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The COVID-19 pandemic and mitigation measures are likely to have a marked effect on mental health. It is important to use longitudinal data to improve inferences.
To quantify the prevalence of depression, anxiety and mental well-being before and during the COVID-19 pandemic. Also, to identify groups at risk of depression and/or anxiety during the pandemic.
Data were from the Avon Longitudinal Study of Parents and Children (ALSPAC) index generation (n = 2850, mean age 28 years) and parent generation (n = 3720, mean age 59 years), and Generation Scotland (n = 4233, mean age 59 years). Depression was measured with the Short Mood and Feelings Questionnaire in ALSPAC and the Patient Health Questionnaire-9 in Generation Scotland. Anxiety and mental well-being were measured with the Generalised Anxiety Disorder Assessment-7 and the Short Warwick Edinburgh Mental Wellbeing Scale.
Depression during the pandemic was similar to pre-pandemic levels in the ALSPAC index generation, but those experiencing anxiety had almost doubled, at 24% (95% CI 23–26%) compared with a pre-pandemic level of 13% (95% CI 12–14%). In both studies, anxiety and depression during the pandemic was greater in younger members, women, those with pre-existing mental/physical health conditions and individuals in socioeconomic adversity, even when controlling for pre-pandemic anxiety and depression.
These results provide evidence for increased anxiety in young people that is coincident with the pandemic. Specific groups are at elevated risk of depression and anxiety during the COVID-19 pandemic. This is important for planning current mental health provisions and for long-term impact beyond this pandemic.
Attention-deficit hyperactivity disorder (ADHD) is associated with later depression and there is considerable genetic overlap between them. This study investigated if ADHD and ADHD genetic liability are causally related to depression using two different methods.
First, a longitudinal population cohort design was used to assess the association between childhood ADHD (age 7 years) and recurrent depression in young-adulthood (age 18–25 years) in N = 8310 individuals in the Avon Longitudinal Study of Parents and Children (ALSPAC). Second, two-sample Mendelian randomization (MR) analyses examined relationships between genetic liability for ADHD and depression utilising published Genome-Wide Association Study (GWAS) data.
Childhood ADHD was associated with an increased risk of recurrent depression in young-adulthood (OR 1.35, 95% CI 1.05–1.73). MR analyses suggested a causal effect of ADHD genetic liability on major depression (OR 1.21, 95% CI 1.12–1.31). MR findings using a broader definition of depression differed, showing a weak influence on depression (OR 1.07, 95% CI 1.02–1.13).
Our findings suggest that ADHD increases the risk of depression later in life and are consistent with a causal effect of ADHD genetic liability on subsequent major depression. However, findings were different for more broadly defined depression.
Depressive symptoms show different trajectories throughout childhood and adolescence that may have different consequences for adult outcomes.
To examine trajectories of childhood depressive symptoms and their association with education and employment outcomes in early adulthood.
We estimated latent trajectory classes from participants with repeated measures of self-reported depressive symptoms between 11 and 24 years of age and examined their association with two distal outcomes: university degree and those not in employment, education or training at age 24.
Our main analyses (n = 9399) yielded five heterogenous trajectories of depressive symptoms. The largest group found (70.5% of participants) had a stable trajectory of low depressive symptoms (stable–low). The other four groups had symptom profiles that reached full-threshold levels at different developmental stages and for different durations. We identified the following groups: childhood–limited (5.1% of participants) with full-threshold symptoms at ages 11–13; childhood–persistent (3.5%) with full-threshold symptoms at ages 13–24; adolescent onset (9.4%) with full-threshold symptoms at ages 17–19; and early-adult onset (11.6%) with full-threshold symptoms at ages 22–24. Relative to the majority ‘stable–low’ group, the other four groups all exhibited higher risks of one or both adult outcomes.
Accurate identification of depressive symptom trajectories requires data spanning the period from early adolescence to early adulthood. Consideration of changes in, as well as levels of, depressive symptoms could improve the targeting of preventative interventions in early-to-mid adolescence.
Interventions to reduce adolescents’ non-core food intake (i.e. foods high in fat and sugar) could target specific people or specific environments, but the relative importance of environmental contexts v. individual characteristics is unknown.
Data from 4d food diaries in the UK National Diet and Nutrition Survey (NDNS) 2008–2012 were analysed. NDNS food items were classified as ‘non-core’ based on fat and sugar cut-off points per 100g of food. Linear multilevel models investigated associations between ‘where’ (home, school, etc.) and ‘with whom’ (parents, friends, etc.) eating contexts and non-core food energy (kcal) per eating occasion (EO), adjusting for variables at the EO (e.g. time of day) and adolescent level (e.g. gender).
Adolescents (n 884) aged 11–18 years.
Only 11 % of variation in non-core energy intake was attributed to differences between adolescents. In adjusted models, non-core food intake was 151 % higher (ratio; 95 % CI) in EO at ‘Eateries’ (2·51; 2·14, 2·95) and 88 % higher at ‘School’ (1·88; 1·65, 2·13) compared with ‘Home’. EO with ‘Friends’ (1·16; CI 1·03, 1·31) and ‘Family & friends’ (1·21; 1·07, 1·37) contained 16–21 % more non-core food compared with eating ‘Alone’. At the individual level, total energy intake and BMI, but not social class, gender or age, were weakly associated with more non-core energy intake.
Regardless of individual characteristics, adolescents’ non-core food consumption was higher outside the home, especially at eateries. Targeting specific eating contexts, not individuals, may contribute to more effective public health interventions.
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