There is clear evidence that installing safety barriers is effective in preventing jumping suicides from high-risk bridges with only moderate displacement to other nearby bridges. However, the impact of barriers on jumping suicides across broader geographical areas is not well understood. We examined patterns in jumping suicides across the state of Victoria, Australia, after a safety barrier was installed at the West Gate Bridge which, before the installation of the barrier, was the site of approximately 40% of Victoria’s jumping suicides.

We used negative binomial regression analyses on Victorian data from 2000 to 2019 to compare rates of jumping suicides at the West Gate Bridge, other bridges and non-bridge jumping locations before, during and after the West Gate Bridge barrier installation. We conducted linear regression analyses to examine whether the distance travelled from the deceased’s usual residence to the location of their jumping suicide changed between the before, during and after barrier installation periods.

After installation of the barrier, there were no jumping suicides at the West Gate Bridge (rate ratio [RR] = 0.00, 95% credible intervals [95% Cr] = 0.00–0.0001) and there was strong evidence that the rate of jumping suicides at all locations declined by 65% (RR = 0.35, 95% Cr = 0.22–0.54). At other bridges, there was also evidence of a reduction (RR = 0.31, 95% Cr = 0.11–0.70), but there was no evidence of a change at non-bridge locations (RR = 0.74, 95% Cr = 0.39–1.30).

After installation of the safety barrier at the West Gate Bridge, jumping suicide in Victoria decreased overall and at other bridges, and did not appear to change at non-bridge locations. Our findings show that when barriers are installed at a site responsible for a disproportionately high number of jumping suicides, they are not only highly effective at the site where the barriers are installed but can also have a prevention impact beyond the immediate locale at similar sites.

The aim of this paper is to present a novel case for the formation, operation and evaluation of a community advisory aboard comprised of Muslims residing in the San Francisco Bay Area, California that utilised a community based participatory approach to address local Muslim mental health needs. The CAB was recruited in partnership with the Muslim Community Association (MCA), one of the largest Islamic centres in the San Franscisco Bay Area. In addition to describing the development of the CAB, the authors present the findings of the evaluation and synthesis of best processes based on CAB members' feedback.

To evaluate the perceived community advisory board members' perceptions of their roles and elicit feedback on how to enhance the relationship between the university team and the CAB, an evaluation was conducted by an independent team who was not part of the research process. Data was collected using anonymous individual surveys and small group open discussions that were conducted over three evaluation meetings. The evaluation utilised mixed method data collection strategies using questions from Schulz et al. (2003, Evaluation and Program Planning 26, 249–262), an instrument for evaluating dimensions of group dynamics within CBPR partnerships.

Results of the evaluation within the sphere of CAB operation indicated that CAB members found the greatest satisfaction from their contributions through direct participation in the research activities that were conducted by the university-CAB team. The collective responses indicated that most CAB members were satisfied with trust built between the university-CAB team and the diversity represented in the members of the board. However, given that the Bay Area is home to a very diverse Muslim community, challenges in recruiting representatives that account for all possible self-identifying groups was reported by the CAB with recommendations to recruit religious leaders. Recommendations also included eliciting funds for potential financial compensation for CAB members.

The Stanford-San Francisco Bay Area CAB demonstrated that empowering community members through direct participation, creating channels and safe spaces for feedback help create community rooted research that carry the true voices of marginalised communities and reflects their evolving needs

Markedly elevated adverse mental health symptoms were widely observed early in the coronavirus disease-2019 (COVID-19) pandemic. Unlike the U.S., where cross-sectional data indicate anxiety and depression symptoms have remained elevated, such symptoms reportedly declined in the U.K., according to analysis of repeated measures from a large-scale longitudinal study. However, nearly 40% of U.K. respondents (those who did not complete multiple follow-up surveys) were excluded from analysis, suggesting that survivorship bias might partially explain this discrepancy. We therefore sought to assess survivorship bias among participants in our longitudinal survey study as part of The COVID-19 Outbreak Public Evaluation (COPE) Initiative.

Survivorship bias was assessed in 4039 U.S. respondents who completed surveys including the assessment of mental health as part of The COPE Initiative in April 2020 and were invited to complete follow-up surveys. Participants completed validated screening instruments for symptoms of anxiety, depression and insomnia. Survivorship bias was assessed for (1) demographic differences in follow-up survey participation, (2) differences in initial adverse mental health symptom prevalence adjusted for demographic factors and (3) differences in follow-up survey participation based on mental health experiences adjusted for demographic factors.

