Background: AI technologies have considerable potential to support mental health practitioners in identifying depression symptoms. However, existing tools are often developed with limited practitioner involvement and evaluated primarily on technical performance, leaving questions of clinical relevance, ethical design, and practitioner trust insufficiently addressed. Objective: This study aimed to examine how iterative co-design with mental health practitioners can inform the development of an AI-based model for identifying depression symptoms, focusing on how practitioner input shaped its clinical relevance and ethical foundations across 2 design cycles. Method: We conducted a 2-stage qualitative co-design study. In stage 1, we developed DepressionSymp 1.0, a fine-tuned GPT-3.5 model trained on the CounselChat dataset, identifying 5 symptom categories based on the () criteria and the Linguistic Inquiry and Word Count (LIWC-22) psycholinguistic framework, reviewed and discussed by 6 mental health practitioners during online co-design workshops. In stage 2, this feedback informed DepressionSymp 2.0, expanded to 10 symptom categories and reviewed by 8 further practitioners in a second round of workshops. Results: Practitioners valued the structured symptom outputs for reducing cognitive workload and supporting intervention planning. In stage 1, indirect indicators such as social withdrawal, financial difficulties, and major life changes were often classified as lifestyle factors rather than symptoms; this informed the expansion to 10 categories in stage 2, described as clearer and more useful for preassessment. Risk categorization was identified as the most ethically sensitive aspect across both stages, alongside broader concerns including the lack of nonverbal information in text-based assessments, cultural and linguistic differences in symptom expression, and the potential for automation bias without sufficient human oversight. Conclusions: Iterative co-design with mental health practitioners is valuable for developing AI-based tools that are clinically relevant and ethically grounded, demonstrating how qualitative practitioner feedback can inform iterative model refinement and ethical design. Findings inform design implications around large language model (LLM) features, practitioner-in-the-loop oversight, and ethical considerations covering clinical standards, informed consent, and data governance. Future work should prioritize larger-scale independent validation, formal safety evaluation, and culturally diverse dataset development.
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Social Desirability Bias in Health Screening Questionnaire Responses Generated by OpenAI Models: Quantitative Experimental Study
<strong>Background:</strong> Social desirability bias, the tendency to respond in ways that present the respondent favorably, is well documented in human self-report and has recently been observed in large language models (LLMs) completing personality inventories. Whether it also affects LLM responses to validated health screening instruments, where LLMs are increasingly proposed as simulated patients or synthetic respondents, has not been examined. <strong>Objective:</strong> This formative study aimed to determine whether 4 OpenAI models alter their responses to health screening questionnaires when the instrument and its evaluative purpose can be inferred from the prompt and to quantify the size and direction of any shift. <strong>Methods:</strong> GPT-4o, GPT-4o mini, o1-mini, and o1 were prompted through the OpenAI application programming interface in April 2025 to complete the Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), Alcohol Use Disorders Identification Test (AUDIT), and FANTASTIC lifestyle assessment while adopting the persona of a random member of the public. Each instrument was administered under a naive condition, wherein every item was presented alone in a separate call so that the instrument could not be recognized, and an informed condition, in which all items were presented together, with 100 completed trials per model, instrument, and condition. Naive and informed scores were compared within each model using independent-samples 2-tailed <i>t</i> tests with Bonferroni correction for 16 comparisons, with effect sizes expressed as Cohen <i>d</i> and in units of published general-population SDs. Published human norms served only as descriptive reference points. <strong>Results:</strong> In 14 of 16 model-instrument combinations, scores shifted significantly toward the socially desirable pole when the