Classification of bipolar disorder in psychiatric hospital. A prospective cohort study

Young Adults with a History of Residential Youth Care: A Cohort Profile of a Hard-to-Reach Population

Adults with a history of living in residential youth care (RYC) face elevated risks across various life domains. In this cohort profile paper, we outline the design of a comprehensive follow-up study-the VINGO study-targeting young adults (22-30 years) with a history of living in RYC (T2). We describe the recruitment strategy and present sample characteristics. Data were collected in the baseline study (T1) from 2011 to 2014. At T1, the 400 adolescent participants showed a high prevalence of mental disorders, maltreatment experiences, substance use, and self-reported suicide attempts. Data collection at T2 10 years later (2021-2023) included self-reported sociodemographic information, physical health, childhood maltreatment, dissociation, quality of life, social support, and self-esteem using standardized and validated instruments. A diagnostic psychiatric assessment and subjective evaluation of service utilization were conducted by telephone interviews. Additionally, a qualitative sub-study involved in-depth interviews of fourteen participants. We reached a 52% response rate at T2. Comparing participants (n = 157, 107 females) to non-participants (n = 243, 123 females) based on T1 data revealed that T2 participants had a higher prevalence of depression, anxiety, and conduct disorder and a lower prevalence of ADHD at T1. Furthermore, T2 participants reported more suicide attempts, experiences of maltreatment, and problematic substance use at T1. Our results show that we reached a burdened population, positioning the VINGO study as a unique opportunity to examine a vulnerable population of emerging adults.

Educational differences in years lived with disability due to mental and substance use disorders: a cohort study using nationwide Norwegian and Danish registries

BACKGROUND

Findings from the Global Burden of Disease (GBD) study have shown that the burden of mental and substance use disorders is considerable, and unevenly distributed across demographic groups in the population. However, there is a lack of knowledge on how this burden differs by socioeconomic position. The aim of this study was to examine educational differences in years lived with disability (YLDs) from mental and substance use disorders among males and females in two high-income countries, taking comorbidity with other diseases into account.

METHODS

The study included all registered residents in Denmark and Norway from 2011 to 2021. Diagnostic information was retrieved from records in the Norwegian National Patient Registry (NPR) and the Danish Psychiatric Central Research Register (PCRR) and used as proxy measures for disorder prevalence. Demographical and educational information was taken from administrative registries. The YLD is a measure of the non-fatal health loss in the population and was calculated by multiplying the duration of a disorder with a disability weight (DW), scaled between 0 and 1. Information on remission and DWs were retrieved from the GBD study and other sources, and disorder specific DWs were averaged by severity levels and adjusted for comorbidity.

RESULTS

Educational gradients in YLD rates were found for mental and substance disorders overall, and for most of the specific disorders. The educational gradient was more pronounced for schizophrenia, intellectual disability and substance use disorders than for eating, anxiety, and affective disorders. Both higher YLD rates, and a larger attributed proportion of the total YLDs, were found for schizophrenia, intellectual disability, and substance use disorders in the groups with low versus high education. YLD rates for eating, anxiety, and affective disorders were more equal across educational levels, but constituted a smaller proportion of the total YLDs among the groups with low versus the groups with high educational level.

CONCLUSION

Most of the disease burden related to mental and substance use disorders falls on those with the fewest years of education. This should be taken into consideration when public health targets aimed at improving mental health and reducing social inequalities in health are developed and implemented.

The prevalence and associated characteristics of Bipolar Disorder diagnosis among admitted patients at three tertiary psychiatric hospitals in Zimbabwe: A cross sectional study

BACKGROUND

Bipolar Affective Disorder (BD) is a serious condition that affects more than 1% of the world's population. If not treated can cause disability, yet its prevalence in Zimbabwe is not known. This study explores the burden of Bipolar Disorder and its associated factors in Zimbabwe.

METHODS

A cross sectional study with a sample of 272 participants was carried out at three tertiary hospitals in Zimbabwe. Data was collected using an interviewer administered questionnaire and the Mini International Neuropsychiatric Interview (M.I.N.I). The study shows the prevalence and factors associated with Bipolar Disorder at tertiary psychiatric hospitals. Data analysis was done using STATA S/E 13.0 for data management.

RESULTS

The prevalence of BD in the sample was 39.3%. Factors associated with BD were, being formally employed (AOR = 3.69, 95%CI: 1.55-8.79), a history of defaulting medications (AOR = 1.90, 95%CI: 1.02-3.57) and a reported previous diagnosis of BD (AOR = 5.66, 95%CI: 2.72-11.8).

