Psychiatric Diagnostic Categories Research Articles

The impact of comorbid psychiatric disorders on chronic obstructive pulmonary disease (COPD) hospitalizations: a nationwide retrospective study.

To characterize the register of a secondary diagnosis of mental illnesses in all chronic obstructive pulmonary disease (COPD) hospitalizations registered in Portugal from 2008 to 2015 and explore their impact on hospitalization outcomes. A retrospective observational study was conducted. Hospitalizations of patients with at least 40 years old, discharged between 2008 and 2015 with a primary diagnosis of COPD (ICD-9-CM codes 491.x, 492.x and 496) were retrieved from a national administrative database. Comorbid psychiatric diagnoses were identified and defined by the HCUP Clinical Classification Software (CCS) category codes 650-670 (excluding 662). Length of hospital stay (LoS), admission type, in-hospital mortality, and estimated hospital charges were analyzed according to psychiatric diagnostic categories using sex and age-adjusted models. Of 66,661 COPD hospitalizations, 25,869 (38.8%) were episodes with a registered psychiatric comorbidity. These were more likely to correspond to younger inpatients (OR = 2.16, 95%CI 2.09-2.23; p < 0.001), to stay longer at the hospital (aOR = 1.08, 95%CI 1.05-1.12; p < 0.001), to incur in higher estimated hospital charges (aOR = 1.37, 95%CI 1.33-1.42; p < 0.001) and to be urgently admitted (aOR = 1.33, 95%CI 1.23-1.44; p < 0.001). After adjustment for age, in-hospital mortality was lower for episodes with psychiatric diagnoses (aOR = 0.90; 95%CI 0.84-0.96; p < 0.001), except for organic and neurodegenerative diseases category and developmental disorders, intellectual disabilities and disorders usually diagnosed in infancy, childhood, or adolescence category. These findings corroborate the additional burden placed by psychiatric disorders on COPD hospitalizations, highlighting the importance of individualizing care to address these comorbidities and minimize their impact on treatment outcomes.

Psychiatric Referrals in a Tertiary Care Hospital in North India: A Retrospective Study

Background and Aim: There is high prevalence of mental disorders in specialist treatment seekers across specialties. Still, psychiatry referral rates have been found to be very low. A physician refers a patient to psychiatric services for reasons like known case of mental disorder, physical symptoms without adequate explanation, or if the physician feels inadequate in dealing with psychological symptoms. A multi disciplinary approach should be encouraged for the management of such patients, facilitating early recognition and management of psychiatric problems. The aim is to study the sociodemographic variables of psychiatric referrals, reasons and diagnostic categories of psychiatric referrals, and the distribution of psychiatric diagnoses according to reasons and sources of referrals. Materials and Methods: A retrospective hospital based study was conducted in the Department of Psychiatry, Hamdard Institute of Medical Sciences and Research, New Delhi, by analyzing the recorded data of referrals to the Psychiatry Department from various other specialties from January 2019 to March 2020. All referred patients were evaluated by a Psychiatrist (MD Psychiatry) and the diagnosis was made as per the International Statistical Classification of Diseases 10 criteria. The data obtained were analyzed using descriptive statistical methods. Results: This study included 315 psychiatric referrals. The mean age and standard deviation of the entire sample were 37.75 ± 17.63. The referral rate was minimum from January to March 2020. The highest number of referrals was from Medicine Department (66.3%). Among those, the most common diagnoses were that of alcohol dependence (27%). The most common overall reason for referral was substance use (26.2%). The most common psychiatric diagnosis was alcohol dependence (24%). Hypertension was the most common physical comorbidity. The most common diagnosis for known case of psychiatric illness and follow ups were psychotic disorders (27.3% and 26.3%). Conclusion: The findings of this study give us insight that consultation liaison psychiatry should focus on early recognition and wholesome management of psychiatric disorders in patients with physical illness through consultation psychiatry services in general hospital settings.

The curse and opportunity of heterogeneity in the pursuit of psychiatric biomarkers.

The curse and opportunity of heterogeneity in the pursuit of psychiatric biomarkers.

