with implications for representativeness and data quality. Marked heterogeneity of encounter profiles was observed across age groups and locations. Household settings afforded greatest opportunity for prolonged close contact, particularly between women and children. Young and middle-aged men reported more age-assortative mixing, often with non-household members. Preliminary comparisons between LGAs suggested that mixing occurred in different settings. In addition, gender differences in mixing with household and non-household members, including strangers, were observed by area. CONCLUSIONS: Survey administration by CATI was challenging, but rich data were obtained, revealing marked heterogeneity of social behaviour. Marked dissimilarities in patterns of prolonged close mixing were demonstrated by gender. In addition, preliminary observations of between-area differences in socialisation warrant further evaluation.

Hepatitis C transmission and treatment as prevention - The role of the injecting network.

BACKGROUND: The hepatitis C virus (HCV) epidemic is a major health issue; in most developed countries it is driven by people who inject drugs (PWID). Injecting networks powerfully influence HCV transmission. In this paper we provide an overview of 10 years of research into injecting networks and HCV, culminating in a network-based approach to provision of direct-acting antiviral therapy. METHODS: Between 2005 and 2010 we followed a cohort of 413 PWID, measuring HCV incidence, prevalence and injecting risk, including network-related factors. We developed an individual-based HCV transmission model, using it to simulate the spread of HCV through the empirical social network of PWID. In addition, we created an empirically grounded network model of injecting relationships using exponential random graph models (ERGMs), allowing simulation of realistic networks for investigating HCV treatment and intervention strategies. Our empirical work and modelling underpins the TAP Study, which is examining the feasibility of community-based treatment of PWID with DAAs. RESULTS: We observed incidence rates of HCV primary infection and reinfection of 12.8 per 100 person-years (PY) (95%CI: 7.7-20.0) and 28.8 per 100 PY (95%CI: 15.0-55.4), respectively, and determined that HCV transmission clusters correlated with reported injecting relationships. Transmission modelling showed that the empirical network provided some protective effect, slowing HCV transmission compared to a fully connected, homogenous PWID population. Our ERGMs revealed that treating PWID and all their contacts was the most effective strategy and targeting treatment to infected PWID with the most contacts the least effective. CONCLUSION: Networks-based approaches greatly increase understanding of HCV transmission and will inform the implementation of treatment as prevention using DAAs.

The impact of injecting networks on hepatitis C transmission and treatment in people who inject drugs.

UNLABELLED: With the development of new highly efficacious direct-acting antiviral (DAA) treatments for hepatitis C virus (HCV), the concept of treatment as prevention is gaining credence. To date, the majority of mathematical models assume perfect mixing, with injectors having equal contact with all other injectors. This article explores how using a networks-based approach to treat people who inject drugs (PWID) with DAAs affects HCV prevalence. Using observational data, we parameterized an exponential random graph model containing 524 nodes. We simulated transmission of HCV through this network using a discrete time, stochastic transmission model. The effect of five treatment strategies on the prevalence of HCV was investigated; two of these strategies were (1) treat randomly selected nodes and (2) "treat your friends," where an individual is chosen at random for treatment and all their infected neighbors are treated. As treatment coverage increases, HCV prevalence at 10 years reduces for both the high- and low-efficacy treatment. Within each set of parameters, the treat your friends strategy performed better than the random strategy being most marked for higher-efficacy treatment. For example, over 10 years of treating 25 per 1,000 PWID, the prevalence drops from 50% to 40% for the random strategy and to 33% for the treat your friends strategy (6.5% difference; 95% confidence interval: 5.1-8.1). CONCLUSION: Treat your friends is a feasible means of utilizing network strategies to improve treatment efficiency. In an era of highly efficacious and highly tolerable treatment, such an approach will benefit not just the individual, but also the community more broadly by reducing the prevalence of HCV among PWID.

Ultrametric distribution of culture vectors in an extended Axelrod model of cultural dissemination.

