R/trauma_14_population.R

In nemsqar: National Emergency Medical Service Quality Alliance Measure Calculations

#' @title Trauma-14 Population
#'
#' @description
#'
#' This function processes EMS data to generate the population needed to
#' calculate the Trauma-14 NEMSQA measure.
#'
#' @param df A data frame or tibble containing EMS data with all relevant
#'   columns.
#' @param patient_scene_table A data frame or tibble containing fields from
#'   epatient and escene needed for this measure's calculations.
#' @param situation_table A data frame or tibble containing fields from
#'   esituation needed for this measure's calculations.
#' @param response_table A data frame or tibble containing fields from eresponse
#'   needed for this measure's calculations.
#' @param disposition_table A data frame or tibble containing fields from
#'   edisposition needed for this measure's calculations.
#' @param vitals_table A data frame or tibble containing fields from evitals
#'   needed for this measure's calculations.
#' @param exam_table A data frame or tibble containing fields from eexam needed
#'   for this measure's calculations.
#' @param procedures_table A data frame or tibble containing fields from
#'   eprocedures needed for this measure's calculations.
#' @param injury_table A data frame or tibble containing fields from einjury
#'   needed for this measure's calculations.
#' @param erecord_01_col The column representing the EMS record unique
#'   identifier.
#' @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 The column for patient age numeric value.
#' @param epatient_16_col The column for patient age unit (e.g., "Years",
#'   "Months").
#' @param esituation_02_col The column containing information on the presence of
#'   injury.
#' @param eresponse_05_col The column representing the 911 response type.
#' @param eresponse_10_col Column name containing scene delay information.
#' @param transport_disposition_col The column for patient transport
#'   disposition.
#' @param edisposition_24_col Column name containing pre-hospital trauma alert
#'   information.
#' @param evitals_06_col Column name containing systolic blood pressure (SBP)
#'   values.
#' @param evitals_10_col Column name containing heart rate values.
#' @param evitals_12_col Column name containing pulse oximetry values.
#' @param evitals_14_col Column name containing capillary refill information.
#' @param evitals_15_col Column name containing respiratory effort values.
#' @param evitals_21_col Column name containing Glasgow Coma Scale (GCS) Motor
#'   values.
#' @param eexam_16_col Column name containing extremities assessment details.
#' @param eexam_20_col Column name containing neurological assessment details.
#' @param eexam_23_col Column name containing lung assessment details.
#' @param eexam_25_col Column name containing chest assessment details.
#' @param eprocedures_03_col Column name containing airway management or
#'   tourniquet usage details.
#' @param einjury_01_col Column name containing injury cause details.
#' @param einjury_03_col Column name containing trauma triage steps 1 and 2
#'   information.
#' @param einjury_04_col Column name containing trauma triage steps 3 and 4
#'   information.
#' @param einjury_09_col Column name containing fall height information.
#'
#' @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),
#'     eresponse_10 = rep(2210011, 5)
#'   )
#'
#'   # situation table
#'   situation_table <- tibble::tibble(
#'
#'     erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
#'     esituation_02 = rep("Yes", 5),
#'   )
#'
#'   # vitals table
#'   vitals_table <- tibble::tibble(
#'
#'     erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
#'     evitals_06 = c(100, 90, 80, 70, 85),
#'     evitals_10 = c(110, 89, 88, 71, 85),
#'     evitals_12 = c(50, 60, 70, 80, 75),
#'     evitals_14 = c(30, 9, 8, 7, 31),
#'     evitals_15 = c("apneic", "labored", "rapid", "shallow", "weak/agonal"),
#'     evitals_21 = c(5, 4, 3, 2, 1)
#'   )
#'
#'   # disposition table
#'   disposition_table <- tibble::tibble(
#'     erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
#'     edisposition_24 = c(4224017, 4224003, 4224017, 4224003, 4224017),
#'     edisposition_30 = c(4230001, 4230003, 4230001, 4230007, 4230007)
#'   )
#'
#'   # injury table
#'   injury_table <- tibble::tibble(
#'     erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
#'     einjury_01 = c("V20", "V36", "V86", "V39", "V32"),
#'     einjury_03 = c(2903011, 2903009, 2903005, 3903003, 2903001),
#'     einjury_04 = c(2904013, 2904011, 2904009, 2904007, 2904001),
#'     einjury_09 = c(11, 12, 13, 14, 15)
#'   )
#'
#'   # exam table
#'   exam_table <- tibble::tibble(
#'     erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
#'     eexam_16 = c(3516043, 3516067, 3516043, 3516067, 3516067),
#'     eexam_20 = c(3520045, 3520043, 3520019, 3520017, 3520017),
#'     eexam_23 = c(3523011, 3523003, 3523001, 3523011, 3523003),
#'     eexam_25 = c(3525039, 3525023, 3525005, 3525039, 3525023)
#'   )
#'
#'   # procedures table
#'   procedures_table <- tibble::tibble(
#'     erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
#'     eprocedures_03 = c(424979004, 427753009, 429705000, 47545007, 243142003)
#'   )
#'
#'   # test the success of the function
#'   result <- trauma_14_population(patient_scene_table = patient_table,
#'                       response_table = response_table,
#'                       situation_table = situation_table,
#'                       vitals_table = vitals_table,
#'                       disposition_table = disposition_table,
#'                       exam_table = exam_table,
#'                       injury_table = injury_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,
#'                       eresponse_10_col = eresponse_10,
#'                       esituation_02_col = esituation_02,
#'                       evitals_06_col = evitals_06,
#'                       evitals_10_col = evitals_10,
#'                       evitals_12_col = evitals_12,
#'                       evitals_14_col = evitals_14,
#'                       evitals_15_col = evitals_15,
#'                       evitals_21_col = evitals_21,
#'                       eexam_16_col = eexam_16,
#'                       eexam_20_col = eexam_20,
#'                       eexam_23_col = eexam_23,
#'                       eexam_25_col = eexam_25,
#'                       edisposition_24_col = edisposition_24,
#'                       transport_disposition_col = edisposition_30,
#'                       eprocedures_03_col = eprocedures_03,
#'                       einjury_01_col = einjury_01,
#'                       einjury_03_col = einjury_03,
#'                       einjury_04_col = einjury_04,
#'                       einjury_09_col = einjury_09
#'   )
#'
#' # show the results of filtering at each step
#' result$filter_process
#'
#' @author Nicolas Foss, Ed.D., MS
#'
#' @export
#'
trauma_14_population <- function(
  df = NULL,
  patient_scene_table = NULL,
  situation_table = NULL,
  response_table = NULL,
  disposition_table = NULL,
  vitals_table = NULL,
  exam_table = NULL,
  procedures_table = NULL,
  injury_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  esituation_02_col,
  eresponse_05_col,
  eresponse_10_col,
  transport_disposition_col,
  edisposition_24_col,
  evitals_06_col,
  evitals_10_col,
  evitals_12_col,
  evitals_14_col,
  evitals_15_col,
  evitals_21_col,
  eexam_16_col,
  eexam_20_col,
  eexam_23_col,
  eexam_25_col,
  eprocedures_03_col,
  einjury_01_col,
  einjury_03_col,
  einjury_04_col,
  einjury_09_col
) {
  # ensure that not all table arguments AND the df argument are fulfilled
  # user only passes df or all table arguments
  if (
    all(
      !is.null(patient_scene_table),
      !is.null(response_table),
      !is.null(situation_table),
      !is.null(vitals_table),
      !is.null(procedures_table),
      !is.null(exam_table),
      !is.null(injury_table),
      !is.null(disposition_table)
    ) &&
      !is.null(df)
  ) {
    cli::cli_abort(
      "{.fn trauma_14_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(situation_table),
      is.null(vitals_table),
      is.null(procedures_table),
      is.null(exam_table),
      is.null(injury_table),
      is.null(disposition_table)
    ) &&
      is.null(df)
  ) {
    cli::cli_abort(
      "{.fn trauma_14_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(erecord_01_col),
      missing(incident_date_col),
      missing(patient_DOB_col),
      missing(epatient_15_col),
      missing(epatient_16_col),
      missing(esituation_02_col),
      missing(eresponse_05_col),
      missing(eresponse_10_col),
      missing(transport_disposition_col),
      missing(edisposition_24_col),
      missing(evitals_06_col),
      missing(evitals_10_col),
      missing(evitals_12_col),
      missing(evitals_14_col),
      missing(evitals_15_col),
      missing(evitals_21_col),
      missing(eexam_16_col),
      missing(eexam_20_col),
      missing(eexam_23_col),
      missing(eexam_25_col),
      missing(eprocedures_03_col),
      missing(einjury_01_col),
      missing(einjury_03_col),
      missing(einjury_04_col),
      missing(einjury_09_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 trauma_14_population}."
    )
  }

