Associations of socioenvironmental features like urbanicity and neighborhood deprivation with psychosis are well-established. An enduring question, however, is whether these associations are causal. Genetic confounding could occur due to downward mobility of individuals at high genetic risk for psychiatric problems into disadvantaged environments.

We examined correlations of five indices of genetic risk [polygenic risk scores (PRS) for schizophrenia and depression, maternal psychotic symptoms, family psychiatric history, and zygosity-based latent genetic risk] with multiple area-, neighborhood-, and family-level risks during upbringing. Data were from the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally-representative cohort of 2232 British twins born in 1994–1995 and followed to age 18 (93% retention). Socioenvironmental risks included urbanicity, air pollution, neighborhood deprivation, neighborhood crime, neighborhood disorder, social cohesion, residential mobility, family poverty, and a cumulative environmental risk scale. At age 18, participants were privately interviewed about psychotic experiences.

Higher genetic risk on all indices was associated with riskier environments during upbringing. For example, participants with higher schizophrenia PRS (OR = 1.19, 95% CI = 1.06–1.33), depression PRS (OR = 1.20, 95% CI = 1.08–1.34), family history (OR = 1.25, 95% CI = 1.11–1.40), and latent genetic risk (OR = 1.21, 95% CI = 1.07–1.38) had accumulated more socioenvironmental risks for schizophrenia by age 18. However, associations between socioenvironmental risks and psychotic experiences mostly remained significant after covariate adjustment for genetic risk.

Genetic risk is correlated with socioenvironmental risk for schizophrenia during upbringing, but the associations between socioenvironmental risk and adolescent psychotic experiences appear, at present, to exist above and beyond this gene-environment correlation.

Few personalised medicine investigations have been conducted for mental health. We aimed to generate and validate a risk tool that predicts adult attention-deficit/hyperactivity disorder (ADHD).

Using logistic regression models, we generated a risk tool in a representative population cohort (ALSPAC – UK, 5113 participants, followed from birth to age 17) using childhood clinical and sociodemographic data with internal validation. Predictors included sex, socioeconomic status, single-parent family, ADHD symptoms, comorbid disruptive disorders, childhood maltreatment, ADHD symptoms, depressive symptoms, mother's depression and intelligence quotient. The outcome was defined as a categorical diagnosis of ADHD in young adulthood without requiring age at onset criteria. We also tested Machine Learning approaches for developing the risk models: Random Forest, Stochastic Gradient Boosting and Artificial Neural Network. The risk tool was externally validated in the E-Risk cohort (UK, 2040 participants, birth to age 18), the 1993 Pelotas Birth Cohort (Brazil, 3911 participants, birth to age 18) and the MTA clinical sample (USA, 476 children with ADHD and 241 controls followed for 16 years from a minimum of 8 and a maximum of 26 years old).

The overall prevalence of adult ADHD ranged from 8.1 to 12% in the population-based samples, and was 28.6% in the clinical sample. The internal performance of the model in the generating sample was good, with an area under the curve (AUC) for predicting adult ADHD of 0.82 (95% confidence interval (CI) 0.79–0.83). Calibration plots showed good agreement between predicted and observed event frequencies from 0 to 60% probability. In the UK birth cohort test sample, the AUC was 0.75 (95% CI 0.71–0.78). In the Brazilian birth cohort test sample, the AUC was significantly lower –0.57 (95% CI 0.54–0.60). In the clinical trial test sample, the AUC was 0.76 (95% CI 0.73–0.80). The risk model did not predict adult anxiety or major depressive disorder. Machine Learning approaches did not outperform logistic regression models. An open-source and free risk calculator was generated for clinical use and is available online at https://ufrgs.br/prodah/adhd-calculator/.

The risk tool based on childhood characteristics specifically predicts adult ADHD in European and North-American population-based and clinical samples with comparable discrimination to commonly used clinical tools in internal medicine and higher than most previous attempts for mental and neurological disorders. However, its use in middle-income settings requires caution.

