Health Diagnosis Research Articles

Development of the assessment for emotional and behavioral problems in school-aged children

BackgroundSeveral assessments have been developed to assess school-aged children’s emotional and behavioral problems (EBPs), but none based on the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, Text Revision. This study aimed to develop the Assessment for Emotional and Behavioral Problems in School-aged children (AEBPS) fitting current knowledge of mental health disorders. Materials and methodsThis study included 2 phases. In Phase I, the assessment construct and its corresponding items were developed. In Phase II, the reliability and validity of the AEBPS were examined. ResultsThe AEBPS contains 120 items in five subscales. The psychometric results showed that the AEBPS subscales had high internal consistency (Cronbach’s alpha = 0.83–0.97) and acceptable to good test–retest reliability (intra-class correlation coefficient = 0.65–0.93). The results of exploratory factor analysis showed that most items within each subscale of the AEBPS significantly contributed to their respective concepts. The AEBPS subscales had small to high correlations with the subscales of the Child Behavior Checklist (r = 0.37–0.87). The AEBPS had good discriminant validity to differentiate children with and without EBPs. ConclusionsThe newly-developed AEBPS fits the current knowledge of mental health diagnoses for assessing school-aged children’s EBPs and has sound psychometric evidence. The AEBPS can be reliably and validly used in a variety of settings.

Bayesian interpretation of likelihood ratios in clinical diagnosis and public health

Purpose of review This study provides a Bayesian interpretation of likelihood ratios and their role in updating the pretest probability of disease, typically represented by the prevalence, to a posttest probability. Recent findings We highlight the importance of likelihood ratios in this process, particularly in low prevalence settings. For example, we demonstrate that a high LR+ can significantly increase the posttest probability of disease, providing crucial information for clinical decision-making. Furthermore, we emphasize the significance of the fold increase in the probability of disease given a positive test result, particularly in low prevalence settings. Summary This study underscores the importance of considering likelihood ratios on our posttest updated probability of disease and highlights their implications for public health and individual diagnosis. Understanding these factors can enhance the accuracy and effectiveness of diagnostic decision-making, ultimately leading to improved public health outcomes and patient care.

Analyzing the relationship between individual and cumulative score of adverse childhood experiences (ACEs) with self-reported mental health disorders in Chile

BackgroundAdverse Childhood Experiences (ACEs) are widely recognized as significant predictors of poor mental health in adulthood. Latin America presents several unique challenges regarding ACEs, such as higher prevalence of violence and income and political inequality. However, little is known about this issue from nationally representative samples in the region, particularly in Chile. ObjectiveThis investigation examines the association between individual and cumulative ACE scores and seven self-reported mental health conditions (substance abuse, depression, PTSD, generalized anxiety, suicide ideation, non-suicidal self-harm, and learning disorders) in a representative Chilean urban sample. MethodsA representative urban sample of 2101 adult Chileans completed the International Adverse Childhood Experiences Questionnaire (ACE-IQ) and disclosed any prior mental health diagnoses. ResultsAround 40 % of the sample had experienced four or more ACEs in their lifetime. For these individuals, the risk of reporting a mental health disorder was significantly higher. Several logistic regression analyses were conducted to address the associations between ACEs and the seven mental health conditions. Notably, sexual abuse, bullying, and exposure to collective violence were the ACEs most strongly associated with self-reported mental health issues. Additionally, a cumulative ACE score was found to be a significant predictor of having a previous mental health diagnosis. ConclusionsWe found a significant predictive association between exposure to collective violence and six out of the seven self-reported mental health disorders. Similarly, sexual abuse demonstrated an association with all mental health conditions. Finally, individuals who experienced four or more ACEs had a notably higher chance of reporting a previous mental health diagnosis. We recommend conducting further ACE research in Latin America due to its cultural and contextual singularities.

The Relationships Among Neighborhood Disadvantage, Mental Health and Developmental Disabilities Diagnoses, and Race/Ethnicity in a U.S. Urban Location.