De-medicalising public mental health with the Power Threat Meaning Framework

To propose that the language and concepts in public mental health are often medicalised and to suggest that the power threat meaning framework (PTMF), can be a useful resource for those wishing to take a de-medicalising approach. Examples of medicalisation from the literature and from practice are discussed and key constructs in the PTMF are explained, drawing from the report which presented its research base. Examples of medicalisation in public mental health include: the uncritical use of psychiatric diagnostic categories; the 'illness like any other' approach in anti-stigma campaigns; and the implicit privileging of biology in the biopsychosocial model. The negative operations of power in society are seen as posing threats to human needs and people make sense of such situations in varied ways, though there are some commonalities. This gives rise to culturally available and bodily enabled threat responses which serve a variety of functions. From a medicalised perspective, these responses to threat are characteristically seen as 'symptoms' of underlying disorders. The PTMF is both a conceptual framework and a practical tool that can be used by individuals, groups and communities. Consistent with social epidemiological research, prevention efforts should focus on preventing adversity rather than 'disorders' but the added value of the PTMF is that varied problems can be understood in an integrated manner as responses to a variety of threats whose functions could be met in different ways. Its message that mental distress is often a response to adversity is comprehensible to the public and can be communicated in an accessible way.

The retrospective and observational study of catatonia - Diagnosis, course of illness, and the response to lorazepam - in a tertiary health-care center.

In patients with catatonia, it has been discovered that benzodiazepines (BZD) have a remarkable impact. However, there is not much evidence reflecting the long-term treatment with only BZDs before considering electroconvulsive therapy. One-year retrospective data of patients obtained from the health management information system (HMIS) portal and records of the department of psychiatry with the diagnosis of catatonia. This data was then analyzed for adequate history, presenting complaints, treatment taken, substance use, and was organized into five groups depending on the primary diagnosis as per the Diagnostic and Statistical Manual of Mental. The scores of Bush-Francis Catatonia Rating Scales of day 1 and subsequent follow-ups were secured. Categorical variables were analyzed with the Chi-squared test. The response overtime for all the groups and its correlation with the number of visits was compared using repeated measures analysis of variance. We found that the lorazepam challenge test versus improvement after 1 week of oral lorazepam had Pearson's correlation of 0.604, this correlation decreased in the following weeks. In the 3 week, the correlation was 0.373, which was statistically significant. This shows that the highest correlation was seen in the 1st week. Hence, our study suggested that lorazepam challenge test is a good predictor of response in the 1st week alone. We observe negative correlation which is significant in the 3rd week (P = 0.048) and not in the 1st and 2nd week. Our study analyzed the patients with catatonia in psychiatric diagnostic categories, history, and the outcome after treating them with lorazepam at every visit over 3 weeks. The correlation in the level of improvement of symptoms at subsequent visits was noteworthy and had a strong association with the lorazepam challenge test. When dose of lorazepam was tapered, on an average dose was reduced in the 2nd week. Suggesting that at least 3 week treatment would be ideal.

DRAGON-Data: a platform and protocol for integrating genomic and phenotypic data across large psychiatric cohorts.

Current psychiatric diagnoses, although heritable, have not been clearly mapped onto distinct underlying pathogenic processes. The same symptoms often occur in multiple disorders, and a substantial proportion of both genetic and environmental risk factors are shared across disorders. However, the relationship between shared symptoms and shared genetic liability is still poorly understood. Well-characterised, cross-disorder samples are needed to investigate this matter, but few currently exist. Our aim is to develop procedures to purposely curate and aggregate genotypic and phenotypic data in psychiatric research. As part of the Cardiff MRC Mental Health Data Pathfinder initiative, we have curated and harmonised phenotypic and genetic information from 15 studies to create a new data repository, DRAGON-Data. To date, DRAGON-Data includes over 45 000 individuals: adults and children with neurodevelopmental or psychiatric diagnoses, affected probands within collected families and individuals who carry a known neurodevelopmental risk copy number variant. We have processed the available phenotype information to derive core variables that can be reliably analysed across groups. In addition, all data-sets with genotype information have undergone rigorous quality control, imputation, copy number variant calling and polygenic score generation. DRAGON-Data combines genetic and non-genetic information, and is available as a resource for research across traditional psychiatric diagnostic categories. Algorithms and pipelines used for data harmonisation are currently publicly available for the scientific community, and an appropriate data-sharing protocol will be developed as part of ongoing projects (DATAMIND) in partnership with Health Data Research UK.

