Mental Health

Analysis of influencing factors and urban-rural difference decomposition of mental health literacy among residents in Chongqing, Southwest China: a large-scale multistage random sampling survey.

TL;DR

The overall MHL attainment rate among Chongqing residents was 32.21% (weighted: 35.18%), with significant urban-rural disparities where 64.83% of the gap remained unexplained by observed covariates, while occupation, education level, and household income were the main explained sources of these differences.

Key Findings

The overall mental health literacy attainment rate among Chongqing residents was 32.21%, with a weighted rate of 35.18% after adjustment for urban-rural population structure.

  • Survey conducted in 2025 using stratified multistage random sampling among 36,874 permanent residents in Chongqing
  • The National Mental Health Literacy Questionnaire (NMHLQ) was used to assess MHL attainment
  • The unweighted overall attainment rate was 32.21%
  • After weighting according to the urban-rural population structure, the overall attainment rate was 35.18%

Urban residents demonstrated significantly higher MHL attainment rates than rural residents.

  • Urban MHL attainment rate: 37.08% vs. rural: 30.26% (p < 0.001)
  • Urban residents had higher odds of MHL attainment than rural residents (OR = 1.36, 95% CI: 1.29–1.43, p < 0.001) in urban-rural weighted logistic regression
  • After adjustment for covariates in stratified analyses, urban residents still had higher odds of MHL attainment
  • The urban-rural difference was robust across sensitivity analyses using repeated Fairlie decomposition with adjusted sample coefficients

The majority (64.83%) of the urban-rural difference in MHL could not be explained by the observed covariates.

  • Weighted Fairlie nonlinear decomposition was applied to quantify contributions to urban-rural differences
  • 0.0442 (64.83%) of the difference could not be explained by observed covariates, 'which may reflect unmeasured structural urban-rural differences or other unobserved factors'
  • A total of 0.0240 (35.17%) of the difference was attributable to observed factors
  • Repeated Fairlie decomposition with adjusted sample coefficients showed 'relatively stable results'

Occupation, education level, monthly household income, and regional distribution were the main observed contributors to urban-rural MHL differences, with regional distribution having a negative contribution.

  • Contribution rates among the explained portion: occupation 27.01%, education level 26.55%, monthly household income 12.90%, and regional distribution -35.13%
  • The negative contribution of regional distribution (-35.13%) indicates it worked to reduce rather than widen the urban-rural gap
  • Occupation and education level together accounted for more than half of the explained difference
  • These findings were from the weighted Fairlie decomposition model

Age, education level, income level, occupation, health behaviors, self-rated health, and chronic disease status were influencing factors of MHL among both urban and rural residents.

  • Multivariable logistic regression identified these seven categories of variables as significant influencing factors
  • Analyses were performed separately for urban and rural residents
  • Generalized variance inflation factor (GVIF) was used to assess multicollinearity among variables
  • Chi-square tests, logistic regression, and urban-rural weighted logistic regression were all employed to identify influencing factors

What This Means

This research suggests that mental health literacy — the ability to understand, recognize, and respond to mental health issues — remains at a moderately high but imperfect level in Chongqing, China, with only about one-third of residents meeting the attainment threshold. The study surveyed nearly 37,000 residents using a rigorous random sampling method and found a notable gap between urban and rural populations, with city dwellers more likely to demonstrate adequate mental health literacy than those in rural areas. Key factors linked to higher mental health literacy included higher education, better income, certain occupations, healthier behaviors, better self-rated health, and the absence of chronic disease. To understand why urban residents fared better, researchers used a statistical technique called Fairlie decomposition. They found that about 35% of the urban-rural gap could be traced to measurable differences — particularly in occupation, education, and household income — while the remaining 65% of the gap was unexplained by any observed variable, suggesting deeper structural or systemic inequalities between urban and rural environments that were not directly captured by the survey. Regional distribution interestingly appeared to slightly narrow the gap rather than widen it. This research suggests that efforts to improve mental health literacy in China should prioritize rural communities and people with lower education or income levels. The authors recommend integrating primary healthcare services, community-based education programs, and digital communication tools to help reduce these persistent disparities. Since much of the urban-rural gap remains unexplained, broader structural factors — such as access to mental health services, media exposure, and social infrastructure — likely need to be addressed to achieve more equitable mental health outcomes.

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Citation

Kong D, Zhong J, Li C, Wang W, Yang H, Liu Z, et al.. (2026). Analysis of influencing factors and urban-rural difference decomposition of mental health literacy among residents in Chongqing, Southwest China: a large-scale multistage random sampling survey.. Frontiers in public health. https://doi.org/10.3389/fpubh.2026.1895974