Psychiatric Quarterly最新文献

This study aimed to estimate the percentage of child RTCs utilizing seclusion and restraint (S/R) practices and examine predictors associated with increased likelihood of S/R use between 2010 and 2020. A secondary analysis of the National Mental Health Services Survey was conducted (n-range = 580-781). Facility-level client demographics and facility characteristics were examined using multi-level logistic regression. One-way ANOVA indicated that the percentage of child RTCs using S/R significantly decreased between 2010 and 2020 [F(4, 58074) = 75.62, p <.001]. A post-hoc Bonferroni test found that the percentage of facilities reporting the use of SR in 2010 was significantly higher than in 2014, 2016, 2018, and 2020 (all p <.001). Multi-level logistic regression analysis found that facility-level client demographics (percent male, white, and involuntarily committed) did not significantly predict facility use of SR in 2010, 2014, or 2016. However, facility-level characteristics of SED program (all p <.01), use of psychotropic medication (all p <.001), and facility size as measured by number of mental health beds (all p <.01) consistently predicted usage of SR in 2010 [F(11) = 68.38, p <.001], 2014 [F(12) = 74.25, p <.001], and 2016 [F(12) = 74.48, p <.001]. Facility ownership and accepting Medicare payments did not significantly predict facility usage of SR. Use of SR within child RTCs significantly declined between 2010 and 2020. Larger facilities, facilities with an SED program, and facilities that used psychotropic medications with clients were more likely to use SR practices between 2010 and 2016. More high-quality, publicly available data for monitoring S/R use in mental health services within the United States is needed.

To examine the risks of long-term de novo psychiatric disease in women with primary infertiltiy compared to age-matched referrent women. Retrospective, population-based cohort of 1,001 women with primary infertility and 1,001 age-matched (± 1 year) referent women aged 18-50. The "index date" was date of first clinical note for infertility and included visits fromJanuary 1, 1980 to December 31, 1999. Baseline characteristics were collected by chart review. Outcome data was evaluated through December 31, 2020. Primary outcomes were baseline prevalence and de novo rates of subsequent psychiatric disorders including depression, anxiety, bipolar disorder, substance abuse, suicidality, and somatization evaluated by Cox proportional hazards modeling. Among women with primary infertility and referent women, the median duration of follow-up was 23.7 years. The risk of de novo psychiatric disorders was not significantly different between groups. Additionally, the risk of de novo psychiatric disorders did not significantly differ between those with isolated male factor versus isolated female factor infertility. Among women with primary infertility, the cumulative incidence of de novo depression and anxiety was significantly higher among women diagnosed with primary infertility in the 1990s compared to the 1980s. Women with primary infertility, in a historical population-based cohort, do not have a significantly different long-term risk of de novo psychiatric diagnoses compared to age-matched referent women. Our findings support the notion that infertility diagnosis and treatment present an acute period of stress and for some psychologic distress, neither of which persist or increase the risk for development of future psychiatric disease.

In this study, our aim was to develop a valid and reliable scale to determine the social anxiety level of people regarding COVID-19. A total of 420 people participated in the research. Explanatory and confirmatory factor analyses were used to test the validity and reliability of the scale. As a result of the explanatory factor analysis, it was determined that the scale consisted of 22 items and four sub-dimensions. These sub-dimensions were named "negative thinking", "sociability", "fear" and "vaccination". In examination of the fit indices obtained from the confirmatory factor analysis results, it was seen that the four-factor scale structure was high and acceptable. Based on these results, we concluded that the COVID-19 social anxiety scale (CAS) was valid and reliable for examining the level of social anxiety of people during the pandemic periods.