  # 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 `trauma_14_population()`",
    total = 33,
    type = "tasks",
    clear = F,
    format = "{cli::pb_name} [Working on {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}]"
  )

  # Create objects that are filter helpers throughout the function

  # injury values
  possible_injury <- "Yes|9922005"

  # 911 codes for eresponse.05
  codes_911 <- "2205001|2205003|2205009|Emergency Response \\(Primary Response Area\\)|Emergency Response \\(Intercept\\)|Emergency Response \\(Mutual Aid\\)"

  # define transports
  transport_responses <- "Transport by This EMS Unit \\(This Crew Only\\)|Transport by This EMS Unit, with a Member of Another Crew|Transport by Another EMS Unit, with a Member of This Crew|Patient Treated, Transported by this EMS Unit|Patient Treated, Transported with this EMS Crew in Another Vehicle|Treat / Transport ALS by this unit|Treat / Transport BLS by this unit|Mutual Aid Tx & Transport|4212033|4230001|4230003|4230007|itDisposition\\.112\\.116|it4212\\.142|itDisposition\\.112\\.165|itDisposition\\.112\\.141|Treat / Transport BLS by this unit|itDisposition\\.112\\.142"

  # GCS motor values
  GCS_motor_values <- "no motor response|extension to pain|flexion to pain|withdrawal from pain|localizing pain|5|4|3|2|1"

  # lung assessment values
  lung_assessment_values <- "Breath Sounds-Absent|Breath Sounds-Decreased|Increased Respiratory Effort|3523001|3523003|3523011"

  # chest assessment values
  chest_assessment_values <- "3525005|Accessory Muscles Used with Breathing|3525023|Flail Segment|3525039|Retraction"

  # respiratory effort values
  respiratory_effort_values <- "Apneic|Labored|Mechanically Assisted \\(BVM, CPAP, etc\\.\\)|Rapid|Shallow|Weak/Agonal|3315001|3315003|3315005|3315009|3315011|3315013"

  # airway management values
  airway_management_values <- "243142003|Dual pressure spontaneous ventilation support \\(regime/therapy\\)|47545007|Continuous positive airway pressure ventilation treatment \\(regime/therapy\\)|429705000|Insertion of esophageal tracheal combitube \\(procedure\\)|427753009|Insertion of esophageal tracheal double lumen supraglottic airway \\(procedure\\)|424979004|Laryngeal mask airway insertion \\(procedure\\)|23674004|Orotracheal intubation \\(procedure\\)|450601000124103|Orotracheal intubation using bougie device \\(procedure\\)|241689008|Rapid sequence induction \\(procedure\\)|450611000124100|Insertion of single lumen supraglottic airway device \\(procedure\\)"

  # trauma triage criteria values for 65+ age group
  trauma_triage_1_2_values_65 <- "2903001|Amputation proximal to wrist or ankle|3903003|Crushed, degloved, mangled, or pulseless extremity|2903005|Chest wall instability or deformity \\(e.g., flail chest\\)|2903009|Open or depressed skull fracture|2903011|Paralysis|3903013|Pelvic fractures|2903015|All Penetrating injuries to head, neck, torso, and extremities proximal to elbow or knee|2903017|Respiratory Rate <10 or >29 breaths per minute \\(<20 in infants aged <1\\) or need for ventilatory support|3903021|Two or more proximal long-bone fractures"