Feelings of loneliness are common among young adults, and are hypothesized to impair the quality of sleep. In the present study, we tested associations between loneliness and sleep quality in a nationally representative sample of young adults. Further, based on the hypothesis that sleep problems in lonely individuals are driven by increased vigilance for threat, we tested whether past exposure to violence exacerbated this association.

Data were drawn from the Environmental Risk (E-Risk) Longitudinal Twin Study, a birth cohort of 2232 twins born in England and Wales in 1994 and 1995. We measured loneliness using items from the UCLA Loneliness Scale, and sleep quality using the Pittsburgh Sleep Quality Index. We controlled for covariates including social isolation, psychopathology, employment status and being a parent of an infant. We examined twin differences to control for unmeasured genetic and family environment factors.

Feelings of loneliness were associated with worse overall sleep quality. Loneliness was associated specifically with subjective sleep quality and daytime dysfunction. These associations were robust to controls for covariates. Among monozygotic twins, within-twin pair differences in loneliness were significantly associated with within-pair differences in sleep quality, indicating an association independent of unmeasured familial influences. The association between loneliness and sleep quality was exacerbated among individuals exposed to violence victimization in adolescence or maltreatment in childhood.

Loneliness is robustly associated with poorer sleep quality in young people, underscoring the importance of early interventions to mitigate the long-term outcomes of loneliness. Special care should be directed towards individuals who have experienced victimization.

Children and adolescents make up almost a quarter of the world's population with 85% living in low- and middle-income countries (LMICs). Globally, mental (and substance use) disorders are the leading cause of disability in young people; however, the representativeness or ‘coverage’ of the prevalence data is unknown. Coverage refers to the proportion of the target population (ages 5–17 years) represented by the available data.

Prevalence data for conduct disorder (CD), attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders (ASDs), eating disorders (EDs), depression, and anxiety disorders were sourced from systematic reviews conducted for the Global Burden of Disease Study 2010 (GBD 2010) and 2013 (GBD 2013). For each study, the location proportion was multiplied by the age proportion to give study coverage. Location proportion was calculated by dividing the total study location population by the total country population. Age proportion was calculated by dividing the population of the country aged within the age range of the study sample by the country population aged 5–17 years. If a study only sampled one sex, study coverage was halved. Coverage across studies was then summed for each country to give coverage by country. This method was repeated at the region and global level, and separately for GBD 2013 and GBD 2010.

Mean global coverage of prevalence data for mental disorders in ages 5–17 years was 6.7% (CD: 5.0%, ADHD: 5.5%, ASDs: 16.1%, EDs: 4.4%, depression: 6.2%, anxiety: 3.2%). Of 187 countries, 124 had no data for any disorder. Many LMICs were poorly represented in the available prevalence data, for example, no region in sub-Saharan Africa had more than 2% coverage for any disorder. While coverage increased between GBD 2010 and GBD 2013, this differed greatly between disorders and few new countries provided data.

The global coverage of prevalence data for mental disorders in children and adolescents is limited. Practical methodology must be developed and epidemiological surveys funded to provide representative prevalence estimates so as to inform appropriate resource allocation and support policies that address mental health needs of children and adolescents.

To our knowledge, there are no universal screening tools for substance dependence that (1) were developed using a population-based sample, (2) estimate total risk briefly and inexpensively by incorporating a relatively small number of well-established risk factors, and (3) aggregate risk factors using a simple algorithm. We created a universal screening tool that incorporates these features to identify adolescents at risk for persistent substance dependence in adulthood.

Participants were members of a representative cohort of 1037 individuals born in Dunedin, New Zealand in 1972–1973 and followed prospectively to age 38 years, with 95% retention. We assessed a small set of childhood and adolescent risk factors: family history of substance dependence, childhood psychopathology (conduct disorder, depression), early exposure to substances, frequent substance use in adolescence, sex, and childhood socioeconomic status. We defined the outcome (persistent substance dependence in adulthood) as dependence on one or more of alcohol, tobacco, cannabis, or hard drugs at ⩾3 assessment ages: 21, 26, 32, and 38 years.

A cumulative risk index, a simple sum of nine childhood and adolescent risk factors, predicted persistent substance dependence in adulthood with considerable accuracy (AUC = 0.80).

