BMC Psychiatry最新文献

Background: Child sexual abuse is a universal social challenge and the victims of childhood sexual abuse suffer a range of short and long term psychological, social, behavioral and physical problems that vary in different cultures. The study was carried out to explore the perceived impacts of childhood sexual abuse in Pakistan, because no such study was conducted in Pakistan earlier.

Methods: Interpretative phenomenological analysis was used to analyze the data. A snowball sampling technique was used to approach the sample of current study. The sample of the study comprised ten female survivors of childhood sexual abuse of age ranged between 18 and 22 years (M_age= 20.10 years) with the education ranging from matric to BSc. Out of these participants, four were married and six were unmarried and belonged to different cities of Punjab, Pakistan. Data were collected via a semi-structured interview schedule and all interviews were verbatim transcribed.

Results: A rigorous iterative process of data analysis resulted in three super-ordinate themes and ten sub-ordinate themes: Experiencing Abuse (emotional trauma, and physical distress), Psycho-social Distress (low self-esteem, negative self-concept, psychological pain, social suffering, and retaliation vs. forgiveness), and Sexual Difficulties (passive role, emotionally aloof, aversion from hetero-sexuality and avoidance).

Conclusion: It was concluded that childhood sexual abuse is an intense experience that has short- and long-term negative impacts on the lives of female survivors and engulfs their lives as a whole. The study has implications for psychiatrists, psychologists, family counselors, social scientists, educationists, and parents.

Background: The national volume-based procurement (NVBP) policy has significantly decreased prices and increased the accessibility of NVBP drugs. Nevertheless, issues such as heightened adverse reactions and suboptimal efficacy have arisen. Concerns regarding the quality of low-cost medications and the absence of long-term research have been widely recognized. This has led to caution among patients with late-life depression (LLD) due to their delicate health and the severity of their condition. This study evaluated the choice intention for NVBP drugs and associated factors in older patients with LLD.

Methods: A weighted sample of 408 participants between December 2022 and March 2023 were included. Data were collected via face-to-face interviews and questionnaires. To identify significant associated factors of choice intention, a multilevel logistic regression model was employed.

Results: Over half (53.68%) of older patients with LLD intended to choose NVBP drugs. Associated factors included self-assessed poor economy, higher out-of-pocket expenses, monthly household income exceeding CNY 6000, absence of other non-communicable chronic diseases, ordinary registration, urban employee medical insurance, no requirements for brand-name drugs, adverse reactions after using NVBP drugs, and rejection of physicians' recommendation for NVBP drugs. The interaction effect between the real economic condition and patients self-assessed economy significantly influences choice intention for NVBP drugs. Among 124 patients with self-assessed poor economy, 75 showed a higher intention to use NVBP drugs. In these patients, age, medical insurance reimbursement, and brand awareness were significantly associated with choice intention.

Conclusion: Economic factors, physical conditions, medical needs, and physician recommendations significantly influenced the choice intention for NVBP drugs. The choice intention can be improved by strengthening physician-patient communication, increasing the scope and proportion of medical insurance reimbursement, improving substitution studies, and conducting post-marketing re-evaluations of NVBP drugs.

Background: Clozapine is the only recommended antipsychotic medication for individuals diagnosed with treatment-resistant schizophrenia. Unfortunately, its wider use is hindered by several possible adverse effects, some of which are rare but potentially life threatening. As such, there is a growing interest in studying clozapine use and safety in routinely collected healthcare data. However, previous attempts to characterise clozapine treatment have had low accuracy.

Aim: To develop a methodology for identifying clozapine treatment dates by combining several data sources and implement this on a large clinical database.

Methods: Non-identifiable electronic health records from a large mental health provider in London and a linked database from a national clozapine blood monitoring service were used to obtain information regarding patients' clozapine treatment status, blood tests and pharmacy dispensing records. A rule-based algorithm was developed to determine the dates of starting and stopping treatment based on these data, and more than 10% of the outcomes were validated by manual review of de-identified case note text.

