AIMS:We provide an overview of nationwide environmental data available for Denmark and its linkage potentials to individual-level records with the aim of promoting research on the potential impact of the local surrounding environment on human health. BACKGROUND:Researchers in Denmark have unique opportunities for conducting large population-based studies treating the entire Danish population as one big, open and dynamic cohort based on nationally complete population and health registries. So far, most research in this area has utilised individual- and family-level information to study the clustering of disease in families, comorbidities, risk of, and prognosis after, disease onset, and social gradients in disease risk. Linking environmental data in time and space to individuals enables novel possibilities for studying the health effects of the social, built and physical environment. METHODS:We describe the possible linkage between individuals and their local surrounding environment to establish the exposome - that is, the total environmental exposure of an individual over their life course. CONCLUSIONS:The currently available nationwide longitudinal environmental data in Denmark constitutes a valuable and globally rare asset that can help explore the impact of the exposome on human health.

BACKGROUND:Schizophrenia spectrum disorders (SSD) comprise a group of related mental disorders, which share clinical features and common genetic disposition, but it is unknown if there is a diagnostic transition between these disorders over time. We aimed to study the incidence at the first SSD diagnosis between 2000 and 2018, defined as schizophrenia, schizotypal or schizoaffective disorder, and the early diagnostic transition between these disorders.METHODS:Using Danish nationwide healthcare registers, we identified all individuals aged 15-64 years during the period from 2000 to 2018 in Denmark and calculated the yearly incidence rates for the specific SSDs. We studied the diagnostic pathways from the first ever diagnosis of an SSD across the subsequent two treatment courses with an SSD diagnosis to evaluate early diagnostic stability, and explore potential changes over time.RESULTS:Among 21,538 patients, yearly incidence rates per 10,000 individuals were similar during the observation period for schizophrenia (2000: 1.8; 2018: 1.6), lower for schizoaffective disorder (2000: 0.3; 2018: 0.1) and increasing for schizotypal disorder (2000: 0.7; 2018: 1.3). Among the subgroup of 13,417 individuals with three separate treatment courses, early diagnostic stability was present among 89.9% which differed between the disorders (schizophrenia: 95.4%; schizotypal disorder: 78.0%; schizoaffective disorder: 80.5%). Among 1352 (10.1%) experiencing an early diagnostic transition, 398 (3.0%) were diagnosed with schizotypal disorder after a schizophrenia or schizoaffective disorder diagnosis.CONCLUSION:This study provides comprehensive incidence rates for SSDs. The majority of patients experienced early diagnostic stability, but sizable proportions of people with initial schizophrenia or schizoaffective disorder are subsequently diagnosed with schizotypal disorder.

The biological mechanisms through which many behavioural traits express and develop are still unknown. This is particularly the case for psychiatric disorders, where diagnosis, prognosis and treatment decisions are not generally informed by molecular biomarkers but by behavioural questionnaires and tests. To better understand these disorders, it is essential to link them to their causal biological mechanisms. A specific study design is required to study the association between psychiatric disorders and biomolecular traits, where genetic information and tissue biomolecule concentrations are ideally measured in the same samples. These types of studies are very expensive, both in time and cost. However, advances in “omic” technologies (proteomics, metabolomics, transcriptomics) have enabled the generation of thousands of publicly available biomolecule blood concentration genome-wide association studies (GWAS). Recent work by Xu et al. (2023) highlights the role of these synthetic multi-omics GWAS datasets for the genetic prediction of disease. For this study, we have projected polygenic scores (PGS) for 17,227 blood concentration biomolecular traits into the iPSYCH data, a Danish genetics case-cohort study with over 90,000 psychiatric cases and a population cohort of 50,000 individuals. For a broad range of psychiatric diagnoses, this study aims to 1) perform a phenome-wide scan of associations to synthetic multi-omics datasets and 2) quantify the effect of these biomolecular traits on disease prediction. First, we projected PGS for the blood concentration of 17,227 biomolecular traits (proteins, metabolites and RNA) on the 134,677 individuals from the iPSYCH data. All PGS weights were developed by Xu et al. 2023 and are publicly available at omicspred.org. Briefly, the PGS were trained using Bayesian Ridge regression on the INTERVAL cohort, which comprised 50,000 genotyped healthy blood donors. Their blood samples have been processed using 5 platforms, including 867 metabolites, 2,692 proteins and 13,668 genes from Illumina RNA-seq. For the phenome-wide scan, models included birth year, sex and the 20 first principal components as covariates and FDR-corrected P Preliminary results indicate that 747 biomolecular traits were genetically associated with psychiatric diagnoses in iPSYCH. Of these, 594 were proteomic profiles, 97 were metabolic profiles and 56 were RNA expressions. For example, the largest effect sizes were for the repressed expression of TMEM161B in ADHD, the increased expression of PPP6C in mental disorders with psychoactive substance abuse and the repressed expression of PANK2 in anxiety-related disorders. Our preliminary results show that PGS for multi-omic traits are closely linked to psychiatric disorder diagnosis and have potential to discover new biological pathways of interest. A limitation of our study is absence of SNPs in iPSYCH that have INTERVAL PGS weights. We will explore ways to impute missing SNPs to finalise our analyses.

