Early Intervention Research Articles

Using the universal design for learning framework to improve child and adolescent mental health

AbstractPoor mental health in childhood and adolescence has a significant impact on both physical and mental health throughout life. However, growing evidence suggests that current levels of child and adolescent mental health (CAMH) care are insufficient to meet the needs of many children and adolescents in the United States as evidenced by increasing rates of mental health problems, inequities in diagnosis, and inaccessibility of services. This article provides a discussion of current problems in CAMH and school mental health services and emphasizes a developmental perspective of CAMH, including the co‐occurrence of school difficulties and mental health concerns. A universal design for learning framework for school‐wide mental health programs is suggested as a means of addressing barriers to care and promoting prevention and early intervention in CAMH.

Efektivitas Tata Kelola Penanganan Anak Stunting oleh Dokter dan Tenaga Kesehatan di Kota Yogyakarta

The governance management of stunting children in Yogyakarta is carried out through the Stunting Case Audit (AKS) program. This program was implemented as an effort to meet the zero stunting target of Yogyakarta City through early intervention to reduce and prevent the appearance of similar cases from the audited areas. To find out that the AKS program has succeeded in achieving its goals, it is necessary to conduct a study to examine the effectiveness of the AKS program. This research is using a qualitative approach by comparing the suitability of program implementation with indicators of effectiveness. The results of this research explain that from the four effectiveness indicators used, only one indicator met the implementation of AKS. Indicators of timeliness have been met, while indicators of target accuracy, goal attainment and the emergence of expected results have not been met the expectation.

Serum anti-mullerian hormone, sex hormone, and nutrient levels in reproductive age women with celiac disease.

This study aimed to investigate the changes in serum Anti-Müllerian Hormone (AMH) levels, sex hormone levels, follicle-stimulating hormone (FSH)/luteinizing hormone (LH) ratio in patients with celiac disease (CeD), and their correlation with clinical characteristics and nutrient levels. This cross-sectional study collected clinical and biochemical data from a total of 67 females diagnosed with CeD and 67 healthy females within the reproductive age range of 18-44 years. The study was conducted at a tertiary hospital between September 2016 and January 2024. Both groups underwent comprehensive clinical and laboratory assessments. Serum levels of AMH and sex hormones were quantified using chemiluminescence immunoassay, and their associations with CeD clinical features and nutrient levels were thoroughly analyzed. The study included 67 patients and 67 controls with a mean age of 36.7±7.6 years. No statistically significant differences were found between the two groups in mean age, BMI, FSH, LH, E2, P levels, FSH/LH, menstrual irregularities, abortions history, parity, and gravidity (all P>0.05). However, AMH, T, FER, FA, Zn, and Se levels were significantly lower, and PRL levels were higher in the CeD group (all P<0.05). Spearman's correlation analysis showed that AMH levels were negatively correlated with age, tTG level, disease duration, and Marsh grading (P<0.05). This study highlights the association between impaired ovarian function in CeD patients and disease severity and nutrient levels. Early detection and intervention for ovarian function abnormalities are imperative to enhance fertility potential in CeD patients.

Prevalence and associated factors of premenstrual dysphoric disorder among high school students in Finote Selam town, northwest Ethiopia.

Premenstrual dysphoric disorder (PMDD) is the most prevalent but neglected psychiatric disorder, with somatic symptoms that are severe enough to markedly affect usual daily activities and have a negative impact on mental health and quality of life by affecting female patients' behavior and cognition. Studies regarding premenstrual dysphoric disorder and associated factors among high school students in low- and middle-income countries are limited. Therefore, the aim of this study was to assess the prevalence and associated factors of PMDD among high school students, and this is pivotal in further investigation. A school-based cross-sectional study was conducted from March 25 to April 17, 2023 using a simple random-sampling technique to select a sample of 564 participants. Premenstrual dysphoric disorder was assessed using the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Self-administered standardized questionnaires were used to collect data. A total of 548 study participants participated, with a 97.2% response rate. The prevalence of premenstrual dysphoric disorder among high school students was found to be 33.03% (95%CI: 29.20-37.09). In a multivariable analysis, irregular menstruation cycle (AOR = 4.242, 95%CI = 2.182-8.246), depression (AOR = 5.272, 95%CI = 2.779-10.002), having greater than 4 days of menstruation bleeding duration (AOR = 2.138, 95%CI = 1.105-4.138), and high perceived stress (AOR = 3.468, 95%CL = 1.217-9.880) were the factors significantly associated with premenstrual dysphoric disorder. The overall prevalence of PMDD which was one-third among high school students was high. Moreover, long duration of menstruation bleeding, depressive symptoms, irregular menstruation cycle, and high perceived stress were significant factors in PMDD. Therefore, it needs early screening and intervention in primary healthcare settings, especially for those who have high perceived stress, having depression, having a long duration of menstruation bleeding, and having an irregular menstruation cycle, so as to have good academic achievement and psychological wellbeing.

