{"id":"cfcfd45f-3146-4b1e-a587-f2930fa68c47","shortId":"nquXMj","kind":"skill","title":"cross-national","tagline":"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.","description":"# Cross-National Comparison Study Skill\n\nYou are assisting a medical researcher in conducting a cross-national comparison study\nusing parallel nationally representative surveys (e.g., KNHANES for Korea, NHANES for the US, CHNS for China).\n\n## When to Use\n\n- Researcher has a clinical question to compare across two countries\n- KNHANES + NHANES data available (or other parallel survey pairs)\n- Goal: produce a complete analysis with country-stratified results + comparison table\n\n## Inputs\n\n1. **Research question**: exposure → outcome association to compare across countries\n2. **Korean data path**: KNHANES CSV file\n3. **US data path**: NHANES CSV directory (multiple tables to merge)\n4. **Harmonization table** (optional): CSV mapping variables across surveys\n   - Default: replicate-study skill's `harmonization_knhanes_nhanes.csv`\n\n## Reference Files\n\n- Harmonization table: `medsci-skills/skills/replicate-study/references/harmonization_knhanes_nhanes.csv`\n- Upstream:\n  - `medsci-skills/skills/write-paper/references/paper_types/cross_national.md` — writing template\n  - `medsci-skills/skills/analyze-stats/references/analysis_guides/survey_weighted.md`\n\n## Workflow\n\n### Phase 1: Study Definition\n\n1. Confirm research question: Exposure → Outcome\n2. Define variable coding for both countries:\n   - Exposure: PHQ-9, BMI category, smoking, etc.\n   - Outcome: diabetes, hypertension, mortality, etc.\n   - Covariates: age, sex, education, income, smoking, alcohol, obesity, CVD\n3. Check harmonization table for variable availability\n4. Output: study protocol summary for user approval\n\n### Phase 2: Data Preparation\n\n**KNHANES (single CSV)**:\n1. Load CSV, filter age ≥20 (or per protocol)\n2. Derive variables using KNHANES coding:\n   - Smoking: BS3_1 (1,2=current, 3=former, 8=never)\n   - Alcohol: BD1_11 (2-6=frequent, 1=occasional, 8=never)\n   - Obesity: HE_obe (≥4=obesity for BMI≥25 Asian cutoff)\n   - PHQ-9: BP_PHQ_1~9, sum score, ≥10=depression\n   - Diabetes: HE_glu≥126 | HE_HbA1c≥6.5 | DE1_dg=1\n   - CVD: DI4_dg=1 | DI5_dg=1 | DI6_dg=1\n3. Set survey design: svydesign(id=~psu, strata=~kstrata, weights=~wt_itvex, nest=TRUE)\n\n**NHANES (multiple CSVs)**:\n1. 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)\n2. Derive variables using NHANES coding:\n   - Smoking: SMQ020 + SMQ040 (100 cigs + now smoke)\n   - Alcohol: ALQ121 (past 12 mo frequency → categories)\n   - Obesity: BMXBMI ≥30 (WHO cutoff, NOT Asian)\n   - PHQ-9: DPQ010~DPQ090, sum score, ≥10=depression\n   - Diabetes: LBXSGL≥126 | LBXGH≥6.5 | DIQ010==\"Yes\" (CRITICAL: LBXSGL not LBXSGLU)\n   - CVD: MCQ160B==\"Yes\" (CHF) | MCQ160C==\"Yes\" (CHD) | MCQ160D==\"Yes\" (angina) | MCQ160E==\"Yes\" (MI)\n   - HTN: BPXOSY3≥140 | BPXODI3≥90 | BPQ020==\"Yes\"\n3. Set survey design: svydesign(id=~SDMVPSU, strata=~SDMVSTRA, weights=~WTMECPRP, nest=TRUE)\n\n### Phase 