Populations with common physical diseases – such as cardiovascular diseases, cancer and neurodegenerative disorders – experience substantially higher rates of major depressive disorder (MDD) than the general population. On the other hand, people living with MDD have a greater risk for many physical diseases. This high level of comorbidity is associated with worse outcomes, reduced adherence to treatment, increased mortality, and greater health care utilization and costs. Comorbidity can also result in a range of clinical challenges, such as a more complicated therapeutic alliance, issues pertaining to adaptive health behaviors, drug‐drug interactions and adverse events induced by medications used for physical and mental disorders. Potential explanations for the high prevalence of the above comorbidity involve shared genetic and biological pathways. These latter include inflammation, the gut microbiome, mitochondrial function and energy metabolism, hypothalamic‐pituitary‐adrenal axis dysregulation, and brain structure and function. Furthermore, MDD and physical diseases have in common several antecedents related to social factors (e.g., socioeconomic status), lifestyle variables (e.g., physical activity, diet, sleep), and stressful live events (e.g., childhood trauma). Pharmacotherapies and psychotherapies are effective treatments for comorbid MDD, and the introduction of lifestyle interventions as well as collaborative care models and digital technologies provide promising strategies for improving management. This paper aims to provide a detailed overview of the epidemiology of the comorbidity of MDD and specific physical diseases, including prevalence and bidirectional risk; of shared biological pathways potentially implicated in the pathogenesis of MDD and common physical diseases; of socio‐environmental factors that serve as both shared risk and protective factors; and of management of MDD and physical diseases, including prevention and treatment. We conclude with future directions and emerging research related to optimal care of people with comorbid MDD and physical diseases.

The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increased prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R2 increases of up to 9-fold for attention-deficit/hyperactivity disorder (ADHD) compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions, with up to 15-fold increases in prediction accuracy. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.

Major depression (MD) is a common mental disorder and a leading cause of disability worldwide. We conducted a GWAS meta-analysis of more than 1.3 million individuals, including 371,184 with MD, identifying 243 risk loci. Sixty-four loci are novel, including glutamate and GABA receptors that are targets for antidepressant drugs. Several biological pathways and components were enriched for genetic MD risk, implicating neuronal development and function. Intersection with functional genomics data prioritized likely causal genes and revealed novel enrichment of prenatal GABAergic neurons, astrocytes and oligodendrocyte lineages. We found MD to be highly polygenic, with around 11,700 variants explaining 90% of the SNP heritability. Bivariate Gaussian mixture modeling estimated that > 97% of risk variants for other psychiatric disorders (anxiety, schizophrenia, bipolar disorder and ADHD) are influencing MD risk when both concordant and discordant variants are considered, and nearly all MD risk variants influence educational attainment. Additionally, we demonstrated that MD genetic risk is associated with impaired complex cognition, including verbal reasoning, attention, abstraction and mental flexibility. Analyzing Danish nation-wide longitudinal data, we dissected the genetic and clinical heterogeneity, revealing distinct polygenic architectures across case subgroups of MD recurrency and psychiatric comorbidity and demonstrating two- to six-fold increases in absolute risks for developing comorbid psychiatric disorders among MD cases with the highest versus the lowest polygenic burden. The results deepen the understanding of the biology underlying MD and its progression and inform precision medicine approaches in MD.