Childhood health disparities by race have been found. Neighborhood disadvantage, which may result from racism, may impact outcomes. The aim of the study is to describe the distribution of mental health (MH) and developmental disabilities (DD) diagnosis across Child Opportunity Index (COI) levels by race/ethnicity. A cross-sectional study using 2022 outpatient visit data for children < 18years living in the Louisville Metropolitan Area (n = 115,738) was conducted. Multivariable logistic regression analyses examined the association between diagnoses and COI levels, controlling for sex and age. Almost 18,000 children (15.5%) had a MH or DD (7,905 [6.8%]) diagnosis. In each COI level, the prevalence of MH diagnosis was lower for non-Hispanic (N-H) Black than for N-H White children. In adjusted analyses, there were no significant associations between diagnoses and COI for non-White children for MH or DD diagnoses. The odds of receiving a MH [OR: 1.74 (95% CI: 1.62, 1.87)] and DD [OR: 1.69 (95% CI: 1.51, 1.88)] diagnosis were higher among N-H White children living in Very Low compared to Very High COI areas. Current findings suggest that COI does not explain disparities in diagnosis for non-White children. More research is needed to identify potential multi-level drivers such as other forms of racism. Identifying programs, policies, and interventions to reduce childhood poverty and link children and families to affordable, family-centered, quality community mental and physical health resources is needed to ensure that families can build trusting relationships with the providers while minimizing stigma.

Perinatal mental health and active-duty military spouses: a scoping review

IntroductionMental health conditions (i.e. depression or anxiety) are the most common complication of pregnancy and childbirth in the United States (U.S.) and are associated with increased maternal and infant morbidity and mortality. Research has demonstrated a relationship between stress and mental health diagnoses in pregnancy; therefore, it is concerning that military families face unique challenges which contribute to additional stressors among spouses of active-duty (AD) military personnel during the perinatal period. The objective of this scoping review was to understand the current state of research on perinatal stress or perinatal mental health among American spouses of AD military personnel.MethodsThe Boolean phrase was created in consultation with 2 health science librarians and the following databases searched in October 2023: PubMed, Embase, Military and Government Collection, CINAHL, and PsychINFO. 2 reviewers identified 481 studies for screening once duplicates were removed. After applying inclusion and exclusion criteria, 21 studies remained for data extraction and analysis.ResultsMost of the studies were quantitative, took place in the southern U.S., and the most represented military branch was Air Force. Most of the studies included both AD military members and AD spouses; 28% focused solely on AD spouses. Samples were not racially diverse, and findings identified racial disparities in perinatal mental health conditions. There was a wide variety in outcome measures, including the following general categories: (1) stress, anxiety, and/or depression, (2) maternal-infant attachment, (3) group prenatal care, and (4) deployment focus. Our review identified the following concepts: spouses most at risk for perinatal mental health conditions, the need for perinatal mental health screening, and the need for social support.ConclusionsFindings from the identified studies indicate a need for additional research in this area. Additionally, findings highlight circumstances unique to this population that result in an increased risk of stress and/or mental health conditions during the perinatal period. Such challenges demand improved mental health screening and additional resources for this population. Meeting the needs of this unique population also requires significant funding and policy change to allow for increased access to mental health resources and to ensure the health of the birthing person and infant.

Measuring Disparities in the Impact of COVID-19 on Pediatric Mental Health in Primary Care Settings.

To examine disparities in mental health diagnosis, depression screening, and depressive symptoms in pediatric primary care settings before and during the COVID-19 pandemic, and to evaluate the use of electronic health records to study temporal trends in pediatric mental and behavioral health (MBH). This is an IRB-approved, retrospective study of pediatric patients (n=10,866) who visited three primary care sites at an academic medical center before (2017-2019) and during (2020-2022) the COVID-19 pandemic. We used logistic regression to compare rates of diagnoses, depression screening, and depression symptom scores among demographic groups. This study demonstrates an increase in both PHQ-9A screening rates and average scores from 2017-2019 to 2020-2022. There were significant disparities in common mental health diagnoses, including higher rates of psychological distress among lower income and Hispanic patients, both before and during the pandemic, despite lower rates of screening among Hispanic patients. This suggests a need for improved equity in routine MBH screening and additional research to better understand the underlying social determinants that may be driving the greater mental health burden for certain marginalized youth.This study also highlights the strengths and challenges of utilizing EHR data to characterize disparities in pediatric mental illness. Although the nature of care delivery in an academic medical center clinic and the limitations of the EHR for collecting relevant data present challenges to this measurement, the EHR is nevertheless a promising tool for measuring and tracking pediatric mental health disparities.

