R/respiratory_02_population.R

In nemsqar: National Emergency Medical Service Quality Alliance Measure Calculations

#' @title Respiratory-02 Populations
#'
#' @description
#'
#' The `respiratory_02_population` function calculates metrics for pediatric and
#' adult respiratory populations based on pre-defined criteria, such as low
#' oxygen saturation and specific medication or procedure codes. It returns a
#' summary table of the overall, pediatric, and adult populations, showing
#' counts and proportions.
#'
#' @param df A data frame containing incident data with each row representing an
#'   observation.
#' @param patient_scene_table A data.frame or tibble containing at least
#'   epatient and escene fields as a fact table.
#' @param response_table A data.frame or tibble containing at least the
#'   eresponse fields needed for this measure's calculations.
#' @param vitals_table A data.frame or tibble containing at least the evitals
#'   fields needed for this measure's calculations.
#' @param medications_table A data.frame or tibble containing only the
#'   emedications fields needed for this measure's calculations.
#' @param procedures_table A data.frame or tibble containing only the
#'   eprocedures fields needed for this measure's calculations.
#' @param erecord_01_col Column name for eRecord.01, used to form a unique
#'   patient ID.
#' @param incident_date_col Column that contains the incident date. This
#'   defaults to `NULL` as it is optional in case not available due to PII
#'   restrictions.
#' @param patient_DOB_col Column that contains the patient's date of birth. This
#'   defaults to `NULL` as it is optional in case not available due to PII
#'   restrictions.
#' @param epatient_15_col integer Column giving the calculated age value.
#' @param epatient_16_col Column giving the provided age unit value.
#' @param eresponse_05_col Column name for response codes (e.g., incident type).
#' @param evitals_12_col Column name for oxygen saturation (SpO2) values.
#' @param emedications_03_col Column name for medication codes.
#' @param eprocedures_03_col Column name for procedure codes.
#'
#' @return A list that contains the following:
#' * a tibble with counts for each filtering step,
#' * a tibble for each population of interest
#' * a tibble for the initial population
#' * a tibble for the total dataset with computations
#'
#' @examples
#' # create tables to test correct functioning
#' # patient table
#'   patient_table <- tibble::tibble(
#'
#'     erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
#'     incident_date = as.Date(c("2025-01-01", "2025-01-05",
#'                               "2025-02-01", "2025-01-01",
#'                               "2025-06-01")
#'                               ),
#'     patient_dob = as.Date(c("2000-01-01", "2020-01-01",
#'                             "2023-02-01", "2023-01-01",
#'                             "1970-06-01")
#'                             ),
#'     epatient_15 = c(25, 5, 2, 2, 55),  # Ages
#'     epatient_16 = c("Years", "Years", "Years", "Years", "Years")
#'
#'   )
#'
#'   # response table
#'   response_table <- tibble::tibble(
#'
#'     erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
#'     eresponse_05 = rep(2205001, 5)
#'
#'   )
#'
#'   # medications table
#'   medications_table <- tibble::tibble(
#'
#'     erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
#'     emedications_03 = c("Oxygen", "Oxygen", "Oxygen", "Oxygen", "Oxygen")
#'
#'   )
#'
#'   # vitals table
#'   vitals_table <- tibble::tibble(
#'
#'     erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
#'     evitals_12 = c(60, 59, 58, 57, 56),
#'
#'   )
#'
#'   # procedures table
#'   procedures_table <- tibble::tibble(
#'
#'     erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
#'     eprocedures_03 = rep("applicable thing", 5)
#'
#'   )
#'
#' # Run the function
#' result <- respiratory_02_population(patient_scene_table = patient_table,
#'                               response_table = response_table,
#'                               medications_table = medications_table,
#'                               vitals_table = vitals_table,
#'                               procedures_table = procedures_table,
#'                               erecord_01_col = erecord_01,
#'                               incident_date_col = incident_date,
#'                               patient_DOB_col = patient_dob,
#'                               epatient_15_col = epatient_15,
#'                               epatient_16_col = epatient_16,
#'                               eresponse_05_col = eresponse_05,
#'                               emedications_03_col = emedications_03,
#'                               evitals_12_col = evitals_12,
#'                               eprocedures_03_col = eprocedures_03
#'                              )
#'
#' # show the results of filtering at each step
#' result$filter_process
#'
#' @author Nicolas Foss, Ed.D., MS
#'
#' @export
#'
respiratory_02_population <- function(df = NULL,
                           patient_scene_table = NULL,
                           response_table = NULL,
                           vitals_table = NULL,
                           medications_table = NULL,
                           procedures_table = NULL,
                           erecord_01_col,
                           incident_date_col = NULL,
                           patient_DOB_col = NULL,
                           epatient_15_col,
                           epatient_16_col,
                           eresponse_05_col,
                           evitals_12_col,
                           emedications_03_col,
                           eprocedures_03_col
                           ) {

