Cognitive variations following exposure to childhood adversity: Evidence from a pre-registered, longitudinal study

Summary

Background

Different methodological approaches to studying the effects and timing of childhood adversity have been proposed and tested. While childhood adversity has primarily been operationalized through specificity (effects of individual adversity types) and cumulative risk (sum of all adversities reported by an individual) models, dimensional models (probeable through latent class and other cluster analyses) have recently gained traction given that it can overcome some of the limitations of the specificity and cumulative risk approaches. On the other hand, structured lifecourse modelling is a new statistical approach that examines the effects of the timing of adversity exposure on health outcomes by comparing sensitive periods and accumulation hypotheses. In this study, we apply these sets of methodological approaches and theoretical models to better understand the complex effects of childhood adversity on cognitive outcomes.

Methods

We analysed data obtained from the Avon Longitudinal Study of Parents and Children for 2965 participants (Male = 1125; Female = 1840). This included parental report of 11 types of childhood adversity when participants were between 8 months and 8.7 years, and performance on inhibition, working memory and emotion recognition neurocognitive tasks when participants were 24 years of age (April 1, 1992–October 31, 2017). We used latent class analysis to classify the participants into subgroups, while we used Kruskal–Wallis test to examine differences in cognitive performance among the adversity subgroups. Additionally, to test whether sensitive period or accumulation models better explain the effects of childhood adversity on cognitive functioning, we carried out separate analyses using structured lifecourse modelling approaches.

Findings

Latent class analysis showed evidence of 5 classes, namely: low adversity (71.6%), dysfunctional family (9.58%); parental deprivation (9.65%); family poverty (6.07%) and global adversity (3.1%). We observed group differences in cognitive performance among the adversity classes in an inhibition control task, χ ²(4) = 15.624, p = 0.003 and working memory task, χ ²(4) = 15.986, p = 0.003. Pairwise comparison tests showed that participants in the family poverty class performed significantly worse than those in the low adversity class, for the inhibition control task (p = 0.007) while participants in the global adversity class significantly performed worse than participants in the low adversity class (p = 0.026) and dysfunctional family class (p = 0.034) on the working memory task. A further analysis revealed that the associations between each individual adversity type and cognitive outcomes were mostly consistent with the observed class performance in which they co-occurred. Follow-up analyses suggested that adversity during specific sensitive periods, namely very early childhood and early childhood, explained more variability in these observed associations, compared to the accumulation of adversities.

Interpretation

These findings suggest that dimensional approaches e.g., latent class analysis or cluster analysis could be good alternatives to studying childhood adversity. Using latent class analysis for example, can help reveal the population distribution of co-occurring adversity patterns among participants who may be at the greatest health risk and thus, enable a targeted intervention. In addition, this approach could be used to investigate specific pathways that link adversity classes to different developmental outcomes that could further complement the specificity or cumulative risk approaches to adversity. On the other hand, findings from a separate analysis using structured lifecourse modelling approaches also highlight the vital developmental timeframes in childhood during which the impact of adversity exposure on cognitive outcomes is greatest, suggesting the need to provide comprehensive academic and mental health support to individuals exposed during those specific timeframes.

Funding

T.N. received funding from doi 10.13039/501100003343, Cambridge Trust; ( doi 10.13039/501100000735, University of Cambridge; ).