  # trauma triage criteria values
  trauma_triage_1_2_values_10_64 <- "2903001|Amputation proximal to wrist or ankle|3903003|Crushed, degloved, mangled, or pulseless extremity|2903005|Chest wall instability or deformity \\(e.g., flail chest\\)|2903009|Open or depressed skull fracture|2903011|Paralysis|3903013|Pelvic fractures|2903015|All Penetrating injuries to head, neck, torso, and extremities proximal to elbow or knee|2903017|Respiratory Rate <10 or >29 breaths per minute \\(<20 in infants aged <1\\) or need for ventilatory support|3903021|Two or more proximal long-bone fractures|2903019|Systolic Blood Pressure <90 mmHg"

  # trauma triage criteria values for < 10 age group
  trauma_triage_1_2_values_10 <- "2903001|Amputation proximal to wrist or ankle|3903003|Crushed, degloved, mangled, or pulseless extremity|2903005|Chest wall instability or deformity \\(e.g., flail chest\\)|2903009|Open or depressed skull fracture|2903011|Paralysis|3903013|Pelvic fractures|2903015|All Penetrating injuries to head, neck, torso, and extremities proximal to elbow or knee|3903021|Two or more proximal long-bone fractures"

  # extremities assessment values
  extremities_assessment_values <- "3516043|Motor Function-Abnormal/Weakness|3516067|Sensation-Absent"

  # neurological assessment values
  neurological_assessment_values <- "3520017|Hemiplegia-Left|3520019|Hemiplegia-Right|3520043|Weakness-Left Sided|3520045|Weakness-Right Sided"

  # procedures values
  tourniquet_values <- "20655006|Application of tourniquet \\(procedure\\)|24173005|Tourniquet procedure \\(procedure\\)|241731009|Tourniquet positioning \\(uninflated\\) \\(procedure\\)|241733007|Tourniquet cuff inflation \\(procedure\\)|241734001|Upper tourniquet cuff inflation \\(procedure\\)|241735000|Lower tourniquet cuff inflation \\(procedure\\)|241736004|Manual tourniquet application \\(procedure\\)|398260007|Tourniquet positioned on patient \\(procedure\\)|447686008|Application of pressure to wound \\(procedure\\)"

  # trauma triage criteria (steps 3 and 4) values
  trauma_triage_3_4_values <- "2904001|Auto v\\. Pedestrian/Bicyclist Thrown, Run Over, or >20 MPH Accident|2904007|Crash Death in Same Passenger Compartment|2904009|Crash Ejection \\(partial or complete\\) from automobile|2904011|Crash Intrusion, Including roof: > 12 in\\. occupant site; > 18 in\\. any site|2904013|Crash Vehicle Telemetry Data \\(AACN\\) Consistent with High Risk of Injury"

  # type of scene delay values
  scene_delay_values <- "2210011|Extrication"

  # cause of injury matches values
  cause_of_injury_values <- "(?:V20|V21|V22|V23|V24|V25|V26|V27|V28|V29|V30|V31|V32|V33|V34|V35|V36|V37|V38|V39|V80|V86)|Motorcycle rider injured in collision with pedestrian or animal|Motorcycle rider injured in collision with pedal cycle|Motorcycle rider injured in collision with two- or three- wheeled motor vehicle|Motorcycle rider injured in collision with car, pick-up truck or van|Motorcycle rider injured in collision with heavy transport vehicle or bus|Motorcycle rider injured in collision with railway train or railway vehicle|Motorcycle rider injured in collision with other nonmotor vehicle|Motorcycle rider injured in collision with fixed or stationary object|Motorcycle rider injured in noncollision transport accident|Motorcycle rider injured in other and unspecified transport accidents|Occupant of three-wheeled motor vehicle injured in collision with pedestrian or animal|Occupant of three-wheeled motor vehicle injured in collision with pedal cycle|Occupant of three-wheeled motor vehicle injured in collision with two- or three- wheeled motor vehicle|Occupant of three-wheeled motor vehicle injured in collision with car, pick-up truck or van|Occupant of three-wheeled motor vehicle injured in collision with heavy transport vehicle or bus|Occupant of three-wheeled motor vehicle injured in collision with railway train or railway vehicle|Occupant of three-wheeled motor vehicle injured in collision with other nonmotor vehicle|Occupant of three-wheeled motor vehicle injured in collision with fixed or stationary object|Occupant of three-wheeled motor vehicle injured in noncollision transport accident|Occupant of three-wheeled motor vehicle injured in other and unspecified transport accidents|Animal-rider or occupant of animal drawn vehicle injured in transport accident|Occupant of special all-terrain or other off-road motor vehicle, injured in transport accident"

  # hospital capability values
  trauma_alert_values_65 <- "4224003|Yes-Adult Trauma|4224017|Yes-Trauma"

  # hospital capability values
  trauma_alert_values_10_64 <- "4224003|Yes-Adult Trauma|4224017|Yes-Trauma \\(General\\)|4224011|Yes-Pediatric Trauma"

  # 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"

  # utilize applicable tables to analyze the data for the measure
  if (
    all(
      !is.null(patient_scene_table),
      !is.null(response_table),
      !is.null(situation_table),
      !is.null(vitals_table),
      !is.null(procedures_table),
      !is.null(exam_table),
      !is.null(injury_table),
      !is.null(disposition_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(situation_table) &&
            tibble::is_tibble(situation_table)) ||

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

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

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

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

          !(is.data.frame(disposition_table) &&
            tibble::is_tibble(disposition_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

    ###_____________________________________________________________________________
    # 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_col = as.numeric(difftime(
            time1 = {{ incident_date_col }},
            time2 = {{ patient_DOB_col }},
            units = "days"
          )) /
            365,