A cumulative risk score can accurately predict which adolescents in the general population will develop persistent substance dependence in adulthood.

Mental and substance use disorders are common and often persistent, with many emerging in early life. Compared to adult mental and substance use disorders, the global burden attributable to these disorders in children and youth has received relatively little attention.

Data from the Global Burden of Disease Study 2010 was used to investigate the burden of mental and substance disorders in children and youth aged 0–24 years. Burden was estimated in terms of disability-adjusted life years (DALYs), derived from the sum of years lived with disability (YLDs) and years of life lost (YLLs).

Globally, mental and substance use disorders are the leading cause of disability in children and youth, accounting for a quarter of all YLDs (54.2 million). In terms of DALYs, they ranked 6th with 55.5 million DALYs (5.7%) and rose to 5th when mortality burden of suicide was reattributed. While mental and substance use disorders were the leading cause of DALYs in high-income countries (HICs), they ranked 7th in low- and middle-income countries (LMICs) due to mortality attributable to infectious diseases.

Mental and substance use disorders are significant contributors to disease burden in children and youth across the globe. As reproductive health and the management of infectious diseases improves in LMICs, the proportion of disease burden in children and youth attributable to mental and substance use disorders will increase, necessitating a realignment of health services in these countries.

We examine prospectively the influence of two separate but potentially inter-related factors in the etiology of post-traumatic stress disorder (PTSD): childhood maltreatment as conferring a susceptibility to the PTSD response to adult trauma and juvenile disorders as precursors of adult PTSD.

The Dunedin Multidisciplinary Health and Development Study (DMHDS) is a birth cohort (n = 1037) from the general population of New Zealand's South Island, with multiple assessments up to age 38 years. DSM-IV PTSD was assessed among participants exposed to trauma at ages 26–38. Complete data were available on 928 participants.

Severe maltreatment in the first decade of life, experienced by 8.5% of the sample, was associated significantly with the risk of PTSD among those exposed to adult trauma [odds ratio (OR) 2.64, 95% confidence interval (CI) 1.16–6.01], compared to no maltreatment. Moderate maltreatment, experienced by 27.2%, was not associated significantly with that risk (OR 1.55, 95% CI 0.85–2.85). However, the two estimates did not differ significantly from one another. Juvenile disorders (ages 11–15), experienced by 35% of the sample, independent of childhood maltreatment, were associated significantly with the risk of PTSD response to adult trauma (OR 2.35, 95% CI 1.32–4.18).

Severe maltreatment is associated with risk of PTSD response to adult trauma, compared to no maltreatment, and juvenile disorders, independent of earlier maltreatment, are associated with that risk. The role of moderate maltreatment remains unresolved. Larger longitudinal studies are needed to assess the impact of moderate maltreatment, experienced by the majority of adult trauma victims with a history of maltreatment.

Childhood psychotic symptoms have been used as a subclinical phenotype of schizophrenia in etiological research and as a target for preventative interventions. However, recent studies have cast doubt on the specificity of these symptoms for schizophrenia, suggesting alternative outcomes such as anxiety and depression. Using a prospective longitudinal birth cohort we investigated whether childhood psychotic symptoms predicted a diagnosis of schizophrenia or other psychiatric disorders by 38 years of age.

Participants were drawn from a birth cohort of 1037 children from Dunedin, New Zealand, who were followed prospectively to 38 years of age (96% retention rate). Structured clinical interviews were administered at age 11 to assess psychotic symptoms and study members underwent psychiatric assessments at ages 18, 21, 26, 32 and 38 to obtain past-year DSM-III-R/IV diagnoses and self-reports of attempted suicides since adolescence.

Psychotic symptoms at age 11 predicted elevated rates of research diagnoses of schizophrenia and post-traumatic stress disorder (PTSD) and also suicide attempts by age 38, even when controlling for gender, social class and childhood psychopathology. No significant associations were found for persistent anxiety, persistent depression, mania or persistent substance dependence. Very few of the children presenting with age-11 psychotic symptoms were free from disorder by age 38.