3: Parallel Analysis\n\nFor EACH country independently:\n1. **Table 1**: Baseline characteristics by exposure (weighted counts + percentages)\n2. **Main analysis**: Sequential logistic regression models\n   - Model 1 (unadjusted)\n   - Model 2 (age + sex)\n   - Model 3 (fully adjusted: + education, income, smoking, alcohol, obesity, CVD)\n3. **Subgroup analyses**: By sex, age group, education, income, alcohol, smoking, CVD, obesity\n4. **Dose-response** (if applicable): RCS with 3 knots\n\n### Phase 4: Cross-National Comparison Table\n\nGenerate a side-by-side comparison:\n\n| Analysis | Korea wOR (95% CI) | US wOR (95% CI) | Direction Agreement |\n|----------|-------------------|-----------------|---------------------|\n| Overall (fully adjusted) | ... | ... | ✓/✗ |\n| Male | ... | ... | |\n| Female | ... | ... | |\n| ... | ... | ... | |\n\n### Phase 5: Output Files\n\n```\n{working_dir}/\n├── cross_national_report.md    — Study summary + comparison tables\n├── variable_mapping.csv        — Variable mapping with match status\n├── analysis_korea.R            — KNHANES analysis (self-contained)\n├── analysis_us.R               — NHANES analysis (self-contained)\n├── results/\n│   ├── table1_korea.csv\n│   ├── table1_us.csv\n│   ├── main_results_comparison.csv\n│   └── subgroup_comparison.csv\n└── manuscript_draft/           — Optional: Methods + Results draft\n    ├── methods_draft.md\n    └── results_draft.md\n```\n\n## Critical Rules\n\n1. **NEVER pool data across countries**. Each country analyzed with its own survey design.\n2. **Country-specific BMI cutoffs**: Korea ≥25 (Asian), US ≥30 (WHO).\n3. **Country-specific income**: KNHANES quartile, NHANES PIR → harmonize to binary.\n4. **Weighted analysis mandatory**: Both KNHANES and NHANES are complex surveys.\n5. **Document all harmonization decisions**: What matches, what needed recoding, what differs.\n6. **Same analytic approach**: Identical model specifications for both countries for fair comparison.\n\n## KNHANES Variable Coding Reference (validated via Joo 2026 replication)\n\n| Variable | Raw Var | Coding |\n|----------|---------|--------|\n| Smoking | BS3_1 | 1,2=Current; 3=Former; 8=Never |\n| Alcohol | BD1_11 | 2-6=Frequent (current drinker); 1=Occasional (past-year abstainer); 8=Never |\n| Obesity | HE_obe | 1-3=Normal; 4-6=Obesity (BMI≥25) |\n| Depression | BP_PHQ_1~9 | Sum ≥10 = depression |\n| Diabetes | HE_glu, HE_HbA1c, DE1_dg | FPG≥126 or HbA1c≥6.5 or DE1_dg=1 |\n| CVD | DI4_dg, DI5_dg, DI6_dg | Any = 1 → CVD yes |\n| Education | edu | 1-3=Non-college; 4=College |\n| Income | incm | 1-3=Bottom 80%; 4=Top 20% |\n| Survey design | kstrata, psu, wt_itvex | strata, cluster, weight |\n\n## NHANES Variable Coding Reference (validated via Joo 2026 cross-national)\n\n**CRITICAL**: NHANES data downloaded via R `nhanesA` package uses TEXT LABELS, not numeric codes.