Predicting the Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Graph Frequency Bands and Functional Connectivity-Based Features.

Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is crucial for disease management. Machine learning techniques have demonstrated success in classifying AD and MCI cases, particularly with the use of resting-state functional magnetic resonance imaging (rs-fMRI) data.This study utilized three years of rs-fMRI data from the ADNI, involving 142 patients with stable MCI (sMCI) and 136 with progressive MCI (pMCI). Graph signal processing was applied to filter rs-fMRI data into low, middle, and high frequency bands. Connectivity-based features were derived from both filtered and unfiltered data, resulting in a comprehensive set of 100 features, including global graph metrics, minimum spanning tree (MST) metrics, triadic interaction metrics, hub tendency metrics, and the number of links. Feature selection was enhanced using particle swarm optimization (PSO) and simulated annealing (SA). A support vector machine (SVM) with a radial basis function (RBF) kernel and a 10-fold cross-validation setup were employed for classification. The proposed approach demonstrated superior performance, achieving optimal accuracy with minimal feature utilization. When PSO selected five features, SVM exhibited accuracy, specificity, and sensitivity rates of 77%, 70%, and 83%, respectively. The identified features were as follows: (Mean of clustering coefficient, Mean of strength)/Radius/(Mean Eccentricity, and Modularity) from low/middle/high frequency bands of graph. The study highlights the efficacy of the proposed framework in identifying individuals at risk of AD development using a parsimonious feature set. This approach holds promise for advancing the precision of MCI to AD progression prediction, aiding in early diagnosis and intervention strategies.

Renal Artery Stenosis: Current Insights and Advances in Management

Renal artery stenosis (RAS) presents a significant challenge in clinical practice, often leading to treatment-resistant hypertension and renal dysfunction. This review provides a comprehensive overview of the current insights and advances in managing RAS, encompassing its pathophysiology, diagnosis, treatment strategies, recent research, and future directions. RAS, predominantly caused by atherosclerosis or fibromuscular dysplasia, poses a substantial burden on affected individuals, necessitating early diagnosis and intervention to mitigate its adverse effects on renal function and cardiovascular health. Recent advancements in diagnostic imaging modalities, such as contrast-enhanced magnetic resonance angiography (CE-MRA) and phase-contrast magnetic resonance angiography (PC-MRA), offer enhanced anatomical and functional insights into RAS without requiring invasive procedures. Furthermore, innovative interventional techniques, including drug-eluting stents and minimally invasive surgical approaches, demonstrate promising outcomes in addressing complex RAS cases. Clinical trials, notably the CORAL trial, provide valuable evidence for the efficacy and safety of various management strategies, guiding clinicians in making informed decisions tailored to individual patient needs. Future directions in RAS research may involve further refinement of imaging techniques, exploration of personalized medicine approaches, and the development of targeted therapies to address underlying pathophysiological mechanisms. Collaborative efforts between clinicians, researchers, and industry partners are essential in driving innovation and advancing the field toward improved outcomes and quality of life for patients with RAS. Through continued research and innovation, the prognosis for patients with RAS promises enhanced management strategies and improved long-term outcomes.

