Impact of the early COVID-19 pandemic on adult mental health-related dispensed medications, hospitalizations and specialist outpatient visits in Norway and Sweden: Interrupted time series analysis.

AIMS: Norway and Sweden had different early pandemic responses that may have impacted mental health management. The aim was to assess the impact of the early COVID-19 pandemic on mental health-related care. METHODS: We used national registries in Norway and Sweden (1 January 2018-31 December 2020) to define 2 cohorts: (i) general adult population; and (ii) mental health adult population. Interrupted times series regression analyses evaluated step and slope changes compared to prepandemic levels for monthly rates of medications (antidepressants, antipsychotics, anxiolytics, hypnotics/sedatives, lithium, opioid analgesics, psychostimulants), hospitalizations (for anxiety, bipolar, depressive/mood, eating and schizophrenia/delusional disorders) and specialist outpatient visits. RESULTS: In Norway, immediate reductions occurred in the general population for medications (-12% antidepressants to -7% hypnotics/sedatives) except for antipsychotics; and hospitalizations (-33% anxiety disorders to -17% bipolar disorders). Increasing slope change occurred for all medications except psychostimulants (+1.1%/month hypnotics/sedatives to +1.7%/month antidepressants); and hospitalization for anxiety disorders (+5.5%/month), depressive/mood disorders (+1.7%/month) and schizophrenia/delusional disorders (+2%/month). In Sweden, immediate reductions occurred for antidepressants (-7%) and opioids (-10%) and depressive/mood disorder hospitalizations (-11%) only with increasing slope change in psychostimulant prescribing of (0.9%/month). In contrast to Norway, increasing slope changes occurred in specialist outpatient visits for depressive/mood disorders, eating disorders and schizophrenia/delusional disorders (+1.5, +1.9 and +2.3%/month, respectively). Similar changes occurred in the pre-existing mental health cohorts. CONCLUSION: Differences in early COVID-19 policy response may have contributed to differences in adult mental healthcare provision in Norway and Sweden.

The structure of psychiatric comorbidity without selection and assortative mating.

The widespread comorbidity observed across psychiatric disorders may be the result of processes such as assortative mating, gene-environment correlation, or selection into population studies. Between-family analyses of comorbidity are subject to these sources of bias, whereas within-family analyses are not. Because of Mendelian inheritance, alleles are randomly assigned within families, conditional on parental alleles. We exploit this variation to compare the structure of comorbidity across broad psychiatric polygenic scores when calculated either between-family (child polygenic scores) or within-family (child polygenic scores regressed on parental polygenic scores) in over 25,000 genotyped parent-offspring trios from the Norwegian Mother Father and Child Cohort study (MoBa). We fitted a series of factor models to the between- and within-family data, which consisted of a single genetic p-factor and a varying number of uncorrelated subfactors. The best-fitting model was identical for between- and within-family analyses and included three subfactors capturing variants associated with neurodevelopment, psychosis, and constraint, in addition to the genetic p-factor. Partner genetic correlations, indicating assortative mating, were not present for the genetic p-factor, but were substantial for the psychosis (b = 0.081;95% CI [0.038,0.124]) and constraint (b = 0.257;95% CI [0.075,0.439]) subfactors. When average factor levels for MoBa mothers and fathers were compared to a population mean of zero we found evidence of sex-specific participation bias, which has implications for the generalizability of findings from cohort studies. Our results demonstrate the power of the within-family design for better understanding the mechanisms driving psychiatric comorbidity and their consequences on population health.

Stressful life events exhibit complex patterns of associations with depressive symptoms in two population-based samples using network analysis.

BACKGROUND: Stressful life events (SLEs) constitute key risk factors for depression. However, previous studies examining associations between SLEs and depression have been limited by focusing on single events, combining events into broad categories, and/or ignoring interrelationships between events in statistical analyses. Network analysis comprises a set of statistical methods well-suited for assessing relationships between multiple variables and can help surpass several limitations of previous studies. METHODS: We applied network analysis using mixed graphical models combining two large-scale population-based samples and >34,600 randomly sampled adults to investigate the associations between SLEs and current depressive symptoms in the general population. RESULTS: Numerous SLEs were uniquely associated with specific symptoms. Strong pairwise links were observed between SLEs during the past year and individual symptoms, e.g., between having experienced illness or injury and sleeping problems, having been degraded or humiliated and feeling blue, and between financial problems and hopelessness and being worried and anxious. Several SLEs, such as financial problems, sexual abuse, and having been degraded or humiliated, were associated with symptoms across more than one timepoint. More recent SLEs were generally more strongly associated with depressive symptoms. Several life events were strongly interrelated, such as multiple forms of abuse, and financial problems, unemployment, divorce, and serious illness or injury. LIMITATIONS: Limitations include a retrospective SLE measure, cross-sectional data, a brief self-report measure of depressive symptoms, and possible attrition bias in the sample. CONCLUSIONS: Our findings may have implications for public health efforts seeking to improve population mental health.

Mental health and environmental factors in adults: A population-based network analysis.