CONCLUSIONS

The prevalence of BD among admitted participants in tertiary psychiatric hospitals in Zimbabwe is high. It is comparable to that from African studies done in clinical settings. There is need for in-service training for clinicians to be more vigilant in diagnosing BD.

Diagnosing major depressive disorder and substance use disorder using the electronic health record: A preliminary validation study

Background

One mechanism to examine if major depressive disorder (MDD) is related to the development of substance use disorder (SUD) is by leveraging naturalistic data available in the electronic health record (EHR). Rules for data extraction and variable construction linked to psychometrics validating their use are needed to extract data accurately.

Objective

We propose and validate a methodologic framework for using EHR variables to identify patients with MDD and non-nicotine SUD.

Methods

Proxy diagnoses and index dates of MDD and/or SUD were established using billing codes, problem lists, patient-reported outcome measures, and prescriptions. Manual chart reviews were conducted for the 1-year period surrounding each index date to determine (1) if proxy diagnoses were supported by chart notes and (2) if the index dates accurately captured disorder onset.

Results

The results demonstrated 100% positive predictive value for proxy diagnoses of MDD. The proxy diagnoses for SUD exhibited strong agreement (Cohen's kappa of 0.84) compared to manual chart review and 92% sensitivity, specificity, positive predictive value, and negative predictive value. Sixteen percent of patients showed inaccurate SUD index dates generated by EHR extraction with discrepancies of over 6 months compared to SUD onset identified through chart review.

Conclusions

Our methodology was very effective in identifying patients with MDD with or without SUD and moderately effective in identifying SUD onset date. These findings support the use of EHR data to make proxy diagnoses of MDD with or without SUD.

The quality of severe mental disorder diagnoses in a national health registry as compared to research diagnoses based on structured interview

BACKGROUND

Utilization of diagnostic information from national patient registries rests on the quality of the registered diagnoses. We aimed to investigate the agreement and consistency of diagnoses of psychotic and bipolar disorders in the Norwegian Patient Registry (NPR) compared to structured interview-based diagnoses given as part of a clinical research project.

METHODS

Diagnostic data from NPR were obtained for the period 01.01.2008-31.12.2013 for all patients who had been included in the Thematically Organized Psychosis (TOP) study between 18.10.2002 and 01.09.2014 with a Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) diagnosis of schizophrenia (n = 537), delusional disorder (n = 48), schizoaffective disorder (n = 118) or bipolar disorder (n = 408). Diagnostic agreement between the primary DSM-IV diagnosis in TOP and the International Classification of Diseases, 10th revision (ICD-10) diagnoses in NPR was evaluated using Cohen's unweighted nominal kappa (κ). Diagnostic consistency was calculated as the proportion of all registered severe mental disorder diagnoses in NPR that were equivalent to the primary diagnosis given in the TOP study.

RESULTS

The proportion of patients registered with the equivalent ICD-10 diagnosis as the primary DSM-IV diagnosis given in TOP was 84.2% for the schizophrenia group, 68.8% for the delusional disorder group, 76.3% for the schizoaffective disorder group, and 78.4% for the bipolar disorder group. Diagnostic agreement was good for schizophrenia (κ = 0.74) and bipolar disorder (κ = 0.72), fair for schizoaffective disorder (κ = 0.63), and poor for delusional disorder (κ = 0.39). Among patients with DSM-IV schizophrenia, 4.7% were diagnosed with ICD-10 bipolar disorder, and among patients with DSM-IV bipolar disorder, 2.5% were diagnosed with ICD-10 schizophrenia. Diagnostic consistency was 84.9% for schizophrenia, 59.1% for delusional disorder, 65.9% for schizoaffective disorder, and 91.0% for bipolar disorder.

CONCLUSIONS

When compared to research-based diagnoses, clinical diagnoses of schizophrenia and bipolar disorder in the NPR are accurate and consistent, with minimal diagnostic overlap between the two disorders.

Big data for bipolar disorder

The delivery of psychiatric care is changing with a new emphasis on integrated care, preventative measures, population health, and the biological basis of disease. Fundamental to this transformation are big data and advances in the ability to analyze these data. The impact of big data on the routine treatment of bipolar disorder today and in the near future is discussed, with examples that relate to health policy, the discovery of new associations, and the study of rare events. The primary sources of big data today are electronic medical records (EMR), claims, and registry data from providers and payers. In the near future, data created by patients from active monitoring, passive monitoring of Internet and smartphone activities, and from sensors may be integrated with the EMR. Diverse data sources from outside of medicine, such as government financial data, will be linked for research. Over the long term, genetic and imaging data will be integrated with the EMR, and there will be more emphasis on predictive models. Many technical challenges remain when analyzing big data that relates to size, heterogeneity, complexity, and unstructured text data in the EMR. Human judgement and subject matter expertise are critical parts of big data analysis, and the active participation of psychiatrists is needed throughout the analytical process.