Decoding Early Psychoses: Unraveling Stable Microstructural Features Associated with Psychopathology Across Independent Cohorts

BackgroundEarly Psychosis patients (EP, within 3 years after psychosis onset) show significant variability, making outcome predictions challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, limiting the development of early interventions. MethodsA data-driven approach, Partial Least Squares (PLS) correlation, was used across two independent datasets to examine multivariate relationships between white matter (WM) properties and symptomatology, to identify stable and generalizable signatures in EP. The primary cohort included EP patients from the Human Connectome Project-Early Psychosis (n=124). The replication cohort included EP patients from the Feinstein Institute for Medical Research (n=78). Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders. ResultsIn both cohorts, a significant latent component (LC) corresponded to a symptom profile combining negative symptoms, primarily diminished expression, with specific somatic symptoms. Both LCs captured comprehensive features of WM disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the PLS model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use. ConclusionsThis data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural WM alterations in EP, across diagnoses and datasets, showing a strong covariance of these alterations with a unique profile of negative and somatic symptoms. This finding suggests the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.

High flow humidified oxygen as an early intervention in children with acute severe asthma: a feasibility randomised controlled trial

High flow humidified oxygen as an early intervention in children with acute severe asthma: a feasibility randomised controlled trial

A mixed-methods study on impact of active case finding on pulmonary tuberculosis treatment outcomes in India

BackgroundTuberculosis (TB) remains a significant public health burden in India, with elimination targets set for 2025. Active case finding (ACF) is crucial for improving TB case detection rates, although conclusive evidence of its association with treatment outcomes is lacking. Our study aims to investigate the impact of ACF on successful TB treatment outcomes among pulmonary TB patients in Gujarat, India, and explore why ACF positively impacts these outcomes.MethodsWe conducted a retrospective cohort analysis in Gujarat, India, including 1,638 pulmonary TB cases identified through ACF and 80,957 cases through passive case finding (PCF) from January 2019 to December 2020. Generalized logistic mixed-model compared treatment outcomes between the ACF and PCF groups. Additionally, in-depth interviews were conducted with 11 TB program functionaries to explore their perceptions of ACF and its impact on TB treatment outcomes.ResultsOur analysis revealed that patients diagnosed through ACF exhibited 1.4 times higher odds of successful treatment outcomes compared to those identified through PCF. Program functionaries emphasized that ACF enhances case detection rates and enables early detection and prompt treatment initiation. This early intervention facilitates faster sputum conversion and helps reduce the infectious period, thereby improving treatment outcomes. Functionaries highlighted that ACF identifies TB cases that might otherwise be missed, ensuring timely and appropriate treatment.ConclusionACF significantly improves TB treatment outcomes in Gujarat, India. The mixed-methods analysis demonstrates a positive association between ACF and successful TB treatment, with early detection and prompt treatment initiation being key factors. Insights from TB program functionaries underscore the importance of ACF in ensuring timely diagnosis and treatment, which are critical for better treatment outcomes. Expanding ACF initiatives, especially among hard-to-reach populations, can further enhance TB control efforts. Future research should focus on optimizing ACF strategies and integrating additional interventions to sustain and improve TB treatment outcomes.

Development of a culturally enhanced caregiver-facilitated language nutrition intervention "+Language is Medicine" to address developmental delay in Diné (Navajo) toddlers.

