Previous studies have found substantial costs to be associated with depression and insomnia (as separate entities).

To estimate healthcare service use and costs associated with insomnia in Australian adults experiencing subthreshold depression or major depressive disorder (MDD).

Healthcare service use and productivity loss were extracted from the cross-sectional 2020–2022 National Survey of Mental Health and Wellbeing data. Insomnia and depression were assessed using questions aligned with DSM-IV criteria. Weighted two-part models were used to calculate average annual costs (presented as 2021–2022 Australian dollars).

The analytical sample meeting subthreshold depression or MDD criteria consisted of 1331 adults (aged 40.5 ± 16.1 years; 59% female; insomnia prevalence: 84%). Healthcare service use and healthcare costs between individuals with insomnia and those without insomnia were similar in the MDD group. For subthreshold depression, healthcare costs were significantly higher for those with insomnia compared with those without insomnia (Δ = A$990, 95% CI: 234 to 1747), but healthcare resource use was not significantly different. Productivity loss among employed people and reduced employment were much greater (although the difference did not reach statistical significance) in adults with insomnia compared with those without insomnia.

Healthcare resource use among adults with depression was similar in those with and without insomnia. However, higher healthcare costs associated with insomnia were observed in adults with subthreshold depression. Further studies are encouraged to understand the nature of the increased healthcare cost associated with insomnia in individuals with subthreshold depression and to optimise healthcare service use in people with comorbid depression and insomnia.

Personality Disorders (PD) - specifically Borderline Personality Disorder (BPD), and certain Eating Disorders (ED) share common clinical features. One of these features is impulsivity, studied individually in each diagnostic group, and scarcely used to encompass specific profiles of these patients.

Understanding the common clinical variables of this patient population would facilitate therapeutic efforts and enable greater precision regarding the prognosis of these patients.

This study aims to study impulsivity in a group collectively formed by BPD and ED, compared to a control group, in contrast to the individualized study approach typically conducted in the literature.

A cross-sectional descriptive study is conducted to assess impulsivity as a common diagnostic variable in a group of PD and ED in comparison with a healthy control group. The sample was collected between 2016 and 2019 at the Hospital Clínico San Carlos, totaling 108 subjects.

A statistically significant difference is observed (p<0.005 in all scales) in total impulsivity, cognitive impulsivity, motor impulsivity, and unplanned impulsivity in the cases group comprising patients diagnosed with PD and ED, compared to the control group from the general population.

Impulsivity is closely related to the concept of borderline personality disorder. This analysis also includes eating disorders, with the difference from the control group still statistically significant.

The presence of common clinical variables in these groups (PD and ED) may have clinical and therapeutic implications that differ from those pursued thus far. This allows moving away from the categorical model and understanding these disorders from a more enriching and advanced dimensional perspective.”

None Declared

Suicide attempts (SA) leading to highly lethal consequences have been associated with heightened suicide planning (Barker et al., 2022), along with deficits in social cognition (Levi-Belz et al., 2022). Hypomentalizing, characterized by excessive uncertainty regarding mental states, may contribute to heightened social withdrawal and an increased risk of SA (Nestor & Sutherland, 2022). Although certain studies have identified a connection between hypomentalizing profiles and self-harm (Badoud et al., 2015), research into the lethality of SA remains limited.

This study aimed to explore the association between hypomentalizing and SA lethality.

Our study encompassed a cohort of 1,371 patients who committed a SA. We conducted assessments of mentalizing using the RFQ-8 instrument, and evaluations of suicidal ideation and behavior employing the CSRSS questionnaire. Demographic and clinical characteristics were compared using the T-student and Chi-square tests. To investigate the relationship between hypomentalizing and the SA lethality, we employed logistic regression models.

Descriptive date are presented in Table 1. Our results show that hypomentalizing do not predict a higher SA lethality. Additionally, hypomentalizing increased the risk of SA planning (p≤0.001, B=-0.182), and SA planning predicted a higher SA lethality (see Table 2).Table 1.

Means Comparison for low and high lethality (N=1371)

While the association between hypomentalizing and high SA lethality was not significant, a discernible trend toward such relationship can be noted. Further studies examining the moderating effects of planning in the association between hypomentalizing and SA lethality are required.

None Declared

Clinical trials provide the “gold standard” evidence for advancing the practice of medicine, even as they evolve to integrate real-world data sources. Modern clinical trials are increasingly incorporating real-world data sources – data not intended for research and often collected in free-living contexts. We refer to trials that incorporate real-world data sources as real-world trials. Such trials may have the potential to enhance the generalizability of findings, facilitate pragmatic study designs, and evaluate real-world effectiveness. However, key differences in the design, conduct, and implementation of real-world vs traditional trials have ramifications in data management that can threaten their desired rigor.