Can mental health diagnoses in administrative data be used for research? A systematic review of the accuracy of routinely collected diagnoses

BACKGROUND

There is increasing availability of data derived from diagnoses made routinely in mental health care, and interest in using these for research. Such data will be subject to both diagnostic (clinical) error and administrative error, and so it is necessary to evaluate its accuracy against a reference-standard. Our aim was to review studies where this had been done to guide the use of other available data.

METHODS

We searched PubMed and EMBASE for studies comparing routinely collected mental health diagnosis data to a reference standard. We produced diagnostic category-specific positive predictive values (PPV) and Cohen's kappa for each study.

RESULTS

We found 39 eligible studies. Studies were heterogeneous in design, with a wide range of outcomes. Administrative error was small compared to diagnostic error. PPV was related to base rate of the respective condition, with overall median of 76 %. Kappa results on average showed a moderate agreement between source data and reference standard for most diagnostic categories (median kappa = 0.45-0.55); anxiety disorders and schizoaffective disorder showed poorer agreement. There was no significant benefit in accuracy for diagnoses made in inpatients.

CONCLUSIONS

The current evidence partly answered our questions. There was wide variation in the quality of source data, with a risk of publication bias. For some diagnoses, especially psychotic categories, administrative data were generally predictive of true diagnosis. For others, such as anxiety disorders, the data were less satisfactory. We discuss the implications of our findings, and the need for researchers to validate routine diagnostic data.

Distinguishing bipolar disorder from borderline personality disorder: A study of current clinical practice

BACKGROUND

Diagnosing mental illness is a central role for psychiatrists. Correct diagnosis informs both treatment and prognosis, and facilitates accurate communication. We sought to explore how psychiatrists distinguished two common psychiatric diagnoses: bipolar disorder (BD) and borderline personality disorder (BPD).

METHODS

We conducted a qualitative study of psychiatrists to explore their practical experience. We then sought to validate these results by conducting a questionnaire study testing the theoretical knowledge and practical experience of a large number of UK psychiatrists. Finally we studied the assessment process in NHS psychiatric teams by analysing GP letters, assessments by psychiatrists, and assessment letters.

RESULTS

There was broad agreement in both the qualitative and questionnaire studies that the two diagnoses can be difficult to distinguish. The majority of psychiatrists demonstrated in survey responses a comprehensive understanding DSM-IV-TR criteria although many felt that these criteria did not necessarily assist diagnostic differentiation. This scepticism about diagnostic criteria appeared to strongly influence clinical practice in the sample of clinicians we observed. In only a minority of assessments were symptoms of mania or BPD sufficiently assessed to establish the presence or absence of each diagnosis.

CONCLUSION

Clinical diagnostic practice was not adequate to differentiate reliably BD and BPD. The absence of reliable diagnostic practice has widespread implications for patient care, service provision and the reliability of clinical case registries.

Substance use disorders in schizophrenia, bipolar disorder, and depressive illness: a registry-based study

PURPOSE

To compare the prevalence and pattern of comorbid substance use disorders (SUD) between patients with schizophrenia, bipolar disorder, and depressive illness.

METHODS

Data on presence of alcohol use disorder (AUD) and non-alcohol drug use disorder (DUD) were retrieved from the Norwegian Patient Register for individuals born between 1950 and 1989 who in the period 2009-2013 were diagnosed with schizophrenia, bipolar disorder or depressive illness according to the 10th version of the WHO International Classification of Diseases. The prevalence of AUD only, DUD only, or both was compared between men and women across age and diagnostic groups.

RESULTS

The prevalence of SUD was 25.1 % in schizophrenia (AUD: 4.6 %, DUD: 15.6 %, AUD and DUD: 4.9 %), 20.1 % in bipolar disorder (AUD: 8.1 %, DUD: 7.6 %, AUD and DUD: 4.4 %), and 10.9 % in depressive illness (AUD: 4.4 %, DUD: 4.3 %, AUD and DUD: 2.2 %). Middle-aged men with bipolar disorder had the highest prevalence of AUD (19.1 %) and young men with schizophrenia had the highest prevalence of DUD (29.6 %). Of the specific DUDs, all but sedative use disorder were more prevalent in schizophrenia than the other groups. Cannabis and stimulant use disorder was found among 8.8 and 8.9 %, respectively, of the men with schizophrenia.