Mental Health Service Utilization Rates Among Commercially Insured Adults in the US During the First Year of the COVID-19 Pandemic

The COVID-19 pandemic has been associated with an elevated prevalence of mental health conditions and disrupted mental health care throughout the US. To examine mental health service use among US adults from January through December 2020. This cohort study used county-level service utilization data from a national US database of commercial medical claims from adults (age >18 years) from January 5 to December 21, 2020. All analyses were conducted in April and May 2021. Per-week use of mental health services per 10 000 beneficiaries was calculated for 5 psychiatric diagnostic categories: major depressive disorder (MDD), anxiety disorders, bipolar disorder, adjustment disorders, and posttraumatic stress disorder (PTSD). Changes in service utilization rates following the declaration of a national public health emergency on March 13, 2020, were examined overall and by service modality (in-person vs telehealth), diagnostic category, patient sex, and age group. The study included 5 142 577 commercially insured adults. The COVID-19 pandemic was associated with more than a 50% decline in in-person mental health care service utilization rates. At baseline, there was a mean (SD) of 11.66 (118.00) weekly beneficiaries receiving services for MDD per 10 000 enrollees; this declined by 6.44 weekly beneficiaries per 10 000 enrollees (β, -6.44; 95% CI, -8.33 to -4.54). For other disorders, these rates were as follows: anxiety disorders (mean [SD] baseline, 12.24 [129.40] beneficiaries per 10 000 enrollees; β, -5.28; 95% CI, -7.50 to -3.05), bipolar disorder (mean [SD] baseline, 3.32 [60.39] beneficiaries per 10 000 enrollees; β, -1.81; 95% CI, -2.75 to -0.87), adjustment disorders (mean [SD] baseline, 12.14 [129.94] beneficiaries per 10 000 enrollees; β, -6.78; 95% CI, -8.51 to -5.04), and PTSD (mean [SD] baseline, 4.93 [114.23] beneficiaries per 10 000 enrollees; β, -2.00; 95% CI, -3.98 to -0.02). Over the same period, there was a 16- to 20-fold increase in telehealth service utilization; the rate of increase was lowest for bipolar disorder (mean [SD] baseline, 0.13 [16.72] beneficiaries per 10 000 enrollees; β, 1.40; 95% CI, 1.04-1.76) and highest for anxiety disorders (mean [SD] baseline, 0.20 [9.28] beneficiaries per 10 000 enrollees; β, 9.12; 95% CI, 7.32-10.92). When combining in-person and telehealth service utilization rates, an overall increase in care for MDD, anxiety, and adjustment disorders was observed over the period. In this cohort study of US adults, we found that the COVID-19 pandemic was associated with a rapid increase in telehealth services for mental health conditions, offsetting a sharp decline in in-person care and generating overall higher service utilization rates for several mental health conditions compared with prepandemic levels.

A critical analysis of digital phenotyping and the neuro-digital complex in psychiatry

This article critically examines the emergence and uses of digital phenotyping in contemporary psychiatry. From an analysis of its discourses and practices, we show that digital phenotyping diffusion is directly related to its promise to solve some of the major impasses of the so-called "neuro-turn" in contemporary psychiatry. However, more than a new tool to address old objects of pre-digital psychiatry, we consider digital phenotyping as participating from a new onto-epistemological matrix, the “neuro-digital complex,” which entails the redefinition of psychiatric objects (e.g., brain and mind), diagnostic categories and procedures, subjectivities (e.g., users of mental health apps), and the emergence of a new regime of truth which promises to reveal the neuropsychological core at the individual scale. Despite this techno-utopia, digital phenotyping does not produce neutral mirrors for self-knowledge. We show that it resorts to population statistics, grounded truth data sets built with pre-digital neuropsychological assumptions, and human categorization processes. Nevertheless, we propose not to approach this gap as a misleading ideological fact but to emphasize its productive possibilities. From this perspective, the gap becomes the measure between whom we think we are and who we really are, working as a guide to conduct our lives in neuropsychological terms. Thus, we conclude that, rather than providing personalized diagnoses and treatments, digital phenotyping produces individualized pathways to normalization and neuropsychologization.

