Cross-National Comparison Study Skill

You are assisting a medical researcher in conducting a cross-national comparison study using parallel nationally representative surveys (e.g., KNHANES for Korea, NHANES for the US, CHNS for China).

When to Use

• Researcher has a clinical question to compare across two countries
• KNHANES + NHANES data available (or other parallel survey pairs)
• Goal: produce a complete analysis with country-stratified results + comparison table

Inputs

1. Research question: exposure → outcome association to compare across countries
2. Korean data path: KNHANES CSV file
3. US data path: NHANES CSV directory (multiple tables to merge)
4. Harmonization table (optional): CSV mapping variables across surveys
   • Default: replicate-study skill's harmonization_knhanes_nhanes.csv

Reference Files

• Harmonization table: medsci-skills/skills/replicate-study/references/harmonization_knhanes_nhanes.csv
• Upstream:
  • medsci-skills/skills/write-paper/references/paper_types/cross_national.md — writing template
  • medsci-skills/skills/analyze-stats/references/analysis_guides/survey_weighted.md

Workflow

Phase 1: Study Definition

1. Confirm research question: Exposure → Outcome
2. Define variable coding for both countries:
   • Exposure: PHQ-9, BMI category, smoking, etc.
   • Outcome: diabetes, hypertension, mortality, etc.
   • Covariates: age, sex, education, income, smoking, alcohol, obesity, CVD
3. Check harmonization table for variable availability
4. Output: study protocol summary for user approval

Phase 2: Data Preparation

KNHANES (single CSV):

1. Load CSV, filter age ≥20 (or per protocol)
2. Derive variables using KNHANES coding:
   • Smoking: BS3_1 (1,2=current, 3=former, 8=never)
   • Alcohol: BD1_11 (2-6=frequent, 1=occasional, 8=never)
   • Obesity: HE_obe (≥4=obesity for BMI≥25 Asian cutoff)
   • PHQ-9: BP_PHQ_1~9, sum score, ≥10=depression
   • Diabetes: HE_glu≥126 | HE_HbA1c≥6.5 | DE1_dg=1
   • CVD: DI4_dg=1 | DI5_dg=1 | DI6_dg=1
3. Set survey design: svydesign(id=~psu, strata=~kstrata, weights=~wt_itvex, nest=TRUE)

NHANES (multiple CSVs):

1. Load and merge tables by SEQN (DEMO_J, DPQ_J, GHB_J, BIOPRO_J, BMX_J, SMQ_J, ALQ_J, DIQ_J, MCQ_J, BPQ_J)
2. Derive variables using NHANES coding:
   • Smoking: SMQ020 + SMQ040 (100 cigs + now smoke)
   • Alcohol: ALQ121 (past 12 mo frequency → categories)
   • Obesity: BMXBMI ≥30 (WHO cutoff, NOT Asian)
   • PHQ-9: DPQ010~DPQ090, sum score, ≥10=depression
   • Diabetes: LBXSGL≥126 | LBXGH≥6.5 | DIQ010=="Yes" (CRITICAL: LBXSGL not LBXSGLU)
   • CVD: MCQ160B=="Yes" (CHF) | MCQ160C=="Yes" (CHD) | MCQ160D=="Yes" (angina) | MCQ160E=="Yes" (MI)
   • HTN: BPXOSY3≥140 | BPXODI3≥90 | BPQ020=="Yes"
3. Set survey design: svydesign(id=~SDMVPSU, strata=~SDMVSTRA, weights=~WTMECPRP, nest=TRUE)

Phase 3: Parallel Analysis

For EACH country independently:

1. Table 1: Baseline characteristics by exposure (weighted counts + percentages)
2. Main analysis: Sequential logistic regression models
   • Model 1 (unadjusted)
   • Model 2 (age + sex)
   • Model 3 (fully adjusted: + education, income, smoking, alcohol, obesity, CVD)
3. Subgroup analyses: By sex, age group, education, income, alcohol, smoking, CVD, obesity
4. Dose-response (if applicable): RCS with 3 knots

Phase 4: Cross-National Comparison Table

Generate a side-by-side comparison:

Phase 5: Output Files

{working_dir}/
├── cross_national_report.md    — Study summary + comparison tables
├── variable_mapping.csv        — Variable mapping with match status
├── analysis_korea.R            — KNHANES analysis (self-contained)
├── analysis_us.R               — NHANES analysis (self-contained)
├── results/
│   ├── table1_korea.csv
│   ├── table1_us.csv
│   ├── main_results_comparison.csv
│   └── subgroup_comparison.csv
└── manuscript_draft/           — Optional: Methods + Results draft
    ├── methods_draft.md
    └── results_draft.md

Critical Rules

1. NEVER pool data across countries. Each country analyzed with its own survey design.
2. Country-specific BMI cutoffs: Korea ≥25 (Asian), US ≥30 (WHO).
3. Country-specific income: KNHANES quartile, NHANES PIR → harmonize to binary.
4. Weighted analysis mandatory: Both KNHANES and NHANES are complex surveys.
5. Document all harmonization decisions: What matches, what needed recoding, what differs.
6. Same analytic approach: Identical model specifications for both countries for fair comparison.

KNHANES Variable Coding Reference (validated via Joo 2026 replication)

NHANES Variable Coding Reference (validated via Joo 2026 cross-national)

CRITICAL: NHANES data downloaded via R nhanesA package uses TEXT LABELS, not numeric codes.

ALQ121 Text Label Mapping (Alcohol Frequency)

• Frequent (current drinker): Any specific frequency except "Never in the last year"
• Occasional (past-year abstainer): "Never in the last year"
• Never (lifetime non-drinker): ALQ111 == "No" (ALQ121 will be NA)

Additional KNHANES Variables (validated via LE8-Asthma replication)

Additional NHANES Variables (validated