Package 'chmsflow'

Description

This function adjusts diastolic blood pressure based on the respondent's diastolic average blood pressure across six measurements. The adjustment is made using specific correction factors. The adjusted diastolic blood pressure is returned as a numeric value.

Usage

adjust_dbp(bpmdpbpd)

Arguments

Details

Blood pressure measurements in survey settings may require adjustment to account for measurement conditions and equipment differences. This function applies a standardized adjustment using the formula: dbp_adj_mmhg = 15.6 + (0.83 * bpmdpbpd).

     **Missing Data Codes:**
     - `996`: Valid skip (e.g., measurement not taken). Handled as `haven::tagged_na("a")`.
     - `997-999`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.

Value

numeric The adjusted diastolic blood pressure as a numeric.

See Also

adjust_sbp() for systolic blood pressure adjustment, derive_hypertension() for hypertension classification

Examples

# Scalar usage: Single respondent
# Example: Adjust for a respondent with average diastolic blood pressure of 80 mmHg.
adjust_dbp(bpmdpbpd = 80)
# Output: 82

# Example: Adjust for a respondent with a non-response diastolic blood pressure of 996.
result <- adjust_dbp(bpmdpbpd = 996)
result # Shows: NA
haven::is_tagged_na(result, "a") # Shows: TRUE (confirms it's tagged NA(a))
format(result, tag = TRUE) # Shows: "NA(a)" (displays the tag)

# Multiple respondents
adjust_dbp(bpmdpbpd = c(80, 90, 100))
# Returns: c(82, 90.3, 98.6)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(dbp_adj_mmhg = adjust_dbp(bpmdpbpd)) |>
  head()

Description

This function adjusts systolic blood pressure based on the respondent's systolic average blood pressure across six measurements. The adjustment is made using specific correction factors. The adjusted systolic blood pressure is returned as a numeric value.

Usage

adjust_sbp(bpmdpbps)

Arguments

Details

Blood pressure measurements in survey settings may require adjustment to account for measurement conditions and equipment differences. This function applies a standardized adjustment using the formula: sbp_adj_mmhg = 11.4 + (0.93 * bpmdpbps).

     **Missing Data Codes:**
     - `996`: Valid skip (e.g., measurement not taken). Handled as `haven::tagged_na("a")`.
     - `997-999`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.

Value

numeric The adjusted systolic blood pressure as a numeric.

See Also

adjust_dbp() for diastolic blood pressure adjustment, derive_hypertension() for hypertension classification

Examples

# Scalar usage: Single respondent
# Example: Adjust for a respondent with average systolic blood pressure of 120 mmHg.
adjust_sbp(bpmdpbps = 120)
# Output: 123

# Example: Adjust for a respondent with a non-response systolic blood pressure of 996.
result <- adjust_sbp(bpmdpbps = 996)
result # Shows: NA
haven::is_tagged_na(result, "a") # Shows: TRUE (confirms it's tagged NA(a))
format(result, tag = TRUE) # Shows: "NA(a)" (displays the tag)

# Multiple respondents
adjust_sbp(bpmdpbps = c(120, 130, 140))
# Returns: c(123, 132.3, 141.6)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(sbp_adj_mmhg = adjust_sbp(bpmdpbps)) |>
  head()

Description

Collapses long-format medication data (cycles 3-6) to one row per respondent by taking the maximum value of each medication variable across all rows. A result of 1 (taking the medication) takes precedence over 0. Tagged NAs are propagated only when all rows for a respondent are missing.

Usage

aggregate_meds_by_person(data, variables, by = "clinicid")

Arguments

Value

data.frame One row per respondent with aggregated medication variables as numeric.

See Also

recode_meds_cycles3to6(), recode_meds_cycles1to2()

Examples

df <- data.frame(
  clinicid    = c(1, 1, 2, 2),
  any_htn_med = c(0, 1, 0, 0),
  diab_med    = c(1, 0, 0, 0)
)
aggregate_meds_by_person(df, variables = c("any_htn_med", "diab_med"))

Description

This function calculates the average daily minutes of moderate-to-vigorous physical activity (MVPA) across a week of accelerometer measurement. It takes seven parameters, each representing the MVPA minutes on a specific day (Day 1 to Day 7). The function computes the daily average across the week.

Usage

calculate_exercise_daily_avg(
  ammdmva1,
  ammdmva2,
  ammdmva3,
  ammdmva4,
  ammdmva5,
  ammdmva6,
  ammdmva7
)

Arguments

Details

This function processes physical activity data from accelerometer measurements to create a weekly activity summary.

     **Data Quality Requirements:**
     - Requires complete 7-day data (missing days result in tagged NA)
     - This conservative approach ensures reliable activity estimates
     - Zero values are preserved (represent valid no-activity days)

     **Missing Data Codes:**
     - For all input variables:
       - `9996`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `9997-9999`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.

Value

numeric The average daily minutes of MVPA across a week of accelerometer use. If inputs are invalid or out of bounds, the function returns a tagged NA.

See Also

calculate_exercise_weekly() for activity unit conversion, categorize_exercise() for activity level classification

Examples

# Scalar usage: Single respondent
# Example: Calculate the average minutes of exercise per day for a week of accelerometer data.
calculate_exercise_daily_avg(30, 40, 25, 35, 20, 45, 50)
# Output: 35

# Example: Respondent has non-response values for all inputs.
result <- calculate_exercise_daily_avg(9998, 9998, 9998, 9998, 9998, 9998, 9998)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
calculate_exercise_daily_avg(
  c(30, 20), c(40, 30), c(25, 35), c(35, 45),
  c(20, 25), c(45, 55), c(50, 60)
)
# Returns: c(35, 39.28571)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(avg_exercise = calculate_exercise_daily_avg(
    ammdmva1, ammdmva2,
    ammdmva3, ammdmva4, ammdmva5, ammdmva6, ammdmva7
  )) |>
  head()

Description

This function takes the average daily minutes of moderate-to-vigorous physical activity (MVPA) across a week of accelerometer use as an input (mvpa_min) and calculates the equivalent weekly MVPA minutes. The result is returned as a numeric value.

Usage

calculate_exercise_weekly(mvpa_min)

Arguments

Details

The function multiplies the average daily MVPA minutes (mvpa_min) by 7 to obtain the equivalent weekly MVPA minutes.

     **Missing Data Codes:**
     - Propagates tagged NAs from the input `mvpa_min`.

Value

numeric The total weekly minutes of MVPA. If inputs are invalid or out of bounds, the function returns a tagged NA.

See Also

calculate_exercise_daily_avg(), categorize_exercise()

Examples

# Scalar usage: Single respondent
# Example: Convert average daily MVPA minutes to weekly MVPA minutes.
calculate_exercise_weekly(35)
# Output: 245

# Multiple respondents
calculate_exercise_weekly(c(35, 40, 20))
# Returns: c(245, 280, 140)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(avg_exercise = calculate_exercise_daily_avg(
    ammdmva1, ammdmva2,
    ammdmva3, ammdmva4, ammdmva5, ammdmva6, ammdmva7
  )) |>
  mutate(min_per_week = calculate_exercise_weekly(avg_exercise)) |>
  head()

Description

This function calculates the daily fruit and vegetable consumption in a year for respondent in the Canadian Health Measures Survey (CHMS) cycles 1-2. It takes seven parameters, each representing the number of times per year a specific fruit or vegetable item was consumed. The function then sums up the consumption frequencies of all these items and divides the total by 365 to obtain the average daily consumption of fruits and vegetables in a year.

Usage

calculate_fv_daily_cycles1to2(
  wsdd14y,
  gfvd17y,
  gfvd18y,
  gfvd19y,
  gfvd20y,
  gfvd22y,
  gfvd23y
)

Arguments

Details

The function calculates the total consumption of fruits and vegetables in a year by summing up the consumption frequencies of all the input items. It then divides the total by 365 to obtain the average daily consumption of fruits and vegetables in a year.

     **Missing Data Codes:**
     - For all input variables:
       - `9996`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `9997-9999`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.

Value

numeric The average times per day fruits and vegetables were consumed in a year. If inputs are invalid or out of bounds, the function returns a tagged NA.

See Also

calculate_fv_daily_cycles3to6() for cycles 3-6 fruit and vegetable consumption, categorize_diet_quality() for overall diet quality

Examples

# Scalar usage: Single respondent
# Example: Calculate average daily fruit and vegetable consumption for a cycle 1-2 respondent.
calculate_fv_daily_cycles1to2(
  wsdd14y = 50, gfvd17y = 150, gfvd18y = 200, gfvd19y = 100, gfvd20y = 80,
  gfvd22y = 120, gfvd23y = 90
)
# Output: 2.164384

# Example: Respondent has non-response values for all inputs.
result <- calculate_fv_daily_cycles1to2(
  wsdd14y = 9998, gfvd17y = 9998, gfvd18y = 9998, gfvd19y = 9998, gfvd20y = 9998,
  gfvd22y = 9998, gfvd23y = 9998
)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
calculate_fv_daily_cycles1to2(
  wsdd14y = c(50, 60), gfvd17y = c(150, 160), gfvd18y = c(200, 210), gfvd19y = c(100, 110),
  gfvd20y = c(80, 90), gfvd22y = c(120, 130), gfvd23y = c(90, 100)
)
# Returns: c(2.164384, 2.356164)

# Database usage: Applied to survey datasets
library(dplyr)
cycle2 |>
  mutate(total_fv = calculate_fv_daily_cycles1to2(
    wsdd14y, gfvd17y, gfvd18y,
    gfvd19y, gfvd20y, gfvd22y, gfvd23y
  )) |>
  head()

Description

This function calculates the daily fruit and vegetable consumption in a year for respondents in the Canadian Health Measures Survey (CHMS) cycles 3-6. It takes eleven parameters, each representing the number of times per year a specific fruit or vegetable item was consumed. The function then sums up the consumption frequencies of all these items and divides the total by 365 to obtain the average daily consumption of fruits and vegetables in a year.

