Objective. To establish differentiated depressive subtypes using a latent class analysis (LCA), including clinical and functional indicators in a sample of depressed patients consulted in Chilean Primary Health Care. Methods. A LCA was performed on a sample of 297 depressed patients consulted in Chilean PHC. The Mini International Neuropsychiatric Interview, the Hamilton Depression Rating Scale, the Outcome Questionnaire -social role, and interpersonal subscales were as instruments. A regression analysis of the different subtypes with sociodemographic and adverse life experiences was performed. Results. In a sample characterized by 87.5% of women, two, three, and four latent class models were obtained. The three-class model likely represents the best clinical implications. In this model, the classes were labeled: “complex depression” (CD) (58% of the sample), “recurrent depression” (RD) (34%), and “single depression episode” (SD) (8%). Members of CD showed a higher probability of history of suicide attempts, interpersonal, and social dysfunction. Psychiatric comorbidities differentiated the RD from SD. According to a multinomial regression model, childhood trauma experiences, recent stressful life experiences, and intimate partner violence events were associated with the CD class ( $p<0.01$ ). Limitations. The vast majority of participants were females from Chile and the sample studied was not random. So, the results may not necessarily represent outpatient clinics. Conclusions. This study can provide additional evidence that depression, specifically in female gender, could be better understood as a complex heterogeneous disorder when clinical and functional indicators are studied. Furthermore, adverse life experiences starting in childhood could lead to a differentiated complex depressive subtype.

Los desastres se caracterizan por generar un impacto en las personas y comunidades expuestas, quienes responderán de manera disruptiva o saludable. El objetivo de esta investigación es definir ese impacto psicosocial mediante el diseño y validación de una escala: SPSI-D. Para eso: se revisó literatura centrada en el impacto de los desastres; se diseñaron ítems que fueron evaluados por jueces-expertos; y se validó la escala en una muestra de personas expuestas al terremoto y tsunami del 27 de Febrero de 2010 en Chile. Se utilizó el análisis factorial paralelo, confirmatorio y curvas ROC. Los resultados evidencian la validez, fiabilidad y precisión de la SPSI-D, la que quedó conformada por 51 ítems, agrupados en 6 dimensiones. El instrumento integra las ventajas de medir respuestas disruptivas y saludables después de un desastre. Se deja la SPSI-D a disposición de los profesionales que la requieran.

This meta-analysis examined how unemployment, a psychosocial stressor, influences post-traumatic stress disorder (PTSD) and complex PTSD (CPTSD). It specifically explores unemployment as a risk factor for trauma, with emphasis on CPTSD, and investigates economic variables, including the GINI coefficient, as potential moderators. A systematic search in Web of Science, Scopus, and PubMed yielded 33 studies comprising more than 57,000 participants. Odds ratios (OR) were computed, and a random-effects model was used to synthesize the findings. Meta-regression analyses were conducted to evaluate the effects of economic inequality (GINI) and nominal gross domestic product (NGDP), but neither moderator reached statistical significance; this is addressed in detail in the Discussion. The results revealed that unemployment significantly elevated the risk for PTSD (OR = 1.500; logOR = 0.3826; PI: 0.457–4.702) and CPTSD (OR = 2.180; logOR = 0.7430; PI: 0.501–8.808), with a stronger impact on CPTSD. These findings emphasize unemployment as a pivotal predictor of trauma, especially CPTSD, broadening the traditional focus on interpersonal factors. They also highlight the importance of integrating psychosocial and economic variables into clinical assessments and public health policies. Addressing both unemployment and economic inequality could be critical for effective interventions and prevention efforts, underscoring the need for further multidisciplinary research.

Recent findings indicate that both disruptive Post-Traumatic Stress Disorder (PTSD) and healthy Post-Traumatic Growth (PTG) responses have some spatial distribution depending on where they are measured and the different degrees of exposure that people may have to a critical event (e.g., earthquake). Less is known about how these responses change as a function of space and time after these events. The objective of this study was to enter deeper into this relationship analysing how PTSD and PTG responses vary in their spatial distribution 6 and 7 years after an earthquake (such as the one that occurred on 27 February, 2010 in Cauquenes City, Chile). Spatial analyses based on Geographic Information Systems (GIS) were performed to detect global and local geographic clustering. Investigating 171 (2016) and 106 (2017) randomly selected adults from Cauquenes, we demonstrated that 7 years after the event only 4 variables were spatially clustered, i.e. personal mental strength, interpersonal relations, new possibilities and appreciation of life), all of them PTG dimensions; This result contrasted with the situation the previous year (2016), when 7 variables were clustered (total PTG, spiritual change, new possibilities, appreciation of life, PTSD symptoms, PTSD reactions and PTSD in total). The spatial identifications found could facilitate the comparison of mental health conditions in populations and the impact of recovery programmes in communities exposed to disasters.

Marcelo Nvo-Fernández, César Villacura-Herrera, Valentina Miño-Reyes, Carlos Serrano, Fabiola Salas, Francisco Ahumada, and Marcelo Leiva-Bianchi. J Korean Acad Child Adolesc Psychiatry 2025;36:196-214. https://doi.org/10.5765/jkacap.250017

Since its first definition, the number of dimensions of the posttraumatic stress disorder (PTSD) has increased (e.g., 1 to 7). This could be caused by 2 methodological reasons. First, the studies do not select representative samples of the population and do not consider the magnitude of exposure to the same event. Second, they use nonrobust data analysis, which increases the number of dimensions artificially. The aim is to determine how many dimensions PTSD has, when both sources of error are controlled. From 25,400 representative participants of the same population exposed to an earthquake and evaluated with the Davidson Trauma Scale, 127 smaller samples of 200 participants were obtained and classified according to the Mercalli magnitude. Parallel factor analyses (PFA), exploratory structural equations models (ESEM), and classic exploratory factor analysis (EFA) were applied to each sample. There were significant differences in fit and number of dimensions between the high and low Mercalli magnitude. In high exposure conditions: There were 1 dimension according to PFA and 3 according to EFA; ESEM and CFA indicated good fit. The main finding was that PTSD has a unique dimension that includes all of its symptoms. The need to use robust methods of analysis to evaluate data from samples homogeneously exposed to the same potentially traumatic event is argued. (PsycInfo Database Record (c) 2022 APA, all rights reserved).