  # ensure that not all table arguments AND the df argument are fulfilled
  # user only passes df or all table arguments
  if(

    any(
      !is.null(patient_scene_table),
      !is.null(response_table),
      !is.null(vitals_table),
      !is.null(medications_table),
      !is.null(procedures_table)
    )

    &&

    !is.null(df)

  ) {

    cli::cli_abort("{.fn respiratory_02_population} will only work by passing a {.cls data.frame} or {.cls tibble} to the {.var df} argument, or by fulfilling all table arguments.  Please choose to either pass an object of class {.cls data.frame} or {.cls tibble} to the {.var df} argument, or fulfill all table arguments.")

  }

  # ensure that df or all table arguments are fulfilled
  if(

    all(
      is.null(patient_scene_table),
      is.null(response_table),
      is.null(vitals_table),
      is.null(medications_table),
      is.null(procedures_table)
    )

    && is.null(df)
  ) {

    cli::cli_abort("{.fn respiratory_02_population} will only work by passing a {.cls data.frame} or {.cls tibble} to the {.var df} argument, or by fulfilling all table arguments.  Please choose to either pass an object of class {.cls data.frame} or {.cls tibble} to the {.var df} argument, or fulfill all table arguments.")

  }

  # ensure all *_col arguments are fulfilled
  if(

    any(

      missing(incident_date_col),
      missing(patient_DOB_col),
      missing(epatient_15_col),
      missing(epatient_16_col),
      missing(eresponse_05_col),
      missing(evitals_12_col),
      missing(emedications_03_col),
      missing(eprocedures_03_col)

      )

  ) {

    cli::cli_abort("One or more of the *_col arguments is missing.  Please make sure you pass an unquoted column to each of the *_col arguments to run {.fn respiratory_02_population}.")

  }

  # Filter incident data for 911 response codes and the corresponding primary/secondary impressions
  # 911 codes for eresponse.05
  codes_911 <- "2205001|2205003|2205009|Emergency Response \\(Primary Response Area\\)|Emergency Response \\(Intercept\\)|Emergency Response \\(Mutual Aid\\)"

  # oxygen
  oxygen_values <- "7806|Oxygen"

  # oxygen therapy
  oxygen_therapy_values <- "57485005|Oxygen Therapy"

  # not values for meds
  not_med <- "8801001|8801003|8801009|8801019|8801027|Contraindication Noted|Denied by Order|Medication Already Taken|Refused|Order Criteria Not Met"

  # not values for procedures
  not_proc <- "8801001|8801023|8801003|8801027|8801019|Contraindicated Noted|Unable to Complete|Denied By Order|Order Criteria Not Met|Refused"

  # days, hours, minutes, months
  minor_values <- "days|2516001|hours|2516003|minutes|2516005|months|2516007"

  year_values <- "2516009|years"

  day_values <- "days|2516001"

  hour_values <- "hours|2516003"

  minute_values <- "minutes|2516005"

  month_values <- "months|2516007"

  # options for the progress bar
  # a green dot for progress
  # a white line for note done yet
  options(cli.progress_bar_style = "dot")

  options(cli.progress_bar_style = list(
    complete = cli::col_green("\u25CF"),  # Black Circle
    incomplete = cli::col_br_white("\u2500")  # Light Horizontal Line
  ))

  # initiate the progress bar process
  progress_bar_population <- cli::cli_progress_bar(
    "Running `respiratory_02_population()`",
    total = 11,
    type = "tasks",
    clear = F,
    format = "{cli::pb_name} [Completed {cli::pb_current} of {cli::pb_total} tasks] {cli::pb_bar} | {cli::col_blue('Progress')}: {cli::pb_percent} | {cli::col_blue('Runtime')}: [{cli::pb_elapsed}]"
  )

  # utilize applicable tables to analyze the data for the measure
  if(
    all(
      !is.null(patient_scene_table),
      !is.null(response_table),
      !is.null(vitals_table),
      !is.null(medications_table),
      !is.null(procedures_table)
    ) && is.null(df)

