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Background: There has recently been an increased emphasis on patient empowerment and collaboration within their healthcare. However, there is widely a lack of clarity to the concept of empowerment and existing measurement tools lack uniformity, covering diverse domains and related concepts.

Objectives: This study aims to conduct a psychometric evaluation of the Swedish version of the Empowerment Scale– Making Decisions, focusing on its structural validity and reliability in assessing patient empowerment. This includes a detailed examination of the factor structure across two different contexts, psychiatric care (n = 211) and primary care (n = 210). We will compare several confirmatory factor analysis (CFA) models proposed in previous research to identify the best fit. If no models provide a good fit, we intend to suggest a new scale for further evaluation.

Method: The dimensionality of the scale was tested by comparing four CFA models, together with a one-factor solution, to identify the best fit for the two samples. Reliability measures were determined by coefficient Omega (ω) as well as Cronbach’s alpha (α).

Results: There was limited support for the one-factor solution in both samples, challenging the scale’s assumed unidimensionality (primary care sample: x2(350) = 1074, p <.001, CFI = 0.58, TLI = 0.54, RMSEA = 0.10 (90% CI: 0.09 − 0.11), SRMR = 0.11; psychiatric care sample: (x2(350) = 1307, p = < 0.001, CFI = 0.66, TLI = 0.63, RMSEA = 0.11 (90% CI:0.11;0.12), SRMR = 0.10). None of the previously suggested factor solutions demonstrated satisfactory fit. However, a three factor-solution entailed the less complexity and best model fit (primary care sample: (x2(270) = 503, p = < 0.001),CFI = 0.85, TLI = 0.84, RMSEA = 0.06 (90% CI 0.06;0.07), SRMR = 0.07; psychiatric care sample: (x2(270) = 622, p <.001), CFI = 0.87, TLI = 0.86, RMSEA = 0.08 (90% CI 0.07;0.09), SRMR = 0.07). Based on this, we continued with exploratory refinements of this solution and arrived at two adjusted three-factor models based on each sample. These two adjusted models displayed only slight differences, and in a last step we removed the items that differed between the samples to arrive at one solution appropriate for use in health care settings in general. As a result, an improved and shortened adaptation of the scale was put forward that included 18 items targeting the subscales Self-Esteem, Powerlessness and Activism. This solution remained relatively clear to the previously proposed solutions (primary care sample:(x2(131) = 240, p <.001), CFI = 0.91, TLI = 0.90, RMSEA = 0.06 (90% CI 0.05;0.08), SRMR = 0.07; psychiatric care sample: (x2(131) = 379, p <.001), CFI = 0.88, TLI = 0.86, RMSEA = 0.09 (90% CI 0.08;0.10), SRMR = 0.07; combined sample: (x2(131) = 432, p <.001), CFI = 0.91, TLI = 0.90, RMSEA = 0.07 (90% CI 0.07;0.08), SRMR = 0.06).

Conclusion: The results reinforce the difficulties in measuring empowerment given the complexity of this concept. The improved and shortened adaptation of the scale could potentially be used within health care settings to measure empowerment, but further research is needed to conceptualize and measure empowerment in patients with mental health problems. Given scarce support for the scale’s unidimensionallity, future research should explore using multiple instruments targeting different constructs to measure patient empowerment more comprehensively.

Objectives: Previous research has suggested antiviral properties for lithium, including potential effectiveness against COVID-19 in vitro. This study aimed to investigate the impact of lithium and other psychotropic drugs on the risks of mortality, hospitalization, and ICU admission due to COVID-19 among individuals with bipolar disorder. The primary objective was to assess whether lithium was beneficial in COVID-19-infection in a real-world population.

Methods: Retrospective register study using data from multiple Swedish patient registers, including 39,063 individuals in Sweden with bipolar disorder and prescribed mood stabilizers. Outcomes included COVID-19-associated death, hospitalization, and ICU admission between 11 March 2020 and 10 March 2021. Multivariate logistic regression adjusted for age, sex, and somatic comorbidities was conducted.