IMPORTANCE People with psychotic disorders have an increased risk of vitamin D deficiency, which is evident during first-episode psychosis (FEP) and associated with unfavorable mental and physical health outcomes. OBJECTIVE To examine whether vitamin D supplementation contributes to improved clinical outcomes in FEP. DESIGN, SETTING, AND PARTICIPANTS This multisite, double-blind, placebo-controlled, parallel-group randomized clinical trial from the UK examined adults 18 to 65 years of age within 3 years of a first presentation with a functional psychotic disorder who had no contraindication to vitamin D supplementation. A total of 2136 patients were assessed for eligibility, 835 were approached, 686 declined participation or were excluded, 149 were randomized, and 104 were followed up at 6 months. The study recruited participants from January 19, 2016, to June 14, 2019, with the final follow-up (after the last dose) completed on December 20, 2019. INTERVENTIONS Monthly augmentation with 120 000 IU of cholecalciferol or placebo. MAIN OUTCOMES AND MEASURES The primary outcome measure was total Positive and Negative Syndrome Scale (PANSS) score at 6 months. Secondary outcomes included total PANSS score at 3 months; PANSS positive, negative, and general psychopathology subscale scores at 3 and 6 months; Global Assessment of Function scores (for symptoms and disability); Calgary Depression Scale score, waist circumference, body mass index, and glycated hemoglobin, total cholesterol, C-reactive protein, and vitamin D concentrations at 6 months; and a planned sensitivity analysis in those with insufficient vitamin D levels at baseline. RESULTS A total of 149 participants (mean [SD] age, 28.1 (8.5) years; 89 [59.7%] male; 65 [43.6%] Black or of other minoritized racial and ethnic group; 84 [56.4%] White [British, Irish, or of other White ethnicity]) were randomized. No differences were observed in the intention-to-treat analysis in the primary outcome, total PANSS score at 6 months (mean difference, 3.57; 95% CI, -1.11 to 8.25; P = .13), or the secondary outcomes at 3 and 6 months (PANSS positive subscore: mean difference, -0.98; 95% CI, -2.23 to 0.27 at 3 months; mean difference, 0.68; 95% CI, -0.69 to 1.99 at 6 months; PANSS negative subscore: mean difference, 0.68; 95% CI, -1.39 to 2.76 at 3 months; mean difference, 1.56; 95% CI, -0.31 to 3.44 at 6 months; and general psychopathology subscore: mean difference, -2.09; 95% CI, -4.36 to 0.18 at 3 months; mean difference, 1.31; 95% CI, -1.42 to 4.05 at 6 months). There also were no significant differences in the Global Assessment of Function symptom score (mean difference, 0.02; 95% CI, -4.60 to 4.94); Global Assessment of Function disability score (mean difference, -0.01; 95% CI, -5.25 to 5.23), or Calgary Depression Scale score (mean difference, -0.39; 95% CI, -2.05 to 1.26) at 6 months. Vitamin D levels were very low in the study group, especially in Black participants and those who identified as another minoritized racial and ethnic group, 57 of 61 (93.4%) of whom had insufficient vitamin D. The treatment was safe and led to a significant increase in 25-hydroxyvitamin D concentrations. CONCLUSIONS AND RELEVANCE In this randomized clinical trial, no association was found between vitamin D supplementation and mental health or metabolic outcomes at 6 months. Because so few patients with FEP were vitamin D replete, the results of this study suggest that this group would benefit from active consideration in future population health strategies.

We have learnt a great deal about the epidemiology of mental disorders over the last 30 years. This has been based on the development and application of a suite of complementary methods—the essential “tool kit” of epidemiology. Surveys based on population-based samples and register-based studies (usually based on help-seeking individuals) have enriched our understanding on how mental disorders impact on society. The content and format of these tools have evolved in the face of shifting diagnostic boundaries and the steady march of improved technology. In this concise perspective, we reflect on the nature and pace of recent changes within the field of psychiatric epidemiology. Mindful of how bad we are at predicting the future, we will also take the risk of speculating on the shape of things to come. While the definition of epidemiology has evolved over time, we will use a simple, easy-to-understand definition. Coggon, Rose and Barker open their (freely available, online) book with this definition: “Epidemiology is the study of how often diseases occur in different groups of people and why. Epidemiological information is used to plan and evaluate strategies to prevent illness and as a guide to the management of patients in whom disease has already developed” (p. 1). Epidemiology is a field of health science that assesses the frequency and rates of disorders and their risk factors (i.e., the “determinants” of health) over time and place (including the distribution of disorders and their risk factors). Within the taxonomy of frequency measures, we have measures of incidence (e.g., a rate that counts new cases per background population per unit of time) and prevalence (e.g., a count of current cases assessed over different periods of time such as 1 month, 1 year, or a lifetime). Around these measures, epidemiology explores the causes and consequences of mental disorders. Research related to identifying the causes of mental disorders operates within an unbounded search space—but progress has been made in the assessment of environmental exposures and genetic risk factors. With respect to the consequences of mental disorders, measures related to morbidity and mortality will continue to figure prominently. If the future follows the past, we will continue to have changes in the diagnostic criteria. While we can build algorithms for backward-looking crosswalks (e.g., DSM-5 to 4), we cannot future-proof studies against unforeseen changes in diagnostic criteria. Sometimes there are grounds to “freeze” items related to the measurement of symptoms and/or diagnoses—especially in panel studies over time (i.e., repeated measures). Here, fixing the items can provide insights into secular changes across time (i.e., cohort effects). Waves of individuals within a fixed age range can be compared across different decades. Recently, the Wellcome Trust and the National Institute of Mental Health have recommended a set of self-report measures for youth anxiety and depression. Having uniform criteria (at the diagnostic or symptom level) can aid between-study comparisons and meta-analyses. This recommendation has attracted some criticism and may hinder innovation. The number of studies based on administrative registers has increased exponentially over the past 2 decades. Population-based registers are a gold mine for epidemiologists; nowadays, it is possible to use de-identified data to link

