The epidemiology of major depression among adults in Norway: an observational study on the concurrence between population surveys and registry data - a NCDNOR project.

BACKGROUND: Mental health problems, and major depression in particular, are important public health issues. Following trends in the prevalence of major depression is difficult because of the costs and complications of diagnostic interviews and general population self-report health surveys. Scandinavian countries, however, have several central, population-based health registries. We aimed to investigate how well these registries capture the epidemiology of major depression in the population. METHODS: In two Norwegian regional surveys of general population health, each repeated after 10 years, responders were asked to report depressive symptoms using the Hopkins Symptom Checklist (HSCL) or the Hospital Anxiety and Depression Scale (HADS). Data were linked to three central health registries capturing contact with primary care, specialist care and prescriptions for antidepressants, to investigate how well these registries reflected self-reported depressive symptoms. RESULTS: Most responders scored low on Hopkins Symptom Checklist (HSCL) and the Hospital Anxiety and Depression Scale (HADS), but 10% and 13%, respectively, scored above cut-off, with only minor changes between the two survey times. Females scored higher than males. Older people scored lower than younger, and a social gradient was visible. Around 12% of those who scored above the cut-off on either scale were recorded in the central health registries during the following year. This correlation was highest in primary care data, followed by prescription data and lowest in specialist care. Females were more often recorded in registries (p < 0.001), as were younger people (p < 0.001). CONCLUSIONS: There was a strong association between scores on screening for major depression in the general population surveys and being recorded in central health registries. There was a low sensitivity of these registries. and there was some variation in how sensitive the central health registries were in picking up depression, especially for males and older people. However, the stability of the measures over time suggests we may get an impression of the prevalence of major depression in the general population by using data from the central health registries. A combination of primary care data, prescription data and specialist care data have a higher sensitivity.

Educational attainment and mental health conditions: a within-sibship Mendelian randomization study.

Importance: Observational studies have demonstrated consistent protective effects of higher educational attainment (EA) on the risk of suffering mental health conditions (MHC). Determining whether these beneficial effects are causal is challenging given the potential role of dynastic effects and demographic factors (assortative mating and population structure) in this association. Objective: To evaluate to what extent the relationship between EA and various MHC is independent from dynastic effects and demographic factors. Design: Within-sibship Mendelian randomization (MR) study. Setting: One-sample MR analyses included participants' data from the Trøndelag Health Study (HUNT, Norway) and UK Biobank (United Kingdom). For two-sample MR analyses we used summary statistics from publicly available genome-wide-association-studies. Participants: 61 880 siblings (27 507 sibships). Exposure: Years of education. Main outcomes: Scores for symptoms of anxiety, depression and neuroticism using the Hospital Anxiety Depression Scale (HADS), the 7-item Generalized Anxiety Disorder Scale (GAD-7), the 9-item Patient Health Questionnaire (PHQ-9), and the Eysenck Personality Questionnaire, as well as self-reported consumption of psychotropic medication. Results: One standard deviation (SD) unit increase in years of education was associated with a lower symptom score of anxiety (-0.20 SD [95%CI: -0.26, -0.14]), depression (-0.11 SD [-0.43, 0.22]), neuroticism (-0.30 SD [-0.53, -0.06]), and lower odds of psychotropic medication consumption (OR: 0.60 [0.52, 0.69]). Estimates from the within-sibship MR analyses showed some attenuation, which however were suggestive of a causal association (anxiety: -0.17 SD [-0.33, -0.00]; depression: -0.18 SD [-1.26, 0.89]; neuroticism: -0.29 SD [-0.43, -0.15]); psychotropic medication consumption: OR, 0.52 [0.34, 0.82]). Conclusions and Relevance: Associations between EA and MHC in adulthood, although to some extend explained by dynastic effects and demographic factors, overall remain robust, indicative of a causal effect. However, larger studies are warranted to improve statistical power and further validate our conclusions.