Interrogating nonsuicidal self-injury disorder through a feminist psychiatric disability theory framework.

This article explores the proposed psychiatric diagnostic category of nonsuicidal self-injury disorder (NSSID) through the lens of feminist psychiatric disability theory. Mobilising insights from labelling theory's accounts of stigma and recovery, in conjunction with critical and feminist disability theory's attention to emotional and physical pain, we suggest that a feminist psychiatric disability approach to NSSID can illuminate the limits of a fully demedicalised engagement with self-injury. While remaining critical of psychiatrisation processes, we explore the implications of 'strategic (dis)identifications' with illness labels as mechanisms through which to manage and access care, treatment and recovery. Such a nuanced account engenders more deeply material considerations of pain, stigma, treatment and 'cure' at the site of NSSID, opening new avenues of engagement with self-injury for sociologists and critical disability scholars alike.

A farewell to psychiatric diagnoses.

Psychiatric diagnostic categories are neither valid nor stable. They tell us little about the causes of the problems, the prognosis or what treatment might be beneficial. A psychiatric diagnosis must not therefore be a prerequisite for providing proper health care.

What can we learn about the psychiatric diagnostic categories by analysing patients' lived experiences with Machine-Learning?

BackgroundTo deliver appropriate mental healthcare interventions and support, it is imperative to be able to distinguish one person from the other. The current classification of mental illness (e.g., DSM) is unable to do that well, indicating the problem of diagnostic heterogeneity between disorders (i.e., the disorder categories have many common symptoms). As a result, the same person might be diagnosed with two different disorders by two independent clinicians. We argue that this problem might have resulted because these disorders were created by a group of humans (APA taskforce members) who relied on more intuition and consensus than data. Literature suggests that human-led decisions are prone to biases, group-thinking, and other factors (such as financial conflict of interest) that can enormously influence creating diagnostic and treatment guidelines. Therefore, in this study, we inquire that if we prevent such human intervention (and thereby their associated biases) and use Artificial Intelligence (A.I.) to form those disorder structures from the data (patient-reported symptoms) directly, then can we come up with homogenous clusters or categories (representing disorders/syndromes: a group of co-occurring symptoms) that are adequately distinguishable from each other for them to be clinically useful. Additionally, we inquired how these A.I.-created categories differ (or are similar) from human-created categories. Finally, to the best of our knowledge, this is the first study, that demonstrated how to use narrative qualitative data from patients with psychopathology and group their experiences using an A.I. Therefore, the current study also attempts to serve as a proof-of-concept.MethodWe used secondary data scraped from online communities and consisting of 10,933 patients’ narratives about their lived experiences. These patients were diagnosed with one or more DSM diagnoses for mental illness. Using Natural Language Processing techniques, we converted the text data into a numeric form. We then used an Unsupervised Machine Learning algorithm called K-Means Clustering to group/cluster the symptoms. ResultsUsing the data mining approach, the A.I. found four categories/clusters formed from the data. We presented ten symptoms or experiences under each cluster to demonstrate the practicality of application and understanding. We also identified the transdiagnostic factors and symptoms that were unique to each of these four clusters. We explored the extent of similarities between these clusters and studied the difference in data density in them. Finally, we reported the silhouette score of + 0.046, indicating that the clusters are poorly distinguishable from each other (i.e., they have high overlapping symptoms).DiscussionWe infer that whether humans attempt to categorise mental illnesses or an A.I., the result is that the categories of mental disorders will not be unique enough to be able to distinguish one service seeker from another. Therefore, the categorical approach of diagnosing mental disorders can be argued to fall short of its purpose. We need to search for a classification system beyond the categorical approaches even if there are secondary merits (such as ease of communication and black-and-white (binary) decision making). However, using our A.I. based data mining approach had several meritorious findings. For example, we found that some symptoms are more exclusive or unique to one cluster. In contrast, others are shared by most other clusters (i.e., identification of transdiagnostic experiences). Such differences are interesting objects of inquiry for future studies. For example, in clear contrast to the traditional diagnostic systems, while some experiences, such as auditory hallucinations, are present in all four clusters, others, such as trouble with eating, are exclusive to one cluster (representing a syndrome: a group of co-occurring symptoms). We argue that trans-diagnostic conditions (e.g., auditory hallucinations) might be prime targets for symptom-level interventions. For syndrome-level grouping and intervention, however, we argue that exclusive symptoms are the main targets.ConclusionCategorical approach to mental disorders is not a way forward because the categories are not unique enough and have several shared symptoms. We argue that the same symptoms can be present in more than one syndrome, although dimensionally different. However, we need additional studies to test this hypothesis. Future directions and implications were discussed.

