Abstract
OBJECTIVES: While childhood circumstances predict mental health outcomes in high-income countries, evidence from low-income and middle-income countries (LMICs) like Indonesia remains scarce. This study examines the long-term association between childhood socioeconomic status (SES), health and depressive symptoms in adulthood, testing the hypothesis that early-life disadvantages increase the odds of depressive symptoms later in life. DESIGN: Cross-sectional analysis using latent class analysis to cluster childhood SES/health and logistic regression to assess associations with depressive symptoms. SETTING: A nationally representative household survey was conducted across 13 provinces in urban and rural areas of Indonesia. PARTICIPANTS: 32 085 adults aged 18 years and older from the 2014-2015 Indonesia Family Life Survey. Participants with missing data on childhood circumstances or depressive symptoms were excluded, resulting in a final analytic sample of 29 140 individuals. OUTCOME MEASURES: The primary outcome was depressive symptoms measured using the 10-item Centre for Epidemiologic Studies Depression Scale, with scores ≥10 indicating clinically significant symptoms. Secondary exposures included latent classes of childhood SES and health (high, moderate and low disadvantage). Analyses adjusted for adult SES, health behaviours, social capital and demographic characteristics. RESULTS: Three latent classes emerged: low (64.85%), moderate (5.73%) and high (29.42%) early-life disadvantage. Adjusted logistic regression showed higher odds of depressive symptoms for high (OR 1.39, 95% CI 1.28 to 1.50) and moderate disadvantage (OR 1.66, 95% CI 1.48 to 1.87) versus low. Significant covariates included age, education, wealth and social capital (all p<0.05). CONCLUSIONS: Early-life disadvantages predict depressive symptoms in adulthood in Indonesia, underscoring the need for child-focused interventions (health, education and poverty reduction) to mitigate long-term mental health risks in LMICs. Further research should explore longitudinal mechanisms.