Sensing emotional valence and arousal dynamics through automated facial action unit analysis

Information about the concordance between dynamic emotional experiences and objective signals is practically useful. Previous studies have shown that valence dynamics can be estimated by recording electrical activity from the muscles in the brows and cheeks. However, whether facial actions based on video data and analyzed without electrodes can be used for sensing emotion dynamics remains unknown. We investigated this issue by recording video of participants’ faces and obtaining dynamic valence and arousal ratings while they observed emotional films. Action units (AUs) 04 (i.e., brow lowering) and 12 (i.e., lip-corner pulling), detected through an automated analysis of the video data, were negatively and positively correlated with dynamic ratings of subjective valence, respectively. Several other AUs were also correlated with dynamic valence or arousal ratings. Random forest regression modeling, interpreted using the SHapley Additive exPlanation tool, revealed non-linear associations between the AUs and dynamic ratings of valence or arousal. These results suggest that an automated analysis of facial expression video data can be used to estimate dynamic emotional states, which could be applied in various fields including mental health diagnosis, security monitoring, and education.

Designing AI for mental health diagnosis: challenges from sub-Saharan African value-laden judgements on mental health disorders

Recently clinicians have become more reliant on technologies such as artificial intelligence (AI) and machine learning (ML) for effective and accurate diagnosis and prognosis of diseases, especially mental health disorders....

BASHH standards for the management of sexual health in UK prisons.

People in prison often experience risk factors that can lead to poor sexual health which is compounded by social deprivation. In response, the British Association for Sexual Health and HIV (BASHH) has published the first national standards in the management of UK prisons, providing recommendations for commissioners, service providers, health care providers and people in prison. Nine standards have been devised which cover aspects of sexual health diagnosis and management, public health measures, and some reproductive health issues. The aim of these standards is to drive improvements in sexual health provision in prisons to ensure access to high quality care that is equitable to that which is provided to the general population.

Mental Health Evacuation Rate in USCENTCOM.

Mental health diagnosis requiring further treatment is one of the top reasons for medical evacuation in the U.S. Central Command (USCENTCOM) area of responsibility (AOR) as of 2022. This study establishes a baseline in which the effectiveness of medical interventions can be measured to determine if they have an impact on the rate of evacuation out of USCENTCOM. The study period was January 1, 2017 to December 31, 2021. Individual evacuation data including date of initial movement and necessary specialty care requirements originating from the USCENTCOM AOR were acquired via U.S. Transportation Command's Regulating and Command & Control Evacuation System. The base evacuation rate was calculated for each month, and evacuation rates were analyzed for variations. For the entire study period, the mean monthly evacuation rate was 0.44 evacuations per 1,000 people in the AOR (95% CI, 0.41-0.47). There was no statistically significant difference between any monthly evacuation rate (P = .505). There is a statistically significant difference in the mean evacuation rates for calendar years (P = .003). The highest evacuation rate occurred in 2021. The study establishes a benchmark mental health evacuation rate. This rate will be useful for assessing mental health evacuation reduction initiatives in the USCENTCOM AOR.

Mental Health Disorders and Surgical Outcomes in Patients With Bone and Soft Tissue Sarcoma.

We conducted a study to investigate the relationship between a mental health diagnosis (MHD) and postoperative outcomes in orthopedic patients with bone and soft tissue sarcoma. We hypothesized that patients with sarcoma with a preoperative MHD would have worse outcomes and more postoperative complications. A retrospective review was performed of 356 patients who underwent surgical treatment for bone or soft tissue sarcoma. Patients were divided into two groups: those with a diagnosis of depression, anxiety, bipolar disorder, and/or schizophrenia and those with no previous MHD. Statistical analysis was performed using independent t, Mann-Whitney U, and chi-square tests. Statistical analysis demonstrated significant differences between the MHD group and the control group in three outcomes: length of stay, 90-day readmission rate, and incidence of surgical site infections. Subgroup analysis of the MHD group yielded significantly higher 90-day readmission rates for patients who were diagnosed during sarcoma treatment. Patients with sarcoma and an MHD had a longer postoperative hospital stay, an increased 90-day readmission rate, and a greater risk of surgical site infection. Given the rising prevalence of mental health disorders nationwide, orthopedic surgeons should be aware of differences in postoperative outcomes between patients with sarcoma with and without mental illness. [Orthopedics. 20XX;4X(X):XXX-XXX.].