          # system age check
          system_age_65 = {{ epatient_15_col }} >= 65 &
            grepl(
              pattern = year_values,
              x = {{ epatient_16_col }},
              ignore.case = TRUE
            ),
          system_age_10_64 = ({{ epatient_15_col }} < 65 &
            {{ epatient_15_col }} >= 10) &
            grepl(
              pattern = year_values,
              x = {{ epatient_16_col }},
              ignore.case = TRUE
            ),
          system_age_10_1 = {{ epatient_15_col }} < 10 &
            grepl(
              pattern = year_values,
              x = {{ epatient_16_col }},
              ignore.case = TRUE
            ),
          system_age_10_2 = {{ epatient_15_col }} < 120 &
            grepl(
              pattern = minor_values,
              x = {{ epatient_16_col }},
              ignore.case = TRUE
            ),
          system_age_10 = system_age_10_1 | system_age_10_2,

          # calculated age check
          calc_age_65 = patient_age_in_years_col >= 65,
          calc_age_10_64 = patient_age_in_years_col < 65 &
            patient_age_in_years_col >= 10,
          calc_age_10 = patient_age_in_years_col < 10
        )
    } 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 check
          system_age_65 = {{ epatient_15_col }} >= 65 &
            grepl(
              pattern = year_values,
              x = {{ epatient_16_col }},
              ignore.case = TRUE
            ),
          system_age_10_64 = ({{ epatient_15_col }} < 65 &
            {{ epatient_15_col }} >= 10) &
            grepl(
              pattern = year_values,
              x = {{ epatient_16_col }},
              ignore.case = TRUE
            ),
          system_age_10_1 = {{ epatient_15_col }} < 10 &
            grepl(
              pattern = year_values,
              x = {{ epatient_16_col }},
              ignore.case = TRUE
            ),
          system_age_10_2 = {{ epatient_15_col }} < 120 &
            grepl(
              pattern = minor_values,
              x = {{ epatient_16_col }},
              ignore.case = TRUE
            ),
          system_age_10 = system_age_10_1 | system_age_10_2
        )
    }

    ###_____________________________________________________________________________
    ### 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
    )

    # possible injury
    possible_injury_data <- situation_table |>
      dplyr::select({{ erecord_01_col }}, {{ esituation_02_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = possible_injury,
          x = {{ esituation_02_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

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

    # transports
    transport_data <- disposition_table |>
      dplyr::select({{ erecord_01_col }}, {{ transport_disposition_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        dplyr::if_any(
          {{ transport_disposition_col }},

          ~ grepl(pattern = transport_responses, x = ., 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
    )

    # 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 = 5,
      id = progress_bar_population,
      force = TRUE
    )

    # GCS
    GCS_data <- vitals_table |>
      dplyr::select({{ erecord_01_col }}, {{ evitals_21_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(grepl(
        pattern = GCS_motor_values,
        x = {{ evitals_21_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
    )

    # lung assessment
    lung_assessment_data <- exam_table |>
      dplyr::select({{ erecord_01_col }}, {{ eexam_23_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = lung_assessment_values,
          x = {{ eexam_23_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

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

    # chest assessment
    chest_data <- exam_table |>
      dplyr::select({{ erecord_01_col }}, {{ eexam_25_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = chest_assessment_values,
          x = {{ eexam_25_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

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

    # respiratory effort
    respiratory_effort_data <- vitals_table |>
      dplyr::select({{ erecord_01_col }}, {{ evitals_15_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = respiratory_effort_values,
          x = {{ evitals_15_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

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

    # airway management
    airway_management_data <- procedures_table |>
      dplyr::select({{ erecord_01_col }}, {{ eprocedures_03_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = airway_management_values,
          x = {{ eprocedures_03_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 10,
      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 = 11,
      id = progress_bar_population,
      force = TRUE
    )

    # SBP
    SBP_data <- vitals_table |>
      dplyr::select({{ erecord_01_col }}, {{ evitals_06_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        {{ evitals_06_col }} < 110
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 12,
      id = progress_bar_population,
      force = TRUE
    )

    # heart rate and SBP
    HR_SBP_data_10_65_plus <- vitals_table |>
      dplyr::select(
        {{ erecord_01_col }},
        {{ evitals_10_col }},
        {{ evitals_06_col }}
      ) |>
      dplyr::distinct() |>
      dplyr::filter(
        {{ evitals_10_col }} > {{ evitals_06_col }}
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 13,
      id = progress_bar_population,
      force = TRUE
    )

    # trauma triage criteria steps 1 and 2 age 65+
    trauma_triage_1_2_data_65 <- injury_table |>
      dplyr::select({{ erecord_01_col }}, {{ einjury_03_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = trauma_triage_1_2_values_65,
          x = {{ einjury_03_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 14,
      id = progress_bar_population,
      force = TRUE
    )

    # trauma triage criteria steps 1 and 2 age 10 - 65
    trauma_triage_1_2_data_10_64 <- injury_table |>
      dplyr::select({{ erecord_01_col }}, {{ einjury_03_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = trauma_triage_1_2_values_10_64,
          x = {{ einjury_03_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 15,
      id = progress_bar_population,
      force = TRUE
    )

    # trauma triage criteria steps 1 and 2 age < 10
    trauma_triage_1_2_data_10 <- injury_table |>
      dplyr::select({{ erecord_01_col }}, {{ einjury_03_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = trauma_triage_1_2_values_10,
          x = {{ einjury_03_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 16,
      id = progress_bar_population,
      force = TRUE
    )

    # extremities assessment
    extremities_assessment_data <- exam_table |>
      dplyr::select({{ erecord_01_col }}, {{ eexam_16_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = extremities_assessment_values,
          x = {{ eexam_16_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 17,
      id = progress_bar_population,
      force = TRUE
    )