Adjusting for demographics, individuals who completed only one or two out of four surveys had significantly higher prevalence of anxiety and depression symptoms in April 2020 (e.g. one-survey v. four-survey, anxiety symptoms, adjusted prevalence ratio [aPR]: 1.30, 95% confidence interval [CI]: 1.08–1.55, p = 0.0045; depression symptoms, aPR: 1.43, 95% CI: 1.17–1.75, p = 0.00052). Moreover, individuals who experienced incident anxiety or depression symptoms had significantly higher adjusted odds of not completing follow-up surveys (adjusted odds ratio [aOR]: 1.68, 95% CI: 1.22–2.31, p = 0.0015, aOR: 1.56, 95% CI: 1.15–2.12, p = 0.0046, respectively).

Our findings reveal significant survivorship bias among longitudinal survey respondents, indicating that restricting analytic samples to only respondents who provide repeated assessments in longitudinal survey studies could lead to overly optimistic interpretations of mental health trends over time. Cross-sectional or planned missing data designs may provide more accurate estimates of population-level adverse mental health symptom prevalence than longitudinal surveys.

There is currently no universally accepted measure for population-based surveillance of mood and anxiety disorders. As such, the use of multiple linked measures could provide a more accurate estimate of population prevalence. Our primary objective was to apply Bayesian methods to two commonly employed population measures of mood and anxiety disorders to make inferences regarding the population prevalence and measurement properties of a combined measure.

We used data from the 2012 Canadian Community Health Survey – Mental Health linked to health administrative databases in Ontario, Canada. Structured interview diagnoses were obtained from the survey, and health administrative diagnoses were identified using a standardised algorithm. These two prevalence estimates, in addition to data on the concordance between these measures and prior estimates of their psychometric properties, were used to inform our combined estimate. The marginal posterior densities of all parameters were estimated using Hamiltonian Monte Carlo (HMC), a Markov Chain Monte Carlo technique. Summaries of posterior distributions, including the means and 95% equally tailed posterior credible intervals, were used for interpretation of the results.

The combined prevalence mean was 8.6%, with a credible interval of 6.8–10.6%. This combined estimate sits between Bayesian-derived prevalence estimates from administrative data-derived diagnoses (mean = 7.4%) and the survey-derived diagnoses (mean = 13.9%). The results of our sensitivity analysis suggest that varying the specificity of the survey-derived measure has an appreciable impact on the combined posterior prevalence estimate. Our combined posterior prevalence estimate remained stable when varying other prior information. We detected no problematic HMC behaviour, and our posterior predictive checks suggest that our model can reliably recreate our data.

Accurate population-based estimates of disease are the cornerstone of health service planning and resource allocation. As a greater number of linked population data sources become available, so too does the opportunity for researchers to fully capitalise on the data. The true population prevalence of mood and anxiety disorders may reside between estimates obtained from survey data and health administrative data. We have demonstrated how the use of Bayesian approaches may provide a more informed and accurate estimate of mood and anxiety disorders in the population. This work provides a blueprint for future population-based estimates of disease using linked health data.

There is currently no gold-standard definition or method for identifying suicide clusters, resulting in considerable heterogeneity in the types of suicide clusters that are detected. This study sought to identify the characteristics, mechanisms and parameters of suicide clusters using three cluster detection methods. Specifically, the study aimed to: (1) determine the overlap in suicide clusters among each method, (2) compare the spatial and temporal parameters associated with different suicide clusters and (3) identify the demographic characteristics and rates of exposure to suicide among cluster and non-cluster members.

Suicide data were obtained from the National Coronial Information System. N = 3027 Australians, aged 10–24 who died by suicide in 2006–2015 were included. Suicide clusters were determined using: (1) poisson scan statistics, (2) a systematic search of coronial inquests and (3) descriptive network analysis. These methods were chosen to operationalise three different definitions of suicide clusters, namely clusters that are: (1) statistically significant, (2) perceived to be significant and (3) characterised by social links among three or more suicide descendants. For each method, the demographic characteristics and rates of exposure to suicide were identified, in addition to the maximum duration of suicide clusters, the geospatial overlap between suicide clusters, and the overlap of individual cluster members.

