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Record W4417293359 · doi:10.64898/2025.12.10.25341979

Childhood food insecurity and youth mental health trajectories in two UK longitudinal cohorts

2025· article· W4417293359 on OpenAlexaboutno aff
Eileen Y. Xu, Amelia Edmondson-Stait, Alex S. F. Kwong, Heather C. Whalley

Bibliographic record

VenuemedRxiv · 2025
Typearticle
Language
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
FundersUniversity of Bristol
KeywordsMental healthLongitudinal studyStrengths and Difficulties QuestionnaireFood insecurityMultilevel modelLongitudinal dataChild developmentNational Child Development StudyChild health

Abstract

fetched live from OpenAlex

Abstract Background Food insecurity (FI) is associated with overall poorer mental health in childhood and adolescence and negatively impacts longer-term health. However, less is known about how differential exposure to FI may shape the development of different mental health symptoms over this period. Methods We used data from two population-based UK birth cohorts: the Avon Longitudinal Study of Parents and Children (ALSPAC; n=6,182; born 1991-1992) and Growing Up in Scotland (GUS; n=3,167; born 2004-2005). FI severity at age ∼5 years (No FI/Low FI/High FI) was determined from parent-reported difficulties affording food. Mental health symptoms were parent-reported over the following 10 years using the conduct, emotional, hyperactivity/inattention and peer problem subscales of the Strengths and Difficulties Questionnaire (SDQ). Trajectories were characterised using multilevel growth curve models adjusted for child sex, household income, maternal mental health and multiple deprivation index. Findings High FI at age ∼5 years was associated with worse trajectories of conduct, emotional, hyperactivity/inattention and peer problems over the following 10 years in both cohorts (vs. No FI). In ALSPAC, differences in conduct and hyperactivity/inattention scores were most pronounced at ages 7 (0.31 [0.11,0.51], p adj <.001) and 9 (0.44 [0.09,0.79], p adj =.008), respectively; these occurred at ages 13 (0.26 [0.04,0.48], p adj =.014) and 15 (0.43 [0.04,0.81], p adj =.023) in GUS. For emotional (ALSPAC: 0.46 [0.22,0.69], p adj <.001; GUS: 0.38 [0.16,0.59], p adj <.001) and peer problems (ALSPAC: 0.34 [0.13,0.55], p adj <.001; GUS: 0.25 [0.05,0.45], p adj =.01), differences between High and No FI groups were most pronounced at age 9 instead. Low FI was also associated – to a lesser degree – with higher peer problem trajectories (vs. No FI); this was most pronounced at age 13 in ALSPAC (0.13 [0.00,0.27], p =.031) and age 15 in GUS (0.28 [0.04,0.52], p =.016). Interpretation Children who experienced more severe FI had greater levels of mental health symptoms over the next 10 years, even after adjusting for sociodemographic confounders. FI severity may also display a dose-response effect for peer problems. While replication using more robust FI measures remains necessary, we provide further evidence of the persistent negative impact of FI on youth mental health – particularly during late childhood and mid-adolescence. Funding University of Edinburgh, Wellcome Trust, University of Bristol Research in context Evidence before this study We searched PubMed for studies examining youth mental health trajectories after early food insecurity (FI) using the following terms: (food) AND (insecur*) AND ((child*) OR (youth) OR (young people) OR (adolescen*)) AND (("mental health") OR (distress) OR (emotion*) OR (behavio*) OR (internali*) OR (externali*)) AND ((trajectory) OR (trajectories)). Three studies investigated the relationship between FI and youth mental health trajectories using data from South Africa, USA and Canada; none investigated the potential impact of marginal food security. Two studies found an association between FI and persistently high depressive and hyperactivity/inattention symptoms over 1-3.5 years of follow-up. The third study used latent growth curve analysis to investigate eight patterns of FI exposure (based on binary FI status over three timepoints) and teacher-reported behaviour problems, reporting no associations. This study, however, only examined linear changes in behavioural problems which typically follow a non-linear trajectory – i.e. age-related differences in rates of change were not considered. Added value of this study To our knowledge, this is the first study to investigate trajectories of mental health symptoms in two generations of UK children and adolescents following exposure to different levels of FI severity. Across two large, population-based birth cohorts, we found that high FI at age 5 was associated with worse 10-year trajectories of emotional, conduct, hyperactivity/inattention and peer problems compared to the no FI group. Low FI – reflecting marginal food security – also associated with heightened peer problems, suggesting a dose-response relationship. Greatest differences between food-secure and food-insecure children in this study occurred in late childhood and mid-adolescence. Findings additionally highlight that the impact of FI has remained consistent for UK children born in the early 1990s and mid-2000s, despite secular changes in youth mental health problems. Implications of all the available evidence Childhood food insecurity casts a long shadow on mental health and well-being outcomes throughout the lifespan, as demonstrated by substantial work from the 20 th and 21 st centuries. Here, we also see that the impact of FI on children and young people’s mental health has remained consistent over two generations of UK youth. If left unchecked, the growing prevalence of FI in the UK will only intensify concerns for the long-term mental health of the nation’s young people – especially those from low-income and marginalised backgrounds who already face a widening gap in health inequalities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score0.524

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.103
GPT teacher head0.407
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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