    # neurological assessment

    neurological_assessment_data <- exam_table |>
      dplyr::select({{ erecord_01_col }}, {{ eexam_20_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = neurological_assessment_values,
          x = {{ eexam_20_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 18,
      id = progress_bar_population,
      force = TRUE
    )

    # tourniquet

    tourniquet_data <- procedures_table |>
      dplyr::select({{ erecord_01_col }}, {{ eprocedures_03_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = tourniquet_values,
          x = {{ eprocedures_03_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 19,
      id = progress_bar_population,
      force = TRUE
    )

    # trauma triage criteria steps 3 and 4

    trauma_triage_3_4_data <- injury_table |>
      dplyr::select({{ erecord_01_col }}, {{ einjury_04_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = trauma_triage_3_4_values,
          x = {{ einjury_04_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 20,
      id = progress_bar_population,
      force = TRUE
    )

    # fall height

    fall_height_data <- injury_table |>
      dplyr::select({{ erecord_01_col }}, {{ einjury_09_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        {{ einjury_09_col }} > 10
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 21,
      id = progress_bar_population,
      force = TRUE
    )

    # scene delay

    scene_delay_data <- response_table |>
      dplyr::select({{ erecord_01_col }}, {{ eresponse_10_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = scene_delay_values,
          x = {{ eresponse_10_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 22,
      id = progress_bar_population,
      force = TRUE
    )

    # cause of injury

    cause_of_injury_data <- injury_table |>
      dplyr::select({{ erecord_01_col }}, {{ einjury_01_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = cause_of_injury_values,
          x = {{ einjury_01_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 23,
      id = progress_bar_population,
      force = TRUE
    )

    # respiratory rate for < 10 yrs population

    respiratory_rate_data <- vitals_table |>
      dplyr::select({{ erecord_01_col }}, {{ evitals_14_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        {{ evitals_14_col }} < 10 | {{ evitals_14_col }} > 29
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 24,
      id = progress_bar_population,
      force = TRUE
    )

    # SBP check variable for ages < 10 years
    # if using calculated and system ages
    if (
      all(
        !rlang::quo_is_null(rlang::enquo(incident_date_col)),
        !rlang::quo_is_null(rlang::enquo(patient_DOB_col))
      )
    ) {
      SBP_age_10_data <- final_data |>
        dplyr::select(
          {{ erecord_01_col }},
          system_age_10,
          calc_age_10,
          {{ epatient_15_col }}
        ) |>
        dplyr::distinct() |>
        dplyr::filter(system_age_10 | calc_age_10) |>
        dplyr::left_join(
          vitals_table |>
            dplyr::select({{ erecord_01_col }}, {{ evitals_06_col }}) |>
            dplyr::distinct(),

          by = rlang::as_name(rlang::enquo(erecord_01_col))
        ) |>
        dplyr::mutate(
          SBP_10 = ({{ evitals_06_col }} + ({{ epatient_15_col }} * 2)) >= 70
        ) |>
        dplyr::filter(SBP_10) |>
        dplyr::distinct({{ erecord_01_col }}) |>
        dplyr::pull({{ erecord_01_col }})

      # SBP check variable for ages < 10 years
      # if using system ages only
    } else if (
      all(
        is.null(incident_date_col),
        is.null(patient_DOB_col)
      )
    ) {
      SBP_age_10_data <- final_data |>
        dplyr::select(
          {{ erecord_01_col }},
          system_age_10,
          {{ epatient_15_col }}
        ) |>
        dplyr::distinct() |>
        dplyr::filter(system_age_10) |>
        dplyr::left_join(
          vitals_table |>
            dplyr::select({{ erecord_01_col }}, {{ evitals_06_col }}) |>
            dplyr::distinct(),

          by = rlang::as_name(rlang::enquo(erecord_01_col))
        ) |>
        dplyr::mutate(
          SBP_10 = ({{ evitals_06_col }} + ({{ epatient_15_col }} * 2)) >= 70
        ) |>
        dplyr::filter(SBP_10) |>
        dplyr::distinct({{ erecord_01_col }}) |>
        dplyr::pull({{ erecord_01_col }})
    }

    cli::cli_progress_update(
      set = 25,
      id = progress_bar_population,
      force = TRUE
    )

    # trauma alert >= 65 years
    trauma_alert_65 <- disposition_table |>
      dplyr::select({{ erecord_01_col }}, {{ edisposition_24_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = trauma_alert_values_65,
          x = {{ edisposition_24_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 26,
      id = progress_bar_population,
      force = TRUE
    )

    # trauma alert > 10 and < 65 years
    trauma_alert_10_64 <- disposition_table |>
      dplyr::select({{ erecord_01_col }}, {{ edisposition_24_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = trauma_alert_values_10_64,
          x = {{ edisposition_24_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 27,
      id = progress_bar_population,
      force = TRUE
    )