  ) {

    # Ensure df is a data frame or tibble
    if (

      any(!(is.data.frame(patient_scene_table) && tibble::is_tibble(patient_scene_table)) ||

          !(is.data.frame(response_table) && tibble::is_tibble(response_table)) ||

          !(is.data.frame(vitals_table) && tibble::is_tibble(vitals_table)) ||

          !(is.data.frame(medications_table) && tibble::is_tibble(medications_table)) ||

          !(is.data.frame(procedures_table) && tibble::is_tibble(procedures_table))

      )

    ) {

      cli::cli_abort(
        c(
          "An object of class {.cls data.frame} or {.cls tibble} is required for each of the *_table arguments."
        )
      )
    }

    # Only check the date columns if they are in fact passed
    if (
      all(
        !rlang::quo_is_null(rlang::enquo(incident_date_col)),
        !rlang::quo_is_null(rlang::enquo(patient_DOB_col))
      )
    ) {
      # Use quasiquotation on the date variables to check format
      incident_date <- rlang::enquo(incident_date_col)
      patient_dob <- rlang::enquo(patient_DOB_col)

      # Convert quosures to names and check the column classes
      incident_date_name <- rlang::as_name(incident_date)
      patient_dob_name <- rlang::as_name(patient_dob)

      if ((!lubridate::is.Date(patient_scene_table[[incident_date_name]]) &
           !lubridate::is.POSIXct(patient_scene_table[[incident_date_name]])) ||
          (!lubridate::is.Date(patient_scene_table[[patient_dob_name]]) &
           !lubridate::is.POSIXct(patient_scene_table[[patient_dob_name]]))) {

        cli::cli_abort(
          "For the variables {.var incident_date_col} and {.var patient_DOB_col}, one or both of these variables were not of class {.cls Date} or a similar class. Please format your {.var incident_date_col} and {.var patient_DOB_col} to class {.cls Date} or a similar class."
        )
      }
    }

    progress_bar_population

  ###_____________________________________________________________________________
  # from the full dataframe with all variables
  # create one fact table and several dimension tables
  # to complete calculations and avoid issues due to row
  # explosion
  ###_____________________________________________________________________________

  # fact table
  # the user should ensure that variables beyond those supplied for calculations
  # are distinct (i.e. one value or cell per patient)

  cli::cli_progress_update(set = 1, id = progress_bar_population, force = TRUE)

  if (
    all(
      !rlang::quo_is_null(rlang::enquo(incident_date_col)),
      !rlang::quo_is_null(rlang::enquo(patient_DOB_col))
    )
  ) {

  final_data <- patient_scene_table |>
    dplyr::distinct({{ erecord_01_col }}, .keep_all = TRUE) |>
    dplyr::mutate(patient_age_in_years = as.numeric(difftime(
      time1 = {{ incident_date_col }},
      time2 = {{ patient_DOB_col }},
      units = "days"
    )) / 365,
    patient_age_in_days = as.numeric(difftime(
      time1 = {{ incident_date_col }},
      time2 = {{ patient_DOB_col }},
      units = "days"
    )),

    # system age checks
    system_age_adult = {{ epatient_15_col }} >= 18 & grepl(pattern = year_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor1 = {{ epatient_15_col }} < 18 & grepl(pattern = year_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor2 = {{ epatient_15_col }} <= 120 & grepl(pattern = minor_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor = system_age_minor1 | system_age_minor2,
    system_age_minor_exclusion1 = {{ epatient_15_col }} < 24 & grepl(pattern = hour_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor_exclusion2 = {{ epatient_15_col }} < 120 & grepl(pattern = minute_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor_exclusion = system_age_minor_exclusion1 | system_age_minor_exclusion2,

    # calculated age checks
    calc_age_adult = patient_age_in_years >= 18,
    calc_age_minor = patient_age_in_years < 18 & patient_age_in_days >= 1
    )

  } else if(

    all(
      is.null(incident_date_col),
      is.null(patient_DOB_col)
    ))

  {

    final_data <- patient_scene_table |>
    dplyr::distinct({{ erecord_01_col }}, .keep_all = TRUE) |>
    dplyr::mutate(