Few studies have assessed the multifactorial nature of environmental influences on population mental health. In this large-scale, population-based study of adults, we applied network analysis to study the relationship between environmental factors and symptoms of depression, anxiety, and well-being. We estimated networks with overall mental health nodes and individual symptoms to assess both broad and fine-grained associations between environmental factors and mental health. Finally, we conducted an out-of-sample replication in an independent large-scale sample to assess the robustness of our results. Across 31,000 adults randomly sampled from the Norwegian population, we identified associations between numerous environmental characteristics and mental health. Recent discrimination and unsupportive social environments were strongly associated with lower population well-being and higher levels of mental illness symptoms, respectively. The most strongly connected variables in the networks were environmental factors, including perceived problems with crime, violence, or vandalism in the residential area, worrying about violence or threats when outside, and problems with noise or contamination at home. Substantial variation in population mental health was explained by environmental factors included in the networks. Replicability of the results was excellent and suggestive of strong robustness of the results across samples. Our findings are indicative of the importance of environmental factors, such as the social environment, housing satisfaction, and residential area characteristics, for multiple aspects of population mental health. We identify several environmental factors that represent potentially useful targets for future studies and public health efforts seeking to improve mental health in the general population. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The structure of well-being: a single underlying factor with genetic and environmental influences.

PURPOSE: The structure of well-being has been debated for millennia. Dominant conceptualisations, such as the hedonic and eudaimonic models, emphasise different constituents of the well-being construct. Some previous studies have suggested that the underlying structure of well-being may consist of one or a few general well-being factors. We conducted three studies to advance knowledge on the structure of well-being comprising more than 21,500 individuals, including a genetically informative twin sample. METHODS: In Study 1, we used hierarchical exploratory factor analysis to identify well-being factors in a population-based sample of Norwegian adults. In Study 2, we used confirmatory factor analysis to examine the model fit of the identified factor model in an independent sample. In Study 3, we used biometric models to examine genetic and environmental influences on general well-being factors. RESULTS: We identified six well-being factors which all loaded on a single higher-order factor. This higher-order factor may represent a general "happiness factor", i.e. an h-factor, akin to the p-factor in psychopathology research. The identified factor model had excellent fit in an independent sample. All well-being factors showed moderate genetic and substantial non-shared environmental influence, with heritability estimates ranging from 26% to 40%. Heritability was highest for the higher-order general happiness factor. CONCLUSION: Our findings yield novel insights into the structure of well-being and genetic and environmental influences on general well-being factors, with implications for well-being and mental health research, including genetically informative studies.

Maternal depression and the polygenic p factor: A family perspective on direct and indirect effects.

Within-family studies typically assess indirect genetic effects of parents on children, however social support theory points to a critical role of partners and children on women's depression. To address this research gap and account for the high heterogeneity of depression, we calculated a general psychiatric factor using eleven major psychiatric polygenic scores (polygenic p), in up to 25,000 parent-offspring trios from the Norwegian Mother, Father and Child Cohort Study (MoBa). Multilevel modeling of trio polygenic p was used to distinguish direct and indirect genetic effects on mothers depression during pregnancy (gestational age 17 and 30 weeks), infancy (6 months, 18 months) and early childhood (3 years, 5 years, and 8 years). We found mothers polygenic p predicts their depression symptoms (b = 0.092; 95 % CI [0.087,0.098]), outperforming prediction using a single major depressive disorder polygenic score (b = 0.070, 95 % CI [0.066,0.075]). Jointly modeling trio polygenic p revealed indirect genetic effects of fathers (b = 0.022, 95 % CI [0.014,0.030]) and children (b = 0.021, 95 % CI [0.010,0.037]) on mothers' depression. Our results support the generalizability of polygenic effects across mental health and highlight the role of close family members on women's depression.

The globalizability of temporal discounting.

Economic inequality is associated with preferences for smaller, immediate gains over larger, delayed ones. Such temporal discounting may feed into rising global inequality, yet it is unclear whether it is a function of choice preferences or norms, or rather the absence of sufficient resources for immediate needs. It is also not clear whether these reflect true differences in choice patterns between income groups. We tested temporal discounting and five intertemporal choice anomalies using local currencies and value standards in 61 countries (N = 13,629). Across a diverse sample, we found consistent, robust rates of choice anomalies. Lower-income groups were not significantly different, but economic inequality and broader financial circumstances were clearly correlated with population choice patterns.

Norwegian judges' knowledge of factors affecting eyewitness testimony: a 12-year follow-up.

Eyewitness evidence often plays a critical role in decisions made in the criminal justice system. To evaluate eyewitness testimony accurately, judges must be aware of factors that can contaminate this type of evidence. In 2008, a survey of judges in Norway revealed a lack of awareness of several factors that affect eyewitness testimony. In the current study, a survey was administered to Norwegian judges (N=98) to evaluate their knowledge of factors that affect eyewitness testimony. Results showed that judges' overall knowledge scores were similar to those reported in 2008, but substantial increases and decreases in knowledge were observed for specific factors. Additional analyses indicated that increased uncertainty regarding some eyewitness factors led to a decline in accuracy when compared to responses observed in 2008. The current study provides an updated assessment of judges' knowledge of eyewitness factors and highlights the need for more comprehensive training for judges regarding these factors.