Usage

calculate_fv_daily_cycles3to6(
  wsdd34y,
  wsdd35y,
  gfvd17ay,
  gfvd17by,
  gfvd17cy,
  gfvd17dy,
  gfvd18y,
  gfvd19y,
  gfvd20y,
  gfvd22y,
  gfvd23y
)

Arguments

Details

The function calculates the total consumption of fruits and vegetables in a year by summing up the consumption frequencies of all the input items. It then divides the total by 365 to obtain the average daily consumption of fruits and vegetables in a year.

     **Missing Data Codes:**
     - For all input variables:
       - `9996`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `9997-9999`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.

Value

numeric The average times per day fruits and vegetables were consumed in a year. If inputs are invalid or out of bounds, the function returns a tagged NA.

See Also

calculate_fv_daily_cycles1to2() for cycles 1-2 fruit and vegetable consumption, categorize_diet_quality() for overall diet quality

Examples

# Scalar usage: Single respondent
# Example: Calculate average daily fruit and vegetable consumption for a cycle 3-6 respondent
calculate_fv_daily_cycles3to6(
  wsdd34y = 50, wsdd35y = 100, gfvd17ay = 150, gfvd17by = 80, gfvd17cy = 40,
  gfvd17dy = 200, gfvd18y = 100, gfvd19y = 80, gfvd20y = 60, gfvd22y = 120, gfvd23y = 90
)
# Output: 2.931507

# Example: Respondent has non-response values for all inputs.
result <- calculate_fv_daily_cycles3to6(
  wsdd34y = 9998, wsdd35y = 9998, gfvd17ay = 9998, gfvd17by = 9998, gfvd17cy = 9998,
  gfvd17dy = 9998, gfvd18y = 9998, gfvd19y = 9998, gfvd20y = 9998, gfvd22y = 9998, gfvd23y = 9998
)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
calculate_fv_daily_cycles3to6(
  wsdd34y = c(50, 60), wsdd35y = c(100, 110), gfvd17ay = c(150, 160), gfvd17by = c(80, 90),
  gfvd17cy = c(40, 50), gfvd17dy = c(200, 210), gfvd18y = c(100, 110), gfvd19y = c(80, 90),
  gfvd20y = c(60, 70), gfvd22y = c(120, 130), gfvd23y = c(90, 100)
)
# Returns: c(2.931507, 3.232877)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(total_fv = calculate_fv_daily_cycles3to6(
    wsdd34y, wsdd35y, gfvd17ay,
    gfvd17by, gfvd17cy, gfvd17dy, gfvd18y, gfvd19y, gfvd20y, gfvd22y, gfvd23y
  )) |>
  head()

Description

This function calculates the estimated glomerular filtration rate (GFR) according to Finlay's formula, where serum creatine is in mg/dL. The calculation takes into account the respondent's ethnicity, sex, and age.

Usage

calculate_gfr(lab_bcre, pgdcgt, clc_sex, clc_age)

Arguments

Details

This function implements the Modification of Diet in Renal Disease (MDRD) equation to estimate glomerular filtration rate, a key indicator of kidney function.

     **Clinical Significance:**
     GFR estimates are essential for:
     - Chronic kidney disease (CKD) classification
     - Medication dosing adjustments
     - Cardiovascular risk assessment

     **Formula Application:**
     Base: GFR = 175 x (creatinine^-1.154) x (age^-0.203)
     Adjustments:
     - Female: x 0.742
     - Black ethnicity: x 1.210

     **Unit Conversion:**
     Serum creatinine converted from umol/L to mg/dL (/ 88.4)

     **Missing Data Codes:**
     - `lab_bcre`: `9996` (Not applicable), `9997-9999` (Missing)
     - `pgdcgt`: `96` (Not applicable), `97-99` (Missing)
     - `clc_sex`: `6` (Not applicable), `7-9` (Missing)
     - `clc_age`: `96` (Not applicable), `97-99` (Missing)

Value

numeric The calculated GFR. If inputs are invalid or out of bounds, the function returns a tagged NA.

See Also

categorize_ckd() for CKD classification based on GFR values

Examples

# Scalar usage: Single respondent
# Example 1: Calculate gfr for a 45-year-old white female with serum creatine of 80 umol/L.
calculate_gfr(lab_bcre = 80, pgdcgt = 1, clc_sex = 2, clc_age = 45)
# Output: 67.27905

# Example 2: Respondent has non-response values for all inputs.
result <- calculate_gfr(lab_bcre = 9998, pgdcgt = 98, clc_sex = 8, clc_age = 98)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
calculate_gfr(
  lab_bcre = c(80, 70, 90), pgdcgt = c(1, 2, 1),
  clc_sex = c(2, 2, 1), clc_age = c(45, 35, 50)
)
# Returns: c(67.27905, 99.94114, 70.38001)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(gfr = calculate_gfr(lab_bcre, pgdcgt, clc_sex, clc_age)) |>
  head()

Description

This function calculates the adjusted total household income based on the respondent's income amount and actual household size, taking into account the weighted household size.

Usage

calculate_household_income(thi_01, dhhdhsz)

Arguments

Details

This function applies equivalence scales to adjust household income for household size, allowing for meaningful income comparisons across different household compositions.

     **Equivalence Scale Logic:**
     - First adult: Weight = 1.0 (full weight)
     - Second adult: Weight = 0.4 (economies of scale)
     - Additional members: Weight = 0.3 each (further economies)

     **Missing Data Codes:**
     - `thi_01`:
       - `99999996`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `99999997-99999999`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.
     - `dhhdhsz`:
       - `96`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `97-99`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.

Value

numeric The calculated adjusted total household income as a numeric. If inputs are invalid or out of bounds, the function returns a tagged NA.

See Also

categorize_income_quintile() for income classification, is_lowest_income_quintile() for poverty indicators

Examples

# Scalar usage: Single respondent
# Example 1: Respondent with $50,000 income and a household size of 3.
calculate_household_income(thi_01 = 50000, dhhdhsz = 3)
# Output: 29411.76

# Example 2: Respondent has non-response values for all inputs.
result <- calculate_household_income(thi_01 = 99999998, dhhdhsz = 98)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
calculate_household_income(thi_01 = c(50000, 75000, 90000), dhhdhsz = c(3, 2, 1))
# Returns: c(29411.76, 53571.43, 90000)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(adj_hh_income = calculate_household_income(thi_01, dhhdhsz)) |>
  head()

Description

This function calculates a respondent's non-HDL cholesterol level by subtracting their HDL cholesterol level from their total cholesterol level. It first checks whether the input values lab_chol (total cholesterol) and lab_hdl (HDL cholesterol) are within valid ranges.

Usage

calculate_nonhdl(lab_chol, lab_hdl)

Arguments

Details

The function calculates the non-HDL cholesterol level by subtracting the HDL cholesterol level from the total cholesterol level.

     **Missing Data Codes:**
     - `lab_chol`:
       - `99.96`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `99.97-99.99`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.
     - `lab_hdl`:
       - `9.96`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `9.97-9.99`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.

Value

numeric The calculated non-HDL cholesterol level (in mmol/L). If inputs are invalid or out of bounds, the function returns a tagged NA.

See Also

categorize_nonhdl()

Examples

# Scalar usage: Single respondent
# Example: Respondent has total cholesterol of 5.0 mmol/L and HDL cholesterol of 1.5 mmol/L.
calculate_nonhdl(lab_chol = 5.0, lab_hdl = 1.5)
# Output: 3.5

# Example: Respondent has non-response values for cholesterol.
result <- calculate_nonhdl(lab_chol = 99.98, lab_hdl = 1.5)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
calculate_nonhdl(lab_chol = c(5.0, 6.0, 7.0), lab_hdl = c(1.5, 1.0, 2.0))
# Returns: c(3.5, 5.0, 5.0)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(non_hdl = calculate_nonhdl(lab_chol, lab_hdl)) |>
  head()

Description

This function calculates an individual's smoking pack-years based on various CHMS smoking variables. Pack years is a measure used by researchers to quantify lifetime exposure to cigarette use.

Usage

calculate_pack_years(
  smkdsty,
  clc_age,
  smk_54,
  smk_52,
  smk_31,
  smk_41,
  smk_53,
  smk_42,
  smk_21,
  smk_11
)

Arguments

Details

Pack-years is a standardized measure of lifetime cigarette exposure used in epidemiological research and clinical practice. The calculation varies by smoking pattern:

     **Smoking Patterns:**
     - Daily smokers: Consistent daily consumption over time period
     - Occasional smokers: Variable consumption adjusted for frequency
     - Former smokers: Historical consumption during smoking periods

     **Minimum Values:**
     The function applies minimum pack-year values (0.0137 or 0.007) to prevent
     underestimation of health risks for light smokers.

     **Missing Data Codes:**
     - `smkdsty`: `96` (Not applicable), `97-99` (Missing)
     - `clc_age`: `96` (Not applicable), `97-99` (Missing)
     - Other variables: Handled within the formula logic.

Value

numeric A numeric representing the pack years for the respondent's smoking history. If inputs are invalid or out of bounds, the function returns a tagged NA.