Open-door policy versus treatment-as-usual in urban psychiatric inpatient wards: a pragmatic, randomised controlled, non-inferiority trial in Norway.

BACKGROUND: Open-door policy is a recommended framework to reduce coercion in psychiatric wards. However, existing observational data might not fully capture potential increases in harm and use of coercion associated with open-door policies. In this first randomised controlled trial, we compared coercive practices in open-door policy and treatment-as-usual wards in an urban hospital setting. We hypothesised that the open-door policy would be non-inferior to treatment-as-usual on the proportion of patients exposed to coercive measures. METHODS: We conducted a pragmatic, randomised controlled, non-inferiority trial comparing two open-door policy wards and three treatment-as-usual acute psychiatric wards at Lovisenberg Diaconal Hospital in Oslo, Norway. An exemption from the consent requirements enabled inclusion and random allocation of all patients admitted to these wards using an open list (2:3 ratio) administrated by a team of ward nurses. The primary outcome was the proportion of patient stays with one or more coercive measures, including involuntary medication, isolation or seclusion, and physical and mechanical restraints. The non-inferiority margin was set to 15%. Primary and safety analyses were assessed using the intention-to-treat population. The trial is registered with ISRCTN registry and is complete, ISRCTN16876467. FINDINGS: Between Feb 10, 2021, and Feb 1, 2022, we randomly assigned 556 patients to either open-door policy wards (n=245; mean age 41·6 [SD 14·5] years; 119 [49%] male; 126 [51%] female; and 180 [73%] admitted to the ward involuntarily) or treatment-as-usual wards (n=311; mean age 41·6 [4·3] years; 172 [55%] male and 138 [45%] female; 233 [75%] admitted involuntarily). Data on race and ethnicity were not collected. The open-door policy was non-inferior to treatment-as-usual on all outcomes: the proportion of patient stays with exposure to coercion was 65 (26·5%) in open-door policy wards and 104 (33·4%) in treatment-as-usual wards (risk difference 6·9%; 95% CI -0·7 to 14·5), with a similar trend for specific measures of coercion. Reported incidents of violence against staff were 0·15 per patient stay in open-door policy wards and 0·18 in treatment-as-usual wards. There were no suicides during the randomised controlled trial period. INTERPRETATION: The open-door policy could be safely implemented without increased use of coercive measures. Our findings underscore the need for more reliable and relevant randomised trials to investigate how a complex intervention, such as open-door policy, can be efficiently implemented across health-care systems and contexts. FUNDING: South-Eastern Norway Regional Health Authority and The Research Council of Norway.

COSGAP: COntainerized Statistical Genetics Analysis Pipelines.

Summary: The collection and analysis of sensitive data in large-scale consortia for statistical genetics is hampered by multiple challenges, due to their non-shareable nature. Time-consuming issues in installing software frequently arise due to different operating systems, software dependencies, and limited internet access. For federated analysis across sites, it can be challenging to resolve different problems, including format requirements, data wrangling, setting up analysis on high-performance computing (HPC) facilities, etc. Easier, more standardized, automated protocols and pipelines can be solutions to overcome these issues. We have developed one such solution for statistical genetic data analysis using software container technologies. This solution, named COSGAP: "COntainerized Statistical Genetics Analysis Pipelines," consists of already established software tools placed into Singularity containers, alongside corresponding code and instructions on how to perform statistical genetic analyses, such as genome-wide association studies, polygenic scoring, LD score regression, Gaussian Mixture Models, and gene-set analysis. Using provided helper scripts written in Python, users can obtain auto-generated scripts to conduct the desired analysis either on HPC facilities or on a personal computer. COSGAP is actively being applied by users from different countries and projects to conduct genetic data analyses without spending much effort on software installation, converting data formats, and other technical requirements. Availability and implementation: COSGAP is freely available on GitHub (https://github.com/comorment/containers) under the GPLv3 license.