\n\n| Variable | Raw Var | Text Labels → Numeric |\n|----------|---------|----------------------|\n| PHQ-9 items | DPQ010~DPQ090 | \"Not at all\"→0, \"Several days\"→1, \"More than half the days\"→2, \"Nearly every day\"→3 |\n| Sex | RIAGENDR | \"Male\" / \"Female\" (NOT 1/2) |\n| Smoking (100 cigs) | SMQ020 | \"Yes\" / \"No\" |\n| Smoking (now) | SMQ040 | \"Every day\" / \"Some days\" / \"Not at all\" |\n| Alcohol freq | ALQ121 | Text labels (see below) |\n| Alcohol ever | ALQ111 | \"Yes\" / \"No\" |\n| Education | DMDEDUC2 | 5 text levels (see SKILL.md Phase 2) |\n| Diabetes dx | DIQ010 | \"Yes\" / \"No\" / \"Borderline\" |\n| CVD (CHF) | MCQ160B | \"Yes\" / \"No\" / \"Don't know\" |\n| CVD (CHD) | MCQ160C | \"Yes\" / \"No\" / \"Don't know\" |\n| CVD (angina) | MCQ160D | \"Yes\" / \"No\" / \"Don't know\" |\n| Fasting glucose | LBXSGL (BIOPRO_J) | Numeric (mg/dL) — note: NOT LBXSGLU |\n| HbA1c | LBXGH (GHB_J) | Numeric (%) |\n| BMI | BMXBMI (BMX_J) | Numeric (kg/m²) |\n| Weight | WTMEC2YR (single-cycle) or WTMECPRP (pre-pandemic pooled) | Numeric |\n| Strata | SDMVSTRA | Numeric |\n| PSU | SDMVPSU | Numeric |\n\n### ALQ121 Text Label Mapping (Alcohol Frequency)\n- Frequent (current drinker): Any specific frequency except \"Never in the last year\"\n- Occasional (past-year abstainer): \"Never in the last year\"\n- Never (lifetime non-drinker): ALQ111 == \"No\" (ALQ121 will be NA)\n\n### Additional KNHANES Variables (validated via LE8-Asthma replication)\n\n| Variable | Raw Var | Coding |\n|----------|---------|--------|\n| Asthma | DJ2_dg | 0=No, 1=Yes (physician dx), 9=Don't know → exclude |\n| Asthma treatment | DJ2_pt | 0=No, 1=Yes, 8=N/A, 9=Don't know |\n| 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 |\n| Sleep (2017-18 weekend) | BP16_21/22/23/24 | Same format as weekday |\n| Sleep (2019-20) | BP16_1/2 | Direct sleep hours (weekday/weekend). 99=Don't know→NA |\n| PA aerobic | pa_aerobic | 0=Doesn't meet, 1=Meets guidelines. **Note: values are 0/1, NOT 1/2** |\n| HTN treatment | DI1_pr | 1=Yes, 0=No (currently treating hypertension) |\n| Dyslipidemia tx | DI3_pr | 1=Yes, 0=No (if available) |\n| Non-HDL chol | HE_chol - HE_HDL_st2 | Derived: total cholesterol minus HDL |\n\n### Additional NHANES Variables (validated via LE8-Asthma replication)\n\n| Variable | Raw Var | Coding |\n|----------|---------|--------|\n| Asthma | MCQ010 | \"Yes\" / \"No\" (ever told by doctor) |\n| Sleep hours | SLD012 | Numeric (hours/night on weekdays) |\n| BP treatment | BPQ020 | \"Yes\" / \"No\" (told by doctor, high BP) |\n| Cholesterol treatment | BPQ100D | \"Yes\" / \"No\" (taking cholesterol Rx) |\n| PA vigorous work | PAQ605/PAQ610/PAD615 | Yes/No, days/week, min/day |\n| PA moderate work | PAQ620/PAQ625/PAD630 | Yes/No, days/week, min/day |\n| PA walk/bike | PAQ635/PAQ640/PAD645 | Yes/No, days/week, min/day |\n| PA vigorous rec | PAQ665/PAQ670/PAD675 | Yes/No, days/week, min/day |\n| PA moderate rec | PAQ650/PAQ655/PAD660 | Yes/No, days/week, min/day |\n| Dietary fiber | DR1TFIBE (DR1TOT_J) | Numeric (grams, day 1 recall) |\n| Dietary sodium | DR1TSODI (DR1TOT_J) | Numeric (mg) |\n| Dietary sat fat | DR1TSFAT (DR1TOT_J) | Numeric (grams) |\n| Total energy | DR1TKCAL (DR1TOT_J) | Numeric (kcal) |\n| Total sugars | DR1TSUGR (DR1TOT_J) | Numeric (grams) |\n| Non-HDL chol | LBXTC - LBDHDD | Derived: TCHOL_J minus HDL_J |\n\n## CHNS Variable Coding Reference (validated via 3-country batch)\n\n**Data source**: cpc.unc.edu/projects/china (free registration)\n**Biomarker wave**: 2009 only (N=9,549). Other variables available 1989-2015.