/cross-national

name: cross-national
description: Use when comparing an exposure-outcome association across countries with parallel national surveys (KNHANES, NHANES, CHNS). Harmonizes variables, runs parallel weighted analyses and builds comparison tables for 2-country (KR+US) or 3-country (KR+US+CN) designs.
model: opus
metadata:
  triggers: "cross-national, 한미 비교, Korea US comparison, KNHANES NHANES, 양국 비교, binational, cross-country, 비교연구, 3국 비교, CHNS, 한미중"

Cross-National Comparison Study Skill

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`'s `references/harmonization_knhanes_nhanes.csv`

Reference Files

• `/write-paper`'s `references/paper_types/cross_national.md` — writing template
• `/analyze-stats`'s `references/analysis_guides/survey_weighted.md` — survey-weighted analysis guide
• `references/chns_coding.md` — CHNS files, merge keys, coding and warnings; read it when the design

includes China (3-country design)

• `references/additional_variables.md` — asthma, sleep, physical activity, diet, treatment and

non-HDL-cholesterol coding, plus composite-score (LE8) warnings; read it when the study uses any of them

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

Never guess a variable name, dataset column name, or variable coding. If a mapping is uncertain, output `[VERIFY: variable_name]` and ask the user to confirm it against the data dictionary.

**KNHANES (single CSV)**: 1. Load CSV and keep every row — the age ≥20 (or per-protocol) restriction is applied to the design in step 3 2. Derive variables using KNHANES coding:

| Variable | Raw Var | Coding | |----------|---------|--------| | Smoking | BS3_1 | 1,2=Current; 3=Former; 8=Never | | Alcohol | BD1_11 | 2-6=Frequent (current drinker); 1=Occasional (past-year abstainer); 8=Never | | Obesity | HE_obe | 1-3=Normal; 4-6=Obesity (BMI≥25, Asian cutoff) | | Depression | BP_PHQ_1~9 | Sum ≥10 = depression | | Diabetes | HE_glu, HE_HbA1c, DE1_dg | FPG≥126 or HbA1c≥6.5 or DE1_dg=1 | | CVD | DI4_dg, DI5_dg, DI6_dg | Any = 1 → CVD yes | | Education | edu | 1-3=Non-college; 4=College | | Income | incm | Quartile (1=lowest … 4=highest): 1-3=Bottom 75%; 4=Top quartile |

3. Set survey design on the full file, then restrict to the analytic domain: des <- svydesign(id=~psu, strata=~kstrata, weights=~wt_itvex, nest=TRUE, data=df); des_ad <- subset(des, age >= 20). Never filter rows before svydesign() — dropping them changes the standard errors (see `survey_weighted.md`, subpopulation analysis)

**NHANES (multiple CSVs)**: 1. Load and merge tables by SEQN (DEMO_J, DPQ_J, GHB_J, GLU_J, BMX_J, SMQ_J, ALQ_J, DIQ_J, MCQ_J, BPQ_J, BPXO_J) 2. Derive variables using NHANES coding. **CRITICAL**: NHANES data downloaded via R `nhanesA` package uses TEXT LABELS, not numeric codes.

| Variable | Raw Var | Coding (text labels) | |----------|---------|----------------------| | Sex | RIAGENDR | "Male" / "Female" (NOT 1/2) | | Smoking | SMQ020 + SMQ040 | 100 cigs (SMQ020 "Yes" / "No") + now smoke (SMQ040 "Every day" / "Some days" / "Not at all") | | Alcohol | ALQ121 + ALQ111 | Frequent (current drinker): any ALQ121 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) | | Obesity | BMXBMI (BMX_J, kg/m²) | ≥30 (WHO cutoff, NOT Asian) | | PHQ-9 | DPQ010~DPQ090 | "Not at all"→0, "Several days"→1, "More than half the days"→2, "Nearly every day"→3; sum ≥10 = depression | | Diabetes | LBXGLU (GLU_J, fasting subsample, mg/dL), LBXGH (GHB_J, %), DIQ010 | LBXGLU≥126 \| LBXGH≥6.5 \| DIQ010=="Yes" (DIQ010: "Yes" / "No" / "Borderline"). CRITICAL: fasting glucose is GLU_J LBXGLU, not BIOPRO_J LBXSGL — CDC says the serum LBXSGL should not be used to determine undiagnosed diabetes; an analysis that uses LBXGLU needs the fasting-subsample weight (step 3). LBXGLU is NA for non-fasters, and in R `NA \| TRUE` is TRUE but `NA \| FALSE` is NA, so this composite goes missing only for non-fasters who would be non-diabetic; dropping those NAs under WTMEC2YR inflates prevalence. Either analyse the FPG composite only in the fasting domain with the fasting weight, or use the non-fasting variant `LBXGH>=6.5 \| DIQ010=="Yes"` on WTMEC2YR, and say which in `variable_mapping.csv` | | CVD | MCQ160B/C/D/E | MCQ160B=="Yes" (CHF) \| MCQ160C=="Yes" (CHD) \| MCQ160D=="Yes" (angina) \| MCQ160E=="Yes" (MI); labels "Yes" / "No" / "Don't know" | | HTN | BPXOSY2+3, BPXODI2+3, BPQ020 | mean(BPXOSY2, BPXOSY3)≥140 \| mean(BPXODI2, BPXODI3)≥90 \| BPQ020=="Yes" (BPXOSY3 is the 3rd reading, not an average; the mean of the 2nd and 3rd matches KNHANES) | | Education | DMDEDUC2 | 5 text levels |

3. Set survey design on the full file, then `subset()` the design object to the analytic domain (e.g. RIDAGEYR >= 20): svydesign(id=~SDMVPSU, strata=~SDMVSTRA, weights=~WTMEC2YR, nest=TRUE). The weight follows the files: the single-cycle `_J` tables above take WTMEC2YR; WTMECPRP goes only with the pre-pandemic `P_` files (P_DEMO, P_BMX, ...). A variable from the fasting subsample (GLU_J LBXGLU, TRIGLY_J LBXTR/LBDLDL) takes the fasting weight instead: WTSAF2YR (`_J`) or WTSAFPRP (`P_`); a variable that combines a fasting-subsample component with full-sa