See Also

https://big-life-lab.github.io/cchsflow/reference/calculate_pack_years.html

Examples

# Scalar usage: Single respondent
# A former occasional smoker who smoked at least 100 cigarettes in their lifetime.
calculate_pack_years(
  smkdsty = 5, clc_age = 50, smk_54 = 40, smk_52 = 18, smk_31 = NA,
  smk_41 = 15, smk_53 = NA, smk_42 = 3, smk_21 = 25, smk_11 = 1
)
# Output: 0.0137

# Example: Respondent has non-response values for all inputs.
result <- calculate_pack_years(
  smkdsty = 98, clc_age = 998, smk_54 = 98, smk_52 = 98, smk_31 = 98,
  smk_41 = 98, smk_53 = 98, smk_42 = 98, smk_21 = 98, smk_11 = 8
)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
calculate_pack_years(
  smkdsty = c(1, 5, 6),
  clc_age = c(40, 50, 60),
  smk_52 = c(20, 18, NA),
  smk_31 = c(30, NA, NA),
  smk_54 = c(NA, 40, NA),
  smk_41 = c(NA, 15, NA),
  smk_53 = c(NA, NA, NA),
  smk_42 = c(NA, 3, NA),
  smk_21 = c(NA, 25, NA),
  smk_11 = c(NA, 1, NA)
)
# Returns: c(30, 0.0137, 0)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(pack_years = calculate_pack_years(
    smkdsty, clc_age, smk_54, smk_52,
    smk_31, smk_41, smk_53, smk_42, smk_21, smk_11
  )) |>
  head()

Description

This function calculates the Waist-to-Height Ratio (WHtR) by dividing the waist circumference by the height of the respondent.

Usage

calculate_waist_height_ratio(hwm_11cm, hwm_14cx)

Arguments

Details

This function calculates the Waist-to-Height Ratio (WHtR), an indicator of central obesity.

     **Missing Data Codes:**
     - `hwm_11cm`:
       - `999.96`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `999.97-999.99`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.
     - `hwm_14cx`:
       - `999.6`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `999.7-999.9`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.

Value

numeric The WHtR. If inputs are invalid or out of bounds, the function returns a tagged NA.

Examples

# Scalar usage: Single respondent
# Example 1: Calculate WHtR for a respondent with height = 170 cm and waist circumference = 85 cm.
calculate_waist_height_ratio(hwm_11cm = 170, hwm_14cx = 85)
# Output: 0.5 (85/170)

# Example 2: Calculate WHtR for a respondent with missing height.
result <- calculate_waist_height_ratio(hwm_11cm = 999.98, hwm_14cx = 85)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
calculate_waist_height_ratio(hwm_11cm = c(170, 180, 160), hwm_14cx = c(85, 90, 80))
# Returns: c(0.5, 0.5, 0.5)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(whtr = calculate_waist_height_ratio(hwm_11cm, hwm_14cx)) |>
  head()

Description

This function categorizes individuals' glomerular filtration rate (GFR) into stages of Chronic Kidney Disease (CKD).

Usage

categorize_ckd(gfr)

Arguments

Details

This function applies the Kidney Disease: Improving Global Outcomes (KDIGO) guideline to classify Chronic Kidney Disease (CKD) based on GFR.

     **Missing Data Codes:**
     - Propagates tagged NAs from the input `gfr`.

Value

integer The CKD stage:

• 1: GFR of 60 or below (indicating CKD)

• 2: GFR above 60 (not indicating CKD)

• haven::tagged_na("a"): Not applicable

• haven::tagged_na("b"): Missing

References

Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. (2013). KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney international supplements, 3(1), 1-150.

See Also

calculate_gfr()

Examples

# Scalar usage: Single respondent
# Example 1: Categorize a GFR of 45
categorize_ckd(45)
# Output: 1

# Example 2: Categorize a GFR of 75
categorize_ckd(75)
# Output: 2

# Example 3: Respondent has a non-response value for GFR.
result <- categorize_ckd(haven::tagged_na("b"))
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
categorize_ckd(c(45, 75, 60))
# Returns: c(1, 2, 1)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(gfr = calculate_gfr(lab_bcre, pgdcgt, clc_sex, clc_age)) |>
  mutate(ckd = categorize_ckd(gfr)) |>
  head()

Description

This function categorizes individuals' diet quality based on their total fruit and vegetable consumption.

Usage

categorize_diet_quality(fv_daily)

Arguments

Details

This function categorizes diet quality based on the widely recognized "5-a-day" recommendation for fruit and vegetable intake.

     **Missing Data Codes:**
     - Propagates tagged NAs from the input `fv_daily`.

Value

integer A categorical indicating the diet quality:

• 1: Good diet (fv_daily >= 5)

• 2: Poor diet (fv_daily < 5)

• haven::tagged_na("a"): Valid skip

• haven::tagged_na("b"): Missing

See Also

calculate_fv_daily_cycles1to2(), calculate_fv_daily_cycles3to6()

Examples

# Scalar usage: Single respondent
# Example 1: Categorize a fv_daily value of 3 as poor diet
categorize_diet_quality(3)
# Output: 2

# Example 2: Categorize a fv_daily value of 7 as good diet
categorize_diet_quality(7)
# Output: 1

# Multiple respondents
categorize_diet_quality(c(3, 7, 5))
# Returns: c(2, 1, 1)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(total_fv = calculate_fv_daily_cycles3to6(
    wsdd34y, wsdd35y, gfvd17ay,
    gfvd17by, gfvd17cy, gfvd17dy, gfvd18y, gfvd19y, gfvd20y, gfvd22y, gfvd23y
  )) |>
  mutate(diet_quality = categorize_diet_quality(total_fv)) |>
  head()

Description

This function categorizes individuals' weekly moderate-to-vigorous physical activity (MVPA) levels against the 150 minutes/week guideline.

Usage

categorize_exercise(exercise_min_week)

Arguments

Details

This function applies the national physical activity guideline of 150 minutes of moderate-to-vigorous physical activity (MVPA) per week.

     **Missing Data Codes:**
     - Propagates tagged NAs from the input `exercise_min_week`.

Value

integer A categorical indicating the MVPA category:

• 1: Meets or exceeds the recommended 150 minutes of MVPA per week (exercise_min_week >= 150)

• 2: Below the recommended 150 minutes of MVPA per week (exercise_min_week < 150)

• haven::tagged_na("a"): Not applicable

• haven::tagged_na("b"): Missing

See Also

calculate_exercise_weekly()

Examples

# Scalar usage: Single respondent
# Example 1: Categorize 180 minutes of MVPA per week as meeting the recommendation
categorize_exercise(180)
# Output: 1

# Example 2: Categorize 120 minutes of MVPA per week as below the recommendation
categorize_exercise(120)
# Output: 2

# Multiple respondents
categorize_exercise(c(180, 120, 150))
# Returns: c(1, 2, 1)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(avg_exercise = calculate_exercise_daily_avg(
    ammdmva1, ammdmva2,
    ammdmva3, ammdmva4, ammdmva5, ammdmva6, ammdmva7
  )) |>
  mutate(min_per_week = calculate_exercise_weekly(avg_exercise)) |>
  mutate(pa_category = categorize_exercise(min_per_week)) |>
  head()

Description

This function categorizes individuals' adjusted household income based on specified income ranges.

Usage

categorize_income_quintile(adj_hh_income)

Arguments

Details

This function segments adjusted household income into quintiles, providing a standardized measure of socioeconomic status.

     **Missing Data Codes:**
     - Propagates tagged NAs from the input `adj_hh_income`.

Value

integer The income category:

• 1: Below or equal to $21,500

• 2: Above $21,500 and up to $35,000

• 3: Above $35,000 and up to $50,000

• 4: Above $50,000 and up to $70,000

• 5: Above $70,000

• haven::tagged_na("a"): Not applicable

• haven::tagged_na("b"): Missing

See Also

calculate_household_income(), is_lowest_income_quintile()

Examples

# Scalar usage: Single respondent
# Example 1: Categorize a household income of $25,000
categorize_income_quintile(25000)
# Output: 2

# Example 2: Categorize a household income of $45,000
categorize_income_quintile(45000)
# Output: 3

# Multiple respondents
categorize_income_quintile(c(25000, 45000, 80000))
# Returns: c(2, 3, 5)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(adj_hh_income = calculate_household_income(thi_01, dhhdhsz)) |>
  mutate(income_category = categorize_income_quintile(adj_hh_income)) |>
  head()

Description

This function categorizes individuals' non-HDL cholesterol levels based on a threshold value.

Usage

categorize_nonhdl(nonhdl)

Arguments

Details

This function categorizes non-HDL cholesterol levels into 'High' or 'Normal' based on a 4.3 mmol/L threshold.

     **Missing Data Codes:**
     - Propagates tagged NAs from the input `nonhdl`.

Value

integer A categorical indicating the non-HDL cholesterol category:

• 1: High non-HDL cholesterol (nonhdl >= 4.3)

• 2: Normal non-HDL cholesterol (nonhdl < 4.3)

• haven::tagged_na("a"): Not applicable

• haven::tagged_na("b"): Missing

See Also

calculate_nonhdl()

Examples

# Scalar usage: Single respondent
# Example 1: Categorize a nonhdl value of 5.0 as high non-HDL cholesterol
categorize_nonhdl(5.0)
# Output: 1

# Example 2: Categorize a nonhdl value of 3.8 as normal non-HDL cholesterol
categorize_nonhdl(3.8)
# Output: 2

# Multiple respondents
categorize_nonhdl(c(5.0, 3.8, 4.3))
# Returns: c(1, 2, 1)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(non_hdl = calculate_nonhdl(lab_chol, lab_hdl)) |>
  mutate(non_hdl_category = categorize_nonhdl(non_hdl)) |>
  head()

Description

This is dummy data representing the second cycle of the Canadian Health Measures Survey (CHMS). The CHMS survey is conducted by Statistics Canada.

