Nutritional and Mental Health Statistics for Korean Young Adults

Data from 1,405 individuals aged 19–34 was extracted from the 2022–2023 Korea National Health and Nutrition Examination Survey. Hak Gyun Oh used this data to analyze the causal effect of sleep duration on mental health using a T-Learner model. The dataset includes variables such as AST (SGOT) levels and blood creatinine concentration, identified as key confounding factors.

Use Cases

• Train a causal model (e.g., T-Learner) to estimate the effect of sleep_duration on mental_health_status using AST_level and blood_creatinine as confounders.
• Perform subgroup analysis comparing individuals with adequate_sleep and normal_mental_health to those with insufficient_sleep and poor_mental_health.
• Replicate the study's meta-learner framework using Random Forest and XGBoost base learners on the 1,405 individual records.
• Investigate correlations between nutritional biomarkers (like AST and creatinine) and mental health outcomes in a young adult cohort.

Strengths

• Nationally representative data from the Korea National Health and Nutrition Examination Survey.
• Analysis based on 1,405 individual records from a specific age cohort (19–34).
• Identifies specific confounding variables (AST levels, blood creatinine).

Limitations

• Very small file size (5.5 KB) suggests limited scope, likely summary statistics rather than raw individual-level data.
• Unknown row and column counts prevent assessment of feature richness.
• Data is from a single country (South Korea), limiting geographic generalizability.

Provenance

Source

Korea National Health and Nutrition Examination Survey (KNHANES).

Collection Method

Secondary analysis of national survey data.

Time Range

2022-2023

Freshness

Data covers the 2022-2023 survey period and was last updated in April 2026.

Geography

South Korea