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:

Analysis	Korea wOR (95% CI)	US wOR (95% CI)	Direction Agreement
Overall (fully adjusted)	...	...	✓/✗
Male	...	...
Female	...	...
...	...	...

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)

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)
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%
Survey design	kstrata, psu, wt_itvex	strata, cluster, weight

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

CRITICAL: NHANES data downloaded via R

nhanesA

package uses TEXT LABELS, not numeric codes.

Variable	Raw Var	Text Labels → Numeric
PHQ-9 items	DPQ010~DPQ090	"Not at all"→0, "Several days"→1, "More than half the days"→2, "Nearly every day"→3
Sex	RIAGENDR	"Male" / "Female" (NOT 1/2)
Smoking (100 cigs)	SMQ020	"Yes" / "No"
Smoking (now)	SMQ040	"Every day" / "Some days" / "Not at all"
Alcohol freq	ALQ121	Text labels (see below)
Alcohol ever	ALQ111	"Yes" / "No"
Education	DMDEDUC2	5 text levels (see SKILL.md Phase 2)
Diabetes dx	DIQ010	"Yes" / "No" / "Borderline"
CVD (CHF)	MCQ160B	"Yes" / "No" / "Don't know"
CVD (CHD)	MCQ160C	"Yes" / "No" / "Don't know"
CVD (angina)	MCQ160D	"Yes" / "No" / "Don't know"
Fasting glucose	LBXSGL (BIOPRO_J)	Numeric (mg/dL) — note: NOT LBXSGLU
HbA1c	LBXGH (GHB_J)	Numeric (%)
BMI	BMXBMI (BMX_J)	Numeric (kg/m²)
Weight	WTMEC2YR (single-cycle) or WTMECPRP (pre-pandemic pooled)	Numeric
Strata	SDMVSTRA	Numeric
PSU	SDMVPSU	Numeric

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)

Variable	Raw Var	Coding
Asthma	DJ2_dg	0=No, 1=Yes (physician dx), 9=Don't know → exclude
Asthma treatment	DJ2_pt	0=No, 1=Yes, 8=N/A, 9=Don't know
Sleep (2017-18)	BP16_11/12/13/14	Clock times, NOT hours! 11=bed hour, 12=bed min, 13=wake hour, 14=wake min. Calculate: duration = wake_time - bed_time (handle midnight crossing). 99=Don't know→NA
Sleep (2017-18 weekend)	BP16_21/22/23/24	Same format as weekday
Sleep (2019-20)	BP16_1/2	Direct sleep hours (weekday/weekend). 99=Don't know→NA
PA aerobic	pa_aerobic	0=Doesn't meet, 1=Meets guidelines. Note: values are 0/1, NOT 1/2
HTN treatment	DI1_pr	1=Yes, 0=No (currently treating hypertension)
Dyslipidemia tx	DI3_pr	1=Yes, 0=No (if available)
Non-HDL chol	HE_chol - HE_HDL_st2	Derived: total cholesterol minus HDL

Additional NHANES Variables (validated via LE8-Asthma replication)

Variable	Raw Var	Coding
Asthma	MCQ010	"Yes" / "No" (ever told by doctor)
Sleep hours	SLD012	Numeric (hours/night on weekdays)
BP treatment	BPQ020	"Yes" / "No" (told by doctor, high BP)
Cholesterol treatment	BPQ100D	"Yes" / "No" (taking cholesterol Rx)
PA vigorous work	PAQ605/PAQ610/PAD615	Yes/No, days/week, min/day
PA moderate work	PAQ620/PAQ625/PAD630	Yes/No, days/week, min/day
PA walk/bike	PAQ635/PAQ640/PAD645	Yes/No, days/week, min/day
PA vigorous rec	PAQ665/PAQ670/PAD675	Yes/No, days/week, min/day
PA moderate rec	PAQ650/PAQ655/PAD660	Yes/No, days/week, min/day
Dietary fiber	DR1TFIBE (DR1TOT_J)	Numeric (grams, day 1 recall)
Dietary sodium	DR1TSODI (DR1TOT_J)	Numeric (mg)
Dietary sat fat	DR1TSFAT (DR1TOT_J)	Numeric (grams)
Total energy	DR1TKCAL (DR1TOT_J)	Numeric (kcal)
Total sugars	DR1TSUGR (DR1TOT_J)	Numeric (grams)
Non-HDL chol	LBXTC - LBDHDD	Derived: TCHOL_J minus HDL_J

CHNS Variable Coding Reference (validated via 3-country batch)

Data source: cpc.unc.edu/projects/china (free registration) Biomarker wave: 2009 only (N=9,549). Other variables available 1989-2015. Survey design: No formal weights. Use

svydesign(id=~COMMID, weights=~1)

or cluster-robust SE.