Usage

data(cycle1)

Source

Statistics Canada

Examples

data(cycle1)
str(cycle1)

Description

This dummy data representing the medication portion of the second cycle of the Canadian Health Measures Survey (CHMS). The CHMS survey is conducted by Statistics Canada.

Usage

data(cycle1_meds)

Format

A data frame with X rows and Y columns.

Source

Statistics Canada

Examples

data(cycle1_meds)
str(cycle1_meds)

Description

This is dummy data representing the second cycle of the Canadian Health Measures Survey (CHMS). The CHMS survey is conducted by Statistics Canada.

Usage

data(cycle2)

Source

Statistics Canada

Examples

data(cycle2)
str(cycle2)

Description

This dummy data representing the medication portion of the second cycle of the Canadian Health Measures Survey (CHMS). The CHMS survey is conducted by Statistics Canada.

Usage

data(cycle2_meds)

Source

Statistics Canada

Examples

data(cycle2_meds)
str(cycle2_meds)

Description

This is dummy data representing the third cycle of the Canadian Health Measures Survey (CHMS). The CHMS survey is conducted by Statistics Canada.

Usage

data(cycle3)

Source

Statistics Canada

Examples

data(cycle3)
str(cycle3)

Description

This dummy data representing the medication portion of the third cycle of the Canadian Health Measures Survey (CHMS). The CHMS survey is conducted by Statistics Canada.

Usage

data(cycle3_meds)

Source

Statistics Canada

Examples

data(cycle3_meds)
str(cycle3_meds)

Description

This is dummy data representing the fourth cycle of the Canadian Health Measures Survey (CHMS). The CHMS survey is conducted by Statistics Canada.

Usage

data(cycle4)

Source

Statistics Canada

Examples

data(cycle4)
str(cycle4)

Description

This dummy data representing the medication portion of the third cycle of the Canadian Health Measures Survey (CHMS). The CHMS survey is conducted by Statistics Canada.

Usage

data(cycle4_meds)

Source

Statistics Canada

Examples

data(cycle4_meds)
str(cycle4_meds)

Description

This is dummy data representing the fifth cycle of the Canadian Health Measures Survey (CHMS). The CHMS survey is conducted by Statistics Canada.

Usage

data(cycle5)

Source

Statistics Canada

Examples

data(cycle5)
str(cycle5)

Description

This dummy data representing the medication portion of the third cycle of the Canadian Health Measures Survey (CHMS). The CHMS survey is conducted by Statistics Canada.

Usage

data(cycle5_meds)

Source

Statistics Canada

Examples

data(cycle5_meds)
str(cycle5_meds)

Description

This is dummy data representing the fifth cycle of the Canadian Health Measures Survey (CHMS). The CHMS survey is conducted by Statistics Canada.

Usage

data(cycle6)

Source

Statistics Canada

Examples

data(cycle6)
str(cycle6)

Description

This dummy data representing the medication portion of the third cycle of the Canadian Health Measures Survey (CHMS). The CHMS survey is conducted by Statistics Canada.

Usage

data(cycle6_meds)

Source

Statistics Canada

Examples

data(cycle6_meds)
str(cycle6_meds)

Description

This function calculates a low drink score (step 1 only) for a respondent using Canada's Low-Risk Alcohol Drinking Guideline. The score is based solely on the number of standard drinks consumed per week and the respondent's sex. (Step 2, which would add additional points based on other drinking habits, is not included.).

Usage

derive_alcohol_risk(clc_sex, alc_11, alcdwky)

Arguments

Details

The scoring is determined by first allocating points (referred to as step1) based on the weekly alcohol consumption and the respondent's sex:

• If the respondent drank in the past year (alc_11 == 1):

  • For alcdwky <= 10, assign 0 points.

  • For alcdwky > 10 and <= 15: assign 0 points for males (clc_sex == 1) and 1 point for females (clc_sex == 2).

  • For alcdwky > 15 and <= 20: assign 1 point for males and 3 points for females.

  • For alcdwky > 20: assign 3 points.

• For respondents who did not drink in the past year (alc_11 == 2), 0 points are assigned.

These step1 points are then mapped to the final categorical score as follows:

• 0 points -> score of 1 ("Low risk"),

• 1-2 points -> score of 2 ("Marginal risk"),

• 3-4 points -> score of 3 ("Medium risk"),

• 5-9 points -> score of 4 ("High risk").

This function implements Canada's Low-Risk Alcohol Drinking Guidelines (Step 1 only) to assess alcohol consumption risk. The scoring system considers both the quantity of alcohol consumed and biological sex differences in alcohol metabolism.

Risk Categories:

• Low risk (0 points): Safe consumption levels

• Marginal risk (1-2 points): Slightly elevated risk

• Medium risk (3-4 points): Moderate health concerns

• High risk (5-9 points): Significant health risks

Sex-Based Differences: Women generally have lower tolerance thresholds due to physiological differences in alcohol metabolism, reflected in the sex-specific point allocations.

Non-response Handling: Invalid inputs or survey non-response values result in tagged NA ("b").

Value

integer The low drink score, with:

• 1 for "Low risk" (0 points),

• 2 for "Marginal risk" (1-2 points),

• 3 for "Medium risk" (3-4 points), and

• 4 for "High risk" (5-9 points). If inputs are invalid or out of bounds, the function returns a tagged NA.

Note

This function implements only Step 1 of the guidelines. Step 2 (additional drinking pattern assessments) is not included due to data limitations in the survey.

References

Canada's Low-Risk Alcohol Drinking Guidelines, Health Canada

See Also

derive_alcohol_risk_detailed() for extended categorization including former/never drinkers

Examples

# Scalar usage: Single respondent
# Example: A male respondent who drank in the past year and consumes 3 standard drinks per week.
derive_alcohol_risk(clc_sex = 1, alc_11 = 1, alcdwky = 3)
# Expected output: 1 (Low risk)

# Missing data examples showing tagged NA patterns
result <- derive_alcohol_risk(clc_sex = 1, alc_11 = 6, alcdwky = 5)
result # Shows: NA
haven::is_tagged_na(result, "a") # Shows: TRUE (confirms it's tagged NA(a))
format(result, tag = TRUE) # Shows: "NA(a)" (displays the tag)

result <- derive_alcohol_risk(clc_sex = 1, alc_11 = 7, alcdwky = 5)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

result <- derive_alcohol_risk(clc_sex = 1, alc_11 = 1, alcdwky = NA)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
derive_alcohol_risk(clc_sex = c(1, 2, 1), alc_11 = c(1, 1, 2), alcdwky = c(3, 12, NA))
# Returns: c(1, 2, 1)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(alc_risk_score = derive_alcohol_risk(clc_sex, alc_11, alcdwky)) |>
  head()

Description

Computes a categorical alcohol consumption score based on Canada's Low-Risk Alcohol Drinking Guidelines (Step 1), while distinguishing between never, former, light, moderate, and heavy drinkers. The function uses information about weekly consumption, past-year use, lifetime drinking, and history of heavy drinking.

Usage

derive_alcohol_risk_detailed(clc_sex, alc_11, alcdwky, alc_17, alc_18)

Arguments

Details

Step 1: Assign points based on weekly alcohol consumption.

• If the respondent drank in the past year (alc_11 == 1):

  • 0 to 10 drinks/week: 0 points

  • 11 to 15 drinks/week: 0 points for males, 1 point for females

  • 16 to 20 drinks/week: 1 point for males, 3 points for females

  • More than 20 drinks/week: 3 points for males, 5 points for females

• If they did not drink in the past year (alc_11 == 2): 0 points

Step 2: Determine the final categorical score.

• If the point score from Step 1 is 0, the final category is determined based on lifetime and past-year drinking habits:

  • A score of 1 (Never drinker) is assigned if the respondent either never drank alcohol in their lifetime or is a former drinker who did not regularly consume more than 12 drinks a week.

  • A score of 2 (Low-risk drinker) is assigned if the respondent drank in the past year (but still scored 0 points) or is a former drinker with a history of regularly consuming more than 12 drinks a week.

• If the point score from Step 1 is 1 or 2, the respondent is classified as a Moderate drinker (Score = 3).

• If the point score from Step 1 is 3 or more, the respondent is classified as a Heavy drinker (Score = 4). If inputs are invalid or out of bounds, the function returns a tagged NA.

Value

integer Score: 1 = Never drank, 2 = Low-risk (former or light) drinker, 3 = Moderate drinker (1–2 points), 4 = Heavy drinker (3–4 points). If inputs are invalid or out of bounds, the function returns a tagged NA.

Note

This function uses only Step 1 of the guidelines, as Step 2 information is unavailable in CHMS.