Towards Defining the Neuroanatomical Basis of Late-Onset Psychiatric Symptoms.

Psychiatric symptoms, including changes in emotional processing, are a common feature of many neurodegenerative disorders, such as Alzheimer's disease, dementia with Lewy Bodies, frontotemporal dementia, and Huntington's disease. However, the neuroanatomical basis of emotional symptoms is not well defined; this stands in contrast to the relatively well-understood neuroanatomical correlates of cognitive and motor symptoms in neurodegenerative disorders. Furthermore, psychiatric diagnostic categories, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Statistical Classification of Diseases and Related Health Problems (ICD), may have limited applicability in patients with late-onset psychiatric symptoms in the context of neurodegenerative disorders. In this clinical review, we suggest that early-onset and late-onset psychiatric symptoms have distinct etiologies, and that late-onset changes in emotional processing are likely underpinned by neurodegenerative disease. Furthermore, we suggest that an improved understanding of the neuroanatomical correlates of emotional changes in neurodegenerative disease may facilitate diagnosis and future treatment development. Finally, we propose a novel clinical approach, in a preliminary attempt to incorporate late-onset emotional symptoms alongside cognitive and motor symptoms into a clinical "algorithm," with a focus on the neuroanatomy implicated when particular combinations of emotional, cognitive, and motor features are present. We anticipate that this clinical approach will assist with the diagnosis of neurodegenerative disorders, and our proposed schema represents a move towards integrating neurologic and psychiatric classification systems.

Col Kirpal Singh Award

Background:Psychiatric disorders are one of the increasing causes of disability and work-related problems. As per the available data, 10% of the total working Indian population have major psychiatric disorders such as depression, anxiety disorders, psychosis, and substance use disorders.Aims:To study the relationship between Psychiatric disorders and Absenteeism in State Government Employees in a Tertiary Healthcare center.Methodology:The retrospective data of the last two years of the candidates referred for psychiatric evaluation to the Department because of absence at work was collected from the Hospital records and were analyzed for the number, gender, education, presence of mental illness, treatment is taken, substance use and outcome of the referral.Statistics:Categorical variables were analyzed with the Chi-squared tests. Significance levels for all analyses were set at P <0.05. Mean ranks were tested using the Kruskal Wallis test.Results:Significant difference was found between Prevalence of Psychiatric disorders, length (p=0.045), and frequent periods of absenteeism (p=0.03).Conclusions:Our study examined the psychiatric diagnostic categories, past absenteeism at work, the outcome of the referral, and associations if any which might help to provide better psychiatric services for the referrals of government employees in the future.