Three examples of real-world trials that leverage different types of data sources – wearables, medical devices, and electronic health records are described. Key insights applicable to all three trials in their relationship to Data and Safety Monitoring Boards (DSMBs) are derived.

Insight and recommendations are given on four topic areas: A. Charge of the DSMB; B. Composition of the DSMB; C. Pre-launch Activities; and D. Post-launch Activities. We recommend stronger and additional focus on data integrity.

Clinical trials can benefit from incorporating real-world data sources, potentially increasing the generalizability of findings and overall trial scale and efficiency. The data, however, present a level of informatic complexity that relies heavily on a robust data science infrastructure. The nature of monitoring the data and safety must evolve to adapt to new trial scenarios to protect the rigor of clinical trials.

More than 700,000 people die by suicide in 2019 globally (World Health Organitation 2021). Mental health problems constitute a risk factor for suicidal behavior and death by suicide (Hoertel et al. Mol Psychiatry 2015; 20 718–726). Different mental disorders have been related to different forms of suicidal ideation and behavior (Conejero et al. Curr Psychiatry Rep 2018; 20, 33) (Quevedo et al. Compr Psychiatry 2020; 102 152194). However, little is known on comorbidity profiles among suicide attempters.

The aim of our work was to identify the psychiatric comorbidity profiles of individuals who were admitted a hospital emergency department due to a suicide attempt. Moreover, it intended to know their clinical characteristics according to comorbidity profile.

A sample of 683 attempters (71.30% female; M age= 40.85, SD= 15.48) from the SURVIVE study was used. Patients were assessed within the 15 days after emergency department admission. Sociodemographic (i.e., sex, age, marital status and employment status) and clinical data were collected. The International Neuropsychiatric Interview (MINI) was used to assess DSM-V Axis 1 mental health diagnoses and the Columbia Suicide Rating Scale (C-SSRS) to assess suicidal ideation and behavior. The Acquired Capacity for Suicide-Fear of Death Scale (ACSS-FAD), the Patient Health Questionnaire (PHQ-9) to assess the frequency of depressive symptoms during the past 2 weeks, and the General Anxiety Disorder-7 (GAD-7) scale to assess symptoms of worry and anxiety were also conducted. For the identification of comorbidity profiles, latent class analysis framework was followed considering diagnosis to each individual disorder as clustering variables. On the other hand, binary logistic regression was used to study the relationship between comorbidity profile membership and clinical factors.

Two classes were found (Class I= mild symptomatology class, mainly featured by emotional disorder endorsement; and Class II= high comorbidity class, featured by a wide amount of endorsed diagnoses) (see figure 1). Individuals from the High comorbidity class were more likely to be female (OR= 0.98, p<.05), younger in age (OR= 0.52, p< .01), with more depressive symptoms (OR=1.09, p<.001) and have greater impulsivity (OR= 1.01, p<.05).

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We found two profiles of people with suicidal behavior based on the presence of mental disorders. Each of the suicidal subtypes had different associated risk factors. They also had a different profile of suicidal behavior.

None Declared

Health services research (HSR) is affected by a widespread problem related to service terminology including non-commensurability (using different units of analysis for comparisons) and terminological unclarity due to ambiguity and vagueness of terms. The aim of this study was to identify the magnitude of the terminological bias in health and social services research and health economics by applying an international classification system.

This study, that was part of the PECUNIA project, followed an ontoterminology approach (disambiguation of technical and scientific terms using a taxonomy and a glossary of terms). A listing of 56 types of health and social services relevant for mental health was compiled from a systematic review of the literature and feedback provided by 29 experts in six European countries. The disambiguation of terms was performed using an ontology-based classification of services (Description and Evaluation of Services and DirectoriEs – DESDE), and its glossary of terms. The analysis focused on the commensurability and the clarity of definitions according to the reference classification system. Interrater reliability was analysed using κ.

The disambiguation revealed that only 13 terms (23%) of the 56 services selected were accurate. Six terms (11%) were confusing as they did not correspond to services as defined in the reference classification system (non-commensurability bias), 27 (48%) did not include a clear definition of the target population for which the service was intended, and the definition of types of services was unclear in 59% of the terms: 15 were ambiguous and 11 vague. The κ analyses were significant for agreements in unit of analysis and assignment of DESDE codes and very high in definition of target population.