Developmental Delay (DD) is highly common in American Indian and Alaska Native (AI/AN; Indigenous) toddlers and leads to high numbers of AI/AN children who eventually need special education services. AI/AN children are 2.89 times more likely to receive special education compared to other children in the U.S., yet developmental disorders are more frequently under diagnosed and untreated in AI/AN infants and toddlers. DD, which can be identified as early as toddlerhood, can lead to negative impacts on developmental trajectories, school readiness, and long-term health. Signs of DD can be identified early with proper developmental screening and remediated with high quality early intervention that includes effective parent training. There are many evidence-based language facilitation interventions often used in Early Intervention programs. However, in communities in rural parts of the Navajo Nation where there are limited services and resources, infants and toddlers with early signs of DD are often missed and do not get the culturally responsive support and evidence-based intervention they deserve. The community-based +Language is Medicine (+LiM) study team partnered with tribal home visitors, community members, and a Diné linguist/elder using a collaborative virtual workgroup approach in 2021 and 2022 to present the +LiM pilot study aims and to discuss strategies for enhancing a language intervention for toddlers experiencing DD in their tribal community. This paper will detail the stages of community engagement, intervention enhancement and preparation for field testing of the +LiM intervention to address elevated rates of DD in toddlers in the Northern Agency of the Navajo Nation. Two major outcomes from this collaborative workgroup included: (1) a team-initiated redefining of language nutrition to align with Indigenous values that center cultural connectedness and native language use and (2) a five-lesson caregiver-facilitated curriculum titled +Language is Medicine which includes caregiver lessons on language nutrition, language facilitation, shared book reading, pretend play, and incorporation of native language into home routines. These two workgroup outcomes were leveraged to develop a pilot pre-/post-intervention study to test the effectiveness of the +LiM intervention with caregiver-toddler dyads living on the Navajo Nation. Delivering tailored child interventions through tribal home visiting are cost-effective and innovative methods for reaching reservation-based families who benefit from culturally responsive parent coaching and instruction. The +LiM team has applied a precision tribal home visiting approach to enhance methods of early intervention for children with DD. Our enhancement process was grounded in Indigenous community-based participatory research that centered culture and language.

Gallbladder Volvulus: A Rare Case of Pre-operative Diagnostic Quandary

&amp;lt;i&amp;gt;Introduction: &amp;lt;/i&amp;gt;Gallbladder volvulus (GBV) is a rare but critical condition characterized by torsion of the gallbladder on its mesentery. Prompt surgical intervention is crucial in preventing complications and improving patient outcomes. Despite advancements in imaging techniques, diagnosing GBV remains challenging due to its nonspecific clinical presentation and low preoperative detection rates.&amp;lt;i&amp;gt; Case Details: &amp;lt;/i&amp;gt;This paper presents the case of a 79-year-old female presenting with symptoms suggestive of acute appendicitis and urinary tract infection. Imaging, including Computed Tomography (CT), ultrasound, (US) and magnetic resonance cholangiopancreatography (MRCP), were indicative of acute acalculous cholecystitis. Intraoperatively, a necrotic gangrenous gallbladder was encountered, resulting from GBV. &amp;lt;i&amp;gt;Discussion: &amp;lt;/i&amp;gt;GBV poses diagnostic difficulties due to its variable presentation and low preoperative detection rates. Misdiagnoses poses significant risk of mismanagement, especially in the elderly and high risk populations, where non-operative management is attempted (antibiotics, percutaneous cholecystostomy, etc.) leading to increased morbidity and mortality. Therefore, high index of suspicion and correlation with imaging findings are crucial for accurate diagnosis. While various imaging modalities can assist in diagnosis, interpretation may be challenging, as demonstrated in this case. Surgical intervention, preferably through laparoscopy, is the cornerstone of treatment and offers favorable outcomes when performed promptly.&amp;lt;i&amp;gt; Conclusion: &amp;lt;/i&amp;gt;In conclusion, GBV is a rare but potentially life-threatening condition that necessitates early recognition and surgical intervention. Despite challenges in diagnosis, a comprehensive clinical and radiological approach is crucial for timely management and optimal patient outcomes. Further research and awareness are warranted to enhance early diagnosis of this uncommon entity.

Machine learning algorithms to predict healthcare-seeking behaviors of mothers for acute respiratory infections and their determinants among children under five in sub-Saharan Africa.