The Axelrod model of cultural diffusion is an apparently simple model that is capable of complex behaviour. A recent work used a real-world dataset of opinions as initial conditions, demonstrating the effects of the ultrametric distribution of empirical opinion vectors in promoting cultural diversity in the model. Here we quantify the degree of ultrametricity of the initial culture vectors and investigate the effect of varying degrees of ultrametricity on the absorbing state of both a simple and extended model. Unlike the simple model, ultrametricity alone is not sufficient to sustain long-term diversity in the extended Axelrod model; rather, the initial conditions must also have sufficiently large variance in intervector distances. Further, we find that a scheme for evolving synthetic opinion vectors from cultural "prototypes" shows the same behaviour as real opinion data in maintaining cultural diversity in the extended model; whereas neutral evolution of cultural vectors does not.

Hepatitis C transmission and treatment in contact networks of people who inject drugs.

Hepatitis C virus (HCV) chronically infects over 180 million people worldwide, with over 350,000 estimated deaths attributed yearly to HCV-related liver diseases. It disproportionally affects people who inject drugs (PWID). Currently there is no preventative vaccine and interventions feature long treatment durations with severe side-effects. Upcoming treatments will improve this situation, making possible large-scale treatment interventions. How these strategies should target HCV-infected PWID remains an important unanswered question. Previous models of HCV have lacked empirically grounded contact models of PWID. Here we report results on HCV transmission and treatment using simulated contact networks generated from an empirically grounded network model using recently developed statistical approaches in social network analysis. Our HCV transmission model is a detailed, stochastic, individual-based model including spontaneously clearing nodes. On transmission we investigate the role of number of contacts and injecting frequency on time to primary infection and the role of spontaneously clearing nodes on incidence rates. On treatment we investigate the effect of nine network-based treatment strategies on chronic prevalence and incidence rates of primary infection and re-infection. Both numbers of contacts and injecting frequency play key roles in reducing time to primary infection. The change from "less-" to "more-frequent" injector is roughly similar to having one additional network contact. Nodes that spontaneously clear their HCV infection have a local effect on infection risk and the total number of such nodes (but not their locations) has a network wide effect on the incidence of both primary and re-infection with HCV. Re-infection plays a large role in the effectiveness of treatment interventions. Strategies that choose PWID and treat all their contacts (analogous to ring vaccination) are most effective in reducing the incidence rates of re-infection and combined infection. A strategy targeting infected PWID with the most contacts (analogous to targeted vaccination) is the least effective.

Hepatitis C virus phylogenetic clustering is associated with the social-injecting network in a cohort of people who inject drugs.

It is hypothesized that social networks facilitate transmission of the hepatitis C virus (HCV). We tested for association between HCV phylogeny and reported injecting relationships using longitudinal data from a social network design study. People who inject drugs were recruited from street drug markets in Melbourne, Australia. Interviews and blood tests took place three monthly (during 2005-2008), with participants asked to nominate up to five injecting partners at each interview. The HCV core region of individual isolates was then sequenced and phylogenetic trees were constructed. Genetic clusters were identified using bootstrapping (cut-off: 70%). An adjusted Jaccard similarity coefficient was used to measure the association between the reported injecting relationships and relationships defined by clustering in the phylogenetic analysis (statistical significance assessed using the quadratic assignment procedure). 402 participants consented to participate; 244 HCV infections were observed in 238 individuals. 26 genetic clusters were identified, with 2-7 infections per cluster. Newly acquired infection (AOR = 2.03, 95% CI: 1.04-3.96, p = 0.037, and HCV genotype 3 (vs. genotype 1, AOR = 2.72, 95% CI: 1.48-4.99) were independent predictors of being in a cluster. 54% of participants whose infections were part of a cluster in the phylogenetic analysis reported injecting with at least one other participant in that cluster during the study. Overall, 16% of participants who were infected at study entry and 40% of participants with newly acquired infections had molecular evidence of related infections with at least one injecting partner. Likely transmission clusters identified in phylogenetic analysis correlated with reported injecting relationships (adjusted Jaccard coefficient: 0.300; p<0.001). This is the first study to show that HCV phylogeny is associated with the injecting network, highlighting the importance of the injecting network in HCV transmission.

Influence of contact definitions in assessment of the relative importance of social settings in disease transmission risk.