    # assign variables to final data
    computing_population <- final_data |>
      dplyr::mutate(
        CALL_911 = {{ erecord_01_col }} %in% call_911_data,
        TRANSPORTS = {{ erecord_01_col }} %in% transport_data,
        POSSIBLE_INJURY = {{ erecord_01_col }} %in% possible_injury_data,
        GCS = {{ erecord_01_col }} %in% GCS_data,
        LUNG = {{ erecord_01_col }} %in% lung_assessment_data,
        CHEST = {{ erecord_01_col }} %in% chest_data,
        RESPIRATORY_EFFORT = {{ erecord_01_col }} %in% respiratory_effort_data,
        AIRWAY_MANAGEMENT = {{ erecord_01_col }} %in% airway_management_data,
        EXTREMITIES = {{ erecord_01_col }} %in% extremities_assessment_data,
        NEURO = {{ erecord_01_col }} %in% neurological_assessment_data,
        TOURNIQUET = {{ erecord_01_col }} %in% tourniquet_data,
        TRAUMA_TRIAGE_3_4 = {{ erecord_01_col }} %in% trauma_triage_3_4_data,
        FALL_HEIGHT = {{ erecord_01_col }} %in% fall_height_data,
        SCENE_DELAY = {{ erecord_01_col }} %in% scene_delay_data,
        INJURY_CAUSE = {{ erecord_01_col }} %in% cause_of_injury_data,
        PULSE_OXIMETRY = {{ erecord_01_col }} %in% pulse_oximetry_data,
        SBP = {{ erecord_01_col }} %in% SBP_data,
        SBP_10 = {{ erecord_01_col }} %in% SBP_age_10_data,
        HR_SBP_10_65_PLUS = {{ erecord_01_col }} %in% HR_SBP_data_10_65_plus,
        TRAUMA_TRIAGE_1_2_65 = {{ erecord_01_col }} %in%
          trauma_triage_1_2_data_65,
        TRAUMA_TRIAGE_1_2_10_64 = {{ erecord_01_col }} %in%
          trauma_triage_1_2_data_10_64,
        TRAUMA_TRIAGE_1_2_10 = {{ erecord_01_col }} %in%
          trauma_triage_1_2_data_10,
        RESPIRATORY_RATE_10 = {{ erecord_01_col }} %in% respiratory_rate_data,
        TRAUMA_ALERT_65 = {{ erecord_01_col }} %in% trauma_alert_65,
        TRAUMA_ALERT_10_64 = {{ erecord_01_col }} %in% trauma_alert_10_64
      )

    cli::cli_progress_update(
      set = 28,
      id = progress_bar_population,
      force = TRUE
    )

    # get the initial population
    initial_population <- computing_population |>
      dplyr::filter(
        POSSIBLE_INJURY,
        CALL_911,
        TRANSPORTS
      )

    # Adult and Pediatric Populations

    cli::cli_progress_update(
      set = 29,
      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))
      )
    ) {
      # filter older adult
      pop_65 <- initial_population |>
        dplyr::filter(system_age_65 | calc_age_65) |>
        dplyr::filter(
          GCS |
            LUNG |
            CHEST |
            RESPIRATORY_EFFORT |
            AIRWAY_MANAGEMENT |
            PULSE_OXIMETRY |
            HR_SBP_10_65_PLUS |
            TRAUMA_TRIAGE_1_2_65 |
            EXTREMITIES |
            NEURO |
            TOURNIQUET |
            TRAUMA_TRIAGE_3_4 |
            FALL_HEIGHT |
            SCENE_DELAY |
            INJURY_CAUSE
        )

      cli::cli_progress_update(
        set = 30,
        id = progress_bar_population,
        force = TRUE
      )

      # filter ages 10 to 65
      pop_10_64 <- initial_population |>
        dplyr::filter(system_age_10_64 | calc_age_10_64) |>
        dplyr::filter(
          GCS |
            LUNG |
            CHEST |
            RESPIRATORY_EFFORT |
            AIRWAY_MANAGEMENT |
            HR_SBP_10_65_PLUS |
            TRAUMA_TRIAGE_1_2_10_64 |
            EXTREMITIES |
            NEURO |
            TOURNIQUET |
            TRAUMA_TRIAGE_3_4 |
            FALL_HEIGHT |
            SCENE_DELAY |
            INJURY_CAUSE
        )

      cli::cli_progress_update(
        set = 31,
        id = progress_bar_population,
        force = TRUE
      )

      # filter ages < 10
      pop_10 <- initial_population |>
        dplyr::filter(system_age_10 | calc_age_10) |>
        dplyr::filter(
          GCS |
            RESPIRATORY_RATE_10 |
            LUNG |
            CHEST |
            RESPIRATORY_EFFORT |
            AIRWAY_MANAGEMENT |
            PULSE_OXIMETRY |
            SBP_10 |
            TRAUMA_TRIAGE_1_2_10 |
            EXTREMITIES |
            NEURO |
            TOURNIQUET |
            TRAUMA_TRIAGE_3_4 |
            FALL_HEIGHT |
            SCENE_DELAY |
            INJURY_CAUSE
        )
    } else if (
      # only use the system generated values

      all(
        is.null(incident_date_col),
        is.null(patient_DOB_col)
      )
    ) {
      # filter older adult
      pop_65 <- initial_population |>
        dplyr::filter(system_age_65) |>
        dplyr::filter(
          GCS |
            LUNG |
            CHEST |
            RESPIRATORY_EFFORT |
            AIRWAY_MANAGEMENT |
            PULSE_OXIMETRY |
            HR_SBP_10_65_PLUS |
            TRAUMA_TRIAGE_1_2_65 |
            EXTREMITIES |
            NEURO |
            TOURNIQUET |
            TRAUMA_TRIAGE_3_4 |
            FALL_HEIGHT |
            SCENE_DELAY |
            INJURY_CAUSE
        )

      cli::cli_progress_update(
        set = 30,
        id = progress_bar_population,
        force = TRUE
      )

      # filter ages 10 to 65
      pop_10_64 <- initial_population |>
        dplyr::filter(system_age_10_64) |>
        dplyr::filter(
          GCS |
            LUNG |
            CHEST |
            RESPIRATORY_EFFORT |
            AIRWAY_MANAGEMENT |
            HR_SBP_10_65_PLUS |
            TRAUMA_TRIAGE_1_2_10_64 |
            EXTREMITIES |
            NEURO |
            TOURNIQUET |
            TRAUMA_TRIAGE_3_4 |
            FALL_HEIGHT |
            SCENE_DELAY |
            INJURY_CAUSE
        )

      cli::cli_progress_update(
        set = 31,
        id = progress_bar_population,
        force = TRUE
      )

      # filter ages < 10
      pop_10 <- initial_population |>
        dplyr::filter(system_age_10) |>
        dplyr::filter(
          GCS |
            RESPIRATORY_RATE_10 |
            LUNG |
            CHEST |
            RESPIRATORY_EFFORT |
            AIRWAY_MANAGEMENT |
            PULSE_OXIMETRY |
            SBP_10 |
            TRAUMA_TRIAGE_1_2_10 |
            EXTREMITIES |
            NEURO |
            TOURNIQUET |
            TRAUMA_TRIAGE_3_4 |
            FALL_HEIGHT |
            SCENE_DELAY |
            INJURY_CAUSE
        )
    }