Importance Polygenic risk scores (PRS) are predictors of the genetic susceptibilities of individuals to diseases. All individuals have DNA risk variants for all common diseases, but genetic susceptibility differences between people reflect the cumulative burden of these. Polygenic risk scores for an individual are calculated as weighted counts of thousands of risk variants that they carry, where the risk variants and their weights have been identified in genome-wide association studies. Here, we review the underlying basic science of PRS, providing a foundation for understanding the potential clinical utility and limitations of PRS. Observations Polygenic risk scores can be calculated for a wide range of diseases from a saliva or blood sample using genotyping technologies that are inexpensive. While genotyping only needs to be done once for each individual in their lifetime, the PRS can be recalculated as identification of risk variants improves. On their own, PRS will never be able to establish or definitively predict future diagnoses of common complex conditions because genetic factors only contribute part of the risk, and PRS will only ever capture part of the genetic contributions. Nonetheless, just as clinical medicine uses a multitude of other predictive measures, PRS either on their own or as part of multivariable predictive algorithms could play a role. Conclusions and Relevance Utility of PRS in clinical medicine and ethical issues related to their use should be evaluated in the context of realistic expectations of what PRS can and cannot deliver. For different diseases, PRS could have utility in community settings (stratification to better triage people into established screening programs) or could contribute to clinical decision-making for those presenting with symptoms but where formal diagnosis is unclear. In principle, PRS could contribute to treatment choices, but more data are needed to allow development of PRS in this context. This review provides a foundation for understanding the basic science as well as the potential clinical utility and limitations of polygenic risk scores.

Abstract Focus of Presentation Life Years Lost represent the reduction in life expectancy for a group of persons, e.g. those with a disease. Calculation of life expectancy among those with a disease is not straightforward for diseases that are not present at birth, and previous studies considered a fixed age-of-onset, e.g. 15 years. A recently-introduced method takes into account the real age-of-onset, and allows to decompose differences according to different causes of death. The aim of this communication is to introduce the Life Years Lost method and the associated R package ‘lillies’. Findings The method uses age at onset for each person with a disease as its starting point and estimates the expected residual lifetime at that age using age-specific mortality rates among the diseased. The number of excess Life Years Lost is estimated by comparing the expected residual lifetime with that of the reference population of same age. A single estimate of excess Life Years Lost is estimated as the average of all the person-specific Life Years Lost, and is interpreted as the average number of Life Years Lost that patients with a given disease experience in excess to those experienced by a reference population of same age. Conclusions/Implications The Life Years Lost method provides accurate estimates of reduced life expectancy in persons with a disease and allows to decompose the total reduction into specific causes of death. Key messages The implementation of the Life Years Lost method – which can be used with individual-level data (e.g. electronic healthcare records) or summary data – overcomes past limitations in the estimation of life expectancy for time-varying conditions.

IMPORTANCE Combining information on polygenic risk scores (PRSs) with other known risk factors could potentially improve the identification of risk of depression in the general population. However, to our knowledge, no study has estimated the association of PRS with the absolute risk of depression, and few have examined combinations of the PRS and other important risk factors, including parental history of psychiatric disorders and socioeconomic status (SES), in the identification of depression risk. OBJECTIVE To assess the individual and joint associations of PRS, parental history, and SES with relative and absolute risk of early-onset depression. DESIGN, SETTING, AND PARTICIPANTS This case-cohort study included participants from the iPSYCH2012 sample, a case-cohort sample of all singletons born in Denmark between May 1, 1981, and December 31, 2005. Hazard ratios (HRs) and absolute risks were estimated using Cox proportional hazards regression for case-cohort designs. EXPOSURES The PRS for depression; SES measured using maternal educational level, maternal marital status, and paternal employment; and parental history of psychiatric disorders (major depression, bipolar disorder, other mood or psychotic disorders, and other psychiatric diagnoses). MAIN OUTCOMES AND MEASURES Hospital-based diagnosis of depression from inpatient, outpatient, or emergency settings. RESULTS Participants included 17 098 patients with depression (11 748 [68.7%] female) and 18 582 (9429 [50.7%] male) individuals randomly selected from the base population. The PRS, parental history, and lower SES were all significantly associated with increased risk of depression, with HRs ranging from 1.32 (95% CI, 1.29-1.35) per 1-SD increase in PRS to 2.23 (95% CI, 1.81-2.64) for maternal history of mood or psychotic disorders. Fully adjusted models had similar effect sizes, suggesting that these risk factors do not confound one another. Absolute risk of depression by the age of 30 years differed substantially, depending on an individual's combination of risk factors, ranging from 1.0% (95% CI, 0.1%-2.0%) among men with high SES in the bottom 2% of the PRS distribution to 23.7% (95% CI, 16.6%-30.2%) among women in the top 2% of PRS distribution with a parental history of psychiatric disorders. CONCLUSIONS AND RELEVANCE This study suggests that current PRSs for depression are not more likely to be associated with major depressive disorder than are other known risk factors; however, they may be useful for the identification of risk in conjunction with other risk factors.