Results: A total of 3,212 possible clozapine treatment periods were identified, of which 425 (13.2%) were excluded due to insufficient data to verify clozapine administration. Of the 2,787 treatments remaining, 1,902 (68.2%) had an identified start-date. On evaluation, the algorithm identified treatments with 96.4% accuracy; start dates were 96.2% accurate within 15 days, and end dates were 85.1% accurate within 30 days.

Conclusions: The algorithm produced a reliable database of clozapine treatment periods. Beyond underpinning future observational clozapine studies, we envisage it will facilitate similar implementations on additional large clinical databases worldwide.

Background: This study was intended to investigate the correlation between depression and suicidal ideation among Chinese college students during the COVID-19 pandemic and the potential mediating roles of chronotype and sleep quality in this relationship .

Methods: A sample of 4,768 college students was selected from four institutions in Anhui Province, China, and the study was conducted during the COVID-19 pandemic (November to December 2020) using a stratified, cluster, multi-stage sampling method. This study used the two-item Patient Health Questionnaire (PHQ-2) to assess depressive symptoms, the Morningness-Eveningness Questionnaire 19 (MEQ-19) to determine individual sleep chronotypes (i.e., morning or evening preference), and the Pittsburgh Sleep Quality Index (PSQI) to evaluate sleep quality. Participants were asked about suicidal ideation. MPLUS 8.3 software was used to analyze the mediating effect of chronotype and sleep quality on the relationship between depression and suicidal ideation.

Results: During the COVID-19 pandemic, the prevalence of suicidal ideation among Chinese college students was 5.4%. Depression was inversely correlated with chronotype (beta = - 0.346, P < 0.01) and positively correlated with sleep quality (beta = 0.846, P < 0.001), indicating that students experiencing depressive symptoms were more likely to have a later chronotype and poor sleep quality. A later chronotype (beta = - 0.019, P < 0.05) and poor sleep quality (beta = 0.066, P < 0.01) predicted suicidal ideation. Depression emerged as a direct and significant risk factor for suicidal ideation (effect value = 0.535, 95% confidence interval: 0.449 ~ 0.622). Chronotype and sleep quality were found to have potential mediating effects on the relationship between depression and suicidal ideation; however, the chain-mediating effect of chronotype and sleep quality was not statistically significant.

Conclusions: Our findings suggest that during the COVID-19 pandemic, depression can precipitate suicidal ideation through its influence on sleep chronotype and quality. These compelling findings highlight the urgency of early intervention strategies intended to mitigate suicidal thoughts, particularly among students exhibiting depressive symptoms, who experience disrupted sleep patterns and poor sleep quality.

Background: Depressive disorders are among the common mental health conditions in the general outpatient setting and affect patients' load and treatment outcomes. People who suffer from depression frequently consult general practitioners and prefer to attribute their symptoms to physical illness rather than mental illness. Little is known about the magnitude and associated factors of depression among patients attending general outpatient services in Somalia. The study aimed at determining the prevalence and associated factors of depression among them.

Methods: This is an institution-based cross-sectional study among randomly selected 422 patients who attended general outpatient services of two hospitals in Mogadishu. We applied three standardized instruments, such as the Somali version of the Patient Health Questionnaire (PHQ-9), the Oslo Social Support Scale (OSSS-3), and the Perceived Stress Scale-10 (PSS-10). We analyzed data using the statistical software SPSS version 29. We calculated prevalence and its 95% Confidence Interval (CI) and identified associated factors by bivariate and Multivariate analysis. We considered the association significant when p value is < 0.05.

Results: The prevalence of depression symptoms was found to be 55% (95% CI 50-60%). The result also showed that 55.0% were females, 50.7% were aged between 26 and 44 years, 44.3% were single, 29.9% achieved tertiary education, and 44.3% were unemployed. Multivariate analysis established that age of between 26 and 44 years (aOR = 2.86, 95%CI:1.30-6.29, p = 0.009), being separated/divorced (aOR = 2.37, 95%CI: 1.16-4.82, p = 0.018), income level of ≤$100 (aOR = 3.71, 95% CI:1.36-10.09, p = 0.010), and high stress levels (aOR = 20.06, 95%CI:7.33-54.94, p < 0.001) were independent factors that significantly associated with depressive symptoms.

