The validity of the residuals approach to measuring resilience to adverse childhood experiences

Abstract

Background Resilience is broadly defined as the ability to maintain or regain functioning in the face of adversity. Recent work to harmonise the quantification and definition of resilience quantifies resilience as the residual variance in psychosocial functioning that remains after accounting for adversity exposure. However, there have been no published studies that have formally investigated the validity of this approach. Considering this, we examine the construct and predictive validity of the residuals approach using participants from the Avon Longitudinal Study of Parents and Children (ALSPAC), a multigenerational, longitudinal cohort study. Methods We regressed exposures of adolescent adversity on adolescent psychopathology scores using the Strength and Difficulties Questionnaire and obtained the residual variance. We investigated construct validity by analysing whether previously identified demographic and resilience factors significantly predicted resilience. Predictive validity of resilience was investigated by comparing the predictive power of resilience with other determinants of psychosocial functioning on two developmental outcomes: depressive symptoms at 18 years, measured by the Short Moods and Feelings Questionnaire, and NEET (Not in Employment, Education or Training) status at 17 and 23 years. The associations between depressive symptoms at 18, resilience, ACEs and covariates were tested using multiple linear regression. NEET status at 17 and 23 were run as separate binary multiple logistic regression models to test associations with resilience and known demographics previously associated with NEET status. Results Seven previously identified protective factors, including self-esteem, positive sibling relationship, temperament, and positive perception of school, significantly predicted resilience to adolescent psychopathology, thus providing strong construct validity. Resilience significantly predicted a reduction in depressive symptoms at 18 years, and significantly decreased the likelihood of having NEET status at both 17 years and 23 years, even after taking into account early childhood adversity and other risk factors. None of the socioeconomic factors were significantly associated with resilience. Conclusions Our study demonstrates that the residuals method of operationalising resilience has good construct and predictive validity yet recommend replication studies. It has the potential to advance research into the mechanisms and modifiability of resilience. Trial Registration: Not applicable.

Resilient phenotypes among bereaved youth: a comparison of trajectory, relative, and cross-domain approaches

“…To calculate relative psychological resilience, we followed methods used previously among trauma-exposed adults [ 21 , 28 , 29 ] as well as in the ALSPAC sample specifically with regards to count of adverse childhood experiences [ 45 ]. We regressed SDQ total difficulties score at age 16 on bereavement and utilized the normed standardized residuals from that regression as an indicator of relative resilience.…”

“…We expect positive resilience following bereavement to significantly predict lower depressive symptoms later in adolescence, reflecting predictive validity of the construct in question. This validation technique was used previously in the ALSPAC sample with relative resilience applied to adverse childhood experiences exposure [ 45 ].…”

Genetic Variants Associated With Resilience in Human and Animal Studies

“…While there is no overlap of this gene with candidate genes found in this review, GPD2 has been previously associated with large GWAS's on cognitive ability ( 283 ) and educational attainment from the Social Science Genetic Consortium 2018 GWAS ( 267 ), and cognitive aspects of educational attainment from a large GWAS meta-analysis combining a cognitive consortium and human intelligence GWAS ( 284 ). While these traits have not been used as a proxy for resilience within this review, they have wide support as individual level resilience factors [e.g., ( 285 – 287 )], with high cognitive skills in childhood predicting resilience to adversity in adolescence ( 288 ).…”

Changes in large-scale neural networks under stress are linked to affective reactivity to stress in real life

“…In resilience research, this measure is derived by regressing the change in mental health onto a measure of exposure to life stressors. The residuals of this regression then indicate changes in mental health that are not explained by the amount of stress exposure [26][27][28] . Here, we adapted this measure for EMA research to quantify individual affective reactivity to acute stressors in real life at a time scale that is comparable to laboratory studies on acute stress 29 .…”