Current application of proteomics in the veterinary field – a short summary and literature review

The biomedical research field’s continuous search for innovative technologies has led to improved disease diagnosis, novel drug discovery, and therapeutic interventions, all aimed at enhancing animal and human health alike. In this context, proteomics has emerged as a critical new technology, focusing on detailed examinations of protein composition, abundance, structure, function, and interactions. Proteomics, the study of the entire set ofproteins expressed by an organism, plays a crucial role in disease diagnosis, drug discovery, and therapeutic interventions in both human and animal health. Proteomics in veterinary medicine and animal health is an evolving field that holds significant promise for fundamental and applied discoveries related to the biology and pathology of domestic and companion species. It encompasses a broad spectrum of applications, including disease diagnosis, comparative medicine, pharmacology, nutrition, reproductive biology, livestock production,and pathology. By analysing protein profiles and interactions, proteomics contributes to enhancing animal health, welfare, and productivity across various fields of veterinary research and practice. Experimental proteomics in domestic animals offers advantages over the use of rodents, such as the ability to conduct multipletime-series samplings of biological samples for extensive analysis, allowing for the investigation of experimental and natural disease processes. This review highlights the current application of proteomics in veterinary medicine, focusing on its potential to advance diagnostics, research, and treatments in veterinary science. The current limitations of proteomics are also discussed.

Incorporating adapted One Health indicators in local Population Health Diagnoses

Incorporating adapted One Health indicators in local Population Health Diagnoses

Intraoral Ultrasound Imaging Using a Rotational Transducer with Periodontal Feature Identification by Machine Learning.

Innovative intraoral ultrasound devices with smart artificial intelligence-based identification for dento-anatomy could provide crucial information for oral health diagnosis and treatment and shed light on real-time detection of developmental dentistry. However, the grand challenge is that the current ultrasound technologies are meant for external use due to their bulkiness and low frequency. We report a compact versatile ultrasound intraoral device that consists of a rotational probe head robustly pivoted around a hand-held and portable handle for real-time imaging of intraoral anatomy using high-frequency ultrasonography (up to 25 MHz). The intraoral ultrasound device that could be adjusted for various orientations of the imaging planes by rotating the head provides real-time, high-resolution ultrasonograms of intraoral structures, including dento-periodontium of most tooth types and maxillary palate. Machine learning-based algorithms are integrated to automate the identification of important structures, including alveolar bone and cementum-enamel junction. The intraoral ultrasound device smartened with artificial intelligence could innovate oral health diagnosis and treatment plans toward precision health and patient care.

Dimensions of posttraumatic stress disorder and menopause-related health in midlife women veterans.

Posttraumatic stress disorder (PTSD) has been linked with menopause symptoms (eg, vasomotor, urinary) and their sequelae (eg, sexual difficulties). However, PTSD is a heterogeneous disorder, and less is known about which aspects may be most associated with menopause-related health. Using confirmatory factor analyses, we evaluated five structural models of PTSD symptoms in 208 predominately postmenopausal women veterans (aged 45-64 years). We investigated associations between PTSD-operationalized as a probable diagnosis and symptom dimensions of the best-fitting model-and common menopause-related health concerns, including (1) vasomotor, urinary, and vaginal symptoms; (2) vasomotor symptom interference; and (3) sexual functioning. A six-factor anhedonia model-comprising re-experiencing, avoidance, negative affect, anhedonia, anxious arousal, and dysphoric arousal-provided optimal fit. Both probable PTSD and greater symptoms across all dimensions were linked with presence of urinary and vasomotor, but not vaginal, symptoms. Comparing dimensions revealed that negative affect and dysphoric arousal were particularly associated with urinary symptoms, whereas dysphoric arousal was the factor most strongly related to vasomotor symptom interference. Associations between PTSD and sexual dysfunction were mixed; whereas there was no relation with probable diagnosis, all dimensions were linked with adverse sexual sequelae. PTSD-considered categorically and dimensionally-was relevant to menopause-related health in midlife women veterans. Further, symptoms of negative affect and dysphoric arousal were particularly related to urinary and vasomotor symptoms. These specific symptoms may drive associations between PTSD and these aspects of menopause-related health. Clinical interventions targeting these symptoms may promote midlife women's health.