Transdiagnostic neurocognitive deficits in patients with type 2 diabetes mellitus, major depressive disorder, bipolar disorder, and schizophrenia: A 1-year follow-up study

BackgroundNeurocognition impairments are critical factors in patients with major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ), and also in those with somatic diseases such as type 2 diabetes mellitus (T2DM). Intriguingly, these severe mental illnesses are associated with an increased co-occurrence of diabetes (direct comorbidity). This study sought to investigate the neurocognition and social functioning across T2DM, MDD, BD, and SZ using a transdiagnostic and longitudinal approach. MethodsA total of 165 participants, including 30 with SZ, 42 with BD, 35 with MDD, 30 with T2DM, and 28 healthy controls (HC), were assessed twice at a 1-year interval using a comprehensive, integrated test battery on neuropsychological and social functioning. ResultsCommon neurocognitive impairments in somatic and psychiatric disorders were identified, including deficits in short-term memory and cognitive reserve (p < 0.01, η²p=0.08–0.31). Social functioning impairments were observed in almost all the disorders (p < 0.0001; η²p=0.29–0.49). Transdiagnostic deficits remained stable across the 1-year follow-up (p < 0.001; η²p=0.13–0.43) and could accurately differentiate individuals with somatic and psychiatric disorders (χ² = 48.0, p < 0.0001). LimitationsThe initial sample size was small, and high experimental mortality was observed after follow-up for one year. ConclusionsThis longitudinal study provides evidence of some possible overlap in neurocognition deficits across somatic and psychiatric diagnostic categories, such as T2DM, MDD, BD, and SZ, which have high comorbidity. This overlap may be a result of shared genetic and environmental etiological factors. The findings open promising avenues for research on transdiagnostic phenotypes of neurocognition in these disorders, in addition to their biological bases.

Symptom changes in patients with pre-existing psychiatric disorders in the initial phase of the COVID-19 pandemic: Vulnerability of female patients and patients with mood disorders.

How patients with pre-existing psychiatric disorders are responding to the COVID-19 pandemic remains unclear, and no comprehensive studies have yet been performed. To elucidate (1) which psychiatric disorders were exacerbated during the initial phase of the COVID-19 pandemic and (2) the contributing factors, we prospectively assessed psychiatric symptoms of 1592 psychiatric outpatients in a single-center study using the Global Assessment of Functioning (GAF) before the state of emergency was declared in Japan and during two months under the state of emergency (study period: April 8 to June 7, 2020). We conducted a chi-squared test for the relationship between psychiatric diagnostic category (ICD-10) and exacerbation. To control for confounders, we conducted a logistic regression analysis using sex, age, diagnostic category, and pre-pandemic GAF score as independent variables. Exacerbation rates of patients with mood disorders (F3) and neurotic disorders (F4) were 4.32% and 5.37%, respectively, and were significantly higher than those for patients with organic disorders (F0) and schizophrenic disorders (F2) (X2 (9, N = 1592) = 27.8, p < .01). Logistic regression analysis revealed that patients with F3 and female patients were significantly more affected than patients with other disorders or male patients, respectively (odds ratio (95% confidence interval) = 2.4 (1.2–4.6), p < .01 for F3; 3.1 (1.5–6.6), p < .01 for females). These findings suggest a need for careful management of patients with mood disorders and female psychiatric patients during a pandemic.

Explosive Outbursts at School.

Explosive outbursts (EO) by students are an intensely distressing experience for that student as well as for all school staff and students present during the outburst. These EO are characterized by rapid escalations, usually far out of proportion to precipitating events, may include significant verbal and/or physical aggression, require intensive staff intervention, are often difficult for the student to process, and are typically recurrent. These explosions cross multiple psychiatric and educational diagnostic categories and require diverse interventions to address behavioral, emotional, impulsive, and sensory components. Interventions for each stage of an EO can be used to deescalate these events.

Characterizing transdiagnostic premorbid biotypes can help progress in selective prevention in psychiatry.

Characterizing transdiagnostic premorbid biotypes can help progress in selective prevention in psychiatry.

Diagnostic Moderators of the Risk-Recidivism Relationship for Offenders with Mental Illness.