References

Canada's Low-Risk Alcohol Drinking Guidelines, Health Canada

See Also

derive_alcohol_risk() for basic risk scoring without drinking history

Examples

# Scalar usage: Single respondent
# Example: Male, drinks 3 drinks/week, drank in past year, no history of heavy drinking
derive_alcohol_risk_detailed(
  clc_sex = 1, alc_11 = 1, alcdwky = 3,
  alc_17 = 1, alc_18 = 2
)
# Expected output: 2

# Missing data examples showing tagged NA patterns
result <- derive_alcohol_risk_detailed(
  clc_sex = 1, alc_11 = 6, alcdwky = 5,
  alc_17 = 1, alc_18 = 2
)
result # Shows: NA
haven::is_tagged_na(result, "a") # Shows: TRUE (confirms it's tagged NA(a))
format(result, tag = TRUE) # Shows: "NA(a)" (displays the tag)

result <- derive_alcohol_risk_detailed(
  clc_sex = 1, alc_11 = 7, alcdwky = 5,
  alc_17 = 1, alc_18 = 2
)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

result <- derive_alcohol_risk_detailed(
  clc_sex = 1, alc_11 = 1, alcdwky = NA,
  alc_17 = 1, alc_18 = 2
)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
derive_alcohol_risk_detailed(
  clc_sex = c(1, 2, 1), alc_11 = c(1, 1, 2),
  alcdwky = c(3, 12, NA), alc_17 = c(1, 1, 1), alc_18 = c(2, 2, 1)
)
# Returns: c(2, 3, 2)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(alc_detailed_risk_score = derive_alcohol_risk_detailed(
    clc_sex, alc_11, alcdwky, alc_17, alc_18
  )) |>
  head()

Description

This function evaluates a respondent's family history of cardiovascular disease (CVD), based on data about diagnoses of heart disease and stroke in immediate family members and the ages at which these diagnoses occurred. It identifies premature CVD if any diagnosis occurred before age 60.

Usage

derive_cvd_family_history(fmh_11, fmh_12, fmh_13, fmh_14)

Arguments

Details

This function assesses family history of premature cardiovascular disease (CVD), a significant risk factor for personal CVD development.

     **Missing Data Codes:**
     - `fmh_11`, `fmh_13`:
       - `6`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `7-9`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.
     - `fmh_12`, `fmh_14`:
       - `996`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `997-999`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.

Value

integer The CVD family history:

• 1: "Yes" – Family history of premature CVD exists (diagnosis before age 60).

• 2: "No" – No family history of premature CVD.

• haven::tagged_na("a"): Not applicable

• haven::tagged_na("b"): Missing

See Also

derive_cvd_personal_history()

Examples

# Scalar usage: Single respondent
# Example 1: Premature CVD due to heart disease diagnosis at age 50
derive_cvd_family_history(fmh_11 = 1, fmh_12 = 50, fmh_13 = 2, fmh_14 = NA)
# Output: 1

# Example 2: Respondent has non-response values for all inputs.
result <- derive_cvd_family_history(fmh_11 = 8, fmh_12 = 998, fmh_13 = 8, fmh_14 = 998)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
derive_cvd_family_history(
  fmh_11 = c(1, 2, 1), fmh_12 = c(50, NA, 70),
  fmh_13 = c(2, 1, 2), fmh_14 = c(NA, 55, NA)
)
# Returns: c(1, 1, 2)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(cvd_family_history = derive_cvd_family_history(fmh_11, fmh_12, fmh_13, fmh_14)) |>
  head()

Description

This function determines a respondent's cardiovascular disease (CVD) personal history based on the presence or absence of specific conditions related to heart disease, heart attack, and stroke.

Usage

derive_cvd_personal_history(ccc_61, ccc_63, ccc_81)

Arguments

Details

This function synthesizes self-reported data on major cardiovascular events (heart disease, heart attack, stroke) into a single binary indicator.

     **Missing Data Codes:**
     - For all input variables:
       - `6`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `7-9`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.

Value

integer The CVD personal history: - 1: "Yes" if the person had heart disease, heart attack, or stroke. - 2: "No" if the person had neither of the conditions. - haven::tagged_na("a"): Not applicable - haven::tagged_na("b"): Missing

See Also

derive_cvd_family_history()

Examples

# Scalar usage: Single respondent
# Determine CVD personal history for a person with heart disease (ccc_61 = 1).
derive_cvd_personal_history(ccc_61 = 1, ccc_63 = 2, ccc_81 = 2)
# Output: 1

# Example: Respondent has non-response values for all inputs.
result <- derive_cvd_personal_history(ccc_61 = 8, ccc_63 = 8, ccc_81 = 8)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
derive_cvd_personal_history(ccc_61 = c(1, 2, 2), ccc_63 = c(2, 1, 2), ccc_81 = c(2, 2, 1))
# Returns: c(1, 1, 1)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(cvd_personal_history = derive_cvd_personal_history(ccc_61, ccc_63, ccc_81)) |>
  head()

Description

This function evaluates diabetes status using a comprehensive approach that combines laboratory measurements, self-reported diagnosis, and medication usage to create an inclusive diabetes classification.

Usage

derive_diabetes_status(diab_a1c, ccc_51, diab_med)

Arguments

Details

This function classifies diabetes status based that considers:

     **Data Sources:**
     - Laboratory: HbA1c levels indicating diabetes (diab_a1c)
     - Self-report: Participant-reported diabetes diagnosis (ccc_51)
     - Medication: Current diabetes medication usage (diab_med)

     **Classification Logic:**
     - ANY positive indicator results in diabetes classification
     - ALL negative indicators required for "no diabetes" classification
     - Sophisticated missing data handling preserves available information

     **Missing Data Codes:**
     - `diab_a1c`, `diab_med`:
       - Tagged NA "a": Valid skip.
       - Tagged NA "b": Don't know, refusal, or not stated.
     - `ccc_51`:
       - `6`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `7-9`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.

Value

integer The inclusive diabetes status: - 1 ("Yes") if any of diab_a1c, ccc_51, or diab_med is 1. - 2 ("No") if all of diab_a1c, ccc_51, and diab_med are 2 or 0. - haven::tagged_na("a"): Not applicable - haven::tagged_na("b"): Missing

See Also

Related health condition functions: derive_hypertension(), calculate_gfr()

Examples

# Scalar usage: Single respondent
# Example: Determine the inclusive diabetes status for a respondent with diabetes based on HbA1c.
derive_diabetes_status(diab_a1c = 1, ccc_51 = 2, diab_med = 0)
# Output: 1 (Inclusive diabetes status is "Yes").

# Example: Determine the inclusive diabetes status for a respondent no diabetes all around.
derive_diabetes_status(diab_a1c = 2, ccc_51 = 2, diab_med = 0)
# Output: 2 (Inclusive diabetes status is "No").

# Example: Determine inclusive diabetes status when only one parameter is NA.
derive_diabetes_status(diab_a1c = 2, ccc_51 = NA, diab_med = 1)
# Output: 1 (Based on `diab_med`, inclusive diabetes status is "Yes").

# Example: Respondent has non-response values for all inputs.
result <- derive_diabetes_status(haven::tagged_na("b"), 8, haven::tagged_na("b"))
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
derive_diabetes_status(diab_a1c = c(1, 2, 2), ccc_51 = c(2, 1, 2), diab_med = c(0, 0, 1))
# Returns: c(1, 1, 1)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(diab_a1c = 1, diab_med = 0) |>
  mutate(diabetes_status = derive_diabetes_status(diab_a1c, ccc_51, diab_med)) |>
  head()

Description

This function determines the hypertension status of a respondent based on their systolic and diastolic blood pressure measurements and medication usage.

Usage

derive_hypertension(
  bpmdpbps,
  bpmdpbpd,
  any_htn_med,
  ccc_32 = 2,
  cvd_status = 2,
  diab_status = 2,
  ckd_status = 2
)

Arguments

Details

This function implements clinical guidelines for hypertension classification:

     **Blood Pressure Thresholds:**
     - General population: >= 140/90 mmHg indicates hypertension
     - Diabetes or CKD patients: >= 130/80 mmHg indicates hypertension (lower threshold)

     **Medication Logic:**
     - Anyone taking hypertension medication is classified as hypertensive
     - Medication status may be adjusted based on comorbidities (diabetes, CKD, cardiovascular disease)

     **Missing Data Codes:**
     - `bpmdpbps`, `bpmdpbpd`:
       - `996`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `997-999`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.
     - `any_htn_med`:
       - Tagged NA "a": Valid skip.
       - Tagged NA "b": Don't know, refusal, or not stated.
     - `ccc_32`, `cvd_status`, `diab_status`, `ckd_status`:
       - `6`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `7-9`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.

Value

integer The hypertension status:

• 1: High blood pressure (BP >= 140/90 mmHg (or >= 130/80 mmHg if diabetes or CKD) or on hypertension medication)

• 2: Normal blood pressure (BP < 140/90 mmHg (or < 130/80 mmHg if diabetes or CKD) and not on hypertension medication)

• haven::tagged_na("a"): Not applicable

• haven::tagged_na("b"): Missing

See Also

adjust_sbp(), adjust_dbp() for blood pressure adjustment, derive_hypertension_adj() for adjusted BP classification

Examples

# Scalar usage: Single respondent
# Example 1: Respondent has systolic BP = 150, diastolic BP = 95, and on medication.
derive_hypertension(bpmdpbps = 150, bpmdpbpd = 95, any_htn_med = 1)
# Output: 1 (High blood pressure due to systolic BP, diastolic BP, and medication usage).

# Example 2: Respondent has systolic BP = 120, diastolic BP = 80, and not on medication.
derive_hypertension(bpmdpbps = 120, bpmdpbpd = 80, any_htn_med = 0)
# Output: 2 (Normal blood pressure as BP is below 140/90 mmHg and not on medication).

