git:20260930.c83ad00 to git:20260930.30387c2

3 added, 3 removed. Audit A to A.

---

name: cross-national

- description: End-to-end cross-national comparison study using KNHANES + NHANES + CHNS (or other parallel surveys). Variable harmonization, parallel weighted analysis, and comparison tables. Supports 2-country (KR+US) and 3-country (KR+US+CN) designs.

- triggers: cross-national, 한미 비교, Korea US comparison, KNHANES NHANES, 양국 비교, binational, cross-country, 비교연구, 3국 비교, CHNS, 한미중

- tools: Read, Write, Edit, Bash, Grep, Glob

+ 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, filter age ≥20 (or per protocol)

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 | 1-3=Bottom 80%; 4=Top 20% |

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. **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 |

| Education | DMDEDUC2 | 5 text levels |

3. Set survey design: svydesign(id=~SDMVPSU, strata=~SDMVSTRA, weights=~WTMECPRP, nest=TRUE) — WTMECPRP

for the pre-pandemic pooled files; for a single cycle (e.g. the `_J` tables) use weights=~WTMEC2YR

**CHNS (3-country design)**: read `references/chns_coding.md` before preparing China data.

### 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:

|----------|-------------------|-----------------|---------------------|

| Overall (fully adjusted) | ... | ... | ✓/✗ |

| Male | ... | ... | |

| Female | ... | ... | |

| ... | ... | ... | |

Every number comes from executed code output (`analysis_korea.R`, `analysis_us.R`) — never an

invented p-value, effect size, confidence interval, or sample size.

### 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

```

Take every citation in the report or draft from `/search-lit` (confirmed DOI/PMID); mark any other

`[UNVERIFIED - NEEDS MANUAL CHECK]`, and never generate references from memory.

## 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. CHNS has no

survey weights — analyse it unweighted (see `references/chns_coding.md`).

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.