Acute respiratory infections (ARIs) are the leading cause of death in children under the age of 5 globally. Maternal healthcare-seeking behavior may help minimize mortality associated with ARIs since they make decisions about the kind and frequency of healthcare services for their children. Therefore, this study aimed to predict the absence of maternal healthcare-seeking behavior and identify its associated factors among children under the age 5 in sub-Saharan Africa (SSA) using machine learning models. The sub-Saharan African countries' demographic health survey was the source of the dataset. We used a weighted sample of 16,832 under-five children in this study. The data were processed using Python (version 3.9), and machine learning models such as extreme gradient boosting (XGB), random forest, decision tree, logistic regression, and Naïve Bayes were applied. In this study, we used evaluation metrics, including the AUC ROC curve, accuracy, precision, recall, and F-measure, to assess the performance of the predictive models. In this study, a weighted sample of 16,832 under-five children was used in the final analysis. Among the proposed machine learning models, the random forest (RF) was the best-predicted model with an accuracy of 88.89%, a precision of 89.5%, an F-measure of 83%, an AUC ROC curve of 95.8%, and a recall of 77.6% in predicting the absence of mothers' healthcare-seeking behavior for ARIs. The accuracy for Naïve Bayes was the lowest (66.41%) when compared to other proposed models. No media exposure, living in rural areas, not breastfeeding, poor wealth status, home delivery, no ANC visit, no maternal education, mothers' age group of 35-49 years, and distance to health facilities were significant predictors for the absence of mothers' healthcare-seeking behaviors for ARIs. On the other hand, undernourished children with stunting, underweight, and wasting status, diarrhea, birth size, married women, being a male or female sex child, and having a maternal occupation were significantly associated with good maternal healthcare-seeking behaviors for ARIs among under-five children. The RF model provides greater predictive power for estimating mothers' healthcare-seeking behaviors based on ARI risk factors. Machine learning could help achieve early prediction and intervention in children with high-risk ARIs. This leads to a recommendation for policy direction to reduce child mortality due to ARIs in sub-Saharan countries.

Testing Machine Learning Models to Predict Postoperative Ileus after Colorectal Surgery.

Background: Postoperative ileus (POI) is a common complication after colorectal surgery, leading to increased hospital stay and costs. This study aimed to explore patient comorbidities that contribute to the development of POI in the colorectal surgical population and compare machine learning (ML) model accuracy to existing risk instruments. Study Design: In a retrospective study, data were collected on 316 adult patients who underwent colorectal surgery from January 2020 to December 2021. The study excluded patients undergoing multi-visceral resections, re-operations, or combined primary and metastatic resections. Patients lacking follow-up within 90 days after surgery were also excluded. Eight different ML models were trained and cross-validated using 29 patient comorbidities and four comorbidity risk indices (ASA Status, NSQIP, CCI, and ECI). Results: The study found that 6.33% of patients experienced POI. Age, BMI, gender, kidney disease, anemia, arrhythmia, rheumatoid arthritis, and NSQIP score were identified as significant predictors of POI. The ML models with the greatest accuracy were AdaBoost tuned with grid search (94.2%) and XG Boost tuned with grid search (85.2%). Conclusions: This study suggests that ML models can predict the risk of POI with high accuracy and may offer a new frontier in early detection and intervention for postoperative outcome optimization. ML models can greatly improve the prediction and prevention of POI in colorectal surgery patients, which can lead to improved patient outcomes and reduced healthcare costs. Further research is required to validate and assess the replicability of these results.

Gallbladder Volvulus: A Rare Case of Pre-operative Diagnostic Quandary

&amp;lt;i&amp;gt;Introduction: &amp;lt;/i&amp;gt;Gallbladder volvulus (GBV) is a rare but critical condition characterized by torsion of the gallbladder on its mesentery. Prompt surgical intervention is crucial in preventing complications and improving patient outcomes. Despite advancements in imaging techniques, diagnosing GBV remains challenging due to its nonspecific clinical presentation and low preoperative detection rates.&amp;lt;i&amp;gt; Case Details: &amp;lt;/i&amp;gt;This paper presents the case of a 79-year-old female presenting with symptoms suggestive of acute appendicitis and urinary tract infection. Imaging, including Computed Tomography (CT), ultrasound, (US) and magnetic resonance cholangiopancreatography (MRCP), were indicative of acute acalculous cholecystitis. Intraoperatively, a necrotic gangrenous gallbladder was encountered, resulting from GBV. &amp;lt;i&amp;gt;Discussion: &amp;lt;/i&amp;gt;GBV poses diagnostic difficulties due to its variable presentation and low preoperative detection rates. Misdiagnoses poses significant risk of mismanagement, especially in the elderly and high risk populations, where non-operative management is attempted (antibiotics, percutaneous cholecystostomy, etc.) leading to increased morbidity and mortality. Therefore, high index of suspicion and correlation with imaging findings are crucial for accurate diagnosis. While various imaging modalities can assist in diagnosis, interpretation may be challenging, as demonstrated in this case. Surgical intervention, preferably through laparoscopy, is the cornerstone of treatment and offers favorable outcomes when performed promptly.&amp;lt;i&amp;gt; Conclusion: &amp;lt;/i&amp;gt;In conclusion, GBV is a rare but potentially life-threatening condition that necessitates early recognition and surgical intervention. Despite challenges in diagnosis, a comprehensive clinical and radiological approach is crucial for timely management and optimal patient outcomes. Further research and awareness are warranted to enhance early diagnosis of this uncommon entity.