instrument was recognizable (all <i>P</i><.001): fewer anxiety and depressive symptoms, lower reported alcohol use, and healthier lifestyle behaviors. Mean shifts were 3.2 to 8.2 points on the GAD-7, 2.4 to 11.9 on the PHQ-9, 2.8 to 4.5 on the AUDIT for the o1 models, and 8.2 to 13.5 on the FANTASTIC lifestyle assessment (Cohen <i>d</i>=1.0-8.6). GPT-4o’s AUDIT shift (0.36 points; <i>P</i>=.04) did not survive correction, and GPT-4o mini shifted in the opposite direction on the AUDIT (–2.69 points; <i>P</i><.001). The largest shifts on the mental health instruments occurred in GPT-4o mini, whereas the largest AUDIT shifts occurred in the 2 reasoning models. <strong>Conclusions:</strong> When a screening instrument was recognizable, the 4 OpenAI models tested consistently produced more socially desirable responses than when the same items were presented in isolation. The design cannot separate evaluative inference from other consequences of presenting items together, and the findings are confined to one vendor’s models. Therefore, they constitute formative evidence that social desirability bias may extend to health screening contexts—not evidence of a general property of LLMs. Studies using LLMs as simulated patients or synthetic respondents should report prompt framing and audit for framing-dependent shifts before relying on generated data. <strong>Trial Registration:</strong>
Engagement Patterns and Clinical Outcomes Among Adolescents and Young Adults Enrolled in the Somethings Digital Peer Support Platform: Retrospective Observational Study
Background: Adolescent mental health needs remain largely unmet, particularly among youth in geographically underserved communities. Digital peer support is a promising approach to expanding access to mental health support, yet real-world evidence on engagement patterns and clinical outcomes remains limited. Objective: The purpose of this study was to characterize engagement patterns and real-world outcomes among adolescents and young adults enrolled in a commercially available, text-based peer support platform. Methods: This retrospective observational study analyzed secondary data from 5673 adolescents and young adults enrolled in a commercially available digital peer support platform from January 29, 2023, through June 3, 2026. Engagement was operationalized as communication days per month. Standardized clinical outcome measures (Patient Health Questionnaire-9 [PHQ-9], Generalized Anxiety Disorder-7 [GAD-7], and World Health Organization-Five Well-Being Index [WHO-5]) were administered as part of routine measurement-based care. Ordinary least squares regression was used to examine predictors of engagement, and complete-case pre-post change scores were evaluated across 4 assessment windows among participants with paired assessments. Results: The sample was predominantly female (4600/5673, 81.1%) with a mean age of 16.4 (SD 2.1) years and moderate-to-severe symptom burden at baseline (PHQ-9: mean 12.6, SD 6.1; GAD-7: mean 10.7, SD 5.0; WHO-5: mean 44.8, SD 21.0). Of enrolled adolescents and young adults, 98.1% (5565/5673) had at least one recorded engagement with the platform, with a mean of 4.56 (SD 6.12) communication days per month. Female sex was a significant positive predictor of engagement intensity (β=.64, SE 0.22; =.004). Hispanic and Latino adolescents and young adults engaged significantly more frequently than White or non-Hispanic adolescents and young adults (β=.61, SE 0.26; =.02). Health Professional Shortage Area (HPSA) shortage score was significantly and positively associated with engagement intensity (β=.08, SE 0.02; <.001). Baseline symptom severity was not associated with engagement intensity (β=.094, SE 0.23; =.68). Among participants with complete baseline and follow-up data (1437/5673, 25.3%), an exploratory complete-case analysis found within-measure paired scores improved significantly from baseline to follow-up across the PHQ-9, GAD-7, and WHO-5. At last assessment, 31.9% (237/742), 36.6% (222/606), and 37.8% (168/442) of adolescents and young adults met clinically meaningful improvement thresholds on the PHQ-9, GAD-7, and WHO-5, respectively. Conclusions: Text-based peer support shows preliminary promise as a scalable approach to expanding mental health access for underserved adolescents, though clinical improvement estimates are based on the subpopulation exposed to the digital peer support platform and may not generalize to the full enrolled population. Prospective, controlled studies with more complete outcome ascertainment are needed to establish causal efficacy.