\n**Survey design**: No formal weights. Use `svydesign(id=~COMMID, weights=~1)` or cluster-robust SE.\n\n### Key Files and Merge Strategy\n\n| File | Key Variables | Join Key |\n|------|--------------|----------|\n| mast_pub_12 | IDind, GENDER (1=M/2=F), WEST_DOB_Y (birth year) | IDind |\n| 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 |\n| biomarker_09 | GLUCOSE_MG, HbA1c, TC_MG, TG_MG, HDL_C_MG, LDL_C_MG, HS_CRP, HGB, WBC, ALT, CRE_MG | IDind |\n| educ_12 | A12 (education 0-6) | IDind + filter WAVE==2009 |\n| indinc_10 | indwage (yuan, continuous → quartiles) | IDind + filter wave==2009 |\n\n### Variable Coding\n\n| Variable | Raw Var | Coding | Notes |\n|----------|---------|--------|-------|\n| Sex | GENDER | 1=Male, 2=Female | Same as KNHANES/NHANES |\n| Age | WEST_DOB_Y | age = wave_year - WEST_DOB_Y | Integer truncation |\n| BMI | HEIGHT, WEIGHT | WEIGHT / (HEIGHT/100)^2 | **Obesity: BMI ≥ 28 (WGOC, NOT 25 or 30)** |\n| Waist | U10 | cm, direct measurement | **Central obesity: ≥90M / ≥80F (IDF-Asian)** |\n| SBP | SYSTOL1-3 | mean(SYSTOL1, SYSTOL2, SYSTOL3) | 3 readings averaged |\n| DBP | DIASTOL1-3 | mean(DIASTOL1, DIASTOL2, DIASTOL3) | 3 readings averaged |\n| HBP diagnosed | U22 | 0=No, 1=Yes, 9=Don't know (→NA) | |\n| HBP medication | U24 | 0=No, 1=Yes | |\n| DM diagnosed | U24A | 0=No, 1=Yes, 9=Don't know (→NA) | |\n| Smoking | U25 + U27 | never(U25==0) / former(U25==1 & U27==0) / current(U25==1 & U27==1) | |\n| 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 |\n| 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 | |\n| Income | indwage | Continuous yuan → quartiles within wave | |\n| Glucose | GLUCOSE_MG | mg/dL (also GLUCOSE in mmol/L) | 2009 only |\n| HbA1c | HbA1c | % (direct) | 2009 only |\n| TC | TC_MG | mg/dL | 2009 only |\n| TG | TG_MG | mg/dL | 2009 only |\n| HDL | HDL_C_MG | mg/dL | 2009 only |\n| hsCRP | HS_CRP | mg/L | 2009 only |\n| Hemoglobin | HGB | **g/L (divide by 10 for g/dL)** | Unit differs from KR/US |\n| Self-health | U48A | Self-reported health status | 2004-2011 |\n| Depression | — | **NOT AVAILABLE** in standard download. CES-D exists but needs separate dataset. | Cannot directly compare with PHQ-9 |\n\n### CHNS-Specific Warnings\n\n1. **No survey weights**: CHNS is NOT a formally weighted survey. Use unweighted analysis with cluster-robust SE by COMMID. Report as limitation.\n2. **Biomarker = 2009 only**: Glucose, HbA1c, lipids, hsCRP available only in 2009 wave. Other waves lack lab data.\n3. **CES-D not in standard download**: Depression comparison requires separate dataset download from cpc.unc.edu.