CONCLUSIONS

The alarmingly high prevalence of DUD among young patients with severe mental disorders should encourage preventive efforts to reduce illicit drug use in the adolescent population.

Population study of disease burden, management, and treatment of bipolar disorder in Sweden: a retrospective observational registry study

OBJECTIVES

The aim of the study was to describe temporal changes in bipolar disorder during 20 years within the Swedish population and to investigate clinical and socioeconomic characteristics, drug treatment, and mortality among patients with bipolar disorder.

METHODS

We conducted a retrospective, nationwide registry study (the Swedish Population Register) that included all patients diagnosed with bipolar disorder (1991-2010) and linked individual data from the Swedish National Patient Register, the National Prescribed Drug Register, and the Population Register (NCT01455961). A cross-sectional cohort analysis was performed for years 2006 versus 2009. Data were analyzed using descriptive statistics.

RESULTS

During the study period, the annual incidence of diagnosed bipolar disorder increased 3.5-fold, and patients were diagnosed at a younger age. Mortality among patients with bipolar disorder was twice that of the general population. Compared to an age-standardized population, 30% fewer patients with bipolar disorder were available for work. Among the 40% employed, 64% reported sick leave (46% >100 days/year). Despite similar education levels, disposable income was lower compared to the general population. The most commonly preceding psychiatric diagnoses were depressive or anxiety disorders. Comparing the data for 2006 and 2009 demonstrated similar somatic comorbidity burdens and socioeconomic levels. There was also a decrease in dispensed antipsychotic medications and lithium, while antiepileptic prescriptions increased slightly. Antidepressant dispenses remained virtually unchanged.

CONCLUSIONS

In Sweden, the incidence and prevalence of diagnosed bipolar disorder have increased during the last 20 years. Compared to the general population, these patients had similar education levels, lower employment levels, less disposable income, more sick leave, and twice the mortality. A trend towards earlier diagnosis, more use of antidepressants, and less use of lithium was seen.

Comparing algorithms for deriving psychosis diagnoses from longitudinal administrative clinical records

PURPOSE

Registers derived from administrative datasets are valuable tools in psychosis research, but diagnostic accuracy can be problematic. We sought to compare the relative performance of four methods for assigning a single diagnosis from longitudinal administrative clinical records when compared with reference diagnoses.

METHODS

Diagnoses recorded in inpatient and community mental health records were compared to research diagnoses of psychotic disorders obtained from semi-structured clinical interviews for 289 persons. Diagnoses were derived from administrative datasets using four algorithms; 'At least one' diagnosis, 'Last' or most recent diagnosis, 'Modal' or most frequently occurring diagnosis, and 'Hierarchy' in which a diagnostic hierarchy was applied. Agreements between algorithm-based and reference diagnoses for overall presence/absence of psychosis and for specific diagnoses of schizophrenia, schizoaffective disorder, and affective psychosis were examined using estimated prevalence rates, overall agreement, ROC analysis, and kappa statistics.

RESULTS

For the presence/absence of psychosis, the most sensitive and least specific algorithm ('At least one' diagnosis) performed best. For schizophrenia, 'Modal' and 'Last' diagnoses had greatest agreement with reference diagnosis. For affective psychosis, 'Hierarchy' diagnosis performed best. Agreement between clinical and reference diagnoses was no better than chance for diagnoses of schizoaffective disorder. Overall agreement between administrative and reference diagnoses was modest, but may have been limited by the use of participants who had been screened for likely psychosis prior to assessment.

CONCLUSION

The choice of algorithm for extracting a psychosis diagnosis from administrative datasets may have a substantial impact on the accuracy of the diagnoses derived. An 'Any diagnosis' algorithm provides a sensitive measure for the presence of any psychosis, while 'Last diagnosis' is more accurate for specific diagnosis of schizophrenia and a hierarchical diagnosis is more accurate for affective psychosis.

Risk of mental illness in offspring of parents with schizophrenia, bipolar disorder, and major depressive disorder: a meta-analysis of family high-risk studies

OBJECTIVE

Offspring of parents with severe mental illness (SMI; schizophrenia, bipolar disorder, major depressive disorder) are at an increased risk of developing mental illness. We aimed to quantify the risk of mental disorders in offspring and determine whether increased risk extends beyond the disorder present in the parent.

METHOD

Meta-analyses of absolute and relative rates of mental disorders in offspring of parents with schizophrenia, bipolar disorder, or depression in family high-risk studies published by December 2012.