# Example 3: Respondent has non-response BP values of 996 for both systolic and diastolic.
result <- derive_hypertension(bpmdpbps = 996, bpmdpbpd = 996, any_htn_med = 0)
result # Shows: NA
haven::is_tagged_na(result, "a") # Shows: TRUE (confirms it's tagged NA(a))
format(result, tag = TRUE) # Shows: "NA(a)" (displays the tag)

# Multiple respondents
derive_hypertension(
  bpmdpbps = c(150, 120, 135), bpmdpbpd = c(95, 80, 85),
  any_htn_med = c(1, 0, 1), diab_status = c(2, 2, 1)
)
# Returns: c(1, 2, 1)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(any_htn_med = 0) |>
  mutate(htn = derive_hypertension(bpmdpbps, bpmdpbpd, any_htn_med)) |>
  select(clinicid, bpmdpbps, bpmdpbpd, htn) |>
  head()

Description

This function determines the hypertension status of a respondent based on their adjusted systolic and diastolic blood pressure measurements and medication usage.

Usage

derive_hypertension_adj(
  sbp_adj_mmhg,
  dbp_adj_mmhg,
  any_htn_med,
  ccc_32 = 2,
  cvd_status = 2,
  diab_status = 2,
  ckd_status = 2
)

Arguments

Details

This function implements clinical guidelines for hypertension classification using adjusted blood pressure values:

     **Blood Pressure Thresholds:**
     - General population: >= 140/90 mmHg indicates hypertension
     - Diabetes or CKD patients: >= 130/80 mmHg indicates hypertension (lower threshold)

     **Medication Logic:**
     - Anyone taking hypertension medication is classified as hypertensive
     - Medication status may be adjusted based on comorbidities (diabetes, CKD, cardiovascular disease)

     **Missing Data Codes:**
     - `sbp_adj_mmhg`, `dbp_adj_mmhg`:
       - `996`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `997-999`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.
     - `any_htn_med`:
       - Tagged NA "a": Valid skip.
       - Tagged NA "b": Don't know, refusal, or not stated.
     - `ccc_32`, `cvd_status`, `diab_status`, `ckd_status`:
       - `6`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `7-9`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.

Value

integer The hypertension status:

• 1: High blood pressure (adjusted BP >= 140/90 mmHg (or >= 130/80 mmHg if diabetes or CKD) or on hypertension medication)

• 2: Normal blood pressure (adjusted BP < 140/90 mmHg (or < 130/80 mmHg if diabetes or CKD) and not on hypertension medication)

• haven::tagged_na("a"): Not applicable

• haven::tagged_na("b"): Missing

See Also

derive_hypertension() for unadjusted BP classification

Examples

# Scalar usage: Single respondent
# Example 1: Respondent has adjusted SBP = 150, adjusted DBP = 95, and on medication.
derive_hypertension_adj(sbp_adj_mmhg = 150, dbp_adj_mmhg = 95, any_htn_med = 1)
# Output: 1 (High blood pressure due to adjusted SBP, adjusted DBP, and medication usage).

# Example 2: Respondent has adjusted SBP = 120, adjusted DBP = 80, and not on medication.
derive_hypertension_adj(sbp_adj_mmhg = 120, dbp_adj_mmhg = 80, any_htn_med = 2)
# Output: 2 (Normal blood pressure as adjusted BP is below 140/90 mmHg and not on medication).

# Example 3: Respondent has non-response BP values of 996 for both systolic and diastolic.
result <- derive_hypertension_adj(sbp_adj_mmhg = 996, dbp_adj_mmhg = 996, any_htn_med = 0)
result # Shows: NA
haven::is_tagged_na(result, "a") # Shows: TRUE (confirms it's tagged NA(a))
format(result, tag = TRUE) # Shows: "NA(a)" (displays the tag)

# Multiple respondents
derive_hypertension_adj(
  sbp_adj_mmhg = c(150, 120, 135), dbp_adj_mmhg = c(95, 80, 85),
  any_htn_med = c(1, 0, 1), diab_status = c(2, 2, 1)
)
# Returns: c(1, 2, 1)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(sbp_adj_mmhg = adjust_sbp(bpmdpbps), dbp_adj_mmhg = adjust_dbp(bpmdpbpd)) |>
  mutate(any_htn_med = 0) |>
  mutate(htn_adj = derive_hypertension_adj(sbp_adj_mmhg, dbp_adj_mmhg, any_htn_med)) |>
  select(clinicid, sbp_adj_mmhg, dbp_adj_mmhg, htn_adj) |>
  head()

Description

This function determines the controlled hypertension status of a respondent based on their systolic and diastolic blood pressure measurements and medication usage.

Usage

derive_hypertension_control(
  bpmdpbps,
  bpmdpbpd,
  any_htn_med,
  ccc_32 = 2,
  cvd_status = 2,
  diab_status = 2,
  ckd_status = 2
)

Arguments

Details

This function assesses whether a respondent's hypertension is controlled:

     **Control Thresholds:**
     - General population: < 140/90 mmHg
     - Diabetes or CKD patients: < 130/80 mmHg

     **Logic:**
     - Only applies to respondents taking hypertension medication.
     - If BP is below the threshold, hypertension is "controlled" (1).
     - If BP is at or above the threshold, it is "not controlled" (2).

     **Missing Data Codes:**
     - `bpmdpbps`, `bpmdpbpd`:
       - `996`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `997-999`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.
     - `any_htn_med`:
       - Tagged NA "a": Valid skip.
       - Tagged NA "b": Don't know, refusal, or not stated.
     - `ccc_32`, `cvd_status`, `diab_status`, `ckd_status`:
       - `6`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `7-9`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.

Value

integer The hypertension status:

• 1: Hypertension controlled (BP < 140/90 mmHg (or < 130/80 mmHg if diabetes or CKD) when on hypertension medication)

• 2: Hypertension not controlled (BP >= 140/90 mmHg (or >= 130/80 mmHg if diabetes or CKD) when on hypertension medication)

• haven::tagged_na("a"): Not applicable

• haven::tagged_na("b"): Missing

See Also

derive_hypertension_control_adj() for controlled status with adjusted BP

Examples

# Scalar usage: Single respondent
# Example 1: Respondent has systolic BP = 150, diastolic BP = 95, and on medication.
derive_hypertension_control(bpmdpbps = 150, bpmdpbpd = 95, any_htn_med = 1)
# Output: 2 (Hypertension not controlled due to high SBP and SBP despite medication usage).

# Example 2: Respondent has systolic BP = 120, diastolic BP = 80, and on medication.
derive_hypertension_control(bpmdpbps = 120, bpmdpbpd = 80, any_htn_med = 1)
# Output: 1 (Hypertension controlled as BP is below 140/90 mmHg and on medication).

# Example 3: Respondent has non-response BP values of 996 for both systolic and diastolic.
result <- derive_hypertension_control(bpmdpbps = 996, bpmdpbpd = 996, any_htn_med = 0)
result # Shows: NA
haven::is_tagged_na(result, "a") # Shows: TRUE (confirms it's tagged NA(a))
format(result, tag = TRUE) # Shows: "NA(a)" (displays the tag)

# Multiple respondents
derive_hypertension_control(
  bpmdpbps = c(150, 120, 135), bpmdpbpd = c(95, 80, 85),
  any_htn_med = c(1, 1, 1), diab_status = c(2, 2, 1)
)
# Returns: c(2, 1, 2)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(any_htn_med = 1) |>
  mutate(ctrl = derive_hypertension_control(bpmdpbps, bpmdpbpd, any_htn_med)) |>
  select(clinicid, bpmdpbps, bpmdpbpd, ctrl) |>
  head()

Description

This function determines the controlled hypertension status of a respondent based on their adjusted systolic and diastolic blood pressure measurements and medication usage.

Usage

derive_hypertension_control_adj(
  sbp_adj_mmhg,
  dbp_adj_mmhg,
  any_htn_med,
  ccc_32 = 2,
  cvd_status = 2,
  diab_status = 2,
  ckd_status = 2
)

Arguments

Details

This function assesses whether a respondent's hypertension is controlled using adjusted BP values:

     **Control Thresholds:**
     - General population: < 140/90 mmHg
     - Diabetes or CKD patients: < 130/80 mmHg

     **Logic:**
     - Only applies to respondents taking hypertension medication.
     - If adjusted BP is below the threshold, hypertension is "controlled" (1).
     - If adjusted BP is at or above the threshold, it is "not controlled" (2).

     **Missing Data Codes:**
     - `sbp_adj_mmhg`, `dbp_adj_mmhg`:
       - `996`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `997-999`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.
     - `any_htn_med`:
       - Tagged NA "a": Valid skip.
       - Tagged NA "b": Don't know, refusal, or not stated.
     - `ccc_32`, `cvd_status`, `diab_status`, `ckd_status`:
       - `6`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `7-9`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.

Value

integer The hypertension status:

• 1: Hypertension controlled (BP < 140/90 mmHg (or < 130/80 mmHg if diabetes or CKD) when on hypertension medication)

• 2: Hypertension not controlled (BP >= 140/90 mmHg (or >= 130/80 mmHg if diabetes or CKD) when on hypertension medication)

• haven::tagged_na("a"): Not applicable

• haven::tagged_na("b"): Missing

See Also

derive_hypertension_control() for controlled status with unadjusted BP

Examples

# Scalar usage: Single respondent
# Example 1: Respondent has adjusted SBP = 150, adjusted DBP = 95, and on medication.
derive_hypertension_control_adj(sbp_adj_mmhg = 150, dbp_adj_mmhg = 95, any_htn_med = 1)
# Output: 2 (Hypertension not controlled due to high adjusted SBP and DBP despite medication usage).

