cross-national

Name: cross-national
Author: Aperivue

208stars53forks

How to install cross-national?

aperivue/medsci-skills/cross-national

$npx -y skills add Aperivue/medsci-skills --skill cross-national

Installs into the current project.

Summary

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.

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SKILL.md

1	# Cross-National Comparison Study Skill
2
3	You are assisting a medical researcher in conducting a cross-national comparison study
4	using parallel nationally representative surveys (e.g., KNHANES for Korea, NHANES for the US, CHNS for China).
5
6	## When to Use
7
8	- Researcher has a clinical question to compare across two countries
9	- KNHANES + NHANES data available (or other parallel survey pairs)
10	- Goal: produce a complete analysis with country-stratified results + comparison table
11
12	## Inputs
13
14	1. Research question: exposure → outcome association to compare across countries
15	2. Korean data path: KNHANES CSV file
16	3. US data path: NHANES CSV directory (multiple tables to merge)
17	4. Harmonization table (optional): CSV mapping variables across surveys
18	- Default: replicate-study skill's `harmonization_knhanes_nhanes.csv`
19
20	## Reference Files
21
22	- Harmonization table: `medsci-skills/skills/replicate-study/references/harmonization_knhanes_nhanes.csv`
23	- Upstream:
24	- `medsci-skills/skills/write-paper/references/paper_types/cross_national.md` — writing template
25	- `medsci-skills/skills/analyze-stats/references/analysis_guides/survey_weighted.md`
26
27	## Workflow
28
29	### Phase 1: Study Definition
30
31	1. Confirm research question: Exposure → Outcome
32	2. Define variable coding for both countries:
33	- Exposure: PHQ-9, BMI category, smoking, etc.
34	- Outcome: diabetes, hypertension, mortality, etc.
35	- Covariates: age, sex, education, income, smoking, alcohol, obesity, CVD
36	3. Check harmonization table for variable availability
37	4. Output: study protocol summary for user approval
38
39	### Phase 2: Data Preparation
40
41	KNHANES (single CSV):
42	1. Load CSV, filter age ≥20 (or per protocol)
43	2. Derive variables using KNHANES coding:
44	- Smoking: BS3_1 (1,2=current, 3=former, 8=never)
45	- Alcohol: BD1_11 (2-6=frequent, 1=occasional, 8=never)
46	- Obesity: HE_obe (≥4=obesity for BMI≥25 Asian cutoff)
47	- PHQ-9: BP_PHQ_1~9, sum score, ≥10=depression
48	- Diabetes: HE_glu≥126 \| HE_HbA1c≥6.5 \| DE1_dg=1
49	- CVD: DI4_dg=1 \| DI5_dg=1 \| DI6_dg=1