Psychotherapeutische Sprechstunde am Arbeitsplatz: Zusammenhänge zwischen Unternehmensgröße und psychosomatischer Gesundheit

ZusammenfassungHintergrundDie Inanspruchnahme der psychotherapeutischen Sprechstunde am Arbeitsplatz (PT-A) wurde bislang in Großunternehmen (GU) untersucht. Diese unterscheiden sich strukturell von mittleren und Klein(st)unternehmen (KMU). Unterschiede der Nutzerprofile einer PT‑A hinsichtlich psychosomatischer Gesundheit, arbeitsbezogener Selbstwirksamkeit und Arbeitsfähigkeit sowie des psychosozialen Sicherheitsklimas (PSC) abhängig von der Unternehmensgröße wurden bisher kaum betrachtet.MethodenIn der Interventionsstudie „Frühe Intervention am Arbeitsplatz“ (friaa) wurden zwischen 09/2021 und 01/2023 an einer PT‑A interessierte Beschäftigte aus GU und KMU deutschlandweit befragt. Mittels t‑ und χ2-Tests wurden Unterschiede zwischen Beschäftigten in GU (n = 439) und KMU (n = 109) hinsichtlich F‑Diagnosen nach ICD-10 („International Statistical Classification of Diseases and Related Health Problems“; psychische und Verhaltensstörungen), Depressivität (PHQ-9), Ängstlichkeit (GAD-2), allgemeinen Funktionsniveaus (GAF), somatischer Symptombelastung (SSS-8), Gesundheit (VR-12), Arbeitsfähigkeit (WAI), Selbstwirksamkeit (SOSES) und psychosozialen Sicherheitsklimas (PSC-4) geprüft und mittels Korrelationsanalyse explorativ deren Zusammenhänge untersucht.ErgebnisseBeide Gruppen zeigten sich ähnlich stark beansprucht. In GU wurden aus Sicht der Beschäftigten psychosoziale Themen signifikant häufiger thematisiert als in KMU mit einer mittleren Effektgröße. Die Studie lieferte erste Hinweise, dass in GU positive Zusammenhänge des PSC‑4 mit SOSES und WAI sowie negative mit PHQ‑9 und SSS‑8 vorliegen.DiskussionDie vergleichbare psychische Beanspruchung der Beschäftigten in GU und KMU weist auf den Bedarf von verhältnis- und verhaltenspräventiven Maßnahmen unabhängig von der Unternehmensgröße hin. Vor allem in KMU sollte die Thematisierung psychosozialer Gesundheit einen größeren Stellenwert einnehmen.

Early childhood development strategy for the world's children with disabilities.

Early childhood is foundational for optimal and inclusive lifelong learning, health and well-being. Young children with disabilities face substantial risks of sub-optimal early childhood development (ECD), requiring targeted support to ensure equitable access to lifelong learning opportunities, especially in low- and middle-income countries. Although the Sustainable Development Goals, 2015-2030 (SDGs) emphasise inclusive education for children under 5 years with disabilities, there is no global strategy for achieving this goal since the launch of the SDGs. This paper explores a global ECD framework for children with disabilities based on a review of national ECD programmes from different world regions and relevant global ECD reports published since 2015. Available evidence suggests that any ECD strategy for young children with disabilities should consists of a twin-track approach, strong legislative support, guidelines for early intervention, family involvement, designated coordinating agencies, performance indicators, workforce recruitment and training, as well as explicit funding mechanisms and monitoring systems. This approach reinforces parental rights and liberty to choose appropriate support pathway for their children. We conclude that without a global disability-focussed ECD strategy that incorporates these key features under a dedicated global leadership, the SDGs vision and commitment for the world's children with disabilities are unlikely to be realised.