Evaluating the prevalence of psychiatric comorbidities associated with pediatric scoliosis utilizing ResearchMatch.

The goal of this study is to characterize the self-reported prevalence of psychiatric comorbidities among patients with adolescent scoliosis. Eligible patients across the US were surveyed using ResearchMatch, a validated online platform. The survey collected patient demographics, type of scoliosis, scoliosis treatment received, and the mental health diagnoses and interventions. Nearly all (98%) of the 162 respondents were patients themselves, the remainder of which were parents. The majority of whom were female (93%), Caucasian (85%), and diagnosed with idiopathic scoliosis (63%). The median age of diagnosis was 13 (IQR 11-18). Most respondents had mild to moderate scoliosis (65%), and 17% received surgical treatment. 76 of 158 (48%) responded that scoliosis affected their overall mental health, and 92 (58%) had received a mental health diagnosis-76% were diagnosed after their scoliosis diagnosis. Of the 92 with mental health diagnoses, the most common diagnoses were clinical depression (83%), anxiety (71%), negative body image (62%). Over 80% of patients received medical treatment or therapy. Of those with depression, 38.4% received counseling and 45.2% received medication. 52% of the respondents also had immediate family members with mental health diagnoses, with siblings (48%) having the highest proportion. According to the CDC, the prevalence of US teenagers with diagnosed depression was found to be 3.9% and anxiety disorder to be 4.7%, notably higher among adolescent girls. In this national sample, over half of adolescent scoliosis patients report psychiatric comorbidity, often diagnosed years later. The most prevalent psychiatric condition is depression, anxiety, and body-image disturbances. These findings highlight the importance of awareness of the psychiatric impact of adolescent scoliosis, and importance of screening and treatment of comorbid mental health conditions. IV.

Childhood Gender Diversity and Mental Health: Protocol for the Longitudinal, Observational Gender Journey Project.

Prepubertal transgender, nonbinary, and gender-diverse (TGD) children (ie, those asserting gender identity, expressing gender-role behavior outside of culturally defined norms for their sex registered at birth, or both) are presenting in greater numbers to pediatric gender clinics across the United States and abroad. A large subset of TGD children experiences gender dysphoria, that is, distress that arises from the incongruence between gender identity and sex registered at birth. A lack of consensus exists regarding care for prepubertal TGD children due, in part, to a dearth of empirical research on longitudinal developmental trajectories of gender identity, role behavior, and gender dysphoria (when present). The objective of this National Institutes of Health-funded study is to provide evidence to inform clinical care for prepubertal TGD children by establishing a US longitudinal cohort (N=248) of prepubertal TGD children and their caregivers that is followed prospectively at 6-month intervals across 18 months. At each timepoint, clinical and behavioral data are collected via web-based visit from child and caregiver reporters. Latent class analysis, among other methods, is used to identify subgroups and longitudinally characterize the gender identity and gender-role behavior of TGD children. These models will define longitudinal patterns of gender identity stability and characterize the relationship between TGD classes and mental and behavioral health outcomes, including the moderating role of social gender transition (when present), on these associations. Baseline data collection (N=248) is complete, and the identification of TGD subgroups based on gender identity and expression using latent class analysis is anticipated in 2024. The completion of all 4 waves of data collection is anticipated in July 2024, coinciding with the start of a no-cost study extension period. We anticipate longitudinal analyses to be completed by winter 2024. Through a longitudinal observational design, this research involving prepubertal TGD children and their caregivers aims to provide empirical knowledge on gender development in a US sample of TGD children, their mental health symptomology and functioning over time, and how family initiated social gender transition may predict or alleviate mental health symptoms or diagnoses. The research findings have promise for clinicians and families aiming to ensure the best developmental outcome for these children as they develop into adolescents. DERR1-10.2196/55558.