Eight suicide clusters (69 suicides) were identified from the scan statistic, seven (40 suicides) from coronial inquests; and 11 (37 suicides) from the descriptive network analysis. Of the eight clusters detected using the scan statistic, two overlapped with clusters detected using the descriptive network analysis and one with clusters identified from coronial inquests. Of the seven clusters from coronial inquests, four overlapped with clusters from the descriptive network analysis and one with clusters from the scan statistic. Overall, 9.2% (12 suicides) of individuals were identified by more than one method. Prior exposure to suicide was 10.1% (N = 7) in clusters from the scan statistic, 32.5% (N = 13) in clusters from coronial inquest and 56.8% (N = 21) in clusters from the descriptive network analysis.

Each method identified markedly different suicide clusters. Evidence of social links between cluster members typically involved clusters detected using the descriptive network analysis. However, these data were limited to the availability information collected as part of the police and coroner investigation. Communities tasked with detecting and responding to suicide clusters may benefit from using the spatial and temporal parameters revealed in descriptive studies to inform analyses of suicide clusters using inferential methods.

The purpose of this review is to examine the replication attempts of psychotherapy clinical trials for depression and anxiety. We focus specifically on replications of trials that exhibit large differences between psychotherapies. The replicability of these trials is especially important for meta-analysis, where the inclusion of false-positive trials can lead to erroneous conclusions about treatment efficacy.

Standard replication criteria were developed to distinguish direct from conceptual replication methodologies. Next, an exhaustive literature search was conducted for published meta-analyses of psychotherapy comparisons. Trials that exhibited large effects (d > 0.8) were culled from these meta-analyses. For each trial, a cited replication was conducted to determine if the trial had been subsequently replicated by either ‘direct’ or ‘conceptual’ methods. Finally, a broader search was conducted to examine the extent of replication efforts in the psychotherapy literature overall.

In the meta-analytic search, a total of N = 10 meta-analyses met the inclusion criteria. From these meta-analyses, N = 12 distinct trials exhibited large effect sizes. The meta-analyses containing more than two large effect trials reported evidence for treatment superiority. A cited replication search yielded no direct replication attempts (N = 0) for the trials with large effects, and N = 4 conceptual replication attempts of average or above average quality. However, of these four attempts, only two partially corroborated the results from their original trial.

Meta-analytic reviews are influenced by trials with large effects, and it is not uncommon for these reviews to contain several such trials. Since we find no evidence that trials with such large effects are directly replicable, treatment superiority conclusions from these reviews are highly questionable. To enhance the quality of clinical science, the development of authoritative replication criteria for clinical trials is needed. Moreover, quality benchmarks should be considered before trials are included in a meta-analysis, or replications are attempted.

To systematically assess the level of evidence for psychotropic drugs approved by the European Medicines Agency (EMA).

Cross-sectional analysis of all European Public Assessment Reports (EPARs) and meta-analyses of the many studies reported in these EPARs. Eligible EPARs were identified from the EMA's website and individual study reports were requested from the Agency when necessary. All marketing authorisation applications (defined by the drug, the route of administration and given indications) for psychotropic medications for adults (including drugs used in psychiatry and addictology) were considered. EPARs solely based on bioequivalence studies were excluded. Our primary outcome measure was the presence of robust evidence of comparative effectiveness, defined as at least two ‘positive’ superiority studies against an active comparator. Various other features of the approvals were assessed, such as evidence of non-inferiority v. active comparator and superiority v. placebo. For studies with available data, effect sizes were computed and pooled using a random effect meta-analysis for each dose of each drug in each indication.

Twenty-seven marketing authorisations were identified. For one, comparative effectiveness was explicitly considered as not needed in the EPAR. Of those remaining, 21/26 (81%) did not provide any evidence of superiority against an active comparator, 2/26 (8%) were based on at least two trials showing superiority against active comparator and three (11%) were based on one positive trial; 1/26 provided evidence for two positive non-inferiority analyses v. active comparator and seven (26%) provided evidence for one. In total, 20/27 (74%) evaluations reported evidence of superiority v. placebo with two or more trials. Among the meta-analyses of initiation studies against active comparator (57 available comparisons), the median effect size was 0.051 (range −0.503; 0.318). Twenty approved evaluations (74%) reported evidence of superiority v. placebo on the basis of two or more initiation trials and seven based on a single trial. Among meta-analyses of initiation studies against placebo (125 available comparisons), the median effect size was −0.283 (range −0.820; 0.091). Importantly, among the 89 study reports requested on the EMA website, only 19 were made available 1 year after our requests.