Abstract Background The Global Burden of Disease (GBD) study uses Years of Life Lost (YLLs) to quantify premature mortality. This is a useful metric from many perspectives, however because GBD acknowledges only a small number of mental disorders as causes of death (CoDs), the true impact of mental disorders on premature mortality is underestimated. Recently, methods have been introduced that compare people with a disorder to the general population by estimating Life Years Lost (LYLs). The aim of this study was to present register-based estimates of both YLLs and LYLs related to mental disorders. Methods We used nationwide registers to examine a cohort of all 6,989,627 people aged 0–94 years living in Denmark in 2000–2015. Using the GBD approved set of mental health-related CoDs (eating disorders, drug use disorders, alcohol use disorder and suicide), YLLs were estimated. In addition, we calculated all-cause and cause-specific differences in life expectancy after a mental disorder diagnosis as excess LYLs between those with a specific mental disorder and the age- and sex-matched general Danish population. The disorders of interest were alcohol use disorder, drug use disorders, schizophrenia, bipolar disorders, depressive disorders, anxiety disorders, eating disorders, personality disorders, developmental intellectual disability, autism spectrum disorders, ADHD and conduct disorder. Excess LYLs related to counts of comorbid mental disorders were also examined (i.e. those diagnosed with at least two, three or four disorders). Results Alcohol use disorder and suicide were the leading causes of YLLs (alcohol use disorder: Men 568.7 YLLs, women 155.5 YLLs per 100,000 person-years; suicide: Men 590.1 YLLs, women 202.3 YLLs per 100,000 person-years). However, all mental disorders were associated with shorter life expectancies using LYLs. Men and women diagnosed with any mental disorder had 11.22 (95% CI 11.09; 11.35) and 7.89 (95% CI 7.76; 8.01) years shorter life expectancies respectively, and the difference increased in those with comorbid mental disorders. Drug use disorders were associated with the largest excess LYLs (17.99 (95% CI 17.49; 18.53) in men and 15.29 (95% CI 14.70; 15.88) in women), however common disorders such as depressive disorders and anxiety disorders were also associated with substantive premature mortality (e.g. in men, 8.27 and 7.52 LYLs, respectively). Schizophrenia was associated with 13.80 (95% CI 13.47; 14.14) excess LYLs in men and 11.77 (95% CI 11.38; 12.13) in women. Discussion Register-based studies allow the calculation of precise individual YLLs and LYLs. The novel LYL metric seems to better capture the true impact of mental disorders on premature mortality and also facilitates the exploration of comorbidity and specific CoDs in those with mental disorders.

Abstract Background People with mental disorders have increased mortality rates and reduced life expectancies. We recently found that, compared to the general population, men and women with any mental disorder experienced 10 and 7 years, respectively, of life-years lost (LYLs), a new metric to estimate reduced life expectancy that takes into account the age of onset of the disorder. Our aim is to examine changes in mortality rate ratios (MRRs) and LYLs for both external and natural causes over twenty years for a comprehensive range of mental disorders, including schizophrenia spectrum disorder. Methods We conducted a cohort study comprising all 7,369,926 people living in Denmark in 1995–2015. Information on mental disorders and mortality was obtained from national registers. We looked at all mental disorders combined and specific groups of diagnoses as defined by the ICD-10 F-subchapters (substance use disorders, schizophrenia spectrum disorder, mood disorders, neurotic disorders, etc.) and classified causes of death into natural and external causes. We estimated MRRs using Poisson regression models, adjusting for sex and age and including an interaction term with calendar time. Differences in remaining life expectancy after disease diagnosis were estimated as excess LYLs (divided into LYLs due to natural and external causes of death) between those with each disorder and the general Danish population (matched on sex and age) for specific periods separately (1995–1999, 2000–2004, 2005–2009, 2010–2015). Results Over the period of observation, mortality rates decreased for those with any diagnosed mental disorder, as well as for those without a diagnosis. Despite these improvements, the MRRs between the two groups increased from 2.38 (95% CI: 2.32–2.44) in 1995 to 2.60 (95% CI: 2.55–2.65) in 2015. For external causes of death, MRRs decreased from 6.64 (95% CI: 6.15–7.17) to 5.27 (95% CI: 4.87–5.70), while MRRs for natural causes increased from 2.19 (95% CI: 2.14–2.25) to 2.52 (95% CI: 2.47–2.56). Remaining life expectancy after disease diagnosis increased 4.6 years from 32.0 to 36.6 years; however, remaining life expectancy increased also in the matched general population of same age and sex by 3.2 years (from 41.7 to 44.9 years). The life expectancy gap between the two periods was therefore shortened by 1.4 years; excess LYLs were 9.7 years in 1995–1999 (5.8/3.8 years due to natural/external causes) and 8.3 years in 2010–2015 (6.6/1.7 years due to natural/external causes). When looking at specific mental disorders, the life expectancy gap was reduced for mood disorders (0.8 years), neurotic disorders (1.7 years), and personality disorders (0.9 years); remained similar for schizophrenia spectrum disorder and substance use disorders; and increased for organic disorders (1.1 years). Discussion Mortality rates for people experiencing mental disorders decreased from 1995 to 2015. However, for natural causes of death, those with mental disorders did not reflect the benefits seen in the general population. Consequently, life lost due to natural causes increased. Overall, life expectancy increased an additional 1.4 years for those with mental disorders compared with the general population, thus reducing the gap. Nevertheless, for some disorders e.g. schizophrenia spectrum disorder and substance use disorders, life expectancy gap did not change. These findings support the hypothesis that service improvements have reduced mortality due to suicide and accidents, but similar benefits are not apparent in natural causes of death, which suggests that interventions related to promoting a healthier lifestyle and optimizing the general medical care of those with mental disorders warrants added investment.