    # summarize
    # progress update, these will be repeated throughout the script
    cli::cli_progress_update(
      set = 32,
      id = progress_bar_population,
      force = TRUE
    )

    # summarize counts for populations filtered
    filter_counts <- tibble::tibble(
      filter = c(
        "Situation possible injury",
        "911 calls",
        "Transports",
        "GCS Motor 1-5",
        "Breath sounds absent, decreased, increased respiratory effort",
        "Flail segment, retraction, accessory muscles used in breathing",
        "Respiratory effort apneic, labored, mech. assist, rapid, shallow, weak/agonal",
        "Airway management procedures",
        "Pulse oximetry < 90",
        "SBP < 110",
        "Respiratory rate < 10 or > 29 ages < 10 yrs",
        "(SBP + (Pt. Age * 2)) >= 70 ages < 10 yrs",
        "Heart rate greater than SBP ages >= 10 yrs",
        "Met trauma triage criteria 1-2 ages 65+ yrs",
        "Met trauma triage criteria 1-2 ages 10-64 yrs",
        "Met trauma triage criteria 1-2 ages < 10 yrs",
        "Motor function abnormal/weakness or sensation absent",
        "Hemiplegia left/right, weakness left/right",
        "Tourniquet procedure", # fixed in 1.1.1
        "Trauma triage criteria 3-4",
        "Cause of Injury matches V20-V39, V80, V86",
        "Scene delay = extrication",
        "Fall > 10 ft.",
        "Pre-arrival trauma alert ages 65+",
        "Pre-arrival trauma alert ages < 65",
        "Patients 65+ yrs denominator",
        "Patients 10-64 yrs denominator",
        "Patients < 10 yrs denominator",
        "Initial population",
        "Total dataset"
      ),
      count = c(
        sum(computing_population$POSSIBLE_INJURY, na.rm = TRUE),
        sum(computing_population$CALL_911, na.rm = TRUE),
        sum(computing_population$TRANSPORTS, na.rm = TRUE),
        sum(computing_population$GCS, na.rm = TRUE),
        sum(computing_population$LUNG, na.rm = TRUE),
        sum(computing_population$CHEST, na.rm = TRUE),
        sum(computing_population$RESPIRATORY_EFFORT, na.rm = TRUE),
        sum(computing_population$AIRWAY_MANAGEMENT, na.rm = TRUE),
        sum(computing_population$PULSE_OXIMETRY, na.rm = TRUE),
        sum(computing_population$SBP, na.rm = TRUE),
        sum(computing_population$RESPIRATORY_RATE_10, na.rm = TRUE),
        sum(computing_population$SBP_10, na.rm = TRUE),
        sum(computing_population$HR_SBP_10_65_PLUS, na.rm = TRUE),
        sum(computing_population$TRAUMA_TRIAGE_1_2_65, na.rm = TRUE),
        sum(computing_population$TRAUMA_TRIAGE_1_2_10_64, na.rm = TRUE),
        sum(computing_population$TRAUMA_TRIAGE_1_2_10, na.rm = TRUE),
        sum(computing_population$EXTREMITIES, na.rm = TRUE),
        sum(computing_population$NEURO, na.rm = TRUE),
        sum(computing_population$TOURNIQUET, na.rm = TRUE),
        sum(computing_population$TRAUMA_TRIAGE_3_4, na.rm = TRUE),
        sum(computing_population$INJURY_CAUSE, na.rm = TRUE),
        sum(computing_population$SCENE_DELAY, na.rm = TRUE),
        sum(computing_population$FALL_HEIGHT, na.rm = TRUE),
        sum(computing_population$TRAUMA_ALERT_65, na.rm = TRUE),
        sum(computing_population$TRAUMA_ALERT_10_64, na.rm = TRUE),
        nrow(pop_65),
        nrow(pop_10_64),
        nrow(pop_10),
        nrow(initial_population),
        nrow(computing_population)
      )
    )

    cli::cli_progress_update(
      set = 33,
      id = progress_bar_population,
      force = TRUE
    )

    # get the population of interest
    trauma.14.population <- list(
      filter_process = filter_counts,
      population_65 = pop_65,
      population_10_64 = pop_10_64,
      population_10 = pop_10,
      initial_population = initial_population,
      computing_population = computing_population
    )

    cli::cli_progress_done(id = progress_bar_population)

    return(trauma.14.population)
  } else if (
    all(
      is.null(patient_scene_table),
      is.null(response_table),
      is.null(situation_table),
      is.null(vitals_table),
      is.null(procedures_table),
      is.null(exam_table),
      is.null(injury_table),
      is.null(disposition_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

    ###_____________________________________________________________________________
    # 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 <- df |>
        dplyr::select(
          -c(
            {{ esituation_02_col }},
            {{ eresponse_05_col }},
            {{ eresponse_10_col }},
            {{ transport_disposition_col }},
            {{ edisposition_24_col }},
            {{ evitals_06_col }},
            {{ evitals_10_col }},
            {{ evitals_12_col }},
            {{ evitals_14_col }},
            {{ evitals_15_col }},
            {{ evitals_21_col }},
            {{ eexam_16_col }},
            {{ eexam_20_col }},
            {{ eexam_23_col }},
            {{ eexam_25_col }},
            {{ eprocedures_03_col }},
            {{ einjury_01_col }},
            {{ einjury_03_col }},
            {{ einjury_04_col }},
            {{ einjury_09_col }}
          )
        ) |>
        dplyr::distinct({{ erecord_01_col }}, .keep_all = TRUE) |>
        dplyr::mutate(
          patient_age_in_years_col = as.numeric(difftime(
            time1 = {{ incident_date_col }},
            time2 = {{ patient_DOB_col }},
            units = "days"
          )) /
            365,