50	3. Set survey design: svydesign(id=~psu, strata=~kstrata, weights=~wt_itvex, nest=TRUE)
51
52	NHANES (multiple CSVs):
53	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)
54	2. Derive variables using NHANES coding:
55	- Smoking: SMQ020 + SMQ040 (100 cigs + now smoke)
56	- Alcohol: ALQ121 (past 12 mo frequency → categories)
57	- Obesity: BMXBMI ≥30 (WHO cutoff, NOT Asian)
58	- PHQ-9: DPQ010~DPQ090, sum score, ≥10=depression
59	- Diabetes: LBXSGL≥126 \| LBXGH≥6.5 \| DIQ010=="Yes" (CRITICAL: LBXSGL not LBXSGLU)
60	- CVD: MCQ160B=="Yes" (CHF) \| MCQ160C=="Yes" (CHD) \| MCQ160D=="Yes" (angina) \| MCQ160E=="Yes" (MI)
61	- HTN: BPXOSY3≥140 \| BPXODI3≥90 \| BPQ020=="Yes"
62	3. Set survey design: svydesign(id=~SDMVPSU, strata=~SDMVSTRA, weights=~WTMECPRP, nest=TRUE)
63
64	### Phase 3: Parallel Analysis
65
66	For EACH country independently:
67	1. Table 1: Baseline characteristics by exposure (weighted counts + percentages)
68	2. Main analysis: Sequential logistic regression models
69	- Model 1 (unadjusted)
70	- Model 2 (age + sex)
71	- Model 3 (fully adjusted: + education, income, smoking, alcohol, obesity, CVD)
72	3. Subgroup analyses: By sex, age group, education, income, alcohol, smoking, CVD, obesity
73	4. Dose-response (if applicable): RCS with 3 knots
74
75	### Phase 4: Cross-National Comparison Table
76
77	Generate a side-by-side comparison:
78
79	\| Analysis \| Korea wOR (95% CI) \| US wOR (95% CI) \| Direction Agreement \|
80	\|----------\|-------------------\|-----------------\|---------------------\|
81	\| Overall (fully adjusted) \| ... \| ... \| ✓/✗ \|
82	\| Male \| ... \| ... \| \|
83	\| Female \| ... \| ... \| \|
84	\| ... \| ... \| ... \| \|
85
86	### Phase 5: Output Files
87
88	```
89	{working_dir}/
90	├── cross_national_report.md — Study summary + comparison tables
91	├── variable_mapping.csv — Variable mapping with match status
92	├── analysis_korea.R — KNHANES analysis (self-contained)
93	├── analysis_us.R — NHANES analysis (self-contained)
94	├── results/
95	│ ├── table1_korea.csv
96	│ ├── table1_us.csv
97	│ ├── main_results_comparison.csv
98	│ └── subgroup_comparison.csv
99	└── manuscript_draft/ — Optional: Methods + Results draft
100	├── methods_draft.md
101	└── results_draft.md
102	```
103
104	## Critical Rules
105
106	1. NEVER pool data across countries. Each country analyzed with its own survey design.
107	2. Country-specific BMI cutoffs: Korea ≥25 (Asian), US ≥30 (WHO).
108	3. Country-specific income: KNHANES quartile, NHANES PIR → harmonize to binary.
109	4. *

