Recoding CHMS medication variables • chmsflow

library(chmsflow)
library(recodeflow)
library(dplyr)

1. Introduction

chmsflow provides 16 functions that classify medications from ATC codes recorded in CHMS clinic data. Each function checks whether a respondent is taking a specific drug class and returns 1 (yes) or 0 (no), with haven::tagged_na() codes for missing or not-applicable responses.

Available medication variables

Variable	Drug class	ATC prefix	Cycles 3–6 function	Cycles 1–2 function
`ace_med`	ACE inhibitors	C09	`is_ace_inhibitor()`	`is_ace_med_cycles1to2()`
`bb_med`	Beta blockers	C07	`is_beta_blocker()`	`is_bb_med_cycles1to2()`
`ccb_med`	Calcium channel blockers	C08	`is_calcium_channel_blocker()`	`is_ccb_med_cycles1to2()`
`diur_med`	Diuretics	C03	`is_diuretic()`	`is_diur_med_cycles1to2()`
`misc_htn_med`	Other antihypertensives	mixed	`is_other_antihtn_med()`	`is_misc_htn_med_cycles1to2()`
`any_htn_med`	Any antihypertensive	combined	`is_any_antihtn_med()`	`is_any_htn_med_cycles1to2()`
`nsaid_med`	NSAIDs	M01A	`is_nsaid()`	`is_nsaid_med_cycles1to2()`
`diab_med`	Diabetes medications	A10	`is_diabetes_med()`	`is_diab_med_cycles1to2()`

Cycle differences

Medication data is structured differently across CHMS cycles:

Cycles 1–2 store medications in a flat format with up to 80 individual columns (atc_101a to atc_235a for ATC codes, mhr_101b to mhr_235b for time last taken). The cycles 1–2 wrapper functions accept all of these columns as parameters.
Cycles 3–6 store medications in a multi-row format with two variables per row: meucatc (ATC code) and npi_25b (time last taken). Each respondent may have multiple rows. After recoding, results must be aggregated by clinicid.

2. When to use medication recoding

If your analysis requires medication variables, always perform medication recoding first, before recoding any other variables. Two downstream health outcome variables depend on medication status:

Hypertension – any_htn_med must be merged into the main cycle dataset before deriving hypertension outcomes.
Diabetes – diab_med must be merged before deriving diabetes outcomes.

3. Workflow

The workflow is the same for all cycles: recode medication variables and merge into the main cycle dataset using recode_meds_cycles1to2() or recode_meds_cycles3to6(), then derive health outcomes using recode_after_meds(). Use recode_after_meds() instead of rec_with_table() – it automatically excludes medication-specific rows from variable_details so pre-computed medication columns are passed through rather than re-derived.

3.1 Cycles 1–2

Cycles 1–2 medication data uses uppercase column names (CLINICID, ATC_101A, etc.). recode_meds_cycles1to2() normalizes these internally.

Step 1 – Recode medication variables and merge with main cycle data. Requires: cycle1, cycle1_meds.

cycle1 <- recode_meds_cycles1to2(cycle1, cycle1_meds, c("any_htn_med", "diab_med"))

Step 2 – Derive diabetes status. Requires: cycle1 from Step 1.

cycle1_diab_data <- recode_after_meds(
  cycle1,
  c("lab_hba1", "diab_a1c", "diab_med", "ccc_51", "diab_status")
)
head(select(cycle1_diab_data, clinicid, diab_status))

  clinicid diab_status
1        1           1
2        2           2
3        3        <NA>
4        4           1
5        5           1
6        6           2

Step 3 – Derive hypertension status. Requires: cycle1 from Step 1.

cycle1_htn_data <- recode_after_meds(
  cycle1,
  c(
    # Blood pressure (raw + adjusted)
    "bpmdpbps", "bpmdpbpd", "sbp_adj_mmhg", "dbp_adj_mmhg",
    # Medication inputs (merged in Step 1)
    "any_htn_med", "ccc_32",
    # Diabetes chain (input to htn functions)
    "lab_hba1", "diab_a1c", "ccc_51", "diab_med", "diab_status",
    # CVD chain
    "ccc_61", "ccc_63", "ccc_81", "cvd_status",
    # CKD chain
    "lab_bcre", "pgdcgt", "clc_sex", "clc_age", "gfr_ml_min", "ckd_status",
    # Hypertension outcomes
    "htn_status", "htn_adj_status", "htn_control_status", "htn_control_adj_status"
  )
)

NOTE for pgdcgt: Respondents who respond as indigenous to previous question are identified as 'not applicable' in this question. Recode to other"

head(select(cycle1_htn_data, clinicid, htn_status, htn_adj_status))

  clinicid htn_status htn_adj_status
1        1          1              1
2        2          1              1
3        3          1              1
4        4          1              1
5        5          1              1
6        6          1              1