Key Files and Merge Strategy

File	Key Variables	Join Key
mast_pub_12	IDind, GENDER (1=M/2=F), WEST_DOB_Y (birth year)	IDind
pexam_00	HEIGHT, WEIGHT, U10 (waist), SYSTOL1-3, DIASTOL1-3, U22 (HBP dx), U24 (HBP meds), U24A (DM dx), U25 (ever smoked), U27 (still smokes), U40 (alcohol), U41 (freq), U48A (self-health), COMMID	IDind + filter WAVE==2009
biomarker_09	GLUCOSE_MG, HbA1c, TC_MG, TG_MG, HDL_C_MG, LDL_C_MG, HS_CRP, HGB, WBC, ALT, CRE_MG	IDind
educ_12	A12 (education 0-6)	IDind + filter WAVE==2009
indinc_10	indwage (yuan, continuous → quartiles)	IDind + filter wave==2009

Variable Coding

Variable	Raw Var	Coding	Notes
Sex	GENDER	1=Male, 2=Female	Same as KNHANES/NHANES
Age	WEST_DOB_Y	age = wave_year - WEST_DOB_Y	Integer truncation
BMI	HEIGHT, WEIGHT	WEIGHT / (HEIGHT/100)^2	Obesity: BMI ≥ 28 (WGOC, NOT 25 or 30)
Waist	U10	cm, direct measurement	Central obesity: ≥90M / ≥80F (IDF-Asian)
SBP	SYSTOL1-3	mean(SYSTOL1, SYSTOL2, SYSTOL3)	3 readings averaged
DBP	DIASTOL1-3	mean(DIASTOL1, DIASTOL2, DIASTOL3)	3 readings averaged
HBP diagnosed	U22	0=No, 1=Yes, 9=Don't know (→NA)
HBP medication	U24	0=No, 1=Yes
DM diagnosed	U24A	0=No, 1=Yes, 9=Don't know (→NA)
Smoking	U25 + U27	never(U25==0) / former(U25==1 & U27==0) / current(U25==1 & U27==1)
Alcohol	U40 + U41	never(U40==0) / occasional(U41≥4) / frequent(U41≤3, ≥1x/week)	U41: 1=daily, 2=3-4x/wk, 3=1-2x/wk, 4=1-2x/mo, 5=<1x/mo
Education	A12	0=none, 1=primary, 2=lower-mid, 3=upper-mid, 4=technical, 5=university, 6=master+. Recode: 0-2→low, 3-4→mid, 5-6→high
Income	indwage	Continuous yuan → quartiles within wave
Glucose	GLUCOSE_MG	mg/dL (also GLUCOSE in mmol/L)	2009 only
HbA1c	HbA1c	% (direct)	2009 only
TC	TC_MG	mg/dL	2009 only
TG	TG_MG	mg/dL	2009 only
HDL	HDL_C_MG	mg/dL	2009 only
hsCRP	HS_CRP	mg/L	2009 only
Hemoglobin	HGB	g/L (divide by 10 for g/dL)	Unit differs from KR/US
Self-health	U48A	Self-reported health status	2004-2011
Depression	—	NOT AVAILABLE in standard download. CES-D exists but needs separate dataset.	Cannot directly compare with PHQ-9

CHNS-Specific Warnings

1. No survey weights: CHNS is NOT a formally weighted survey. Use unweighted analysis with cluster-robust SE by COMMID. Report as limitation.
2. Biomarker = 2009 only: Glucose, HbA1c, lipids, hsCRP available only in 2009 wave. Other waves lack lab data.
3. CES-D not in standard download: Depression comparison requires separate dataset download from cpc.unc.edu.
4. BMI cutoff ≠ KR ≠ US: China=28, Korea=25, US=30. Use country-specific cutoffs AND sensitivity analysis with WHO cutoff=25.
5. SES-health gradient may reverse: Low education and low income are NOT always risk factors in China (null/protective). This is the "developing country health transition" — do NOT treat as a bug.
6. Hemoglobin unit: CHNS reports g/L (KR/US report g/dL). Divide by 10 when comparing.
7. Education scale: 7-level (0-6) vs KR 4-level vs US 5-level. Harmonize to 3-level for comparison.

Composite Score Replication Warnings (learned from LE8 replication)

1. BMI cutoff mismatch: LE8 uses WHO <25 which classifies most Koreans as "ideal" → Factor subscore loses BMI discriminatory power in Asian populations. Report this limitation.
2. KNHANES sleep = clock times: BP16_11-14 are bedtime/waketime (hour:min), NOT sleep duration. Must compute

   wake_time - bed_time

   with midnight crossing.
3. pa_aerobic codes: Values are 0/1 (not 1/2). Binary → MET-hours approximation is coarse.
4. Diet quality scoring: AHEI-2010 requires detailed food group data; nutrient-based proxy gives different distribution. Recommend downloading NHANES DR1TOT_J for dietary recall nutrients.
5. LE8 sensitivity to implementation: Small scoring differences compound across 8 components → overall score can diverge substantially, especially in the "moderate" range where most people cluster.

Anti-Hallucination

• Never fabricate variable names, dataset column names, or variable codings. If a variable mapping is uncertain, output

  [VERIFY: variable_name]

  and ask the user to confirm against the data dictionary.
• Never fabricate statistical results — no invented p-values, effect sizes, confidence intervals, or sample sizes. All numbers must come from executed code output.
• Never generate references from memory. Use

  /search-lit

  for all citations.
• If a function, package, or API does not exist or you are unsure, say so explicitly rather than guessing.