\n4. **BMI cutoff ≠ KR ≠ US**: China=28, Korea=25, US=30. Use country-specific cutoffs AND sensitivity analysis with WHO cutoff=25.\n5. **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.\n6. **Hemoglobin unit**: CHNS reports g/L (KR/US report g/dL). Divide by 10 when comparing.\n7. **Education scale**: 7-level (0-6) vs KR 4-level vs US 5-level. Harmonize to 3-level for comparison.\n\n### Composite Score Replication Warnings (learned from LE8 replication)\n\n1. **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.\n2. **KNHANES sleep = clock times**: BP16_11-14 are bedtime/waketime (hour:min), NOT sleep duration. Must compute `wake_time - bed_time` with midnight crossing.\n3. **pa_aerobic codes**: Values are 0/1 (not 1/2). Binary → MET-hours approximation is coarse.\n4. **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.\n5. **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.\n\n## Anti-Hallucination\n\n- **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.\n- **Never fabricate statistical results** — no invented p-values, effect sizes, confidence intervals, or sample sizes. All numbers must come from executed code output.\n- **Never generate references from memory.** Use `/search-lit` for all citations.\n- If a function, package, or API does not exist or you are unsure, say so explicitly rather than guessing.","tags":["cross","national","medsci","skills","aperivue","agent-skills","biostatistics","claude-code","claude-skills","clinical-research","diagnostic-accuracy","irb-protocol"],"capabilities":["skill","source-aperivue","skill-cross-national","topic-agent-skills","topic-biostatistics","topic-claude-code","topic-claude-skills","topic-clinical-research","topic-diagnostic-accuracy","topic-irb-protocol","topic-literature-review","topic-manuscript","topic-medical-ai","topic-medical-research","topic-meta-analysis"],"categories":["medsci-skills"],"synonyms":[],"warnings":[],"endpointUrl":"https://skills.sh/Aperivue/medsci-skills/cross-national","protocol":"skill","transport":"skills-sh","auth":{"type":"none","details":{"cli":"npx skills add Aperivue/medsci-skills","source_repo":"https://github.com/Aperivue/medsci-skills","install_from":"skills.sh"}},"qualityScore":"0.499","qualityRationale":"deterministic score 0.50 from registry signals: · indexed on github topic:agent-skills · 98 github stars · SKILL.md body (14,209 chars)","verified":false,"liveness":"unknown","lastLivenessCheck":null,"agentReviews":{"count":0,"score_avg":null,"cost_usd_avg":null,"success_rate":null,"latency_p50_ms":null,"narrative_summary":null,"summary_updated_at":null},"enrichmentModel":"deterministic:skill-github:v1","enrichmentVersion":1,"enrichedAt":"2026-05-18T18:56:29.094Z","embedding":null,"createdAt":"2026-05-13T12:57:44.208Z","updatedAt":"2026-05-18T18:56:29.094Z","lastSeenAt":"2026-05-18T18:56:29.094Z","tsv":"'-14':1862 '-18':1008,1043 '-2':1540,1544,1570 '-20':1053 '-2010':1900 '-2011':1647 '-2015':1275 '-3':692,737,746,1323,1325,1453,1463 '-4':1536,1573 '-6':265,676,695,1382,1576,1806 '-9':195,282,383,793,1667 '/projects/china':1261 '/search-lit':2011 '/skills/analyze-stats/references/analysis_guides/survey_weighted.md':174 '/skills/replicate-study/references/harmonization_knhanes_nhanes.csv':163 '/skills/write-paper/references/paper_types/cross_national.md':168 '0':800,981,996,1069,1088,1099,1381,1474,1486,1493,1507,1512,1523,1550,1569,1805 '0/1':1079,1885 '00':1317 '09':1355 '1':112,177,180,236,253,254,267,285,300,304,307,310,328,442,444,460,575,664,665,680,691,702,722,731,736,745,803,983,998,1073,1086,1097,1205,1286,1307,1406,1476,1488,1495,1510,1515,1517,1532,1539,1543,1552,1672,1829 '1/2':819,1055,1081,1887 '10':289,388,705,1388,1630,1797 '100':364,821 '11':263,674,1015,1861 '11/12/13/14':1010 '12':371,1018,1304,1378 '126':294,392,715 '13':1021 '14':1024 '140':416 '1989':1274 '1x/mo':1547 '1x/week':1530 '2':30,122,186,230,245,255,264,355,452,463,589,666,675,809,856,1408,1430,1534,1554,1696,1855 '20':241,751 '2004':1646 '2009':1266,1353,1386,1396,1593,1598,1604,1610,1617,1623,1698,1707 '2017':1007,1042 '2019':1052 '2026':656,768 '21/22/23/24':1046 '25':278,596,698,1436,1738,1752,1836 '28':1433,1736 '3':35,129,214,257,311,421,435,467,476,497,601,668,813,1254,1458,1468,1529,1535,1538,1558,1572,1714,1817,1879 '30':377,599,1438,1740 '4':140,221,274,489,500,613,694,741,749,1526,1542,1562,1730,1809,1895 '5':530,624,850,1546,1564,1575,1753,1813,1922 '549':1270 '6':636,1566,1786 '6.5':297,394,718 '7':1800,1803 '8':259,269,670,686,1000,1932 '80':748 '80f':1447 '9':987,1002,1269,1478,1497 '90':418 '90m':1446 '95':516,520 '99':1036,1060 'a12':1379,1549 'abstain':685,948 'across':87,120,147,579,1931 'addit':965,1117 'adjust':469,526 'aerob':1066,1068,1881 'age':206,240,464,481,1413,1417 'agreement':523 'ahei':1899 'alcohol':211,261,368,473,485,672,836,843,930,1342,1518 'alq':347 'alq111':845,959 'alq121':369,838,926,961 'also':1589 'alt':1373 'alway':1767 'analys':478 'analysi':25,103,437,454,513,546,549,553,556,615,1685,1748 'analyt':638 'analyz':583 'angina':410,880 'anti':1949 'anti-hallucin':1948 'api':2020 'applic':494 'approach':639 'approv':228 'approxim':1892 'asian':279,381,597,1450,1850 'ask':1972 'assist':49 'associ':117 'asthma':972,978,992,1124,1130 'avail':93,220,1102,1273,1650,1704 'averag':1460,1470 'base':1908 'baselin':445 'batch':1256 'bd1':262,673 'bed':1016,1019,1031,1874 'bedtime/waketime':1864 'binari':612,1888 'biomark':1264,1354,1697 'biopro':341,890 'birth':1313 'bmi':196,277,593,697,902,1425,1432,1731,1830,1846 'bmx':343,904 'bmxbmi':376,903 'borderlin':862 'bottom':747 'bp':283,700,1145,1154 'bp16':1009,1045,1054,1860 'bpq':353 'bpq020':419,1147 'bpq100d':1157 'bpxodi3':417 'bpxosy3':415 'bs3':252,663 'bug':1785 'c':1364,1367,1614 'calcul':1027 'cannot':1662 'categori':197,374 'central':1444 'ces':1655,1716 'ces-d':1654,1715 'characterist':446 'chd':407,872 'check':215 'chf':404,864 'china':76,1735,1771 'chns':16,74,1248,1669,1676,1789 'chns-specif':1668 'chol':1106,1108,1239 'cholesterol':1114,1155,1161 'ci':517,521 'cig':365,822 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