The evidence base came from the China Health and Retirement Longitudinal Study, or CHARLS, a nationally representative survey organized by the Center for Healthy Ageing and Development Studies at Peking University. The researchers drew on two separate waves of the survey in a deliberate temporal design. Twenty-four measures of adverse childhood experiences, or ACEs, were extracted from the 2014 wave, while individual characteristics and depressive symptoms were taken from the 2018 wave. This four-year separation matters scientifically. By measuring childhood adversity well before assessing current mental health, the design reduces the risk that recall of past hardship was contaminated by present mood, a common pitfall in ACE research.

The ACEs framework rests on a well-established body of evidence showing that early-life adversity, from abuse and household dysfunction to serious illness, produces lasting biological and psychological consequences. Chronic activation of the stress response during sensitive developmental periods can alter the regulation of the hypothalamic-pituitary-adrenal axis, reshape neural circuits involved in emotion processing, and increase vulnerability to depression across the entire life course. What has been less clear is whether these early wounds contribute to population-level geographic disparities in mental health, or whether such gaps are driven entirely by contemporary circumstances such as income, education, and health care access. The new study addresses that question directly.

The pattern of ACEs itself differed strikingly between settings. Parental anxiety, parental strictness, and childhood adverse health conditions emerged as risk factors for depressive symptoms in rural areas but not in urban ones. Conversely, adverse intra-parental relationships and childhood poor friendships were significant risk factors in urban areas but not in rural ones. Parental divorce, perhaps counterintuitively, acted as a protective factor in rural areas, while parental depression was a risk factor in both settings. These divergent patterns suggest that the same categories of childhood adversity can carry different meanings and different consequences depending on the social and cultural context in which they occur, a finding with important implications for how interventions are designed.

The decomposition results form the analytical core of the paper. The Fairlie model revealed that 55.65 percent of the urban-rural difference in depressive symptoms could be explained by the measured factors combined. Self-rated health status was the single largest contributor, accounting for 18.24 percent of the gap. Internet use followed at 7.70 percent, retirement at 6.32 percent, and parental depression, one of the childhood adversity measures, at 6.19 percent. Educational level explained 5.35 percent and life satisfaction 4.68 percent. Smaller but still meaningful contributions came from childhood starvation at 1.68 percent, parental disability at 1.44 percent, age at 0.79 percent, childhood disease at 0.63 percent, childhood bullying at 0.60 percent, and childhood adverse health conditions at 0.55 percent.

Not every factor pushed the gap in the same direction. Gender contributed a negative 2.56 percent and marital status a negative 0.34 percent, meaning these characteristics actually worked to narrow the urban-rural difference rather than widen it. In a decomposition analysis, negative contributions indicate that the distribution of that factor favors the group with worse outcomes, offsetting part of the gap produced by other variables. The inclusion of such offsetting terms is a technical strength of the Fairlie approach, because it prevents the model from overstating the role of harmful factors and provides a more honest accounting of the full structure of the disparity.

The prominence of parental depression in the decomposition is scientifically striking. It indicates that a childhood exposure measured years earlier still explains more than six percent of the current urban-rural mental health gap among middle-aged and older adults, rivaling the contributions of contemporary factors such as retirement status and educational attainment. Childhood starvation, a measure of early material deprivation, similarly retains explanatory power at 1.68 percent. Together, the childhood adversity measures collectively account for a nontrivial share of the disparity, confirming the study’s central claim that adverse childhood experiences are a genuine contributing factor to the geographic mental health divide, not merely a background correlate.

The strong role of self-rated health status, explaining nearly a fifth of the gap, aligns with a large literature documenting that rural Chinese residents face greater burdens of chronic disease, functional limitation, and reduced access to health services. Internet use, the second-largest contributor at 7.70 percent, is a particularly modern finding. Digital connectivity has become intertwined with social participation, information access, and even the delivery of mental health support, and the digital divide between urban and rural China has widened as services migrate online. Retirement status and educational level, both structural determinants, further reinforce the picture of a disparity rooted in the differential accumulation of advantage and disadvantage across the life course.

The authors argue that the findings carry direct policy implications. Because the urban-rural disparity is attributable to both individual circumstances and childhood adversity, interventions should address structural determinants, such as improving rural health care, education, and digital inclusion, alongside efforts to prevent and mitigate early-life adversity. The divergent ACE patterns between settings add a layer of nuance: programs targeting parental mental health, family relationships, and childhood social environments may need to be tailored differently for urban and rural communities rather than applied uniformly.

The study also demonstrates the value of decomposition methodology for health equity research. Where a simple comparison of prevalence rates reveals that a gap exists, the Fairlie model reveals what the gap is made of, factor by factor, with each contribution expressed as a percentage of the total difference. That transparency makes the findings immediately actionable for policymakers, who can see precisely which levers, from health status and connectivity to parental depression and childhood starvation, offer the greatest potential for narrowing the divide. As China’s population continues to age and urbanization reshapes the geography of disadvantage, understanding the full life-course architecture of mental health disparities, from the childhoods of today’s older adults to the structural conditions of the present, will be essential for building interventions that reach the communities where the burden is heaviest.

Subject of Research: Decomposition analysis of the disparities in depressive symptoms among middle-aged and older adults between urban and rural areas in China: adverse childhood experiences are a contributing factor

Article Title: Decomposition analysis of the disparities in depressive symptoms among middle-aged and older adults between urban and rural areas in China: adverse childhood experiences are a contributing factor

Article References: Jiang, Q., Shi, Z., Yan, H., Shi, L., Ye, Y., & Wang, D. (2026). Decomposition analysis of the disparities in depressive symptoms among middle-aged and older adults between urban and rural areas in China: adverse childhood experiences are a contributing factor. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09306-1

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09306-1

Keywords: Decomposition, analysis, disparities, depressive, symptoms, middle-aged, older, adults, between, urban, rural, areas