RESULTS

We included 33 studies with 3863 offspring of parents with SMI and 3158 control offspring. Offspring of parents with SMI had a 32% probability of developing SMI (95% CI: 24%-42%) by adulthood (age >20). This risk was more than twice that of control offspring (risk ratio [RR] 2.52; 95% CI 2.08-3.06, P < .001). High-risk offspring had a significantly increased rate of the disorder present in the parent (RR = 3.59; 95% CI: 2.57-5.02, P < .001) and of other types of SMI (RR = 1.92; 95% CI: 1.48-2.49, P < .001). The risk of mood disorders was significantly increased among offspring of parents with schizophrenia (RR = 1.62; 95% CI: 1.02-2.58; P = .042). The risk of schizophrenia was significantly increased in offspring of parents with bipolar disorder (RR = 6.42; 95% CI: 2.20-18.78, P < .001) but not among offspring of parents with depression (RR = 1.71; 95% CI: 0.19-15.16, P = .631).

CONCLUSIONS

Offspring of parents with SMI are at increased risk for a range of psychiatric disorders and one third of them may develop a SMI by early adulthood.

A comparison of Cohen's Kappa and Gwet's AC1 when calculating inter-rater reliability coefficients: a study conducted with personality disorder samples

Background

Rater agreement is important in clinical research, and Cohen’s Kappa is a widely used method for assessing inter-rater reliability; however, there are well documented statistical problems associated with the measure. In order to assess its utility, we evaluated it against Gwet’s AC1 and compared the results.

Methods

This study was carried out across 67 patients (56% males) aged 18 to 67, with a mean SD of 44.13 ± 12.68 years. Nine raters (7 psychiatrists, a psychiatry resident and a social worker) participated as interviewers, either for the first or the second interviews, which were held 4 to 6 weeks apart. The interviews were held in order to establish a personality disorder (PD) diagnosis using DSM-IV criteria. Cohen’s Kappa and Gwet’s AC1 were used and the level of agreement between raters was assessed in terms of a simple categorical diagnosis (i.e., the presence or absence of a disorder). Data were also compared with a previous analysis in order to evaluate the effects of trait prevalence.

Results

Gwet’s AC1 was shown to have higher inter-rater reliability coefficients for all the PD criteria, ranging from .752 to 1.000, whereas Cohen’s Kappa ranged from 0 to 1.00. Cohen’s Kappa values were high and close to the percentage of agreement when the prevalence was high, whereas Gwet’s AC1 values appeared not to change much with a change in prevalence, but remained close to the percentage of agreement. For example a Schizoid sample revealed a mean Cohen’s Kappa of .726 and a Gwet’s AC1of .853 , which fell within the different level of agreement according to criteria developed by Landis and Koch, and Altman and Fleiss.

Conclusions

Based on the different formulae used to calculate the level of chance-corrected agreement, Gwet’s AC1 was shown to provide a more stable inter-rater reliability coefficient than Cohen’s Kappa. It was also found to be less affected by prevalence and marginal probability than that of Cohen’s Kappa, and therefore should be considered for use with inter-rater reliability analysis.

Diagnosing comorbidity in psychiatric hospital: challenging the validity of administrative registers

BACKGROUND

This study will explore the validity of psychiatric diagnoses in administrative registers with special emphasis on comorbid anxiety and substance use disorders.

METHODS

All new patients admitted to psychiatric hospital in northern Norway during one year were asked to participate. Of 477 patients found eligible, 272 gave their informed consent. 250 patients (52%) with hospital diagnoses comprised the study sample. Expert diagnoses were given on the basis of a structured diagnostic interview (M.I.N.I.PLUS) together with retrospective checking of the records. The hospital diagnoses were blind to the expert. The agreement between the expert's and the clinicians' diagnoses was estimated using Cohen's kappa statistics.

RESULTS

The expert gave a mean of 3.4 diagnoses per patient, the clinicians gave 1.4. The agreement ranged from poor to good (schizophrenia). For anxiety disorders (F40-41) the agreement is poor (kappa = 0.12). While the expert gave an anxiety disorder diagnosis to 122 patients, the clinicians only gave it to 17. The agreement is fair concerning substance use disorders (F10-19) (kappa = 0.27). Only two out of 76 patients with concurrent anxiety and substance use disorders were identified by the clinicians.

CONCLUSIONS

The validity of administrative registers in psychiatry seems dubious for research purposes and even for administrative and clinical purposes. The diagnostic process in the clinic should be more structured and treatment guidelines should include comorbidity.