Service terminology is a source of systematic bias in health service research, and certainly in mental healthcare. The magnitude of the problem is substantial. This finding has major implications for the international comparability of resource use in health economics, quality and equality research. The approach presented in this paper contributes to minimise differentiation between services by taking into account key features such as target population, care setting, main activities and type and number of professionals among others. This approach also contributes to support financial incentives for effective health promotion and disease prevention. A detailed analysis of services in terms of cost measurement for economic evaluations reveals the necessity and usefulness of defining services using a coding system and taxonomical criteria rather than by ‘text-based descriptions’.

In response to increasing caseloads of foodborne illnesses and high consequence infectious disease investigations, the Texas Department of State Health Services (DSHS) requested funding from the Texas Legislature in 2013 and 2015 for a new state-funded epidemiologist (SFE) program.

Primary cross-sectional survey data were collected from 32 of 40 local health departments (LHDs) via an online instrument and analyzed to quantify roles, responsibilities, and training of epidemiologists in Texas in 2017 and compared to similar state health department assessments.

Sixty-six percent of SFEs had epidemiology-specific training (eg, master’s in public health) compared to 45% in state health department estimates. For LHDs included in this study, the mean number of epidemiologists per 100 000 was 0.73 in medium LHDs and 0.46 in large LHDs. SFE positions make up approximately 40% of the LHD epidemiology workforce of all sizes and 56% of medium-sized LHD epidemiology staff in Texas specifically.

Through this program, DSHS increased epidemiology capacity almost twofold from 0.28 to 0.47 epidemiologists per 100 000 people. These findings suggest that capacity funding programs like this improve epidemiology capacity in local jurisdictions and should be considered in other regions to improve general public health preparedness and epidemiology capacity.

While taxonomy segregates anxiety symptoms into diagnoses, patients typically present with multiple diagnoses; this poses major challenges, particularly for youth, where mixed presentation is particularly common. Anxiety comorbidity could reflect multivariate, cross-domain interactions insufficiently emphasized in current taxonomy. We utilize network analytic approaches that model these interactions by characterizing pediatric anxiety as involving distinct, inter-connected, symptom domains. Quantifying this network structure could inform views of pediatric anxiety that shape clinical practice and research.

Participants were 4964 youths (ages 5–17 years) from seven international sites. Participants completed standard symptom inventory assessing severity along distinct domains that follow pediatric anxiety diagnostic categories. We first applied network analytic tools to quantify the anxiety domain network structure. We then examined whether variation in the network structure related to age (3-year longitudinal assessments) and sex, key moderators of pediatric anxiety expression.

The anxiety network featured a highly inter-connected structure; all domains correlated positively but to varying degrees. Anxiety patients and healthy youth differed in severity but demonstrated a comparable network structure. We noted specific sex differences in the network structure; longitudinal data indicated additional structural changes during childhood. Generalized-anxiety and panic symptoms consistently emerged as central domains.

Pediatric anxiety manifests along multiple, inter-connected symptom domains. By quantifying cross-domain associations and related moderation effects, the current study might shape views on the diagnosis, treatment, and study of pediatric anxiety.

Social cognition is presently an important focus in rehabilitation of patients wish schizophrenic psychoses. Emotion recognition in one of its different components, the Training of Affect Recognition (TAR) is a therapy program specifically developed aiming at improving affect recognition.

To test if the material of the TAR program in terms a series of facial affect cards is reliable for emotion recognition in a Spanish control population.

A sample of Spanish control subjects were shown the TAR's series of facial affect cards. It includes the following emotions: happiness, sadness, surprise, fear, angry and disgust, and the neutral facial affect. Reliability was assessed by means of the kappa coefficient. Kappa values higher than 0.4 were considered reliable, the following criterion was accepted: kappa values < 0.4, low reliability; 0.4 to 0.75, moderate reliability; > 0.76, strong reliability.

The sample was formed by subjects from the general population with no previous history of psychiatric disorders (n = 20). The following kappa values were obtained: happiness, 0.8406 (CI 0.7744–0.9068), sadness, 0.6171 (CI 0.5377–0.6964), surprise, 0.6582 (CI 0.5859–0.7304), fear, 0.4509 (CI 0.3569–0.5448), angry, 0.6252 (CI 0.5464–0.7040), disgust, 0.7700 (CI 0.6943–0.8456), neutral, 0.5534 (CI 0.4663–0.6404).

In a preliminary study in a sample of 20 Spanish control subjects using the TAR's series of facial affect cards, the six basic emotions and the neutral facial affect were recognized with at least a moderate reliability. A strong reliability was obtained in happiness and disgust, and a moderate reliability in surprise, angry, sadness, neutral, fear.