    # system age checks
    system_age_adult = {{ epatient_15_col }} >= 18 & grepl(pattern = year_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor1 = {{ epatient_15_col }} < 18 & grepl(pattern = year_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor2 = {{ epatient_15_col }} <= 120 & grepl(pattern = minor_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor = system_age_minor1 | system_age_minor2,
    system_age_minor_exclusion1 = {{ epatient_15_col }} < 24 & grepl(pattern = hour_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor_exclusion2 = {{ epatient_15_col }} < 120 & grepl(pattern = minute_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor_exclusion = system_age_minor_exclusion1 | system_age_minor_exclusion2

    )

  }


  ###_____________________________________________________________________________
  ### dimension tables
  ### each dimension table is turned into a vector of unique IDs
  ### that are then utilized on the fact table to create distinct variables
  ### that tell if the patient had the characteristic or not for final
  ### calculations of the numerator and filtering
  ###_____________________________________________________________________________

  cli::cli_progress_update(set = 2, id = progress_bar_population, force = TRUE)

  # pulse oximetry
  pulse_oximetry_data <- vitals_table |>
    dplyr::select({{ erecord_01_col }}, {{ evitals_12_col }}) |>
    dplyr::distinct() |>
    dplyr::filter(

      {{ evitals_12_col }} < 90

    ) |>
    dplyr::distinct({{ erecord_01_col }}) |>
    dplyr::pull({{ erecord_01_col }})

  cli::cli_progress_update(set = 3, id = progress_bar_population, force = TRUE)

  # oxygen med used
  oxygen_med_data <- medications_table |>
    dplyr::select({{ erecord_01_col }}, {{ emedications_03_col }}) |>
    dplyr::distinct() |>
    dplyr::filter(

      grepl(pattern = oxygen_values, x = {{ emedications_03_col }}, ignore.case = TRUE) &
        !grepl(pattern = not_med, x = {{ emedications_03_col }}, ignore.case = TRUE)

    ) |>
    dplyr::distinct({{ erecord_01_col }}) |>
    dplyr::pull({{ erecord_01_col }})

  cli::cli_progress_update(set = 4, id = progress_bar_population, force = TRUE)

  # oxygen procedure used
  oxygen_proc_data <- procedures_table |>
    dplyr::select({{ erecord_01_col }}, {{ eprocedures_03_col }}) |>
    dplyr::distinct() |>
    dplyr::filter(

      grepl(pattern = oxygen_therapy_values, x = {{ eprocedures_03_col }}, ignore.case = TRUE) &
        !grepl(pattern = not_proc, x = {{ eprocedures_03_col }}, ignore.case = TRUE)

    ) |>
    dplyr::distinct({{ erecord_01_col }}) |>
    dplyr::pull({{ erecord_01_col }})

  cli::cli_progress_update(set = 5, id = progress_bar_population, force = TRUE)

  # 911 calls
  call_911_data <- response_table |>
    dplyr::select({{ erecord_01_col }}, {{ eresponse_05_col }}) |>
    dplyr::distinct() |>
    dplyr::filter(grepl(pattern = codes_911, x = {{ eresponse_05_col }}, ignore.case = TRUE)) |>
    dplyr::distinct({{ erecord_01_col }}) |>
    dplyr::pull({{ erecord_01_col }})

  cli::cli_progress_update(set = 6, id = progress_bar_population, force = TRUE)

  # assign variables to final data
  computing_population <- final_data |>
    dplyr::mutate(PULSE_OXIMETRY = {{ erecord_01_col }} %in% pulse_oximetry_data,
                  OXYGEN1 = {{ erecord_01_col }} %in% oxygen_med_data,
                  OXYGEN2 = {{ erecord_01_col }} %in% oxygen_proc_data,
                  OXYGEN = OXYGEN1 | OXYGEN2,
                  CALL_911 = {{ erecord_01_col }} %in% call_911_data
    )

  cli::cli_progress_update(set = 7, id = progress_bar_population, force = TRUE)

  # get the initial population
  initial_population <- computing_population |>
    dplyr::filter(

      PULSE_OXIMETRY,

      CALL_911

    )

  # Adult and Pediatric Populations

  cli::cli_progress_update(set = 8, id = progress_bar_population, force = TRUE)

  if(

    # use the system generated and calculated ages

    all(
      !rlang::quo_is_null(rlang::enquo(incident_date_col)),
      !rlang::quo_is_null(rlang::enquo(patient_DOB_col))
    )
  ) {