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cross-national

208stars53forks

View on GitHub

How to install cross-national?

aperivue/medsci-skills/cross-national

$npx -y skills add Aperivue/medsci-skills --skill cross-national

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Cross-National Comparison Study Skill
2
3	You are assisting a medical researcher in conducting a cross-national comparison study
4	using parallel nationally representative surveys (e.g., KNHANES for Korea, NHANES for the US, CHNS for China).
5
6	## When to Use
7
8	- Researcher has a clinical question to compare across two countries
9	- KNHANES + NHANES data available (or other parallel survey pairs)
10	- Goal: produce a complete analysis with country-stratified results + comparison table
11
12	## Inputs
13
14	1. Research question: exposure → outcome association to compare across countries
15	2. Korean data path: KNHANES CSV file
16	3. US data path: NHANES CSV directory (multiple tables to merge)
17	4. Harmonization table (optional): CSV mapping variables across surveys
18	- Default: replicate-study skill's `harmonization_knhanes_nhanes.csv`
19
20	## Reference Files
21
22	- Harmonization table: `medsci-skills/skills/replicate-study/references/harmonization_knhanes_nhanes.csv`
23	- Upstream:
24	- `medsci-skills/skills/write-paper/references/paper_types/cross_national.md` — writing template
25	- `medsci-skills/skills/analyze-stats/references/analysis_guides/survey_weighted.md`
26
27	## Workflow
28
29	### Phase 1: Study Definition
30
31	1. Confirm research question: Exposure → Outcome
32	2. Define variable coding for both countries:
33	- Exposure: PHQ-9, BMI category, smoking, etc.
34	- Outcome: diabetes, hypertension, mortality, etc.
35	- Covariates: age, sex, education, income, smoking, alcohol, obesity, CVD
36	3. Check harmonization table for variable availability
37	4. Output: study protocol summary for user approval
38
39	### Phase 2: Data Preparation
40
41	KNHANES (single CSV):
42	1. Load CSV, filter age ≥20 (or per protocol)
43	2. Derive variables using KNHANES coding:
44	- Smoking: BS3_1 (1,2=current, 3=former, 8=never)
45	- Alcohol: BD1_11 (2-6=frequent, 1=occasional, 8=never)
46	- Obesity: HE_obe (≥4=obesity for BMI≥25 Asian cutoff)
47	- PHQ-9: BP_PHQ_1~9, sum score, ≥10=depression
48	- Diabetes: HE_glu≥126 \| HE_HbA1c≥6.5 \| DE1_dg=1
49	- CVD: DI4_dg=1 \| DI5_dg=1 \| DI6_dg=1
50	3. Set survey design: svydesign(id=~psu, strata=~kstrata, weights=~wt_itvex, nest=TRUE)
51
52	NHANES (multiple CSVs):
53	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)
54	2. Derive variables using NHANES coding:
55	- Smoking: SMQ020 + SMQ040 (100 cigs + now smoke)
56	- Alcohol: ALQ121 (past 12 mo frequency → categories)
57	- Obesity: BMXBMI ≥30 (WHO cutoff, NOT Asian)
58	- PHQ-9: DPQ010~DPQ090, sum score, ≥10=depression
59	- Diabetes: LBXSGL≥126 \| LBXGH≥6.5 \| DIQ010=="Yes" (CRITICAL: LBXSGL not LBXSGLU)
60	- CVD: MCQ160B=="Yes" (CHF) \| MCQ160C=="Yes" (CHD) \| MCQ160D=="Yes" (angina) \| MCQ160E=="Yes" (MI)
61	- HTN: BPXOSY3≥140 \| BPXODI3≥90 \| BPQ020=="Yes"
62	3. Set survey design: svydesign(id=~SDMVPSU, strata=~SDMVSTRA, weights=~WTMECPRP, nest=TRUE)
63
64	### Phase 3: Parallel Analysis
65
66	For EACH country independently:
67	1. Table 1: Baseline characteristics by exposure (weighted counts + percentages)
68	2. Main analysis: Sequential logistic regression models
69	- Model 1 (unadjusted)
70	- Model 2 (age + sex)
71	- Model 3 (fully adjusted: + education, income, smoking, alcohol, obesity, CVD)
72	3. Subgroup analyses: By sex, age group, education, income, alcohol, smoking, CVD, obesity
73	4. Dose-response (if applicable): RCS with 3 knots
74
75	### Phase 4: Cross-National Comparison Table
76
77	Generate a side-by-side comparison:
78
79	\| Analysis \| Korea wOR (95% CI) \| US wOR (95% CI) \| Direction Agreement \|
80	\|----------\|-------------------\|-----------------\|---------------------\|
81	\| Overall (fully adjusted) \| ... \| ... \| ✓/✗ \|
82	\| Male \| ... \| ... \| \|
83	\| Female \| ... \| ... \| \|
84	\| ... \| ... \| ... \| \|
85
86	### Phase 5: Output Files
87
88	```
89	{working_dir}/
90	├── cross_national_report.md — Study summary + comparison tables
91	├── variable_mapping.csv — Variable mapping with match status
92	├── analysis_korea.R — KNHANES analysis (self-contained)
93	├── analysis_us.R — NHANES analysis (self-contained)
94	├── results/
95	│ ├── table1_korea.csv
96	│ ├── table1_us.csv
97	│ ├── main_results_comparison.csv
98	│ └── subgroup_comparison.csv
99	└── manuscript_draft/ — Optional: Methods + Results draft
100	├── methods_draft.md
101	└── results_draft.md
102	```
103
104	## Critical Rules
105
106	1. NEVER pool data across countries. Each country analyzed with its own survey design.
107	2. Country-specific BMI cutoffs: Korea ≥25 (Asian), US ≥30 (WHO).
108	3. Country-specific income: KNHANES quartile, NHANES PIR → harmonize to binary.
109	4. *

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