Evaluation of the Efficiency of Neural Enolase (NSA) in the Diagnosis of Viral Encephalitis: A Medical Review

Encephalitis is an inflammation that occurs in the brain when a virus in some cases, bacteria attack it directly, and can also be triggered by other factors. It is an inflammatory process of the meninges, the membranes that cover the central nervous system. Enolase isoenzyme found in neuronal and neuroendocrine tissues. Its levels in tissues other than erythrocytes are negligible. Its measurement is useful for monitoring tumors of neuroendocrine origin, such as neuroblastoma, pheochromocytoma, medullary thyroid carcinoma, small cell lung carcinoma, melanoma, and some pancreatic tumors. NSE concentrations in cerebrospinal fluid (CSF) are often elevated in diseases that result in relatively rapid neuronal destruction. This review aims to evaluate the efficiency of neural enolase (NSA) in diagnosing viral encephalitis. The methodology used an integrative literature review and a synthesis process to develop the study, expand the understanding of knowledge, and achieve the expected results. The integrative literature review is a method that aims to synthesize results obtained in research on a topic or issue, in a systematic, ordered, and comprehensive way. It is called integrative because it provides broader information on a subject/problem, thus constituting a body of knowledge. To carry out the study, a search for scientific articles was carried out through the Virtual Health Library, in the SCIELO, LILACS, and PUBMED databases, using the terminologies registered in the Health Sciences descriptors, encephalitis, enolase, virus, and diagnosis.

Cross-domain diagnosis for polymer electrolyte membrane fuel cell based on digital twins and transfer learning network✰

Existing research on fault diagnosis for polymer electrolyte membrane fuel cells (PEMFC) has advanced significantly, yet performance is hindered by variations in data distributions and the requirement for extensive fault data. In this study, a cross-domain adaptive health diagnosis method for PEMFC is proposed, integrating the digital twin model and transfer convolutional diagnosis model. A physical-based high-fidelity digital twin model is developed to obtain diverse and high-quality datasets for training diagnosis method. To extract long-term time series features from the data, a temporal convolutional network (TCN) is proposed as a pre-trained diagnosis model for the source domain, with feature extraction layers that can be reused to the transfer learning network. It is demonstrated that the proposed pre-trained model can hold the ability to accurately diagnose the various fuel cell faults, including pressure, drying, flow, and flooding faults, with 99.92 % accuracy, through the effective capture of the long-term dependencies in time series data. Finally, a domain adaptive transfer convolutional network (DATCN) is established to improve the diagnosis accuracy across diverse fuel cells by learning domain-invariant features. The results show that the DATCN model, tested on three different target domain devices with adversarial training using only 10 % normal data, can achieve an average accuracy of 98.5 % (30 % improved over traditional diagnosis models). This proposed method provides an effective solution for accurate cross-domain diagnosis of PEMFC devices, significantly reducing the reliance on extensive fault data.

Potentially inappropriate anticholinergic drug use among older adults in primary healthcare: prevalence and determinants

ABSTRACT Background Considering the vulnerability of older adults to adverse drug reactions, medications with strong anticholinergic properties are considered potentially inappropriate for this population. This study aims to characterize older adults’ profile of anticholinergics use and to identify the factors associated with their potentially inappropriate use. Research design and methods A retrospective study was conducted on 1200 older adults in primary health care centers of Portugal between April 2021 and August 2022. Potentially inappropriate use was assessed according to the 2023 Beers criteria. Logistic regression analyses were performed to determine associations between independent variables and potentially inappropriate use. Results A 8.9% (95% CI 0.074–0.107) of the older adults were exposed to one or more potentially inappropriate anticholinergics, and amitriptyline was the most used (2.0%). Multivariate analysis revealed that use was associated with a higher mean number of medications (OR 1.173, 95% CI 1.115–1.234), diagnoses of depression (OR 2.889, 95% CI 1.785–4.674) and psychiatric disorders (OR 1.654, 95% CI 1.003–2.729). Conclusions This study underscores the importance of vigilance in prescribing anticholinergic medications to older adults, particularly those with higher medication burdens and mental health diagnoses. By identifying factors associated with potentially inappropriate use, healthcare providers can better tailor medication regimens to mitigate risks and optimize the well-being of older adults.