Cross National

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

• Research question: exposure → outcome association to compare across countries
• Korean data path: KNHANES CSV file
• US data path: NHANES CSV directory (multiple tables to merge)
• 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

• Confirm research question: Exposure → Outcome
• 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
• Check harmonization table for variable availability
• Output: study protocol summary for user approval

Phase 2: Data Preparation

KNHANES (single CSV):

• Load CSV, filter age ≥20 (or per protocol)
• 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
• Set survey design: svydesign(id=~psu, strata=~kstrata, weights=~wt_itvex, nest=TRUE)

NHANES (multiple CSVs):

• 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)
• 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"
• Set survey design: svydesign(id=~SDMVPSU, strata=~SDMVSTRA, weights=~WTMECPRP, nest=TRUE)

Phase 3: Parallel Analysis

For EACH country independently:

• Table 1: Baseline characteristics by exposure (weighted counts + percentages)
• Main analysis: Sequential logistic regression models
• Model 1 (unadjusted)
• Model 2 (age + sex)
• Model 3 (fully adjusted: + education, income, smoking, alcohol, obesity, CVD)
• Subgroup analyses: By sex, age group, education, income, alcohol, smoking, CVD, obesity
• 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

• NEVER pool data across countries. Each country analyzed with its own survey design.
• Country-specific BMI cutoffs: Korea ≥25 (Asian), US ≥30 (WHO).
• Country-specific income: KNHANES quartile, NHANES PIR → harmonize to binary.
• Weighted analysis mandatory: Both KNHANES and NHANES are complex surveys.
• Document all harmonization decisions: What matches, what needed recoding, what differs.
• 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

• No survey weights: CHNS is NOT a formally weighted survey. Use unweighted analysis with cluster-robust SE by COMMID. Report as limitation.
• Biomarker = 2009 only: Glucose, HbA1c, lipids, hsCRP available only in 2009 wave. Other waves lack lab data.
• CES-D not in standard download: Depression comparison requires separate dataset download from cpc.unc.edu.
• BMI cutoff ≠ KR ≠ US: China=28, Korea=25, US=30. Use country-specific cutoffs AND sensitivity analysis with WHO cutoff=25.
• 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.
• Hemoglobin unit: CHNS reports g/L (KR/US report g/dL). Divide by 10 when comparing.
• 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)

• 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.
• KNHANES sleep = clock times: BP16_11-14 are bedtime/waketime (hour:min), NOT sleep duration. Must compute wake_time - bed_time with midnight crossing.
• pa_aerobic codes: Values are 0/1 (not 1/2). Binary → MET-hours approximation is coarse.
• 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.
• 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.