Psychische Gesundheit am Arbeitsplatz – Welche Rolle spielen sozioökonomische, geschlechterspezifische und migrationsbedingte Ungleichheiten?

ZusammenfassungHintergrundRisikofaktoren für die psychische Gesundheit sind häufig in sozioökonomischen, geschlechter- und migrationsspezifischen Ungleichheiten begründet. Diese sowie die Ausprägung der Depressivität, Ängstlichkeit und Somatisierung von Beschäftigten wurden in der vorliegenden Studie untersucht.MethodenIm Rahmen der friaa-Studie („Frühe Intervention am Arbeitsplatz“) wurden psychisch belastete Beschäftigte an 5 Standorten in Deutschland zu soziodemografischen, arbeits-, migrations- und gesundheitsbezogenen Inhalten befragt. Mittels Regressionsanalysen wurde der Zusammenhang dieser Faktoren mit Depressivität (Patient-Health-Questionnaire-9, PHQ-9), Ängstlichkeit (Generalized Anxiety Disorder-2, GAD-2) und Somatisierung (Somatic Symptom Scale-8, SSS-8) an der gesamten Stichprobe sowie bei Personen mit Migrationshintergrund (MH) untersucht. Bei Letzteren wurden zusätzlich die Akkulturation (Frankfurter Akkulturationsskala, FRAKK) und das Belastungsempfinden gegenüber Migrationsanforderungen (Demands of Immigration Scale, DIS) berücksichtigt.ErgebnisseDie 550 Beschäftigten (12 % mit MH) wiesen im Durchschnitt eine klinisch relevante Depressivität (M = 13,0, SD = 5,1) (PHQ-9 ≥ 10), Ängstlichkeit (M = 3,5, SD = 1,7) (GAD ≥ 3) sowie Somatisierung (M = 13,0, SD = 5,8) (SSS-8 ≥ 12) auf. Das weibliche Geschlecht war mit höheren Werten für Ängstlichkeit und Somatisierung assoziiert. Höheres Alter und Nachtschichtarbeit hingen mit stärkerer Somatisierung zusammen.DiskussionDie Ergebnisse bestätigen die hohe psychische Belastung der Beschäftigten in dieser Stichprobe aus Deutschland. Um deren psychische Gesundheit aufrechtzuerhalten, sollten Unterstützungsmaßnahmen insbesondere für vulnerable Gruppen wie Frauen, ältere Beschäftigte und Nachtschichtarbeitende angeboten werden.

Exercise4Psychosis: A randomised control trial assessing the effect of moderate-to-vigorous exercise on inflammatory biomarkers and negative symptom profiles in men with first-episode psychosis

IntroductionFirst-Episode Psychosis (FEP) is a devastating mental health condition that commonly emerges during early adulthood, and is characterised by a disconnect in perceptions of reality. Current evidence suggests that inflammation and perturbed immune responses are involved in the pathology of FEP and may be associated specifically with negative symptoms. Exercise training is a potent anti-inflammatory stimulus that can reduce persistent inflammation, and can improve mood profiles in general populations. Therefore, exercise may represent a novel adjunct therapy for FEP. The aim of this study was to assess the effect of exercise on biomarkers of inflammation, negative symptoms of psychosis, and physiological health markers in FEP. MethodsSeventeen young males (26.67 ± 6.64 years) were recruited from Birmingham Early Intervention in Psychosis Services and randomised to a 6-week exercise programme consisting of two-to-three sessions per week that targeted 60–70 % heart-rate max (HRMax), or a treatment as usual (TAU) condition. Immune T-helper (Th-) cell phenotypes and cytokines, symptom severity, functional wellbeing, and cognition were assessed before and after 6-weeks of regular exercise. ResultsParticipants in the exercise group (n = 10) achieved 81.11 % attendance to the intervention, with an average exercise intensity of 67.54 % ± 7.75 % HRMax. This led to favourable changes in immune cell phenotypes, and a significant reduction in the Th1:Th2 ratio (−3.86 %) compared to the TAU group (p = 0.014). After the exercise intervention, there was also a significant reduction in plasma IL-6 concentration (–22.17 %) when compared to the TAU group (p = 0.006). IL-8, and IL-10 did not show statistically significant differences between the groups after exercise. Symptomatically, there was a significant reduction in negative symptoms after exercise (−13.54 %, Positive and Negative Syndrome Scale, (PANSS) Negative) when compared to the TAU group (p = 0.008). There were no significant change in positive or general symptoms, functional outcomes, or cognition (all p > 0.05). DiscussionRegular moderate-to-vigorous physical activity is feasible and attainable in clinical populations. Exercise represents a physiological tool that is capable of causing significant inflammatory biomarker change and concomitant symptom improvements in FEP cohorts, and may be useful for treatment of symptom profiles that are not targeted by currently prescribed antipsychotic medication.