# Example 2: Respondent has adjusted SBP = 120, adjusted DBP = 80, and on medication.
derive_hypertension_control_adj(sbp_adj_mmhg = 120, dbp_adj_mmhg = 80, any_htn_med = 1)
# Output: 1 (Hypertension controlled as adjusted BP is below 140/90 mmHg and on medication).

# Example 3: Respondent has non-response BP values of 996 for both systolic and diastolic.
result <- derive_hypertension_control_adj(sbp_adj_mmhg = 996, dbp_adj_mmhg = 996, any_htn_med = 0)
result # Shows: NA
haven::is_tagged_na(result, "a") # Shows: TRUE (confirms it's tagged NA(a))
format(result, tag = TRUE) # Shows: "NA(a)" (displays the tag)

# Multiple respondents
derive_hypertension_control_adj(
  sbp_adj_mmhg = c(150, 120, 135), dbp_adj_mmhg = c(95, 80, 85),
  any_htn_med = c(1, 1, 1), diab_status = c(2, 2, 1)
)
# Returns: c(2, 1, 2)

# Database usage: Applied to survey datasets
library(dplyr)
cycle4 |>
  mutate(sbp_adj_mmhg = adjust_sbp(bpmdpbps), dbp_adj_mmhg = adjust_dbp(bpmdpbpd)) |>
  mutate(any_htn_med = 1) |>
  mutate(ctrl_adj = derive_hypertension_control_adj(sbp_adj_mmhg, dbp_adj_mmhg, any_htn_med)) |>
  select(clinicid, sbp_adj_mmhg, dbp_adj_mmhg, ctrl_adj) |>
  head()

Description

This function checks if a given medication is an ACE inhibitor. This function processes multiple inputs efficiently.

Usage

is_ace_inhibitor(meucatc, npi_25b)

Arguments

Details

Identifies ACE inhibitors based on ATC codes starting with "C09".

     **Missing Data Codes:**
     - `meucatc`: `9999996` (Not applicable), `9999997-9999999` (Missing)
     - `npi_25b`: `6` (Not applicable), `7-9` (Missing)

Value

numeric 1 if medication is an ACE inhibitor, 0 otherwise. If inputs are invalid or out of bounds, the function returns a tagged NA.

Examples

# Scalar usage: Single respondent
is_ace_inhibitor("C09AB03", 2)
# Returns: 1

# Example: Respondent has non-response values for all inputs.
result <- is_ace_inhibitor("9999998", 8)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
is_ace_inhibitor(c("C09AB03", "C01AA05"), c(2, 1))
# Returns: c(1, 0)

# Database usage: Applied to survey datasets
library(dplyr)
cycle3_meds |>
  mutate(ace_inhibitor = is_ace_inhibitor(meucatc, npi_25b)) |>
  head()

Description

This function checks if a person is taking ACE inhibitors based on the provided Anatomical Therapeutic Chemical (ATC) codes for medications and the Canadian Health Measures Survey (CHMS) response for the time when the medication was last taken.

Usage

is_ace_med_cycles1to2(
  atc_101a = NULL,
  atc_102a = NULL,
  atc_103a = NULL,
  atc_104a = NULL,
  atc_105a = NULL,
  atc_106a = NULL,
  atc_107a = NULL,
  atc_108a = NULL,
  atc_109a = NULL,
  atc_110a = NULL,
  atc_111a = NULL,
  atc_112a = NULL,
  atc_113a = NULL,
  atc_114a = NULL,
  atc_115a = NULL,
  atc_201a = NULL,
  atc_202a = NULL,
  atc_203a = NULL,
  atc_204a = NULL,
  atc_205a = NULL,
  atc_206a = NULL,
  atc_207a = NULL,
  atc_208a = NULL,
  atc_209a = NULL,
  atc_210a = NULL,
  atc_211a = NULL,
  atc_212a = NULL,
  atc_213a = NULL,
  atc_214a = NULL,
  atc_215a = NULL,
  atc_131a = NULL,
  atc_132a = NULL,
  atc_133a = NULL,
  atc_134a = NULL,
  atc_135a = NULL,
  atc_231a = NULL,
  atc_232a = NULL,
  atc_233a = NULL,
  atc_234a = NULL,
  atc_235a = NULL,
  mhr_101b = NULL,
  mhr_102b = NULL,
  mhr_103b = NULL,
  mhr_104b = NULL,
  mhr_105b = NULL,
  mhr_106b = NULL,
  mhr_107b = NULL,
  mhr_108b = NULL,
  mhr_109b = NULL,
  mhr_110b = NULL,
  mhr_111b = NULL,
  mhr_112b = NULL,
  mhr_113b = NULL,
  mhr_114b = NULL,
  mhr_115b = NULL,
  mhr_201b = NULL,
  mhr_202b = NULL,
  mhr_203b = NULL,
  mhr_204b = NULL,
  mhr_205b = NULL,
  mhr_206b = NULL,
  mhr_207b = NULL,
  mhr_208b = NULL,
  mhr_209b = NULL,
  mhr_210b = NULL,
  mhr_211b = NULL,
  mhr_212b = NULL,
  mhr_213b = NULL,
  mhr_214b = NULL,
  mhr_215b = NULL,
  mhr_131b = NULL,
  mhr_132b = NULL,
  mhr_133b = NULL,
  mhr_134b = NULL,
  mhr_135b = NULL,
  mhr_231b = NULL,
  mhr_232b = NULL,
  mhr_233b = NULL,
  mhr_234b = NULL,
  mhr_235b = NULL
)

Arguments

Details

The function identifies ACE inhibitors based on ATC codes starting with "C09". It checks all medication variables provided in the input data frame.

     **Missing Data Codes:**
     - The function handles tagged NAs from the `is_ace_inhibitor` function and propagates them.

Value

numeric Returns 1 if the person is taking ACE inhibitors, 0 otherwise. If all medication information is missing, it returns a tagged NA.

See Also

is_ace_inhibitor

Examples

# This is a wrapper function and is not intended to be called directly by the user.
# See `is_ace_inhibitor` for usage examples.

Description

This function checks if a given medication is any anti-hypertensive drug. This function processes multiple inputs efficiently.

Usage

is_any_antihtn_med(meucatc, npi_25b)

Arguments

Details

Identifies anti-hypertensive drugs based on ATC codes starting with "C02", "C03", "C07", "C08", or "C09", excluding specific sub-codes.

     **Missing Data Codes:**
     - `meucatc`: `9999996` (Not applicable), `9999997-9999999` (Missing)
     - `npi_25b`: `6` (Not applicable), `7-9` (Missing)

Value

numeric 1 if medication is an anti-hypertensive drug, 0 otherwise. If inputs are invalid or out of bounds, the function returns a tagged NA.

Examples

# Scalar usage: Single respondent
is_any_antihtn_med("C07AB02", 4)
# Returns: 1

# Example: Respondent has non-response values for all inputs.
result <- is_any_antihtn_med("9999998", 8)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
is_any_antihtn_med(c("C07AB02", "C07AA07"), c(4, 2))
# Returns: c(1, 0)

# Database usage: Applied to survey datasets
library(dplyr)
cycle3_meds |>
  mutate(any_antihtn = is_any_antihtn_med(meucatc, npi_25b)) |>
  head()

Description

This function checks if a person is taking any anti-hypertensive medication based on the provided Anatomical Therapeutic Chemical (ATC) codes for medications and the Canadian Health Measures Survey (CHMS) response for the time when the medication was last taken.

Usage

is_any_htn_med_cycles1to2(
  atc_101a = NULL,
  atc_102a = NULL,
  atc_103a = NULL,
  atc_104a = NULL,
  atc_105a = NULL,
  atc_106a = NULL,
  atc_107a = NULL,
  atc_108a = NULL,
  atc_109a = NULL,
  atc_110a = NULL,
  atc_111a = NULL,
  atc_112a = NULL,
  atc_113a = NULL,
  atc_114a = NULL,
  atc_115a = NULL,
  atc_201a = NULL,
  atc_202a = NULL,
  atc_203a = NULL,
  atc_204a = NULL,
  atc_205a = NULL,
  atc_206a = NULL,
  atc_207a = NULL,
  atc_208a = NULL,
  atc_209a = NULL,
  atc_210a = NULL,
  atc_211a = NULL,
  atc_212a = NULL,
  atc_213a = NULL,
  atc_214a = NULL,
  atc_215a = NULL,
  atc_131a = NULL,
  atc_132a = NULL,
  atc_133a = NULL,
  atc_134a = NULL,
  atc_135a = NULL,
  atc_231a = NULL,
  atc_232a = NULL,
  atc_233a = NULL,
  atc_234a = NULL,
  atc_235a = NULL,
  mhr_101b = NULL,
  mhr_102b = NULL,
  mhr_103b = NULL,
  mhr_104b = NULL,
  mhr_105b = NULL,
  mhr_106b = NULL,
  mhr_107b = NULL,
  mhr_108b = NULL,
  mhr_109b = NULL,
  mhr_110b = NULL,
  mhr_111b = NULL,
  mhr_112b = NULL,
  mhr_113b = NULL,
  mhr_114b = NULL,
  mhr_115b = NULL,
  mhr_201b = NULL,
  mhr_202b = NULL,
  mhr_203b = NULL,
  mhr_204b = NULL,
  mhr_205b = NULL,
  mhr_206b = NULL,
  mhr_207b = NULL,
  mhr_208b = NULL,
  mhr_209b = NULL,
  mhr_210b = NULL,
  mhr_211b = NULL,
  mhr_212b = NULL,
  mhr_213b = NULL,
  mhr_214b = NULL,
  mhr_215b = NULL,
  mhr_131b = NULL,
  mhr_132b = NULL,
  mhr_133b = NULL,
  mhr_134b = NULL,
  mhr_135b = NULL,
  mhr_231b = NULL,
  mhr_232b = NULL,
  mhr_233b = NULL,
  mhr_234b = NULL,
  mhr_235b = NULL
)

Arguments

Details

The function identifies anti-hypertensive drugs based on ATC codes starting with "C02", "C03", "C07", "C08", or "C09", excluding specific sub-codes. It checks all medication variables provided in the input data frame.

