Cross National

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-sample ones (diabetes above) is defined only in that fasting domain, so it cannot enter a WTMEC2YR model. Pooling cycles follows the NCHS rules: divide each cycle's weight by the number of cycles pooled (1999–2002 has its own 4-year weights), and to combine 2015–2016 with 2017–March 2020 use 2/5.2 × WTMEC2YR and 3.2/5.2 × WTMECPRP.

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