AI-Generated Exposure Hierarchies for Body Dysmorphic Disorder: Proof-of-Concept Study
Background: Body dysmorphic disorder (BDD) is a chronic, impairing condition for which cognitive behavioral therapy, including exposure and response prevention (ERP), is the first-line treatment, but few clinicians are trained in BDD-specific ERP. Constructing personalized exposure hierarchies is time-intensive and requires clinical expertise, particularly for patients with absent insight who may hold appearance beliefs with delusional conviction. Generative AI (GenAI)–powered chatbots, such as ChatGPT, have shown utility for clinical content, but their capacity to produce safe, appropriate ERP hierarchies for BDD has not been examined. Objective: This study evaluated the feasibility of using ChatGPT (GPT-5) to generate ERP hierarchies for BDD, assessed the influence of clinical and demographic characteristics on output quality, benchmarked AI-generated hierarchies against those created by doctoral-level BDD specialists, and tested whether blinded experts could distinguish AI-generated from expert-generated hierarchies. Methods: GPT-5 generated 10-item graded exposure hierarchies for 72 simulated vignettes that varied by body area, insight level (good, limited, or absent), symptom specificity, age, and gender. Expert clinicians independently generated hierarchies for a subset of 18 vignettes. Two researchers rated all GPT-5 hierarchies for task completion and information integration. Three BDD expert clinicians, blinded to the hierarchy source (expert vs GPT-5), rated all 90 hierarchies for relevance, specificity, variability, safety, and overall quality. After unblinding, raters attempted to identify the AI-generated hierarchy in the 18 matched pairs. Results: GPT-5 generated complete hierarchies for 69 of 72 (95.8%) vignettes. Blinded experts rated GPT-5 hierarchies highly across all dimensions (mean scores ranging from 4.39, SD 0.26 to 4.90, SD 0.16 out of 5), with no significant differences from expert-generated hierarchies except for variability, where experts scored modestly higher (=.02; =0.24). After unblinding, raters correctly identified AI-generated hierarchies only 31.5% (17/54) of the time, significantly below chance (=.009); this below-chance result was mostly due to one rater, and no rater performed above chance. Insight level was the most consistent predictor of quality: absent-insight hierarchies received significantly lower safety (<.001; =0.73) and overall quality ratings (=.01; =0.43). This pattern was not unique to GPT-5; safety ratings for expert-generated hierarchies also declined significantly by insight level (=.03). Miscalibration of exposure difficulty, particularly for low- and absent-insight cases, was the most common rater concern (30/43, 69.8% comments), disproportionately directed at GPT-5 hierarchies. Conclusions: GPT-5 generated ERP hierarchies for BDD that blinded experts rated as comparable to clinician-created hierarchies and could not reliably distinguish them from clinician-created hierarchies. However, hierarchies for patients with absent insight received lower safety and quality ratings from both AI and expert sources, and qualitative comments suggested AI-generated hierarchies were less well calibrated to delusional-level presentations. GenAI thus shows promise as a clinician-facing aid for ERP hierarchy generation, but these findings underscore the need for clinician oversight, especially for patients with delusional conviction.
Integration of an FDA-Authorized Digital Autism Diagnostic Tool Into an Outpatient Developmental Pediatrics Workflow: Preliminary Quality Improvement Study
<strong>Background:</strong> Long wait times and specialist shortages delay autism spectrum disorder (ASD) diagnosis, often beyond the period when early intervention is most effective. Digital diagnostic tools may help increase diagnostic capacity by supporting clinicians in delivering timely autism evaluations within routine outpatient care. <strong>Objective:</strong> This study aimed to conduct a preliminary assessment of the integration of a digital autism diagnostic device (Canvas Dx) into an outpatient developmental pediatrics workflow, including a descriptive evaluation of agreement between device outputs and specialist clinical diagnoses and an exploration of clinician and caregiver experience. <strong>Methods:</strong> A single-center, prospective quality improvement study evaluated the implementation and performance of Canvas Dx in a specialty developmental pediatrics clinic. Canvas Dx is a Food and Drug Administration (FDA)–authorized digital ASD diagnostic device approved for use in children aged 18 to 72 months with caregiver or health care provider concern for developmental delay. Eligible children were consecutively prescribed Canvas Dx during the study period and completed the assessment as part of routine clinical care. Device diagnostic outputs were compared with the final specialist clinical diagnoses. Clinician and caregiver feedback was collected to explore their experience with the assessment process and perceived workflow impact via poststudy surveys that captured both structured quantitative questions and open-ended qualitative questions. <strong>Results:</strong> A total of 28 children were included (mean age 52.2, SD 13.8 months; 7/28, 25% female), with an autism prevalence of 67.9% (19/28) based on specialist diagnosis. The device generated a determinate result for 22 (78.6%) participants. Among participants with determinate device results, sensitivity was 94.1% (95% CI 73%-99%) and specificity was 80% (95% CI 37.6%-96.4%). Positive predictive value was 94.1% (95% CI 73%-99%), and negative predictive value was 80% (95% CI 37.6-96.4%) when compared with specialist diagnosis. These estimates were imprecise, particularly for specificity and negative predictive value, because of the small number of participants without an ASD diagnosis. Clinicians reported improved insights and ease of use during evaluation visits, and caregivers reported high satisfaction with the assessment process. <strong>Conclusions:</strong> This preliminary quality improvement assessment demonstrates the feasibility of integrating Canvas Dx into a routine outpatient developmental pediatrics workflow and provides initial descriptive data on agreement with specialist diagnostic determinations and clinician and caregiver experience. Given the small sample size and resulting imprecision of diagnostic performance estimates, these findings should be interpreted as preliminary and used to inform larger prospective evaluations of diagnostic performance and workflow impact.