     **Missing Data Codes:**
     - The function handles tagged NAs from the `is_any_antihtn_med` function and propagates them.

Value

numeric Returns 1 if the person is taking any anti-hypertensive medication, 0 otherwise. If all medication information is missing, it returns a tagged NA.

See Also

is_any_antihtn_med

Examples

# This is a wrapper function and is not intended to be called directly by the user.
# See `is_any_antihtn_med` for usage examples.

Description

This function checks if a person is taking beta blockers based on the provided Anatomical Therapeutic Chemical (ATC) codes for medications and the Canadian Health Measures Survey (CHMS) response for the time when the medication was last taken.

Usage

is_bb_med_cycles1to2(
  atc_101a = NULL,
  atc_102a = NULL,
  atc_103a = NULL,
  atc_104a = NULL,
  atc_105a = NULL,
  atc_106a = NULL,
  atc_107a = NULL,
  atc_108a = NULL,
  atc_109a = NULL,
  atc_110a = NULL,
  atc_111a = NULL,
  atc_112a = NULL,
  atc_113a = NULL,
  atc_114a = NULL,
  atc_115a = NULL,
  atc_201a = NULL,
  atc_202a = NULL,
  atc_203a = NULL,
  atc_204a = NULL,
  atc_205a = NULL,
  atc_206a = NULL,
  atc_207a = NULL,
  atc_208a = NULL,
  atc_209a = NULL,
  atc_210a = NULL,
  atc_211a = NULL,
  atc_212a = NULL,
  atc_213a = NULL,
  atc_214a = NULL,
  atc_215a = NULL,
  atc_131a = NULL,
  atc_132a = NULL,
  atc_133a = NULL,
  atc_134a = NULL,
  atc_135a = NULL,
  atc_231a = NULL,
  atc_232a = NULL,
  atc_233a = NULL,
  atc_234a = NULL,
  atc_235a = NULL,
  mhr_101b = NULL,
  mhr_102b = NULL,
  mhr_103b = NULL,
  mhr_104b = NULL,
  mhr_105b = NULL,
  mhr_106b = NULL,
  mhr_107b = NULL,
  mhr_108b = NULL,
  mhr_109b = NULL,
  mhr_110b = NULL,
  mhr_111b = NULL,
  mhr_112b = NULL,
  mhr_113b = NULL,
  mhr_114b = NULL,
  mhr_115b = NULL,
  mhr_201b = NULL,
  mhr_202b = NULL,
  mhr_203b = NULL,
  mhr_204b = NULL,
  mhr_205b = NULL,
  mhr_206b = NULL,
  mhr_207b = NULL,
  mhr_208b = NULL,
  mhr_209b = NULL,
  mhr_210b = NULL,
  mhr_211b = NULL,
  mhr_212b = NULL,
  mhr_213b = NULL,
  mhr_214b = NULL,
  mhr_215b = NULL,
  mhr_131b = NULL,
  mhr_132b = NULL,
  mhr_133b = NULL,
  mhr_134b = NULL,
  mhr_135b = NULL,
  mhr_231b = NULL,
  mhr_232b = NULL,
  mhr_233b = NULL,
  mhr_234b = NULL,
  mhr_235b = NULL
)

Arguments

Details

The function identifies beta blockers based on ATC codes starting with "C07", excluding specific sub-codes. It checks all medication variables provided in the input data frame.

     **Missing Data Codes:**
     - The function handles tagged NAs from the `is_beta_blocker` function and propagates them.

Value

numeric Returns 1 if the respondent is taking beta blockers, 0 otherwise. If all medication information is missing, returns a tagged NA.

See Also

is_beta_blocker

Examples

# This is a wrapper function and is not intended to be called directly by the user.
# See `is_beta_blocker` for usage examples.

Description

This function determines whether a given medication is a beta blocker. This function processes multiple inputs efficiently.

Usage

is_beta_blocker(meucatc, npi_25b)

Arguments

Details

Identifies beta blockers based on ATC codes starting with "C07", excluding specific sub-codes.

     **Missing Data Codes:**
     - `meucatc`: `9999996` (Not applicable), `9999997-9999999` (Missing)
     - `npi_25b`: `6` (Not applicable), `7-9` (Missing)

Value

numeric 1 if medication is a beta blocker, 0 otherwise. If inputs are invalid or out of bounds, the function returns a tagged NA.

Examples

# Scalar usage: Single respondent
is_beta_blocker("C07AA13", 3)
# Returns: 1

# Example: Respondent has non-response values for all inputs.
result <- is_beta_blocker("9999998", 8)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
is_beta_blocker(c("C07AA13", "C07AA07"), c(3, 4))
# Returns: c(1, 0)

# Database usage: Applied to survey datasets
library(dplyr)
cycle3_meds |>
  mutate(beta_blocker = is_beta_blocker(meucatc, npi_25b)) |>
  head()

Description

This function checks if a given medication is a calcium channel blocker. This function processes multiple inputs efficiently.

Usage

is_calcium_channel_blocker(meucatc, npi_25b)

Arguments

Details

Identifies calcium channel blockers based on ATC codes starting with "C08".

     **Missing Data Codes:**
     - `meucatc`: `9999996` (Not applicable), `9999997-9999999` (Missing)
     - `npi_25b`: `6` (Not applicable), `7-9` (Missing)

Value

numeric 1 if medication is a calcium channel blocker, 0 otherwise. If inputs are invalid or out of bounds, the function returns a tagged NA.

Examples

# Scalar usage: Single respondent
is_calcium_channel_blocker("C08CA05", 1)
# Returns: 1

# Example: Respondent has non-response values for all inputs.
result <- is_calcium_channel_blocker("9999998", 8)
result # Shows: NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)

# Multiple respondents
is_calcium_channel_blocker(c("C08CA05", "C01AA05"), c(1, 2))
# Returns: c(1, 0)

# Database usage: Applied to survey datasets
library(dplyr)
cycle3_meds |>
  mutate(ccb = is_calcium_channel_blocker(meucatc, npi_25b)) |>
  head()

Description

This function checks if a person is taking calcium channel blockers based on the provided Anatomical Therapeutic Chemical (ATC) codes for medications and the Canadian Health Measures Survey (CHMS) response for the time when the medication was last taken.

Usage

is_ccb_med_cycles1to2(
  atc_101a = NULL,
  atc_102a = NULL,
  atc_103a = NULL,
  atc_104a = NULL,
  atc_105a = NULL,
  atc_106a = NULL,
  atc_107a = NULL,
  atc_108a = NULL,
  atc_109a = NULL,
  atc_110a = NULL,
  atc_111a = NULL,
  atc_112a = NULL,
  atc_113a = NULL,
  atc_114a = NULL,
  atc_115a = NULL,
  atc_201a = NULL,
  atc_202a = NULL,
  atc_203a = NULL,
  atc_204a = NULL,
  atc_205a = NULL,
  atc_206a = NULL,
  atc_207a = NULL,
  atc_208a = NULL,
  atc_209a = NULL,
  atc_210a = NULL,
  atc_211a = NULL,
  atc_212a = NULL,
  atc_213a = NULL,
  atc_214a = NULL,
  atc_215a = NULL,
  atc_131a = NULL,
  atc_132a = NULL,
  atc_133a = NULL,
  atc_134a = NULL,
  atc_135a = NULL,
  atc_231a = NULL,
  atc_232a = NULL,
  atc_233a = NULL,
  atc_234a = NULL,
  atc_235a = NULL,
  mhr_101b = NULL,
  mhr_102b = NULL,
  mhr_103b = NULL,
  mhr_104b = NULL,
  mhr_105b = NULL,
  mhr_106b = NULL,
  mhr_107b = NULL,
  mhr_108b = NULL,
  mhr_109b = NULL,
  mhr_110b = NULL,
  mhr_111b = NULL,
  mhr_112b = NULL,
  mhr_113b = NULL,
  mhr_114b = NULL,
  mhr_115b = NULL,
  mhr_201b = NULL,
  mhr_202b = NULL,
  mhr_203b = NULL,
  mhr_204b = NULL,
  mhr_205b = NULL,
  mhr_206b = NULL,
  mhr_207b = NULL,
  mhr_208b = NULL,
  mhr_209b = NULL,
  mhr_210b = NULL,
  mhr_211b = NULL,
  mhr_212b = NULL,
  mhr_213b = NULL,
  mhr_214b = NULL,
  mhr_215b = NULL,
  mhr_131b = NULL,
  mhr_132b = NULL,
  mhr_133b = NULL,
  mhr_134b = NULL,
  mhr_135b = NULL,
  mhr_231b = NULL,
  mhr_232b = NULL,
  mhr_233b = NULL,
  mhr_234b = NULL,
  mhr_235b = NULL
)

Arguments