  # get population 1 for respiratory-02, peds
  peds_pop <- initial_population |>
    dplyr::filter(

      (system_age_minor & !system_age_minor_exclusion) | calc_age_minor

    )

  cli::cli_progress_update(set = 9, id = progress_bar_population, force = TRUE)

  # get population 2 for respiratory-02, adults
  adult_pop <- initial_population |>
    dplyr::filter(system_age_adult | calc_age_adult)

  } else if(

    # only use the system generated values

    all(
      is.null(incident_date_col),
      is.null(patient_DOB_col)
    )

  ) {

    # get population 1 for respiratory-02, peds
    peds_pop <- initial_population |>
      dplyr::filter(

        system_age_minor & !system_age_minor_exclusion

      )

    cli::cli_progress_update(set = 9, id = progress_bar_population, force = TRUE)

    # get population 2 for respiratory-02, adults
    adult_pop <- initial_population |>
      dplyr::filter(system_age_adult)

  }

  # summarize

  # progress update, these will be repeated throughout the script
  cli::cli_progress_update(set = 10, id = progress_bar_population, force = TRUE)

  # summarize counts for populations filtered
  filter_counts <- tibble::tibble(
    filter = c("Oxygen given as med",
               "Oxygen therapy procedure",
               "Pulse oximetry < 90",
               "911 calls",
               "Adults denominator",
               "Peds denominator",
               "Initial population",
               "Total dataset"
    ),
    count = c(
      sum(computing_population$OXYGEN1, na.rm = TRUE),
      sum(computing_population$OXYGEN2, na.rm = TRUE),
      sum(computing_population$PULSE_OXIMETRY, na.rm = TRUE),
      sum(computing_population$CALL_911, na.rm = TRUE),
      nrow(adult_pop),
      nrow(peds_pop),
      nrow(initial_population),
      nrow(computing_population)
    )
  )

  cli::cli_progress_update(set = 11, id = progress_bar_population, force = TRUE)

  # get the population of interest
  respiratory.02.population <- list(
    filter_process = filter_counts,
    adults = adult_pop,
    peds = peds_pop,
    initial_population = initial_population,
    computing_population = computing_population
  )

  cli::cli_progress_done(id = progress_bar_population)

  return(respiratory.02.population)

  } else if(

    all(
      is.null(patient_scene_table),
      is.null(response_table),
      is.null(vitals_table),
      is.null(medications_table),
      is.null(procedures_table)
    )

    && !is.null(df)

  )

  # utilize a dataframe to analyze the data for the measure analytics

  {

  # Ensure df is a data frame or tibble
  if (!is.data.frame(df) && !tibble::is_tibble(df)) {
    cli::cli_abort(
      c(
        "An object of class {.cls data.frame} or {.cls tibble} is required as the first argument.",
        "i" = "The passed object is of class {.val {class(df)}}."
      )
    )
  }

    # only check the date columns if they are in fact passed
    if(
      all(
        !rlang::quo_is_null(rlang::enquo(incident_date_col)),
        !rlang::quo_is_null(rlang::enquo(patient_DOB_col))
      )
    )

    {

      # use quasiquotation on the date variables to check format
      incident_date <- rlang::enquo(incident_date_col)
      patient_dob <- rlang::enquo(patient_DOB_col)

      if ((!lubridate::is.Date(df[[rlang::as_name(incident_date)]]) &
           !lubridate::is.POSIXct(df[[rlang::as_name(incident_date)]])) ||
          (!lubridate::is.Date(df[[rlang::as_name(patient_dob)]]) &
           !lubridate::is.POSIXct(df[[rlang::as_name(patient_dob)]]))) {

        cli::cli_abort(
          "For the variables {.var incident_date_col} and {.var patient_DOB_col}, one or both of these variables were not of class {.cls Date} or a similar class.  Please format your {.var incident_date_col} and {.var patient_DOB_col} to class {.cls Date} or similar class."
        )

      }
    }

  progress_bar_population

  # progress update, these will be repeated throughout the script
  cli::cli_progress_update(set = 1, id = progress_bar_population, force = TRUE)

  ###_____________________________________________________________________________
  # from the full dataframe with all variables
  # create one fact table and several dimension tables
  # to complete calculations and avoid issues due to row
  # explosion
  ###_____________________________________________________________________________

