The Multiple Layers of Childhood Adversity and Mortality: Population-based Study Synthesizing Life-course Data on Individual, Family and Neighborhood Adversity in 1.2 million individuals
Bibliographic record
Abstract
ABSTRACT Background Childhood adversity is multi-layered, extending beyond the family to include broader neighborhood and health contexts. Methods Children were followed from birth into young adulthood (16-42 years) using nationwide register data on multi-layered childhood adversity and mortality. Individual adversity included perinatal adversity (preterm or small-for-gestational-age) and mental and physical health service use. Family adversity included five distinct groups using group-based multi-trajectory modelling based on 12 adversities. Neighborhood adversity included material deprivation in small-area geographical zones. We evaluated associations of these layers with all-cause mortality using survival analyses. Findings 1,235,519 individuals born between 1980 and 2001, were followed up until Dec 31, 2022, capturing 7,320 deaths. Children facing high family adversity were more likely to have perinatal adversity, use health services, and live in deprived neighborhoods. Each layer separately predicted mortality, with for example high physical health service use (HR: 2.36; 95% CI: 2.24; 2.48) and living in a deprived neighborhood (HR: 1.20; 95% CI: 1.14; 1.26) being associated with higher mortality. Cross-layer interactions were most pronounced between family adversity and child health or perinatal adversity. The highest mortality risk was observed among those with both high family and individual adversity: HR: 7.16 (95% CI: 6.40; 8.01) compared to those with low adversity. Interpretation The co-occurrence and interaction of childhood adversities across different layers can create highly vulnerable groups, deepening lifelong health inequalities. This underscores the importance of a comprehensive, multi-layered approach that targets individual vulnerabilities as well as the broader social environment. Funding The European Research Council. RESEARCH IN CONTEXT Evidence before this study We searched for relevant studies in PubMed with no language restrictions from inception to March 31, 2025, using the terms ((“Childhood adversity” OR “Adverse Childhood Experiences” OR “Early life adversity OR “Perinatal” OR Neighbo*rhood deprivation”) and (“[mortality] OR “premature mortality” OR “death” OR “suicide”)”. We identified studies investigating how individual forms of childhood adversity contribute to the risk of mortality in young adulthood, but only a limited number of studies investigating intersections of different forms of adversity in smaller samples. We also identified several conceptual frameworks that have emphasized the importance of considering multiple, intersecting forms of adversity across developmental contexts. Empirical studies that comprehensively investigate the intersections of multiple forms of adversity simultaneously remain limited. Added value of this study By leveraging large-scale, multi-layer life-course data, the study examines emergent patterns of childhood adversity arising from co-occurring and interacting adversities across individual, family, and neighborhood layers, and relate this to mortality in young adulthood. We demonstrate that adversity is not confined to isolated domains: children exposed to family adversity are more likely to experience perinatal complications, health difficulties, and residence in deprived neighborhoods. Importantly, we show that these adversities are not only independently associated with mortality but can also interact across layers, where adversity at one level amplifies the harmful effects of adversity at another. Such cross-layer interactions help identify highly vulnerable subgroups of children who experience multiple interacting adversities already during childhood. Implications of the available evidence Childhood adversity is a multi-layered phenomenon that cannot be fully understood through single-domain analyses. Our findings suggest that public health and social policies must address adversity more broadly, considering the interplay between individual vulnerability, family adversity, and the structural conditions of the communities in which children grow up. These insights can inform more targeted interventions; from reducing premature births and supporting families in adversity to promoting mental health and tackling broader social determinants such as poverty, education, and neighborhood conditions. By adopting a multi-layered, life course approach to childhood adversity, health and social systems can better identify and support highly vulnerable subgroups of children facing adversity, ultimately reducing intergenerational cycles of disadvantage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".