BACKGROUND: Realistic models of disease transmission incorporating complex population heterogeneities require input from quantitative population mixing studies. We use contact diaries to assess the relative importance of social settings in respiratory pathogen spread using three measures of person contact hours (PCH) as proxies for transmission risk with an aim to inform bipartite network models of respiratory pathogen transmission. METHODS AND FINDINGS: Our survey examines the contact behaviour for a convenience sample of 65 adults, with each encounter classified as occurring in a work, retail, home, social, travel or "other" setting. The diary design allows for extraction of PCH-interaction (cumulative time in face-face conversational or touch interaction with contacts)--analogous to the contact measure used in several existing surveys--as well as PCH-setting (product of time spent in setting and number of people present) and PCH-reach (product of time spent in setting and number of people in close proximity). Heterogeneities in day-dependent distribution of risk across settings are analysed using partitioning and cluster analyses and compared between days and contact measures. Although home is typically the highest-risk setting when PCH measures isolate two-way interactions, its relative importance compared to social and work settings may reduce when adopting a more inclusive contact measure that considers the number and duration of potential exposure events. CONCLUSIONS: Heterogeneities in location-dependent contact behaviour as measured by contact diary studies depend on the adopted contact definition. We find that contact measures isolating face-face conversational or touch interactions suggest that contact in the home dominates, whereas more inclusive contact measures indicate that home and work settings may be of higher importance. In the absence of definitive knowledge of the contact required to facilitate transmission of various respiratory pathogens, it is important for surveys to consider alternative contact measures.

Homophily and contagion as explanations for weight similarities among adolescent friends.

PURPOSE: To determine whether weight-based similarities among adolescent friends result from social influence processes, after controlling for the role of weight on friendship selection and other confounding influences. METHODS: Four waves of data were collected from a grade 8 cohort of adolescents (N = 156, mean age = 13.6 years) over their initial 2 years of high school. At each wave, participants reported on their friendship relations with grade-mates and had their height and weight measured by researchers to calculate their body mass index (BMI). Newly developed stochastic actor-oriented models for social networks were used to simultaneously assess the role of weight on adolescents' friendship choices, and the effect of friends' BMIs on changes in adolescent BMI. RESULTS: Adolescents' BMIs were not significantly predicted by the BMI of their friends over the 16 months of this study. Similarities in the weights of friends were found to be driven predominantly by friendship selection, whereby adolescents, particularly those who were not overweight, preferred to initiate friendships with peers whose weight status (overweight/nonoverweight) was the same as their own. CONCLUSIONS: Weight-based similarities among friends were largely explained by the marginalization of overweight adolescents by their peers, rather than by the "contagion" of excess weight among friends. These findings highlight the importance of adequately modeling friendship selection processes when estimating social influence effects on adiposity.

How physical activity shapes, and is shaped by, adolescent friendships.

The current study explored the role of school-based friendship networks in adolescents' engagement in physical activity (PA). It was hypothesized that similar participation in PA would be a basis for friendship formation, and that friends would also influence behavior. Whether these processes were mediated through cognitive mechanisms was also explored. Self-reported participation in PA, cognitions about PA, and friendship ties to grade-mates were measured in two cohorts of Australian grade eight students (N = 378; M age = 13.7) three times over the 2008 school year. Interdependence between the friendship networks and PA was tested using stochastic actor-based models for social networks and behavior. The results showed that participants tended to befriend peers who did similar amounts of PA, and subsequently emulated their friends' behaviors. Friends' influence on PA was not found to be mediated through adolescents' cognitions about PA. These findings show that there is a mutually dependent relationship between adolescent friendship networks and PA; they highlight how novel network-based strategies may be effective in supporting young people to be physically active.

Comparison of three methods for ascertainment of contact information relevant to respiratory pathogen transmission in encounter networks.