  # fact table
  # the user should ensure that variables beyond those supplied for calculations
  # are distinct (i.e. one value or cell per patient)

  if (
    all(
      !rlang::quo_is_null(rlang::enquo(incident_date_col)),
      !rlang::quo_is_null(rlang::enquo(patient_DOB_col))
    )
  ) {

  final_data <- df |>
    dplyr::select(-c({{ eresponse_05_col }},
                     {{ evitals_12_col }},
                     {{ emedications_03_col }},
                     {{ eprocedures_03_col }}
    )) |>
    dplyr::distinct({{ erecord_01_col }}, .keep_all = TRUE) |>
    dplyr::mutate(patient_age_in_years = as.numeric(difftime(
      time1 = {{ incident_date_col }},
      time2 = {{ patient_DOB_col }},
      units = "days"
    )) / 365,
    patient_age_in_days = as.numeric(difftime(
      time1 = {{ incident_date_col }},
      time2 = {{ patient_DOB_col }},
      units = "days"
    )),

    # system age checks
    system_age_adult = {{ epatient_15_col }} >= 18 & grepl(pattern = year_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor1 = {{ epatient_15_col }} < 18 & grepl(pattern = year_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor2 = {{ epatient_15_col }} <= 120 & grepl(pattern = minor_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor = system_age_minor1 | system_age_minor2,
    system_age_minor_exclusion1 = {{ epatient_15_col }} < 24 & grepl(pattern = hour_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor_exclusion2 = {{ epatient_15_col }} < 120 & grepl(pattern = minute_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor_exclusion = system_age_minor_exclusion1 | system_age_minor_exclusion2,

    # calculated age checks
    calc_age_adult = patient_age_in_years >= 18,
    calc_age_minor = patient_age_in_years < 18 & patient_age_in_days >= 1
    )

  } else if(

    all(
      is.null(incident_date_col),
      is.null(patient_DOB_col)
    ))

  {

    final_data <- df |>
    dplyr::select(-c({{ eresponse_05_col }},
                     {{ evitals_12_col }},
                     {{ emedications_03_col }},
                     {{ eprocedures_03_col }}
    )) |>
    dplyr::distinct({{ erecord_01_col }}, .keep_all = TRUE) |>
    dplyr::mutate(

    # system age checks
    system_age_adult = {{ epatient_15_col }} >= 18 & grepl(pattern = year_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor1 = {{ epatient_15_col }} < 18 & grepl(pattern = year_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor2 = {{ epatient_15_col }} <= 120 & grepl(pattern = minor_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor = system_age_minor1 | system_age_minor2,
    system_age_minor_exclusion1 = {{ epatient_15_col }} < 24 & grepl(pattern = hour_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor_exclusion2 = {{ epatient_15_col }} < 120 & grepl(pattern = minute_values, x = {{ epatient_16_col }}, ignore.case = TRUE),
    system_age_minor_exclusion = system_age_minor_exclusion1 | system_age_minor_exclusion2

    )

  }


  ###_____________________________________________________________________________
  ### dimension tables
  ### each dimension table is turned into a vector of unique IDs
  ### that are then utilized on the fact table to create distinct variables
  ### that tell if the patient had the characteristic or not for final
  ### calculations of the numerator and filtering
  ###_____________________________________________________________________________

  cli::cli_progress_update(set = 2, id = progress_bar_population, force = TRUE)

  # pulse oximetry
  pulse_oximetry_data <- df |>
    dplyr::select({{ erecord_01_col }}, {{ evitals_12_col }}) |>
    dplyr::distinct() |>
    dplyr::filter(

      {{ evitals_12_col }} < 90

    ) |>
    dplyr::distinct({{ erecord_01_col }}) |>
    dplyr::pull({{ erecord_01_col }})

  cli::cli_progress_update(set = 3, id = progress_bar_population, force = TRUE)

  # oxygen med used
  oxygen_med_data <- df |>
    dplyr::select({{ erecord_01_col }}, {{ emedications_03_col }}) |>
    dplyr::distinct() |>
    dplyr::filter(

      grepl(pattern = oxygen_values, x = {{ emedications_03_col }}, ignore.case = TRUE) &
        !grepl(pattern = not_med, x = {{ emedications_03_col }}, ignore.case = TRUE)

    ) |>
    dplyr::distinct({{ erecord_01_col }}) |>
    dplyr::pull({{ erecord_01_col }})

  cli::cli_progress_update(set = 4, id = progress_bar_population, force = TRUE)

