Childhood correlates of adult positive mental well-being in three British longitudinal studies

The Long-Term Effect of Early-Life Uncertainty on Mental Health in Adolescence and Adulthood: A Meta-Analysis

Turbulent changes in early life are a hidden source of childhood trauma, increasing potential risks for mental illness. Many studies have identified the link between childhood uncertainty and mental health. However, research on the long-term effect of early-life uncertainty (EU) on mental health has not been systematically synthesized. This meta-analysis aims to provide a quantitative estimate of the association between EU and subsequent mental health outcomes. Eight electronic databases and gray literature were searched. Twenty-eight studies met our inclusion criteria: samples of non-clinical adolescents or adults and clear and valid assessments. Random-effect models were used to calculate the pooled effect sizes of EU on internalizing problems, externalizing problems, and well-being. Meta-regression and subgroup analysis were used to explore potential moderators. Results indicated small to moderate associations involving EU and internalizing problem (r = .28; 95% confidence interval [CI] [0.228, 0.326]) and externalizing problem (r = .16; 95% CI [0.102, 0.220]). EU was not significantly associated with well-being (r = -.41; 95% CI [-0.738, 0.071]). Furthermore, moderator analyses found that composite uncertain experiences in childhood had a stronger negative effect than single experiences. EU was a stronger predictor of mental health problems in adults than in adolescents. Cross-sectional studies would amplify the correlation between EU and mental illness compared to longitudinal studies. In the future, childhood uncertain and unpredictable risks should receive more attention. More research needs to focus on positive psychological indicators and samples from non-Western countries.

The effect of childhood socioeconomic status on depressive symptoms in middle-old age: the mediating role of life satisfaction

BACKGROUND

Some studies have shown that childhood socioeconomic status (SES) can influence the development and progression of depression in adulthood. This study aimed to analyze the effects of childhood SES on depressive symptoms in individuals of middle-old age and examine the potential mediating role of life satisfaction based on national data in China.

METHODS

Data were derived from the 2018 China Health and Retirement Longitudinal Study (CHARLS) data. A total of 17,129 individuals who were aged 45 years and older were included. The dependent variable was depressive symptoms in middle-old age. Childhood SES was the independent variable, and life satisfaction was the mediator. This study controlled four factors: general demographic characteristics, adult SES, childhood adversity and health and living status. Pearson correlations and hierarchical multiple regression analysis were performed, and the Baron and Kenny method was used to test the mediating role.

RESULTS

The score of depressive symptoms among all participants was 7.88 ± 6.65. Gender, age, adult SES, childhood adversity and health and living status all affected the development of depression symptom in middle-aged and elderly individuals in China. After adjusting for all covariates, the higher the childhood SES, the lower the depressive symptom score (β = - 0.422, P < 0.001). Life satisfaction had a partial mediating effect between childhood SES and depressive symptoms. Low childhood SES may improve life satisfaction (β = 0.051, P < 0.001) and life satisfaction indirectly decreases depressive symptom scores (β = - 0.403, P < 0.001).

CONCLUSIONS

Life satisfaction was a partial mediator between childhood SES and depressive symptoms in middle and old age. Improving life satisfaction may serve as an intervention to reduce the incidence of depression symptoms in the future.

Risk factors relate to the variability of health outcomes as well as the mean: A GAMLSS tutorial

Background:

Risk factors or interventions may affect the variability as well as the mean of health outcomes. Understanding this can aid aetiological understanding and public health translation, in that interventions which shift the outcome mean and reduce variability are typically preferable to those which affect only the mean. However, most commonly used statistical tools do not test for differences in variability. Tools that do have few epidemiological applications to date, and fewer applications still have attempted to explain their resulting findings. We thus provide a tutorial for investigating this using GAMLSS (Generalised Additive Models for Location, Scale and Shape).

Methods:

The 1970 British birth cohort study was used, with body mass index (BMI; N = 6007) and mental wellbeing (Warwick-Edinburgh Mental Wellbeing Scale; N = 7104) measured in midlife (42–46 years) as outcomes. We used GAMLSS to investigate how multiple risk factors (sex, childhood social class, and midlife physical inactivity) related to differences in health outcome mean and variability.

Results:

Risk factors were related to sizable differences in outcome variability—for example males had marginally higher mean BMI yet 28% lower variability; lower social class and physical inactivity were each associated with higher mean and higher variability (6.1% and 13.5% higher variability, respectively). For mental wellbeing, gender was not associated with the mean while males had lower variability (–3.9%); lower social class and physical inactivity were each associated with lower mean yet higher variability (7.2% and 10.9% higher variability, respectively).

Conclusions:

The results highlight how GAMLSS can be used to investigate how risk factors or interventions may influence the variability in health outcomes. This underutilised approach to the analysis of continuously distributed outcomes may have broader utility in epidemiologic, medical, and psychological sciences. A tutorial and replication syntax is provided online to facilitate this (https://osf.io/5tvz6/).

Funding:

DB is supported by the Economic and Social Research Council (grant number ES/M001660/1), The Academy of Medical Sciences / Wellcome Trust (“Springboard Health of the Public in 2040” award: HOP001/1025); DB and LW are supported by the Medical Research Council (MR/V002147/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.