BACKGROUND: Mathematical models of infection that consider targeted interventions are exquisitely dependent on the assumed mixing patterns of the population. We report on a pilot study designed to assess three different methods (one retrospective, two prospective) for obtaining contact data relevant to the determination of these mixing patterns. METHODS: 65 adults were asked to record their social encounters in each location visited during 6 study days using a novel method whereby a change in physical location of the study participant triggered data entry. Using a cross-over design, all participants recorded encounters on 3 days in a paper diary and 3 days using an electronic recording device (PDA). Participants were randomised to first prospective recording method. RESULTS: Both methods captured more contacts than a pre-study questionnaire, but ascertainment using the paper diary was superior to the PDA (mean difference: 4.52 (95% CI 0.28, 8.77). Paper diaries were found more acceptable to the participants compared with the PDA. Statistical analysis confirms that our results are broadly consistent with those reported from large-scale European based surveys. An association between household size (trend 0.14, 95% CI (0.06, 0.22), P < 0.001) and composition (presence of child 0.37, 95% CI (0.17, 0.56), P < 0.001) and the total number of reported contacts was observed, highlighting the importance of sampling study populations based on household characteristics as well as age. New contacts were still being recorded on the third study day, but compliance had declined, indicating that the optimal number of sample days represents a trade-off between completeness and quality of data for an individual. CONCLUSIONS: The study's location-based reporting design allows greater scope compared to other methods for examining differences in the characteristics of encounters over a range of environments. Improved parameterisation of dynamic transmission models gained from work of this type will aid in the development of more robust decision support tools to assist health policy makers and planners.

Ceremonies of the whole: does social participation moderate the mood consequences of neuroticism?

BACKGROUND: The aim of this research was to test whether social participation is associated with improvements in mood and well-being, and in particular to test whether social participation might moderate the chronic distress associated with high levels of neuroticism (N). METHOD: A rural Australian sample of 394 adults (54.3% female) completed questionnaires and participated in follow-up interviews. Social participation was indexed by community group membership, and operationalised for analysis in two forms: extent (number of group memberships) and presence (zero vs. one or more memberships). Mood was measured as Positive Affect (PA) and Negative Affect (NA) as rated on the Positive and Negative Affect Schedule and well-being was measured with Diener's Satisfaction with Life (SWL) questionnaire. Items from Goldberg's International Personality Item Pool were used to measure N. RESULTS: The extent of social participation was significantly associated with all three mood/well-being variables in bivariate analyses, and remained as a significant net predictor of PA and NA (beta = 0.11, P < 0.05, beta = -0.13, P < 0.05) when modeled with age, gender and income. In parallel, categorical social participation was found to be significantly associated with PA, NA and SWL in bivariate analyses and in multivariate analyses controlling for age, gender and income (beta = 0.11, P < 0.05, beta = -0.15, P < 0.01 and beta = 0.11, P < 0.05, respectively). The interaction term N*Social interaction was significantly correlated with NA in bivariate analyses involving both continuous (r = -0.14, P < 0.01) and categorical (r = -0.13, P < 0.01) measures of social participation, and in its continuous form remained a significant net predictor of NA after controlling for the main effects of N and Social participation (beta = -0.09, P < 0.05). CONCLUSIONS: The present findings extend upon existing evidence that social participation tends to be positively associated with mental health by demonstrating the predicted effect across a comprehensive set of mood/well-being variables. Preliminary evidence was also obtained that social participation may serve as a moderator of the chronic distress associated with N. It is concluded that further research seeking to confirm the causal direction of the identified pathways is warranted.

Understanding suicide in Australian farmers.

OBJECTIVE: Male farmers in Australia have an elevated risk of suicide. The aims of this study were to investigate the rate of mental health problems amongst farmers compared with non-farmer rural residents and to investigate what additional factors might contribute to an increased risk of suicide amongst farmers. METHOD: This study used a combination of quantitative and qualitative approaches. First, using self-report questionnaire data, we compared rates of mental health problems (a common correlate of suicide) and a number of personality measures between farmers (n=371) and non-farming rural residents (n=380). In addition, semi-structured interviews with farmers (n=32) were used to gain a richer understanding of how the context of farming and mental health interact. RESULTS: Five key findings emerged from the study. First, in the quantitative study, we found no support for the proposition that farmers experience higher rates of mental health problems than do non-farmer rural residents, but we identified potentially important personality differences between farmers and non-farmers, with levels of conscientiousness being significantly higher amongst farmers and levels of neuroticism being significantly lower. A strong association between maleness and farming was also found. In the qualitative study, participants indicated that farming is an environment in which individuals experienced a range of stressors but have limited capacity to acknowledge or express these. In addition, there appeared to be significant attitudinal barriers to seeking help for those who may have mental health problems, particularly male farmers. CONCLUSION: The elevated rate of suicide amongst farmers does not seem to be simply explained by an elevated rate of mental health problems. Individual personality, gender and community attitudes that limit a person's ability to acknowledge or express mental health problems and seek help for these may be significant risk factors for suicide in farmers.