  # oxygen procedure used
  oxygen_proc_data <- df |>
    dplyr::select({{ erecord_01_col }}, {{ eprocedures_03_col }}) |>
    dplyr::distinct() |>
    dplyr::filter(

      grepl(pattern = oxygen_therapy_values, x = {{ eprocedures_03_col }}, ignore.case = TRUE) &
        !grepl(pattern = not_proc, x = {{ eprocedures_03_col }}, ignore.case = TRUE)

    ) |>
    dplyr::distinct({{ erecord_01_col }}) |>
    dplyr::pull({{ erecord_01_col }})

  cli::cli_progress_update(set = 5, id = progress_bar_population, force = TRUE)

  # 911 calls
  call_911_data <- df |>
    dplyr::select({{ erecord_01_col }}, {{ eresponse_05_col }}) |>
    dplyr::distinct() |>
    dplyr::filter(grepl(pattern = codes_911, x = {{ eresponse_05_col }}, ignore.case = TRUE)) |>
    dplyr::distinct({{ erecord_01_col }}) |>
    dplyr::pull({{ erecord_01_col }})

  cli::cli_progress_update(set = 6, id = progress_bar_population, force = TRUE)

  # assign variables to final data
  computing_population <- final_data |>
    dplyr::mutate(PULSE_OXIMETRY = {{ erecord_01_col }} %in% pulse_oximetry_data,
                  OXYGEN1 = {{ erecord_01_col }} %in% oxygen_med_data,
                  OXYGEN2 = {{ erecord_01_col }} %in% oxygen_proc_data,
                  OXYGEN = OXYGEN1 | OXYGEN2,
                  CALL_911 = {{ erecord_01_col }} %in% call_911_data
    )

  cli::cli_progress_update(set = 7, id = progress_bar_population, force = TRUE)

  # get the initial population
  initial_population <- computing_population |>
    dplyr::filter(

      PULSE_OXIMETRY,

      CALL_911

    )

  # Adult and Pediatric Populations

  cli::cli_progress_update(set = 8, id = progress_bar_population, force = TRUE)

  if(

    # use the system generated and calculated ages

    all(
      !rlang::quo_is_null(rlang::enquo(incident_date_col)),
      !rlang::quo_is_null(rlang::enquo(patient_DOB_col))
    )
  ) {

  # get population 1 for respiratory-02, peds
  peds_pop <- initial_population |>
    dplyr::filter(

      (system_age_minor & !system_age_minor_exclusion) | calc_age_minor

    )

  cli::cli_progress_update(set = 9, id = progress_bar_population, force = TRUE)

  # get population 2 for respiratory-02, adults
  adult_pop <- initial_population |>
    dplyr::filter(system_age_adult | calc_age_adult)

  } else if(

    # only use the system generated values

    all(
      is.null(incident_date_col),
      is.null(patient_DOB_col)
    )

  ) {

    # get population 1 for respiratory-02, peds
    peds_pop <- initial_population |>
      dplyr::filter(

        system_age_minor & !system_age_minor_exclusion

      )

    cli::cli_progress_update(set = 9, id = progress_bar_population, force = TRUE)

    # get population 2 for respiratory-02, adults
    adult_pop <- initial_population |>
      dplyr::filter(system_age_adult)

  }

  # summarize

  # progress update, these will be repeated throughout the script
  cli::cli_progress_update(set = 10, id = progress_bar_population, force = TRUE)

  # summarize counts for populations filtered
  filter_counts <- tibble::tibble(
    filter = c("Oxygen given as med",
               "Oxygen therapy procedure",
               "Pulse oximetry < 90",
               "911 calls",
               "Adults denominator",
               "Peds denominator",
               "Initial population",
               "Total dataset"
    ),
    count = c(
      sum(computing_population$OXYGEN1, na.rm = TRUE),
      sum(computing_population$OXYGEN2, na.rm = TRUE),
      sum(computing_population$PULSE_OXIMETRY, na.rm = TRUE),
      sum(computing_population$CALL_911, na.rm = TRUE),
      nrow(adult_pop),
      nrow(peds_pop),
      nrow(initial_population),
      nrow(computing_population)
    )
  )

  cli::cli_progress_update(set = 11, id = progress_bar_population, force = TRUE)

  # get the population of interest
  respiratory.02.population <- list(
    filter_process = filter_counts,
    adults = adult_pop,
    peds = peds_pop,
    initial_population = initial_population,
    computing_population = computing_population
  )

  cli::cli_progress_done(id = progress_bar_population)

  return(respiratory.02.population)

  }

}