Caregiver burden, family resilience, and depressive and anxiety symptoms in parents of depressed adolescents: a cross-sectional mediation study
BackgroundParents of adolescents with depression experience substantial caregiving burden, contributing to an increased risk of psychological distress. While individual resilience has been associated with this relationship, the role of family-level resilience remains underexplored. This study explored whether family resilience statistically accounted for the association between caregiver burden and psychological distress, and whether social support moderated this pathway.MethodsA cross-sectional study was conducted with 178 parents of adolescents diagnosed with depression. Participants completed measures of caregiver burden (Zarit Burden Interview, ZBI), family resilience (Family Resilience Assessment Scale, FRAS), social support (Social Support Rating Scale, SSRS), depressive symptoms (Patient Health Questionnaire-9, PHQ-9), and anxiety symptoms (Generalized Anxiety Disorder-7, GAD-7). A moderated mediation model (PROCESS Model 7) was tested as the primary analysis, with 5,000 bootstrap resamples, adjusting for child age, child sex, illness duration, caregiver age, caregiver sex, caregiver education and economic status. All continuous predictors were standardized. Sensitivity analyses were re-run on the primary model excluding outliers and using 10,000 bootstrap resamples.ResultsThe index of moderated mediation was not significant for depressive symptoms (β = −0.029, 95% CI [−0.086, 0.029], p = 0.262) or anxiety symptoms (β = −0.023, 95% CI [−0.073, 0.021], p = 0.266), indicating that social support did not moderate the indirect pathway. Conditional indirect effects of caregiver burden on depressive symptoms through family resilience were significant at low, mean, and high levels of social support (β = 0.132, 0.103, and 0.075, respectively), and similarly for anxiety symptoms (β = 0.106, 0.083, and 0.059). However, these effects did not differ significantly across levels. Social support was directly and positively associated with family resilience (β = 0.496, p < 0.001; r = 0.62, p < 0.001). Sensitivity analyses supported the robustness of these findings.ConclusionsFamily resilience partially accounted for the association between caregiver burden and psychological distress in parents of adolescents with MDD. Social support did not moderate this pathway but was independently associated with higher family resilience. Interventions targeting family resilience may be promising, with social support as a supplementary resource. Longitudinal studies are needed to establish causality and to clarify which aspects of social support are most valued by these caregivers.
Inflammatory Process Identified as Link Between Pulmonary Hypertension and Cognitive Decline
A study by University of Pittsburgh and UPMC researcher-clinicians has uncovered why many pulmonary hypertension (PH) patients report symptoms of cognitive decline.
The team carried out a multimodal investigation involving brain MRI studies, blood-based molecular profiling, and functional studies in mouse models, and linked specific brain abnormalities in pulmonary hypertension to the same inflammatory processes that drive the disease in the lungs and heart. The findings suggest that cognitive decline may be present in other vascular and inflammatory conditions beyond pulmonary hypertension. The results also point to neural targets that may be relevant for future diagnostic, prognostic, and therapeutic approaches.
“Patients often assume memory issues or trouble concentrating are unrelated to their pulmonary hypertension,” said research lead Stephen Chan, MD, PhD, Vitalant Chair in Vascular Medicine and director of the Vascular Medicine Institute and Center for Pulmonary Vascular Biology and Medicine at the Pitt School of Medicine and UPMC. “Our findings suggest those symptoms are a central part of the disease process and deserve attention.”
Chan is senior author of the researchers’ published paper in Journal of Clinical Investigation, titled “Cerebellar and frontal lobe alterations in pulmonary hypertension implicate an inflammatory lung vascular/brain axis.”
Millions of people are affected by pulmonary hypertension, which causes high blood pressure in the blood vessels of the lungs. Common symptoms include shortness of breath, chest pain, and feeling lightheaded. More recently scientists have linked pulmonary hypertension with cognitive issues such as impaired executive function and memory problems. “Cognitive impairment is increasingly recognized in pulmonary hypertension (PH), a disease traditionally viewed as limited to the heart and lungs,” the authors wrote. “Given that inflammation is shared by PH and neurodegenerative diseases, we investigated its role in driving brain and cognitive deficits in PH.”