Recent research has shown that people who gamble are more likely to have suicidal thoughts and attempts compared to the general population. Despite the advancements made, no study to date has predicted suicide risk factors in people who gamble using machine learning algorithms. Therefore, current study aimed to identify the most critical predictors of suicidal ideation and suicidal attempts among people who gamble using a machine learning approach. An online survey conducted a cross-sectional analysis of 741 people who gamble (mean age: 25.9 ± 5.56). To predict the risk of suicide attempts and ideation, we employed a comprehensive set of 40 biological, psychological, social, and socio-demographic variables. The predictive models were developed using Logistic Regression, Random Forest (RF), robust eXtreme Gradient Boosting (XGBoost), and ensemble machine learning algorithms. Data analysis was performed using R-Studio software. Random Forest emerged as the top-performing algorithm for predicting suicidal ideation, with an impressive AUC of 0.934, sensitivity of 0.7514, specificity of 0.9885, PPV of 0.9473, and NPV of 0.9347. Across all models, dissociation, depression, and anxiety symptoms consistently emerged as crucial predictors of suicidal ideation. However, for suicide attempt prediction, all models exhibited weaker performance. XGBoost showed the best performance in this regard, with an AUC of 0.663, sensitivity of 0.78, specificity of 0.8990, PPV of 0.34, NPV of 0.984, and accuracy of 0.8918. Depressive symptoms and rumination severity were highlighted as the most important predictors of suicide attempts according to this model. These findings have important implications for clinical practice and public health interventions. Machine learning could help detect individuals prone to suicidal ideation and suicide attempts among people who gamble, assisting in creating tailored prevention programs to address future suicide risks more effectively.

This study aimed to evaluate the role of neuroinflammation in neuronal and glial cells in the pathophysiology of Major Depressive Disorder (MDD) through different biomarkers.S100-B and VILIP-1 levels of patients diagnosed with MDD were evaluated before and after antidepressant treatment. A total of 65 patients diagnosed with MDD and 69 healthy controls were included. Serum levels of S100B and VILIP-1 were measured at the time of diagnosis and after eight weeks antidepressant treatment and compared with healthy controls. Hamilton Depression Rating Scale (HDRS) and the Clinical Global Impression Scale (CGI) were applied to assess the severity of depression. In our study, although serum S100B levels were higher in patients before treatment compared to healthy controls, this difference was not statistically significant. Regarding VILIP-1 levels, there was no statistically significant difference between patients and healthy controls. A positive and statistically significant correlation was found between S100B and VILIP-1 levels in MDD group before the treatment. At the eighth week of treatment, a statistically significant positive correlation was also found between S100B and VILIP-1 levels. Our research is the first study to evaluate MDD through two separate biomarkers specific to glial and neuronal cells.The fact that S100B and VILIP-1 levels showed significant correlations in patients diagnosed with MDD both before and after treatment suggests that they may play a shared role in the pathophysiology of the disorder. The correlation between S100B and VILIP-1 may serve as a guide in understanding the pathophysiology of the disorder and in identifying new drug development targets.

The COVID-19 pandemic has significantly impacted mental health worldwide, leading to increased rates of suicidal behavior. This systematic review and meta-regression aim to investigate the global prevalence and risk factors associated with suicidal behaviors in the general population during the pandemic. The study included 202 articles from January 1, 2019, to October 31, 2023, sourced from databases such as Embase, MEDLINE, CINAHL, Web of Science, and Cochrane Library. The meta-analysis revealed a prevalence of 13.5% for suicidal ideation, 10.4% for suicide attempts, and a death rate of 0.5%, translating to 4.52 per 100,000 person-years. Significant risk factors identified include being transgender, young adults (18-44 years), unmarried status, low education, retirement, living alone, low social support, a history of suicide attempts, substance use, depression, anxiety, PTSD, sleep problems, poor perceived physical health, loneliness, quarantine, and residing in the Americas or multiple regions. The findings underscore the urgent need for targeted mental health interventions during pandemics, focusing on high-risk groups such as young adults, transgender individuals, those with low social support, and people with mental health issues. This comprehensive analysis provides valuable insights for policymakers and healthcare providers to develop effective strategies to mitigate the heightened risk of suicide during global health crises.

Patients with acute psychosis are often confronted with the prejudice that they refuse all treatment due to lack of insight. This study examined and compared the aims and preferences for treatment of patients with acute psychosis and of psychiatric inpatient staff. A total of 142 inpatients being treated for a psychotic disorder on either a locked or an open ward indicated which of a range of symptoms they want to be treated and which of various biopsychosocial treatment options they would like to receive. Staff members from the same wards reported which psychiatric symptoms they deemed relevant in the treatment of psychosis and which treatment options should be offered. Patients assigned the highest treatment need to neurocognitive symptoms, followed by affective and positive symptoms. In contrast, staff assigned the highest treatment need to positive symptoms, followed by neurocognitive and affective symptoms. Patients and staff on open wards expressed more treatment needs overall than did patients and staff on locked wards. Patients' desire for treatment differed across treatment types. In comparing patients on locked versus open wards, patients on open wards expressed higher approval of medication than patients on locked wards. Even patients with highly acute psychosis being treated on locked psychiatric wards endorsed treatment. Treatment preferences of this group deviated markedly in some instances from staffs' preferences. Considering their specific needs may contribute to increasing patient self-efficacy, and improving adherence to treatment.

