Methodology

## Why CHMS harmonization is non-trivial

The Canadian Health Measures Survey (CHMS) is a repeated cross-sectional survey conducted by Statistics Canada since 2007. Each cycle collects questionnaire, physical measurement, and laboratory data from a nationally representative sample of Canadians. Combining data across cycles enables trend analyses and increases statistical power, but the raw data is not directly comparable across cycles for several reasons.

### Variable naming changes

Variable names sometimes change between cycles. For example, the accelerometer variable for moderate-to-vigorous physical activity on day 1 is named `amsdmva1` in cycle 1, but `ammdmva1` in cycles 2–6. A researcher pooling cycles must know about every such rename, or risk silent misalignment.

chmsflow handles these renames through metadata in `variable-details.csv`. The `variableStart` column uses a mixed format to specify cycle-specific exceptions:

    cycle1::amsdmva1, [ammdmva1]

This means: use `amsdmva1` for cycle 1, and `ammdmva1` for all other cycles. The `recodeflow` package reads this format and applies the correct mapping automatically. For the full list of variable naming patterns, see `inst/metadata/schemas/chms/chms_database_config.yaml` in the package source.

### Coding scheme differences

Even when variable names are stable, the coding categories may differ. CHMS response codes for missing data, valid skips, and refusals vary across variables and cycles. chmsflow’s recoding rules in `variable-details.csv` define explicit mappings for each response code, ensuring consistent treatment across cycles.

### Medication data format changes

Cycles 1–2 store medication data in a wide format: up to 80 columns of ATC code and time-last-taken pairs per respondent. Cycles 3–6 use a long format: one row per medication per respondent with two columns (`meucatc` and `npi_25b`). chmsflow provides separate functions for each format (`recode_meds_cycles1to2()` and `recode_meds_cycles3to6()`) with identical call signatures, so the downstream workflow is the same regardless of cycle. See Recoding medications for details.

## How chmsflow works

### Rules as data, derivation as code

chmsflow builds on the recodeflow package, which separates recoding rules from recoding logic. The rules live in two CSV metadata files:

- `variables.csv` – lists every harmonized variable with its name, label, section, subject, type, and unit
- `variable-details.csv` – defines how each raw CHMS value maps to a harmonized value, row by row

The recoding logic lives in `recodeflow::rec_with_table()`, which reads the metadata and applies the mappings. This separation means that adding or correcting a recoding rule is a CSV edit, not a code change.

For detailed schema documentation, see Variable schema reference.

### The `variableStart` column

The `variableStart` column in `variable-details.csv` tells `rec_with_table()` where to find the source data. It supports several formats:

Format | Meaning | Example
--- | --- | ---
`[variable_name]` | Same name across all cycles | `[clc_age]`
`cycle1::name1, [default_name]` | Cycle-specific exception with a default | `cycle1::amsdmva1, [ammdmva1]`
`DerivedVar::[var1, var2, ...]` | Computed by a function from listed inputs | `DerivedVar::[lab_bcre, pgdcgt, clc_sex, clc_age]`
`Func::function_name` | The R function that computes the derived variable (in `recTo`) | `Func::calculate_gfr`

### The `recStart` and `recEnd` columns

These columns define the mapping from source values to harmonized values:

- Continuous pass-through: `recStart = "copy"` passes the value unchanged
- Category mapping: `recStart` and `recEnd` define source ranges (e.g., `[1, 3]`), and the row’s `recTo` column gives the harmonized value
- Missing data: rows with `recTo = "NA::a"` or `recTo = "NA::b"` map specific source codes to tagged NA values

### Missing data semantics

CHMS uses numeric codes for missing data (e.g., `996` for valid skip, `997--999` for don’t know / refusal / not stated). chmsflow converts these to `haven::tagged_na()` values that preserve the reason for missingness:

- `tagged_na("a")` – valid skip (not applicable to this respondent)
- `tagged_na("b")` – missing (don’t know, refusal, or not stated)

This distinction matters for analysis. For example, a respondent who was never asked about smoking (valid skip) should be treated differently from one who refused to answer (missing). See Missing data (tagged_na) for a full explanation.

### Derived variables

Some harmonized variables cannot be created by simple value mapping. These are computed by R functions referenced in `variable-details.csv` with the `Func::` prefix. Examples include:

- `calculate_gfr()` – estimated glomerular filtration rate from creatinine, ethnicity, sex, and age
- `derive_hypertension()` – hypertension status from adjusted blood pressure, medication use, and comorbidities
- `calculate_pack_years()` – smoking pack-years from smoking history variables

The `DerivedVar::` prefix in `variableStart` lists the input variables that must be present in the data before the function can run. See Derived variables for details.

## Known limits

- Cycle 1 naming divergences require mixed-format `variableStart` entries. These are documented in the metadata but can be surprising if you inspect the raw CHMS data directly.
- Not all CHMS variables are harmonized. chmsflow currently covers variables used in cardiometabolic health research. The 613apps data dictionary provides a broader reference for all CHMS variables.
- Harmonization decisions are embedded in the metadata. Researchers should review `variable-details.csv` to confirm that the recoding rules are appropriate for their specific research question. The mappings follow clinical and epidemiological conventions but may not suit every analytic purpose.
- Survey design is out of scope. chmsflow harmonizes and derives variables but does not implement CHMS survey design, weights, or bootstrap variance estimation.

## Next steps

- See it in action – Follow the Analysis walkthrough to run a complete hypertension prevalence analysis using chmsflow.
- Inspect the recoding rules – Browse the metadata column by column in Variable schema reference.
- Understand missing data – Learn how `tagged_na()` preserves the reason for missingness in Missing data (tagged_na).
- Add your own variables – Extend chmsflow with custom harmonized variables in How to add variables.

## References

- Statistics Canada. Canadian Health Measures Survey (CHMS). Survey overview and documentation.
- Statistics Canada. CHMS user guide materials. Available at OSF.
- 613apps data dictionary builder. Interactive CHMS variable lookup (select “CHMS” under “Choose survey”).
- Big Life Lab. recodeflow. The general-purpose harmonization engine underlying chmsflow.