Conclusion: This study found high levels of depression among patients attending outpatient clinics, with age, marital status, education level, income level, family history of psychiatry disorder, and stress level being key predictors. Regular screening among patients in outpatient clinics and proper referral are crucial in ensuring that those at high risk of depression are managed effectively.

Objective: Patients with Postherpetic Neuralgia (PHN) often exhibit depressive-like symptoms, significantly impacting their quality of life. Esketamine, known for its analgesic properties, has also been recognized for its rapid antidepressant effects. However, its efficacy in the treatment of PHN requires further exploration. This study aims to evaluate the impact of intravenous patient-controlled analgesia(PICA) with esketamine on depressive mood in PHN patients.

Methods: This retrospective study analyzed PHN patients hospitalized and treated at the affiliated hospital of Southwest Medical University from June 2021 to March 2023. Patients were divided into the esketamine group (E group) and the sufentanil group (S group) based on their treatment regimens. Primary outcomes included pain numerical rating scale(NRS), depression patient health questionaire-9(PHQ-9), and anxiety generalized anxiety disorder-7(GAD-7) scores measured before treatment, and at 3 days, 7 days, 1 month, 2 months, and 3 months post-treatment.

Results: A total of 83 patients were included in the analysis. Before treatment, there were no statistically significant differences in pain NRS, depression PHQ-9, and anxiety GAD-7 scores between the two groups (P > 0.05). Compared to before treatment, significant reductions in pain NRS scores were observed at all post-treatment time points in both groups (P < 0.05), with no differences between groups (P > 0.05). The E group exhibited significantly lower depression PHQ-9 scores than the S group at 3 days and 7 days post-treatment (P < 0.05), but no significant differences were observed at 1 month, 2 months, and 3 months (P > 0.05). Anxiety GAD-7 scores were significantly lower in the E group compared to the S group at 3 days, 7 days post-treatment (P < 0.05), with no statistical differences at 1 month, 2 months, and 3 months post-treatment (P > 0.05).

Conclusion: Both PICA with esketamine and sufentanil alleviated pain equally in PHN patients. However, PICA with esketamine specifically improved early symptoms of anxiety and depression.

Background: Precisely estimating the probability of mental health challenges among college students is pivotal for facilitating timely intervention and preventative measures. However, to date, no specific artificial intelligence (AI) models have been reported to effectively forecast severe mental distress. This study aimed to develop and validate an advanced AI tool for predicting the likelihood of severe mental distress in college students.

Methods: A total of 2088 college students from five universities were enrolled in this study. Participants were randomly divided into a training group (80%) and a validation group (20%). Various machine learning models, including logistic regression (LR), extreme gradient boosting machine (eXGBM), decision tree (DT), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were employed and trained in this study. Model performance was evaluated using 11 metrics, and the highest scoring model was selected. In addition, external validation was conducted on 751 participants from three universities. The AI tool was then deployed as a web-based AI application.

Results: Among the models developed, the eXGBM model achieved the highest area under the curve (AUC) value of 0.932 (95% CI: 0.911-0.949), closely followed by RF with an AUC of 0.927 (95% CI: 0.905-0.943). The eXGBM model demonstrated superior performance in accuracy (0.850), precision (0.824), recall (0.890), specificity (0.810), F1 score (0.856), Brier score (0.103), log loss (0.326), and discrimination slope (0.598). The eXGBM model also received the highest score of 60 based on the evaluation scoring system, while RF achieved a score of 49. The scores of LR, DT, and SVM were only 19, 32, and 36, respectively. External validation yielded an impressive AUC value of 0.918.

Conclusions: The AI tool demonstrates promising predictive performance for identifying college students at risk of severe mental distress. It has the potential to guide intervention strategies and support early identification and preventive measures.

