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library(chmsflow)

1. Introduction

chmsflow harmonizes variables from the Canadian Health Measures Survey (CHMS) across cycles 1–6 and derives health indicators used in health research. It works with the recodeflow package to transform raw CHMS variables into analysis-ready versions using recoding rules defined in CSV metadata files.

What chmsflow provides

The package includes two metadata CSV files (variables.csv and variable-details.csv) that define how raw CHMS variables are recoded, and 42 functions that derive new health indicators. The table below summarizes the available variables, organized by section and subject as defined in variables.csv:

Section Subject Examples
Sociodemographics Age, sex, ethnicity clc_age, clc_sex, pgdcgt
Socioeconomic Income, education, occupation, marital status adj_hh_income, income_quintile, edudr04
Health status Blood pressure, hypertension sbp_adj_mmhg, htn_status, htn_control_status
Health status Chronic disease (diabetes, CKD, CVD) diab_status, ckd_status, cvd_status
Health status Medication (8 drug classes from ATC codes) ace_med, any_htn_med, diab_med
Health status Weight, height, cholesterol nonhdl_mmoll, waist_height_ratio, hwmdbmi
Health status Family history cvd_premature_famhist_status, fam_bp
Health behaviour Alcohol, diet alc_risk_score, fv_daily_times, healthy_diet_indicator
Health behaviour Exercise exercise_min_week, enough_exercise_indicator
Health behaviour Smoking pack_years, smoke

For the full variable list, see Variable schema reference.

Typical workflow

  1. Merge cycle components - At the RDC, combine household, clinic, and lab data into one object per cycle (e.g., cycle4). Keep medication data separate as cyclex_meds.
  2. Recode medications first - If your analysis needs medication variables, always recode them before other variables. See Recoding medications.
  3. Recode other variables - Use rec_with_table() from recodeflow to transform source variables and derive new ones.

2. Installation

# Install release version from CRAN
install.packages("chmsflow")

# Install the most recent version from GitHub
devtools::install_github("Big-Life-Lab/chmsflow")

3. Quick start

Use rec_with_table() from recodeflow to transform CHMS variables. The cycle data object must be named cyclex for recoding to work properly.

library(recodeflow)

# Recode a source variable (age)
cycle4_ages <- rec_with_table(
  cycle4, "clc_age",
  variable_details = variable_details, log = TRUE
)
  value_to       From rows_recoded
1     copy    [3, 80]           50
2    NA::a        996            0
3    NA::b [997, 999]            0
4     <NA>       else            0
head(cycle4_ages)
  clc_age
1      73
2      33
3      22
4      47
5      74
6      22

4. Variable types

chmsflow handles three types of variables, each recoded differently.

4.1 Source variables (direct mapping)

Source variables are mapped directly from raw CHMS columns. Variable names may differ across cycles, but chmsflow harmonizes them to a single name.

# Recode sex (same variable name across all cycles)
cycle4_sexes <- rec_with_table(
  cycle4, "clc_sex",
  variable_details = variable_details, log = TRUE
)
  value_to   From rows_recoded
1        1      1           27
2        2      2           23
3    NA::a      6            0
4    NA::b [7, 9]            0
5    NA(b)   else            0
head(cycle4_sexes)
  clc_sex
1       2
2       2
3       2
4       2
5       2
6       2

4.2 Transformed variables (continuous to categorical)

Some variables convert continuous measurements into categories using thresholds defined in variable-details.csv.

# Recode age into 4 groups
cycle4_categorical_ages <- rec_with_table(
  cycle4, "agegroup4",
  variable_details = variable_details, log = TRUE
)
  value_to       From rows_recoded
1        1   [20, 39]           12
2        2   [40, 59]           23
3        3   [60, 69]            6
4        4   [70, 79]            9
5    NA::a        996            0
6    NA::b [997, 999]            0
7    NA(b)       else            0
head(cycle4_categorical_ages)
  agegroup4
1         4
2         1
3         1
4         2
5         4
6         1

4.3 Derived variables (computed by functions)

Derived variables are computed by R functions referenced as Func:: entries in variable-details.csv. These require their input variables to be present in the data. See Derived variables for details.

# Derive adjusted systolic blood pressure
# bpmdpbps (raw SBP) must be in the data for sbp_adj_mmhg to be computed
cycle4_adjusted_SBPs <- rec_with_table(
  cycle4, c("bpmdpbps", "sbp_adj_mmhg"),
  variable_details = variable_details, log = TRUE
)
  value_to       From rows_recoded
1     copy  [73, 216]           50
2    NA::a        996            0
3    NA::b [997, 999]            0
4     <NA>       else            0
head(cycle4_adjusted_SBPs)
  bpmdpbps sbp_adj_mmhg
1      207       203.91
2      101       105.33
3       92        96.96
4      196       193.68
5      152       152.76
6       79        84.87

5. Next steps