IMPORTANCE Observational studies have reported an association between high maternal vitamin D levels and improved neurodevelopment in offspring, but no randomized clinical trial (RCT) has investigated these observations. OBJECTIVE To determine whether high-dose vitamin D supplementation during pregnancy improves offspring neurodevelopment from birth to age 6 years. DESIGN, SETTING, AND PARTICIPANTS This prespecified secondary analysis of a double-blinded, placebo-controlled RCT of high-dose vitamin D-3 supplementation vs standard dose during the third trimester of pregnancy was conducted in the unselected prospective mother-child birth cohort at a single-center research unit in Denmark as part of the Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC-2010). Participants included pregnant women; women with vitamin D intake greater than 600 IU/d or an endocrine, heart, or kidney disorder, and those who did not speak Danish fluently were excluded. Neurodevelopmental assessments for offspring of these women were performed at ages 0 to 6 years. Children born prematurely (gestational week <37), with low birth weight (<2500 g), or with a neurological disease affecting neurodevelopment were excluded. Data were analyzed from August 2019 to February 2020. INTERVENTIONS High-dose (ie, 2800 IU/d) vs standard dose (ie, 400 IU/d) vitamin D-3 supplementation from pregnancy week 24 until 1 week after birth. MAIN OUTCOMES AND MEASURES The primary outcome of interest was cognitive development assessed at 2.5 years using the Bayley Scales of Infant and Toddler Development. Other neurodevelopmental outcomes included age of motor milestone achievement (Denver Developmental Index and World Health Organization milestone registration), language development (MacArthur-Bates Communicative Development Inventories), general neurodevelopment at age 3 years (Ages and Stages Questionnaire), and emotional and behavioral problems at age 6 years (Strengths and Difficulties Questionnaire). RESULTS Among 623 women randomized, 315 were randomized to high-dose vitamin D-3 and 308 were randomized to standard dose placebo. A total of 551 children were evaluated from birth to age 6 years, (282 [51.2%] boys; 528 [95.8%] White), with 277 children in the high-dose vitamin D-3 group and 274 children in the standard dose group. There was no effect of the high-dose compared with standard dose of vitamin D-3 supplementation during pregnancy on offspring achievement of motor milestones (beta = 0.08 [95% CI, -0.26 to 0.43]; P = .64), cognitive development (score difference: 0.34 [95% CI, -1.32 to 1.99]; P = .70), general neurodevelopment (median [IQR] communication score: 50 [50-55] vs 50 [50-55]; P = .62), or emotional and behavioral problems (odds ratio, 0.76 [95% CI, 0.53 to 1.09]; P = .14). There was no effect on language development expressed by the word production at 1 year (median [IQR], 2 [0-6] words vs 3 [1-6] words; P = .16), although a decreased word production was apparent at 2 years in children in the high-dose vitamin D-3 group (median [IQR], 232 [113-346] words vs 253 [149-382.5] words; P = .02). CONCLUSIONS AND RELEVANCE In this prespecified secondary analysis of an RCT, maternal high-dose vitamin D-3 supplementation during the third trimester of pregnancy did not improve neurodevelopmental outcomes in the offspring during the first 6 years of life. These findings contribute essential information clarifying the effects of prenatal exposure to vitamin D on neurodevelopment in childhood.