Loneliness and depression are serious public mental health problems. Meaning in life (MIL) is associated with reduced loneliness and depression. This study aimed to: (1) investigate associations between loneliness, MIL, and depression, differentiated by sex in individuals aged ≥ 50 years, residing in 26 European countries and Israel, and (2) to examine in men and women separately whether MIL mediated the relationship between loneliness and depression. We included 41,372 individuals (23,789 women) who responded to wave 8 of the SHARE project. The variables analyzed were loneliness (UCLA), depression (EURO-D scale), and MIL (CASP-19). The analytical procedures included regression analysis and exploratory mediation analysis. Among men and women, the odds of loneliness increasing depression were 3.6 and 3.3 times higher, respectively. Among men, feeling MIL sometimes or often had odds for reducing depression by 0.53 and 0.21, respectively. In women, feeling MIL sometimes or frequently reduced the odds of depression by 0.37 and 0.19, respectively. Regardless of sex, mediation analyses showed a positive association between loneliness and depression, while MIL was negatively associated with loneliness and depression. MIL partially mediated the association between LON and depression in male and female models by approximately 83.2% and 80.7%, respectively. No differences were found between men's and women's mediation models. Regardless of sex, high levels of MIL seem to be effective in benefiting the mental health of Europeans aged 50 and over. MIL proved to be a significant mediator of the relationship between loneliness and depression, while loneliness and depression strengthened each other.

Major depressive disorder (MDD) patients' personality traits and illness representations are linked to MDD severity. However, the associations between personality and illness representations in MDD and the mediating role of illness representations between personality and MDD severity have not been investigated. This study aimed to prospectively investigate the aforementioned associations and the possible mediating role of illness representations between personality and MDD severity. One hundred twenty-five patients with a MDD diagnosis, aged 48.18 ± 13.92 (84% females), participated in the study. Personality traits were measured with the Traits Personality Questionnaire at baseline. Illness representations were measured with the Illness Perception Questionnaire-Mental Health about five months later (mean = 5.08 ± 1.14 months). MDD severity was measured about 10 months after the baseline assessment (mean = 9.53 ± 2.36 months) with the Beck Depression Inventory. SPSS 29 and AMOS 27 were used to conduct correlational and parallel mediation analyses. According to the results, Neuroticism was positively and Extraversion was negatively linked to MDD severity. Negative MDD impact representations fully mediated these associations. Neuroticism and Extraversion are linked to future MDD severity through patients' representations of MDD's impact. Restructuring maladaptive representations about MDD's impact can be a promising way to reduce symptom severity in patients with high Neuroticism and low Extraversion levels.

Background: Military veterans often encounter multiple obstacles to mental health care, such as stigma, practical barriers (e.g., high cost), and negative beliefs about mental health care. To date, however, nationally representative data on the prevalence and key correlates of these barriers to care are lacking. Such data are critical to informing population-based efforts to reduce barriers and promote engagement in mental health treatment in this population.

Methods: Data were analyzed from the National Health and Resilience in Veterans Study, which surveyed 4,069 US veterans, 531 (weighted 15.0%) of whom screened positive for a mental disorder but never received mental health treatment. Multivariable logistic regression and relative importance analyses were conducted to identify key predisposing, enabling, and need-based factors associated with endorsement of stigma, instrumental barriers, and negative beliefs about mental health care.

Results: A total 47.1% of veterans endorsed any barrier to care, with 38.7% endorsing instrumental barriers to care, 28.8% perceived stigma, and 22.0% negative beliefs about mental health care. Lower purpose in life, grit, and received social support were most consistently associated with these barriers to care.

Conclusions: Nearly half of US veterans with psychiatric need and no history of mental health treatment report barriers to care. Modifiable characteristics such as a low purpose in life, grit, and received support were associated with endorsement of these barriers. Results may help inform resource allocation, as well as prevention, psychoeducation, and treatment efforts to help reduce barriers and promote engagement with mental health services in this population.