Results: Lithium were prescribed to 44.2 % of patients, either as mono- or combination therapy; other mood stabilizers were prescribed to 55.8 %. There were no significant associations between lithium and COVID-19-associated death, hospitalization, or ICU admission. Atypical antipsychotics were associated with increased odds ratios for COVID-19-associated death (OR 1.58 [95 % CI 1.01–2.47]), hospitalization (OR 1.80 [95 % CI 1.49–2.18]), and ICU admission (OR 2.25 [95 % CI 1.33–3.80]). Benzodiazepines were associated with a significant increase in COVID-19-associated death (OR 1.54 [95 % CI 1.01–2.35]) and hospitalization OR 1.26 [95 % CI 1.03–1.53]). In an ad hoc analysis, lithium monotherapy was, however, associated with reduced hospitalizations and ICU admissions.

Conclusions: Our findings weaken the hypothesis that lithium reduces the risk of severe events associated with COVID-19 infection in bipolar disorder.

With the impetus of Digital Mental Health Interventions (DMHIs), complex data can be leveraged to improve and personalize mental health care. However, most approaches rely on a very limited number of often costly features. Computer mouse trajectories can be unobtrusively and cost-efficiently gathered and seamlessly integrated into current baseline processes. Empirical evidence suggests that mouse movements hold information on user motivation and attention, both valuable aspects otherwise difficult to measure at scale. Further, mouse trajectories can already be collected on pre-treatment questionnaires, making them a promising candidate for early predictions informing treatment allocation. Therefore, this paper discusses how to collect and process mouse trajectory data on questionnaires in DMHIs. Covering different complexity levels, we combine hand-crafted features with non-sequential machine learning models, as well as spatiotemporal raw mouse data with state-of-the-art sequential neural networks. The data processing pipeline for the latter includes task-specific pre-processing to convert the variable length trajectories into a single prediction per user. As a feasibility study, we collected mouse trajectory data from 183 patients filling out a pre-intervention depression questionnaire. While the hand-crafted features slightly improve baseline predictions, the spatiotemporal models underperform. However, considering our small data set size, we propose more research to investigate the potential value of this novel and promising data type and provide the necessary steps and open-source code to do so.

Importance: In pregnancy, the benefits of lithium treatment for relapse prevention in psychiatric conditions must be weighed against potential teratogenic effects. Currently, there is a paucity of information on how and when lithium is used by pregnant women.

Objective: To examine lithium use in the perinatal period.

Design, Setting, and Participants: This cohort study used individual-level data of pregnancies from January 1, 2000, to December 31, 2021, in Australia, Denmark, Finland, Germany, Hong Kong, Iceland, Israel, New Zealand, Norway, South Korea, Sweden, Taiwan, the UK, and 2 cohorts in the US. Analyses were performed from September 1 to November 30, 2023.

Exposures: The prevalence of lithium use as the proportion of pregnancies with at least 1 prescription fill or prescription within 3 months before pregnancy until childbirth was estimated using a common protocol. Lithium use during pregnancy by trimester and in the 3 months before and after pregnancy was examined.

Main Outcomes and Measures: Comparison of prevalence between the first and last 3-year periods of available data.

Results: Among 21 659 454 pregnancies from all collaborating sites, the prevalence of lithium use ranged from 0.07 per 1000 pregnancies in Hong Kong to 1.56 per 1000 in the US publicly insured population. Lithium use increased per 1000 pregnancies in 10 populations (Australia [0.60 to 0.74], Denmark [0.09 to 0.51], Finland [0.10 to 0.29], Iceland [0.24 to 0.99], Israel [0.25 to 0.37], Norway [0.24 to 0.47], South Korea [0.30 to 0.44], Sweden [0.42 to 1.07], the UK [0.07 to 0.10], and Taiwan [0.15 to 0.19]), remained stable in 4 populations (Germany [0.17 to 0.16], Hong Kong [0.06 to 0.06], and the publicly [1.50 to 1.34] and commercially [0.38 to 0.36] insured US populations), and decreased in 1 population (New Zealand [0.54 to 0.39]). Use of lithium decreased with each trimester of pregnancy, while prevalence of postpartum use was similar to prepregnancy levels. The proportion of lithium use in the second trimester compared with the prepregnancy period ranged from 2% in South Korea to 80% in Denmark.

Conclusions and Relevance: Prevalence of lithium use in pregnant women over the past 2 decades varied markedly between populations. Patterns of use before, during, and after pregnancy suggest that many women discontinued lithium use during pregnancy and reinitiated treatment after childbirth, with large variations between countries. These findings underscore the need for internationally harmonized guidelines, specifically for psychiatric conditions among pregnant women that may benefit from lithium treatment.