3.2 Cycles 3–6

Step 1 – Recode medication variables and merge with main cycle data. Requires: cycle3, cycle3_meds.

cycle3 <- recode_meds_cycles3to6(cycle3, cycle3_meds, c("any_htn_med", "diab_med"))

Step 2 – Derive diabetes status. Requires: cycle3 from Step 1.

cycle3_diab_data <- recode_after_meds(
  cycle3,
  c("lab_hba1", "diab_a1c", "diab_med", "ccc_51", "diab_status")
)
head(select(cycle3_diab_data, clinicid, diab_status))

  clinicid diab_status
1        1           1
2        2           1
3        3           1
4        4           1
5        5           1
6        6           1

Step 3 – Derive hypertension status. Requires: cycle3 from Step 1.

cvd_status, diab_status, and ckd_status are intermediate inputs to the hypertension functions. Their full input chains must also be listed so recode_after_meds() can derive them.

cycle3_htn_data <- recode_after_meds(
  cycle3,
  c(
    # Blood pressure (raw + adjusted)
    "bpmdpbps", "bpmdpbpd", "sbp_adj_mmhg", "dbp_adj_mmhg",
    # Medication inputs (merged in Step 1)
    "any_htn_med", "ccc_32",
    # Diabetes chain (input to htn functions)
    "lab_hba1", "diab_a1c", "ccc_51", "diab_med", "diab_status",
    # CVD chain
    "ccc_61", "ccc_63", "ccc_81", "cvd_status",
    # CKD chain
    "lab_bcre", "pgdcgt", "clc_sex", "clc_age", "gfr_ml_min", "ckd_status",
    # Hypertension outcomes
    "htn_status", "htn_adj_status", "htn_control_status", "htn_control_adj_status"
  )
)

NOTE for pgdcgt: Respondents who respond as indigenous to previous question are identified as 'not applicable' in this question. Recode to other"

head(select(cycle3_htn_data, clinicid, htn_status, htn_adj_status))

  clinicid htn_status htn_adj_status
1        1          1              1
2        2          1              1
3        3          1              1
4        4          1              1
5        5          1              1
6        6          1              1

4. Advanced: using individual classification functions

The is_* functions underlie the wrapper functions and are available directly for custom workflows – for example, deriving a single drug class without the full pipeline, or integrating classification logic into your own aggregation steps.

Each function accepts an ATC code and a time-last-taken value and returns 1, 0, or a tagged_na() code:

# Single medication classification
is_beta_blocker("C07AA05", 1) # returns 1

[1] 1

is_ace_inhibitor("C09AA02", 1) # returns 1

[1] 1

is_diabetes_med("A10BA02", 1) # returns 1

[1] 1

Cycle format differences

Cycles 1–2 – one row per respondent with up to 80 atc_*/mhr_* column pairs. The is_*_med_cycles1to2() variants accept named arguments for each slot:

# Classification using cycles 1--2 wide-format columns
is_ace_med_cycles1to2(atc_101a = "C09AA02", mhr_101b = 1) # returns 1

[1] 1

is_ace_med_cycles1to2(atc_101a = "C09AA02", mhr_101b = 6) # returns 0 (not taken recently)

[1] 0

Cycles 3–6 – one row per medication per respondent with two columns: meucatc (ATC code) and npi_25b (time last taken). Classify per row, then aggregate across rows per respondent:

cycle3_meds |>
  mutate(ace_med = is_ace_inhibitor(meucatc, npi_25b)) |>
  aggregate_meds_by_person(variables = "ace_med")

# A tibble: 50 × 2
   clinicid ace_med
      <int>   <dbl>
 1        1       1
 2        2       1
 3        3       1
 4        4       1
 5        5       1
 6        6       1
 7        7       1
 8        8       1
 9        9       1
10       10       1
# ℹ 40 more rows

Warning

Avoid using as.numeric(as.character(.x)) to aggregate medication columns. That pattern strips tagged_na("a") (valid skip) and tagged_na("b") (missing/refused) distinctions, collapsing them into plain NA. Use aggregate_meds_by_person() instead – it preserves tagged-NA semantics across the aggregation.

Next steps

Full analysis example – See how medication recoding fits into an end-to-end workflow in Analysis walkthrough.
Understand missing data – Learn how tagged_na("a") and tagged_na("b") are preserved through the medication pipeline in Missing data (tagged_na).
Inspect the metadata – See how medication variables are defined in variable-details.csv in Variable schema reference.
Work at an RDC – For loading real CHMS medication data at a Research Data Centre, see Using chmsflow at an RDC.