Life expectancy at a given age is a summary measure of mortality rates present in a population (estimated as the area under the survival curve), and represents the average number of years an individual at that age is expected to live if current age-specific mortality rates apply now and in the future. A complementary metric is the number of Life Years Lost, which is used to measure the reduction in life expectancy for a specific group of persons, for example those diagnosed with a specific disease or condition (e.g. smoking). However, calculation of life expectancy among those with a specific disease is not straightforward for diseases that are not present at birth, and previous studies have considered a fixed age at onset of the disease, e.g. at age 15 or 20 years. In this paper, we present the R package lillies (freely available through the Comprehensive R Archive Network; CRAN) to guide the reader on how to implement a recently-introduced method to estimate excess Life Years Lost associated with a disease or condition that overcomes these limitations. In addition, we show how to decompose the total number of Life Years Lost into specific causes of death through a competing risks model, and how to calculate confidence intervals for the estimates using non-parametric bootstrap. We provide a description on how to use the method when the researcher has access to individual-level data (e.g. electronic healthcare and mortality records) and when only aggregated-level data are available.

IMPORTANCE Knowledge about the epidemiology of mental disorders in children and adolescents is essential for research and planning of health services. Surveys can provide prevalence rates, whereas population-based registers are instrumental to obtain precise estimates of incidence rates and risks. OBJECTIVE To estimate age- and sex-specific incidence rates and risks of being diagnosed with any mental disorder during childhood and adolescence. DESIGN This cohort study included all individuals born in Denmark from January 1, 1995, through December 31, 2016 (1.3 million), and followed up from birth until December 31, 2016, or the date of death, emigration, disappearance, or diagnosis of 1 of the mental disorders examined (14.4 million person-years of follow-up). Data were analyzed from September 14, 2018, through June 11, 2019. EXPOSURES Age and sex. MAIN OUTCOMES AND MEASURES Incidence rates and cumulative incidences of all mental disorders according to the ICD-10 Classification of Mental and Behavioral Disorders: Diagnostic Criteria for Research, diagnosed before 18 years of age during the study period. RESULTS A total of 99 926 individuals (15.01%; 95% CI, 14.98%-15.17%), including 41 350 girls (14.63%; 95% CI, 14.48%-14.77%) and 58 576 boys (15.51%; 95% CI, 15.18%-15.84%), were diagnosed with a mental disorder before 18 years of age. Anxiety disorder was the most common diagnosis in girls (7.85%; 95% CI, 7.74%-7.97%); attention-deficit/hyperactivity disorder (ADHD) was the most common in boys (5.90%; 95% CI, 5.76%-6.03%). Girls had a higher risk than boys of schizophrenia (0.76% [95% CI, 0.72%-0.80%] vs 0.48% [95% CI, 0.39%-0.59%]), obsessive-compulsive disorder (0.96% [95% CI, 0.92%-1.00%] vs 0.63% [95% CI, 0.56%-0.72%]), and mood disorders (2.54% [95% CI, 2.47%-2.61%] vs 1.10% [95% CI, 0.84%-1.21%]). Incidence peaked earlier in boys than girls in ADHD (8 vs 17 years of age), intellectual disability (5 vs 14 years of age), and other developmental disorders (5 vs 16 years of age). The overall risk of being diagnosed with a mental disorder before 6 years of age was 2.13% (95% CI, 2.11%-2.16%) and was higher in boys (2.78% [95% CI, 2.44%-3.15%]) than in girls (1.45% [95% CI, 1.42%-1.49%]). CONCLUSIONS AND RELEVANCE This nationwide population-based cohort study provides a first comprehensive assessment of the incidence and risks of mental disorders in childhood and adolescence. By 18 years of age, 15.01% of children and adolescents in this study were diagnosed with a mental disorder. The incidence of several neurodevelopmental disorders peaked in late adolescence in girls, suggesting possible delayed detection. The distinct signatures of the different mental disorders with respect to sex and age may have important implications for service planning and etiological research.