The evidence for psychiatric drug approved by the EMA was in general poor. Small to modest effects v. placebo were considered sufficient in indications where an earlier drug exists. Data retrieval was incomplete after 1 year despite EMA's commitment to transparency. Improvements are needed.

People with a mental illness have a shorter lifespan and higher rates of somatic illnesses than the general population. They also face multiple barriers which interfere with access to healthcare. Our objective was to assess the effect of mental illness on the timeliness and optimality of access to healthcare for somatic reasons by comparing indicators reflecting the quality of prior somatic care in hospitalised patients.

An observational nation-wide study was carried out using exhaustive national hospital discharge databases for the years 2009–2013. All adult inpatient stays for somatic reasons in acute care hospitals were included with the exception of obstetrics and day admissions. Admissions with coding errors were excluded. Patients with a mental illness were identified by their admissions for a psychiatric reason and/or contacts with psychiatric hospitals. The quality of prior somatic care was assessed using the number of admissions, admissions through the emergency room (ER), avoidable hospitalisations, high-severity hospitalisations, mean length of stay (LOS) and in-hospital death. Generalised linear models studied the factors associated with poor quality of primary care.

A total of 17 620 770 patients were included, and 6.58% had been admitted at least once for a mental illness, corresponding to 8.96% of hospital admissions. Mentally ill patients were more often hospitalised (+41% compared with non-mentally patients) and for a longer LOS (+16%). They also had more high-severity hospitalisations (+77%), were more often admitted to the ER (+113%) and had more avoidable hospitalisations (+50%). After adjusting for other covariates, regression models found that suffering from a mental illness was significantly associated with a worse state for each indicator of the quality of care except in-hospital death.

Inadequate primary care of mentally ill patients leads to more serious conditions upon admission to hospital and avoidable hospitalisations. It is, therefore, necessary to improve primary care and prevention for those patients.

This article explores an alternative understanding of how psychiatric drugs work that is referred to as the drug-centred model of drug action. Unlike the current disease-centred model, which suggests that psychiatric drugs work by correcting an underlying brain abnormality, the drug-centred model emphasises how psychiatric drugs affect mental states and behaviour by modifying normal brain processes. The alterations produced may impact on the emotional and behavioural problems that constitute the symptoms of mental disorders.

Arguments are put forward that justify the consideration of the drug-centred model. The research necessary to support the prescription of drugs according to such a model is explored.

Evidence from neurochemistry and comparative drug trials do not confirm the disease-centred model of drug action. Since psychiatric drugs are recognised to have mind- and behaviour-altering properties, the drug-centred model constitutes a plausible alternative. The drug-centred model suggests that research is needed to identify all the alterations produced by various sorts of drugs, both acute and long term, and how these might interact with the symptoms and problems associated with different mental disorders. This requires detailed animal and volunteer studies and data from patients prescribed drug treatment long term, along with placebo-controlled and comparative trials that look at the overall impact of drug-induced alterations on well-being and functioning as well as symptoms. Research is also needed on alternative ways of fulfilling the function of drug treatment. The moral aspect of using drugs to modify behaviour rather than treat disease needs honest and transparent consideration.

It is hoped this discussion will encourage the psychiatric and pharmaceutical research community to provide more of the information that is required to use psychiatric drugs safely and effectively.

Inequalities in mental health are well documented using individual social statuses such as socioeconomic status (SES), ethnicity and migration status. However, few studies have taken an intersectional approach to investigate inequalities in mental health using latent class analysis (LCA). This study will examine the association between multiple indicator classes of social identity with common mental disorder (CMD).

Data on CMD symptoms were assessed in a diverse inner London sample of 1052 participants in the second wave of the South East London Community Health study. LCA was used to define classes of social identity using multiple indicators of SES, ethnicity and migration status. Adjusted associations between CMD and both individual indicators and multiple indicators of social identity are presented.

LCA identified six groups that were differentiated by varying levels of privilege and disadvantage based on multiple SES indicators. This intersectional approach highlighted nuanced differences in odds of CMD, with the economically inactive group with multiple levels of disadvantage most likely to have a CMD. Adding ethnicity and migration status further differentiated between groups. The migrant, economically inactive and White British, economically inactive classes both had increased odds of CMD.

This is the first study to examine the intersections of SES, ethnicity and migration status with CMD using LCA. Results showed that both the migrant, economically inactive and the White British, economically inactive classes had a similarly high prevalence of CMD. Findings suggest that LCA is a useful methodology for investigating health inequalities by intersectional identities.

Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful.

We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments.

Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials.

Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.