Background: Individuals with severe mental disorder (SMD) have a higher risk of somatic comorbidity and mortality than the rest of the population. We set up a population-based study to assess whether individuals with SMD had a higher risk of death associated with a COVID-19 infection (COVID-19 associated death) than individuals without SMD.

Methods: Exploratory analysis with a cross-sectional design in the framework of a population-based register study covering the entire Swedish population. The Swedish Board for Health and Welfare (Socialstyrelsen) provided anonymized tabulated summary data for further analysis. We compared numbers of COVID-19 associated death in individuals with SMD (cases) and without SMD (controls). We calculated the odds ratio (OR) for the whole sample and by age group and four comorbidities, namely diabetes, cardiovascular disease, hypertension, chronic lung disease.

Results: The sample comprised of 7,923,859 individuals, 103,999 with SMD and 7,819,860 controls. There were 130 (0.1%) COVID-19 associated deaths in the SMD group and 4,945 (0.06%) in the control group, corresponding to an OR of 1.98 (CI 1.66-2.35; p < 0.001). The odds were 4-fold for the age groups between 60 and 79 years and 1.5-fold for cardiovascular diseases. Individuals with SMD without any of the risk factors under study had 3-fold odds of COVID-19 associated death.

Conclusion: Our preliminary results identify individuals with SMD as a further group at increased risk of COVID-19 associated death. In regard to comorbidities, future studies should explore the potential confounding or mediation role in the relationship between SMD and COVID-19 associated deaths.

 Individuals with severe mental disorders (SMDs) such as psychotic disorders, bipolar disorders, and single manic episodes have increased mortality associated with COVID-19 infection. We set up a population-based study to examine whether individuals with SMD also had a higher risk of hospitalization and death from other infectious conditions. Anonymized and summarized data from multiple Swedish patient registers covering the entire Swedish population were supplied by the Swedish National Board of Health and Welfare. The frequencies of hospitalizations and deaths associated with influenza/pneumonia and sepsis in individuals with SMD were compared with the rest of the population during 2018–2019. Possible contributing comorbidities were also examined, of which diabetes, cardiovascular disease, chronic lung disease, and hypertension were chosen. A total of 7,780,727 individuals were included in the study; 97,034 (1.2%) cases with SMD and 7,683,693 (98.8%) controls. Individuals with SMD had increased risk of death associated with influenza/pneumonia (OR = 2.06, 95% CI [1.87–2.27]) and sepsis (OR = 1.61, 95% CI [1.38–1.89]). They also had an increased risk of hospitalization associated with influenza/pneumonia (OR = 2.12, 95% CI [2.03–2.20]) and sepsis (OR = 1.89, 95% CI [1.75–2.03]). Our results identify a need for further evaluation of whether these individuals should be included in prioritized risk groups for vaccination against infectious diseases other than COVID-19.

Background: Postpartum affective disorders may be associated with dysregulation of gonadal steroids. We investigated peripheral levels of allopregnanolone and progesterone in a combined group of women with postpartum onset of severe depression and/or psychosis who, as previously reported, responded with rapid symptom remission during sublingual estradiol treatment. The aim was to assess differences in allopregnanolone and progesterone between patients and healthy controls at baseline, and hormonal changes during estradiol treatment and symptom remission in patients.

Methods: Allopregnanolone and progesterone in serum were analyzed with radioimmunoassay before and four weeks after initiation of sublingual estradiol treatment in ten women with postpartum depression and four women with postpartum psychosis (ICD-10). Twenty-eight healthy postpartum controls were included for baseline comparison.

Results: Allopregnanolone declined significantly during estradiol treatment while there was a trend for lower baseline allopregnanolone levels in patients compared with healthy postpartum controls. The ratio between allopregnanolone and progesterone was significantly lower in patients compared with controls and it remained unchanged after clinical recovery.

Limitations: This study is a secondary analysis of two estradiol treatment studies based on availability of samples for the analysis of allopregnanolone. Healthy controls were assessed earlier after delivery. Data on potential confounders (somatic health, breastfeeding, other medication) were not available.

Conclusions: These preliminary findings suggest that clinical recovery of severe postpartum depression and psychosis during estradiol treatment does not seem to depend on increasing levels of allopregnanolone. Differences in progesterone metabolism may constitute a risk factor for severe postnatal affective dysregulation.