Recent research by Chan identified a genetic variant in a gene called NCOA7, which produces a protein of the same name. In a quarter of pulmonary hypertension patients, this variant drives inflammatory responses and worsens the odds of survival. Chan hypothesized that the same variant might be connected to brain inflammation that leads to cognitive impairment in people with pulmonary hypertension.
To test that theory, Chan and colleagues used brain MRI scans and blood tests to look for brain changes that were unique to pulmonary hypertension. They first compared the brain scans of people with pulmonary hypertension against those without, using data from a large public health database and a smaller group of UPMC patients to test and confirm their hypothesis.
Patients with pulmonary hypertension in both test groups had a consistent and substantial loss of brain tissue and weakened communication between two brain regions involved in memory, attention and planning—the cerebellum, at the base of the brain, and the frontal lobe, just behind the forehead. Notably, the same brain changes were absent in people with other heart, lung, and psychiatric conditions. “Using structural and functional MRI data from UK Biobank (N = 248 PH) and a prospective validation cohort, we identified reduced cerebellar white matter volume, thinner frontal gyri, and weakened cerebellar-frontal connectivity in patients with PH but not in other heart or lung diseases,” the team reported. These changes were accompanied by worsened executive function and processing speed.”
The brain differences could not be explained by low blood oxygen, poor heart function, or the medications used to treat the disease—evidence that the changes are specific to pulmonary hypertension, rather than a side effect of the disease’s effects on the heart and lungs.
By analyzing genetic data from both groups of patients and conducting additional laboratory studies, the team found that the NCOA7 variant already linked to blood vessel inflammation in pulmonary hypertension is also connected to inflammation in the cerebellum of these patients.
Examining cerebellum tissue from patients and mice with pulmonary hypertension, they saw signs that brain immune cells—microglia—had become overactive, and evidence that neurons were changing in response. Mice that both developed the disease and lacked the NCOA7 gene had more severe cerebellar damage than mice with pulmonary hypertension alone, establishing that the gene plays a causal role in regulating brain inflammation in this disease. “Postmortem analysis and PH mouse models showed cerebellar microglial activation and structural deficits worsened by NCOA7 deficiency,” the investigators wrote in summary. “Collectively, these findings support PH-associated cerebellar–frontal brain deficiencies linked to chronic neuroinflammation and NCOA7 dysfunction.”
Chan suspects that this mechanism is active in other diseases beyond pulmonary hypertension. “I believe that NCOA7 deficiency may underlie a spectrum of inflammatory conditions—including those that may contribute to dementia and aging,” Chan said. “If so, these findings could introduce new diagnostic and therapeutic avenues for preserving brain function in a much broader group of people.” The authors added: “From a translational perspective, the association of inflammatory plasma biomarkers with structural and functional brain alterations may inform new diagnostic and prognostic strategies … Our findings also open avenues for testing whether PH-associated neuroinflammatory changes are therapeutically reversible.”
Current guidelines for managing pulmonary hypertension do not include cognitive screening, and no biological marker exists to predict or track how much the disease affects the brain. This study points to a potential marker and identifies inflammation as a potential therapeutic target for preserving brain function and quality of life.
“This changes the way we look at a disease like pulmonary hypertension,” said Chan. “In medicine, we typically treat the symptoms of one organ system; but the reality is that our bodies are very complex, and disease processes may be affecting multiple parts of the body in a variety of ways.”
The post Inflammatory Process Identified as Link Between Pulmonary Hypertension and Cognitive Decline appeared first on GEN – Genetic Engineering and Biotechnology News.
