On the transience or stability of subthreshold psychopathology

Prevalence and risk of subthreshold anxiety developing into threshold anxiety disorder in the general population

BACKGROUND

Subthreshold anxiety may act as a critical precursor and risk factor for the onset of threshold anxiety. However, accurate prevalence rates of subthreshold anxiety and its role in leading to threshold anxiety require further elucidation.

METHODS

We conducted a search on PubMed and Web of Science using predefined criteria and identified 45 articles with a total of 278,971 individuals to estimate the prevalence rates using a random effects model. The incidence risk ratio (IRR) was estimated by comparing the proportion of individuals with subthreshold anxiety who developed threshold anxiety to those without subthreshold anxiety in seven articles involving 18,693 individuals.

RESULTS

Our analysis revealed an overall prevalence of subthreshold anxiety of 6.19%. Specifically, among individuals with subthreshold generalized anxiety disorders, adolescents show the highest prevalence (9.47%), outpacing adults (4.69%) and the elderly (3.49%). Further analysis of seven studies showed an increased risk of developing threshold anxiety in individuals with subthreshold anxiety (IRR = 2.63), with a higher transition rate (9.59%) compared to those without subthreshold anxiety (3.65%).

CONCLUSIONS

Anxiety disorders may be conceptualized as a spectrum, with subthreshold anxiety serving as a significant prodromal state and risk factor for the development of threshold anxiety. Proactive management of subthreshold anxiety represents an effective approach for the prevention of its progression to threshold anxiety. Future research should investigate the risk of progression from subthreshold to threshold anxiety across various types, and explore how factors, such as social support and personality traits facilitate this progression.

A longitudinal study of the impact of childhood adversity dimensions on social and psychological factors and symptoms of psychosis, depression, and anxiety

The present study examined three empirically-derived childhood adversity dimensions as predictors of social, psychological, and symptom outcomes across three prospective assessments of a young adult sample. Participants were assessed five times over eight years with semi-structured interviews and questionnaires. The analyses used the dimensions underlying multiple subscales from well-established childhood adversity measures administered at the first two assessment waves (described in a previous report). Outcome data pertain to the last three assessment waves, with sample sizes ranging from 89 to 169. As hypothesized, the childhood adversity dimensions demonstrated overlapping and differential longitudinal associations with the outcomes. Deprivation predicted the negative (deficit-like) dimension of psychosis, while Threat and Intrafamilial Adversity predicted the positive (psychotic-like) dimension. Depression and anxiety symptoms were predicted by different childhood adversity dimensions over time. Furthermore, Threat predicted a smaller and less diverse social network, Intrafamilial Adversity predicted anxious attachment, and Deprivation predicted a smaller social network, anxious and avoidant attachment, perceived social support, and loneliness. The three adversity dimensions combined accounted for moderate to large proportions of variance in several outcomes. These results extend prior work by identifying associations of three meaningful dimensions of childhood adversity with different risk profiles across psychological, social, and psychopathological domains. The findings enhance our understanding of the impact of childhood adversity across young adulthood.

Using unsupervised clustering approaches to identify common mental health profiles and associated mental health-care service-use patterns in Ontario, Canada

Mental health is a complex, multidimensional concept that goes beyond clinical diagnoses, including psychological distress, life stress, and well-being. In this study, we aimed to use unsupervised clustering approaches to identify multidimensional mental health profiles that exist in the population, and their associated service-use patterns. The data source was the 2012 Canadian Community Health Survey-Mental Health, linked to administrative health-care data; all Ontario, Canada, adult respondents were included. We used a partitioning around medoids clustering algorithm with Gower's proximity to identify groups with distinct combinations of mental health indicators and described them according to their sociodemographic and service-use characteristics. We identified 4 groups with distinct mental health profiles, including 1 group that met the clinical threshold for a depressive diagnosis, with the remaining 3 groups expressing differences in positive mental health, life stress, and self-rated mental health. The 4 groups had different age, employment, and income profiles and exhibited differential access to mental health-care services. This study represents the first step in identifying complex profiles of mental health at the population level in Ontario. Further research is required to better understand the potential causes and consequences of belonging to each of the mental health profiles identified. This article is part of a Special Collection on Mental Health.

Transdiagnostic symptom networks in relation to mental health service use in community youth

OBJECTIVE

The objective of this study is to scrutinize whether psychopathology symptom networks differ between those with and without lifetime: treatment seeking, treatment and treatment of longer duration.

METHODS

We created non-exclusive groups of subjects with versus without lifetime treatment seeking, treatment and treatment of mid-long-term duration. We estimated Ising models and carried out network comparison tests (NCTs) to compare (a) overall connectivity and (b) network structure. Furthermore, we examined node strength. We used propensity score matching (PSM) to minimize potential confounding by indication for service use.

RESULTS

Based on data from 9,172 participants, there were no statistically significant differences in overall connectivity and network structure in those with versus without lifetime: treatment seeking (p = .75 and p = .82, respectively), treatment (p = .63 and p = .49, respectively) and treatment of mid-longterm duration (p = .15 and p = .62, respectively). Notably, comparing networks with versus without service use consistently revealed higher node strength in 'obsessions' and 'aggression' and lower node strength in 'elevated mood' in all networks with service use.

CONCLUSIONS

Findings suggest that after adjusting for potential confounding by indication for service use, there was no indication of an association in overall connectivity or network structure for lifetime treatment seeking, treatment and treatment of longer duration. However, selected structurally important symptoms differed consistently in all three comparisons. Our findings highlight the potential of network analysis methods to examine treatment mechanisms and outcomes. Specifically, more granular network characteristics on the node level may complement and enrich traditional outcomes in clinical research.