Abstract Background Comorbidity within mental disorders is common – individuals with one type of mental disorder are at increased risk of subsequently developing other types of disorders. Previous studies are usually restricted to temporally-ordered pairs of disorders. While more complex patterns of comorbidity have been described (e.g. internalizing and externalizing disorders), there is a lack of detailed information on the nature of the different sets of comorbid mental disorders. Additionally, mental disorders are associated with premature mortality, and people with two or more types of mental disorders have a shorter life expectancy compared to those with exactly one type of mental disorder. The aims of this study were to: (a) describe the prevalence and demographic correlates of combinations of mental disorders; and (b) estimate the excess mortality for each of these combinations. Methods We conducted a population-based cohort study including all 7,505,576 persons living in Denmark in 1995–2016. Information on mental disorders and mortality was obtained from national registers. First, we described the most common combinations of mental disorders defined by the ICD-10 F-subchapters (substance use disorders, schizophrenia spectrum disorder, mood disorders, neurotic disorders, etc.). Then, we investigated excess mortality using mortality rate ratios (MRRs) and differences in life expectancy after disease diagnosis compared to the general population of same sex and age. Results At the end of the 22-year observation, 6.2% individuals were diagnosed with exactly one type of disorder, 2.7% with exactly two, 1.1% with exactly three, and 0.5% with four or more types. The most prevalent mental disorders were neurotic disorders (4.6%) and mood disorders (3.8%), even when looking particularly at persons with a specific number of disorders (exactly one type, exactly two types, etc.). We observed 616 out of 1,024 possible sets of disorders, but the 52 most common sets (with at least 1,000 individuals each) represented 92.8% of all persons with diagnosed mental disorders. Mood and/or neurotic disorders, alone or in combination with other disorders, were present in 64.8% of individuals diagnosed with mental disorders. People with all combinations of mental disorders had higher mortality rates than those without any mental disorder diagnosis, with MRRs ranging from 1.10 (95% CI 0.67 – 1.84) for the two-disorder set of developmental-behavioral disorders to 5.97 (95% CI 5.52 – 6.45) for the three-disorder set of schizophrenia-neurotic-substance use disorders. Additionally, any combination of mental disorders was associated with shorter life expectancies compared to the general population, with estimates ranging from 5.06 years [95% CI 5.01 – 5.11] for the one-disorder set of organic disorders to 17.46 years [95% CI 16.86 – 18.03] for the three-disorder set of schizophrenia-personality-substance use disorders. Discussion Within those with mental disorders, approximately 2 out of 5 had two more types of mental disorders. Our study provides prevalence estimates of the most common sets of mental disorders – mood disorders (e.g. depression) and neurotic disorders (e.g. anxiety) commonly co-occur, and contribute to many different sets of comorbid mental disorders. The association between mental disorders comorbidity and mortality-related estimates revealed the prominent role of substance use disorders with respect to both elevated mortality rates and reduced life expectancies. Substance use disorders are relatively common, and these disorders often feature in sets of mental disorders. In light of the substantial contribution to premature mortality, efforts related to the ‘primary prevention of secondary comorbidity’ warrant added scrutiny.

Abstract Introduction and Aims The gender difference in alcohol use seems to have narrowed in the Nordic countries, but it is not clear to what extent this may have affected differences in levels of harm. We compared gender differences in all‐cause and cause‐specific alcohol‐attributed disease burden, as measured by disability‐adjusted life‐years (DALY), in four Nordic countries in 2000–2017, to find out if gender gaps in DALYs had narrowed. Design and Methods Alcohol‐attributed disease burden by DALYs per 100 000 population with 95% uncertainty intervals were extracted from the Global Burden of Disease database. Results In 2017, all‐cause DALYs in males varied between 2531 in Finland and 976 in Norway, and in females between 620 in Denmark and 270 in Norway. Finland had the largest gender differences and Norway the smallest, closely followed by Sweden. During 2000–2017, absolute gender differences in all‐cause DALYs declined by 31% in Denmark, 26% in Finland, 19% in Sweden and 18% in Norway. In Finland, this was driven by a larger relative decline in males than females; in Norway, it was due to increased burden in females. In Denmark, the burden in females declined slightly more than in males, in relative terms, while in Sweden the relative decline was similar in males and females. Discussion and Conclusions The gender gaps in harm narrowed to a different extent in the Nordic countries, with the differences driven by different conditions. Findings are informative about how inequality, policy and sociocultural differences affect levels of harm by gender.

The '16Up' study conducted at the QIMR Berghofer Medical Research Institute from January 2014 to December 2018 aimed to examine the physical and mental health of young Australian twins aged 16-18 years (N = 876; 371 twin pairs and 18 triplet sets). Measurements included online questionnaires covering physical and mental health as well as information and communication technology (ICT) use, actigraphy, sleep diaries and hair samples to determine cortisol concentrations. Study participants generally rated themselves as being in good physical (79%) and mental (73%) health and reported lower rates of psychological distress and exposure to alcohol, tobacco products or other substances than previously reported for this age group in the Australian population. Daily or near-daily online activity was almost universal among study participants, with no differences noted between males and females in terms of frequency or duration of internet access. Patterns of ICT use in this sample indicated that the respondents were more likely to use online information sources for researching physical health issues than for mental health or substance use issues, and that they generally reported partial levels of satisfaction with the mental health information they found online. This suggests that internet-based mental health resources can be readily accessed by adolescent Australians, and their computer literacy augurs well for future access to online health resources. In combination with other data collected as part of the ongoing Brisbane Longitudinal Twin Study, the 16Up project provides a valuable resource for the longitudinal investigation of genetic and environmental contributions to phenotypic variation in a variety of human traits.