AI-Assisted Analysis of Spontaneous Affective Facial Responses for Cognitive Status Assessment in Older Adults: Exploratory Feasibility Study
<strong>Background:</strong> As Taiwan enters a super-aged society, the prevalence of dementia continues to rise, highlighting the need for scalable, noninvasive approaches to identify behavioral alterations associated with cognitive decline. AI-based analysis of spontaneous affective facial responses has emerged as a potential digital phenotyping tool, but its feasibility for assessing cognitive performance remains underexplored. <strong>Objective:</strong> This study aimed to examine the feasibility of using AI-derived spontaneous affective facial response metrics, including affective expression congruency and onset latency, to characterize differences across cognitive performance levels in older adults with healthy cognition (HC), mild cognitive impairment (MCI), or dementia. <strong>Methods:</strong> A total of 60 participants (HC, MCI, and dementia; 20 per group) were presented with positive and negative emotion-eliciting videos and completed Mini-Mental State Examination (MMSE) recall and calculation tasks. Facial expressions were video-recorded and analyzed using a convolutional neural network–based AI system. AI-derived metrics included affective expression congruency, categorized as congruent, neutral, or incongruent according to the detected facial response, and affective expression onset latency, defined as the time from stimulus presentation to the emergence of a nonneutral facial expression detected by the AI model. Group differences were analyzed using 1-way ANOVA, Pearson chi-square or Fisher-Freeman-Halton exact tests, Bonferroni-adjusted pairwise post hoc comparisons, analysis of covariance adjusting for age and education, and exploratory receiver operating characteristic analyses. <strong>Results:</strong> Affectively congruent responses were generally less frequent among participants with lower cognitive performance. During the 14-second positive stimulus, congruent positive expressions were observed in 65% (13/20) of HC participants, 20% (4/20) of MCI participants, and 0% (0/20) of participants with dementia. During the 48-second negative stimulus, congruent negative expressions were observed in 60% (12/20) of HC participants compared with 5% (1/20) of both MCI and dementia participants. Onset latency differed significantly across groups for the 14-second positive stimulus (<i>F</i><sub>2,57</sub>=19.63; <i>P</i><.001; η<sup>2</sup>=0.41), 52-second positive stimulus (<i>F</i><sub>2,57</sub>=4.25; <i>P</i>=.02; η<sup>2</sup>=0.13), 48-second negative stimulus (<i>F</i><sub>2,57</sub>=15.61; <i>P</i><.001; η<sup>2</sup>=0.35), and MMSE-based cognitive-task condition (<i>F</i><sub>2,57</sub>=11.28; <i>P</i><.001; η<sup>2</sup>=0.28). After adjustment for age and education, the group effect remained significant for all latency outcomes (<i>F</i><sub>2,55</sub>=3.89-10.68; all <i>P</i>≤.03; partial η<sup>2</sup>=0.12-0.28). Exploratory receiver operating characteristic analyses showed stronger separation in several HC vs MCI and HC vs dementia comparisons, whereas MCI vs dementia comparisons showed limited separability. <strong>Conclusions:</strong> AI-assisted analysis of spontaneous affective facial responses revealed differences in expression congruency and onset latency across cognitive performance levels. These preliminary findings suggest that AI-derived facial behavioral metrics may serve as noninvasive adjunctive features for observational cognitive assessment. However, the findings should be interpreted cautiously because of the exploratory design, modest sample size, and absence of internal or external validation.
Experiences of suicidal behaviour and perceived triggers among young people and caregivers at a tertiary healthcare facility in Kenya
BackgroundSuicidal behavior among adolescents and young adults remains a major public health concern. In sub-Saharan Africa, however, qualitative work that captures both youth and caregiver perspectives is still limited. This study explored the experiences of suicidal behavior, perceived triggers and responses to it among young people and their caregivers attending a tertiary facility in Nairobi, Kenya.MethodsThis qualitative study was conducted at the outpatient youth mental health clinic of a tertiary facility in Kenya, which receives both walk-in clients and referrals. It included 42 young people aged 13–24 years with personal experience of suicidal behavior and 11 caregivers. Participants were recruited purposively, and semi-structured interviews were conducted separately with young people and caregivers. Interviews were audio-recorded, transcribed verbatim, and analyzed thematically using NVivo.FindingsYoung people described suicidal behavior as developing gradually through cumulative and interacting stressors. Perceived triggers included academic and work-related pressure, family conflict, relationship difficulties, harmful social media experiences, socioeconomic hardship, and psychological or health-related challenges, including depression, substance use, trauma, and abuse. Caregivers expressed concern and willingness to help but frequently reported uncertainty about recognizing distress and responding appropriately.ConclusionSuicidal behavior in this setting reflected the interaction of personal and structural disadvantage rather than a single precipitating event. Prevention should combine youth-friendly mental health care, caregiver psychoeducation, family involvement, school-based support, and community-level action on poverty, stigma, and service access.
Trends in Autism Spectrum Disorder Cumulative Incidence and Incidence Density: A Systematic Review
Claims of an “autism epidemic” imply a true increase in ASD occurrence and changing underlying risk yet largely rely on rising prevalence estimates rather than incidence. Both measures are influenced by diagnostic practices, service access, documentation, and ascertainment; prevalence is additionally affected by service-related migration. Incidence studies follow a defined population previously not identified, to examine outcomes over a specified period and study area; incidence is more likely to capture temporal changes in underlying risk.