Childhood psychotic symptoms were not specific to a diagnosis of schizophrenia in adulthood and thus future studies of early symptoms should be cautious in extrapolating findings only to this clinical disorder. However, these symptoms may be useful as a marker of adult mental health problems more broadly.

Very few longitudinal studies have evaluated prospective neurodevelopmental and psychosocial risk factors for obsessive–compulsive disorder (OCD). Furthermore, despite the heterogeneous nature of OCD, no research has examined risk factors for its primary symptom dimensions, such as contamination/washing.

Potential risk factors for symptoms or diagnosis of OCD in adulthood and for specific adult obsessive–compulsive (OC) symptom dimensions were examined in the Dunedin Study birth cohort. The presence of obsessions and compulsions and psychological disorders was assessed using the Diagnostic Interview Schedule (DIS) at ages 26 and 32 years. Individuals with a diagnosis of OCD at either age (n=36) were compared to both a healthy control group (n=613) and an anxious control group (n=310) to determine whether associations between a risk factor and an OCD diagnosis were specific.

Childhood neurodevelopmental, behavioral, personality and environmental risk factors were associated with a diagnosis of OCD and with OC symptoms at ages 26 and 32. Social isolation, retrospectively reported physical abuse and negative emotionality were specific predictors of an adult OCD diagnosis. Of note, most risk factors were associated with OC symptoms in adulthood and several risk factors predicted specific OCD dimensions. Perinatal insults were linked to increased risk for symmetry/ordering and shameful thoughts dimensions, whereas poor childhood motor skills predicted the harm/checking dimension. Difficult temperament, internalizing symptoms and conduct problems in childhood also predicted specific symptom dimensions and lower IQ non-specifically predicted increased risk for most dimensions.

The current findings underscore the need for a dimensional approach in evaluating childhood risk factors for obsessions and compulsions.

Most information about the lifetime prevalence of mental disorders comes from retrospective surveys, but how much these surveys have undercounted due to recall failure is unknown. We compared results from a prospective study with those from retrospective studies.

The representative 1972–1973 Dunedin New Zealand birth cohort (n=1037) was followed to age 32 years with 96% retention, and compared to the national New Zealand Mental Health Survey (NZMHS) and two US National Comorbidity Surveys (NCS and NCS-R). Measures were research diagnoses of anxiety, depression, alcohol dependence and cannabis dependence from ages 18 to 32 years.

The prevalence of lifetime disorder to age 32 was approximately doubled in prospective as compared to retrospective data for all four disorder types. Moreover, across disorders, prospective measurement yielded a mean past-year-to-lifetime ratio of 38% whereas retrospective measurement yielded higher mean past-year-to-lifetime ratios of 57% (NZMHS, NCS-R) and 65% (NCS).

Prospective longitudinal studies complement retrospective surveys by providing unique information about lifetime prevalence. The experience of at least one episode of DSM-defined disorder during a lifetime may be far more common in the population than previously thought. Research should ask what this means for etiological theory, construct validity of the DSM approach, public perception of stigma, estimates of the burden of disease and public health policy.

There is increased interest in assessing the family history of psychiatric disorders for both genetic research and public health screening. It is unclear how best to combine family history reports into an overall score. We compare the predictive validity of different family history scores.

Probands from the Dunedin Study (n=981, 51% male) had their family history assessed for nine different conditions. We computed four family history scores for each disorder: (1) a simple dichotomous categorization of whether or not probands had any disordered first-degree relatives; (2) the observed number of disordered first-degree relatives; (3) the proportion of first-degree relatives who are disordered; and (4) Reed's score, which expressed the observed number of disordered first-degree relatives in terms of the number expected given the age and sex of each relative. We compared the strength of association between each family history score and probands' disorder outcome.

Each score produced significant family history associations for all disorders. The scores that took account of the number of disordered relatives within families (i.e. the observed, proportion, and Reed's scores) produced significantly stronger associations than the dichotomous score for conduct disorder, alcohol dependence and smoking. Taking account of family size (i.e. using the proportion or Reed's score) produced stronger family history associations depending on the prevalence of the disorder among family members.

Dichotomous family history scores can be improved upon by considering the number of disordered relatives in a family and the population prevalence of the disorder.