Recent research suggests that early onset of criminality is a potential marker for a subgroup of offenders with mental illness (OMI) for whom general risk/need factors are more relevant. The present study examines whether the presence of certain psychiatric diagnostic categories (i.e., Psychotic Disorders, Mood Disorders, Personality Disorders, Substance use Disorders) moderates the prediction of arrest outcomes using Level of Service Inventory-Revised (LSI-R) total scores in an archival sample of N = 179 OMI probationers. Moderator analyses demonstrated that LSI-R total scores predicted arrest outcomes significantly better for those with a diagnosis of Personality Disorder using path and survival analytic methods. Hierarchical regressions using LSI-R subscales as predictors revealed lower pro-criminal Attitudes for Mood Disordered participants, while more severe Criminal History and Family/Marital problems characterized those diagnosed with Personality Disorders. This study adds to the developing literature on subgroups of OMI for whom general risk/need factors are particularly important and highlights personality disturbance as another potential characterizing feature.

Child Sexual Abuse and Risk of Revictimization: Impact of Child Demographics, Sexual Abuse Characteristics, and Psychiatric Disorders.

Approximately half of child sexual abuse (CSA) victims report sexual revictimization later in life; however, there is limited rigorous evidence concerning factors contributing to sexual and nonsexual forms of revictimization. This article investigates the relationships between CSA and a range of revictimization experiences. It also examines the role of other individual-level factors (demographics, CSA characteristics, psychiatric disorders) in the risk of revictimization. The study compares data from a prospective-longitudinal study of 2,759 Australian children (<17 years old) alleged to have experienced contact-CSA between 1964 and 1995, and a comparison group matched on sex and age. In each case, CSA was deemed likely to have occurred according to expert forensic medical opinion. Abused children and comparisons were followed to age 35 years on average, and their lifetime official crime victimization histories and public mental health service records were extracted from statewide population-level administrative databases. Relative to comparisons, CSA victims experienced significantly higher rates of revictimization, with marked elevations in odds for interpersonal revictimization (i.e., sexual assault, physical assault, threats of violence, and stalking). The CSA-physical assault relationship was moderated by sex, with a stronger association for female victims. Among CSA victims, victim sex, age at index abuse, and several psychiatric diagnostic categories were independently associated with revictimization risk, with different patterns of vulnerability emerging depending on the nature of revictimization. Overall, CSA victims are vulnerable to a range of revictimization experiences later in life. Findings have implications for the identification of particular groups of sexually abused children at heightened risk for revictimization and the role mental health services may play in mitigating risk.

Mortality among male forensic and non-forensic psychiatric patients: matched cohort study of rates, predictors and causes-of-death

Background: The mortality of forensic psychiatric (FP) patients compared to non-forensic psychiatric (non-FP) patients has been sparsely examined. Methods: We conducted a matched cohort study and compared Danish male FP patients (n = 490) who underwent pre-trial forensic psychiatric assessment (FPA) 1980–1992 and were subsequently sentenced to psychiatric treatment with matched (on year of birth, marital status, and municipality of residence) male non-FP patients (n = 490) and male general population controls (n = 1716). FP and non-FP patients were also matched on major psychiatric diagnostic categories. To determine mortality and identify potential predictors of mortality, we linked nationwide register data (demographics, education, employment, psychiatric admission pattern and diagnoses, cause of death) to study cohorts. Average follow-up time was 19 years from FPA assessment/sampling until death/censoring or 31 December 2010 and risk factors were studied/controlled with Cox proportional hazard analysis. Results: Overall, psychiatric patients had significantly higher mortality compared to matched general population controls (medium to large effects). Among patients, 44% (213) of FP vs. 36% (178) of matched non-FP patients died during follow-up (p = 0.02). When we used Cox regression modeling to control for potential risk factors; age, education, immigrant background, employed/studying at index, length of psychiatric inpatient stay/year, and ever being diagnosed with substance use disorder (SUD), FP patient status was no longer significantly associated with increased mortality, whereas SUD and longer inpatient time per year were independently associated with increased mortality. Discussion: This study suggests that SUD and longer inpatient time per year are independent risk factors for increased mortality in psychiatric patients.