Botulinum toxin type A in treating early-stage patients with small-angle acute acquired comitant esotropia.

To investigate botulinum toxin A (BTXA) efficacy on small-angle (≤25Δ) acute acquired concomitant esotropia (AACE) in early-stage patients. The electronic medical record data of AACE patients during March 2019 and June 2023 were collected in this retrospective and hospital-based cohort study. A total of 72 small-angle AACE patients received BTXA extraocular muscle injection. Patients were grouped by onset-to-treatment time (Group A: ≤6mo, Group B: >6mo). Deviation of esotropia, eye alignment and stereopsis were analyzed at the period of pre/post-injection (1wk, 1, 3, and 6mo). Orthophoria rate at 6mo (horizontal deviation <10Δ and binocular single vision) were considered as outcome index. There were no significant baseline differences (P>0.05) between two groups except onset-to-treatment time (2mo vs 11mo, P<0.001). Higher orthophoria rates were in Group A at last follow-up (94.74% vs 73.53%, P=0.013). Post-BTXA deviations of two groups at 1mo showed no difference (P>0.05); while in 3 and 6mo Group A was significantly smaller than group B (all P<0.001). No statistically significant differences were observed among all post-BTXA deviations of near and distance in Group A. In Group B, deviation at 3mo (near: 2Δ vs 0, P<0.001; distance: 4Δ vs 0, P<0.001) and 6mo (near: 6Δ vs 0, P<0.001; distance: 6Δ vs 0, P<0.001) was significant increased compared to deviation at 1wk after treatment. Group A showed better stereopsis recovery in last follow-up compared to Group B (80″ vs 200″, P=0.002). Both groups obtained improved stereopsis after treatment (Group A: 80″ vs 300″, P<0.001; Group B: 200″ vs 300″, P=0.037). BTXA is effective for AACE with small deviation (≤25Δ) in early stage. Delayed treatment (>6mo) may reduce BTXA efficacy. Early BTXA intervention benefits long-term eye alignment and stereopsis recovery.

Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning.

Alzheimer's disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and graph theory metrics, derived from resting-state functional magnetic resonance imaging (rs-fMRI) data to predict AD. Using Southwest University Adult Lifespan Dataset (SALD, age 21-76 years) and the Open Access Series of Imaging Studies (OASIS, age 64-95 years) dataset, containing 112 participants, various ML models were developed for the purpose of AD prediction. The study identifies key features for a comprehensive understanding of brain network topology and functional connectivity in AD. Through a 5-fold cross-validation, all models demonstrate substantial predictive capabilities (accuracy in 82-92% range), with the support vector machine model standing out as the best having an accuracy of 92%. Present study suggests that top 13 regions, identified based on most important discriminating features, have lost significant connections with thalamus. The functional connection strengths were consistently declined for substantia nigra, pars reticulata, substantia nigra, pars compacta, and nucleus accumbens among AD subjects as compared to healthy adults and aging individuals. The present finding corroborate with the earlier studies, employing various neuroimagining techniques. This research signifies the translational potential of a comprehensive approach integrating ML, graph theory and rs-fMRI analysis in AD prediction, offering potential biomarker for more accurate diagnostics and early prediction of AD.