Background: Depression is prevalent among lung cancer patients undergoing chemotherapy, and the symptom cluster of fatigue-pain-insomnia may influence their depression. Identifying characteristics of patients with different depression trajectories can aid in developing more targeted interventions. This study aimed to identify the trajectories of depression and the fatigue-pain-insomnia symptom cluster, and to explore the predictive factors associated with the categories of depression trajectories.

Methods: In this longitudinal study, 187 lung cancer patients who were undergoing chemotherapy were recruited and assessed at the first (T1), second(T2), and fourth(T3) months using the Patient Health Questionnaire-9 (PHQ-9), the Brief Pain Inventory (BPI), the Brief Fatigue Inventory (BFI), and the Athens Insomnia Scale (AIS). Growth Mixture Model (GMM) and Latent Class Analysis (LCA) were used to identify the different trajectories of the fatigue-pain-insomnia symptom cluster and depression. Binary logistic regression was utilized to analyze the predictive factors of different depressive trajectories.

Results: GMM identified two depressive trajectories: a high decreasing depression trajectory (40.64%) and a low increasing depression trajectory (59.36%). LCA showed that 48.66% of patients were likely members of the high symptom cluster trajectory. Binary logistic regression analysis indicated that having a history of alcohol consumption, a higher symptom cluster burden, unemployed, and a lower monthly income predicted a high decreasing depression trajectory.

Conclusions: Depression and fatigue-pain-insomnia symptom cluster in lung cancer chemotherapy patients exhibited two distinct trajectories. When managing depression in these patients, it is recommended to strengthen symptom management and pay particular attention to individuals with a history of alcohol consumption, unemployed, and a lower monthly income.

Background: Major depressive disorder (MDD) is a highly heterogeneous disease, with differences in clinical manifestations among depression patients based on onset ages and genders. The neural mechanisms underlying these differences remain unclear. In this study, we utilized resting state functional imaging data from a large sample database and adopted the ReHo method to investigate gender differences in local brain function in MDD patients across different onset age groups.

Methods: The study included 364 MDD patients and 695 healthy participants who were part of the REST-meta-MDD project. Regional homogeneity (ReHo) assessed gender disparities in MDD and healthy individuals within groups delineated by gender and onset age (young group: 18-29 years; middle-aged group: 30-45 years).

Results: Among the young MDD groups, there were significant gender differences in the right superior frontal gyrus, right inferior frontal gyrus, left superior temporal gyrus, and right superior parietal lobule, with male MDD patients having higher ReHo values compared to females. When compared to healthy males, male MDD patients exhibited elevated ReHo values in the right superior parietal lobule. In the middle-aged groups, a marked ReHo difference was observed in the bilateral cerebellum posterior lobe, with female MDD patients showing higher ReHo values.

Conclusions: The functional mechanisms of MDD differ between genders and show distinct variations across different onset age groups. These findings underscore the importance of developing personalized interventions that address the unique needs of MDD patients, tailored to their gender and age, and necessitate the development of antidepressant medications targeted at each gender-age subgroup.

Background: Drug addiction is a significant public health concern, and aggression is common among people with drug addiction. Despite mounting evidence showing that the Dark Triad is a risk factor for aggression, the mediating and moderating mechanisms underlying this relationship are less known. This study tested the mediation effect of self-control in the association between the Dark Triad and aggression and whether this mediation was moderated by physical exercise.

Methods: A cross-sectional study was conducted in two compulsory drug rehabilitation centers in Nanning, China. A convenience sample of 564 drug abstainers completed a questionnaire to assess their Dark Triad, self-control, aggression, and physical exercise levels. Mediation and moderation analyses were carried out in SPSS macro-PROCESS.

Results: Self-control partially mediated the positive association between the Dark Triad and aggression. Physical exercise moderated the indirect effect of the Dark Triad on aggression via self-control, with the effect decreasing with the increase in physical exercise levels.

Conclusions: This study offers fresh insights into the underlying mediating and moderating mechanisms between the Dark Triad and aggression. The findings provide important practical implications for future intervention and prevention programs to address aggression among drug abstainers, which may be realized through strengthening self-control and physical exercise.