          # system age check
          system_age_65 = {{ epatient_15_col }} >= 65 &
            grepl(
              pattern = year_values,
              x = {{ epatient_16_col }},
              ignore.case = TRUE
            ),
          system_age_10_64 = ({{ epatient_15_col }} < 65 &
            {{ epatient_15_col }} >= 10) &
            grepl(
              pattern = year_values,
              x = {{ epatient_16_col }},
              ignore.case = TRUE
            ),
          system_age_10_1 = {{ epatient_15_col }} < 10 &
            grepl(
              pattern = year_values,
              x = {{ epatient_16_col }},
              ignore.case = TRUE
            ),
          system_age_10_2 = {{ epatient_15_col }} < 120 &
            grepl(
              pattern = minor_values,
              x = {{ epatient_16_col }},
              ignore.case = TRUE
            ),
          system_age_10 = system_age_10_1 | system_age_10_2,

          # calculated age check
          calc_age_65 = patient_age_in_years_col >= 65,
          calc_age_10_64 = patient_age_in_years_col < 65 &
            patient_age_in_years_col >= 10,
          calc_age_10 = patient_age_in_years_col < 10
        )
    } else if (
      all(
        is.null(incident_date_col),
        is.null(patient_DOB_col)
      )
    ) {
      final_data <- df |>
        dplyr::select(
          -c(
            {{ esituation_02_col }},
            {{ eresponse_05_col }},
            {{ eresponse_10_col }},
            {{ transport_disposition_col }},
            {{ edisposition_24_col }},
            {{ evitals_06_col }},
            {{ evitals_10_col }},
            {{ evitals_12_col }},
            {{ evitals_14_col }},
            {{ evitals_15_col }},
            {{ evitals_21_col }},
            {{ eexam_16_col }},
            {{ eexam_20_col }},
            {{ eexam_23_col }},
            {{ eexam_25_col }},
            {{ eprocedures_03_col }},
            {{ einjury_01_col }},
            {{ einjury_03_col }},
            {{ einjury_04_col }},
            {{ einjury_09_col }}
          )
        ) |>
        dplyr::distinct({{ erecord_01_col }}, .keep_all = TRUE) |>
        dplyr::mutate(
          # system age check
          system_age_65 = {{ epatient_15_col }} >= 65 &
            grepl(
              pattern = year_values,
              x = {{ epatient_16_col }},
              ignore.case = TRUE
            ),
          system_age_10_64 = ({{ epatient_15_col }} < 65 &
            {{ epatient_15_col }} >= 10) &
            grepl(
              pattern = year_values,
              x = {{ epatient_16_col }},
              ignore.case = TRUE
            ),
          system_age_10_1 = {{ epatient_15_col }} < 10 &
            grepl(
              pattern = year_values,
              x = {{ epatient_16_col }},
              ignore.case = TRUE
            ),
          system_age_10_2 = {{ epatient_15_col }} < 120 &
            grepl(
              pattern = minor_values,
              x = {{ epatient_16_col }},
              ignore.case = TRUE
            ),
          system_age_10 = system_age_10_1 | system_age_10_2
        )
    }

    ###_____________________________________________________________________________
    ### 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
    )

    # possible injury
    possible_injury_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{ esituation_02_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = possible_injury,
          x = {{ esituation_02_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

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

    # transports
    transport_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{ transport_disposition_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        dplyr::if_any(
          {{ transport_disposition_col }},

          ~ grepl(pattern = transport_responses, x = ., 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
    )

    # 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 = 5,
      id = progress_bar_population,
      force = TRUE
    )

    # GCS
    GCS_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{ evitals_21_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(grepl(
        pattern = GCS_motor_values,
        x = {{ evitals_21_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
    )

    # lung assessment
    lung_assessment_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{ eexam_23_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = lung_assessment_values,
          x = {{ eexam_23_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

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

    # chest assessment
    chest_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{ eexam_25_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = chest_assessment_values,
          x = {{ eexam_25_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

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

    # respiratory effort
    respiratory_effort_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{ evitals_15_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = respiratory_effort_values,
          x = {{ evitals_15_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

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

    # airway management
    airway_management_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{ eprocedures_03_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = airway_management_values,
          x = {{ eprocedures_03_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 10,
      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 = 11,
      id = progress_bar_population,
      force = TRUE
    )

    # SBP
    SBP_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{ evitals_06_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        {{ evitals_06_col }} < 110
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 12,
      id = progress_bar_population,
      force = TRUE
    )

    # heart rate and SBP
    HR_SBP_data_10_65_plus <- df |>
      dplyr::select(
        {{ erecord_01_col }},
        {{ evitals_10_col }},
        {{ evitals_06_col }}
      ) |>
      dplyr::distinct() |>
      dplyr::filter(
        {{ evitals_10_col }} > {{ evitals_06_col }}
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 13,
      id = progress_bar_population,
      force = TRUE
    )

    # trauma triage criteria steps 1 and 2 age 65+
    trauma_triage_1_2_data_65 <- df |>
      dplyr::select({{ erecord_01_col }}, {{ einjury_03_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = trauma_triage_1_2_values_65,
          x = {{ einjury_03_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 14,
      id = progress_bar_population,
      force = TRUE
    )

    # trauma triage criteria steps 1 and 2 age 10 - 65
    trauma_triage_1_2_data_10_64 <- df |>
      dplyr::select({{ erecord_01_col }}, {{ einjury_03_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = trauma_triage_1_2_values_10_64,
          x = {{ einjury_03_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 15,
      id = progress_bar_population,
      force = TRUE
    )

    # trauma triage criteria steps 1 and 2 age < 10
    trauma_triage_1_2_data_10 <- df |>
      dplyr::select({{ erecord_01_col }}, {{ einjury_03_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = trauma_triage_1_2_values_10,
          x = {{ einjury_03_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 16,
      id = progress_bar_population,
      force = TRUE
    )

    # extremities assessment
    extremities_assessment_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{ eexam_16_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = extremities_assessment_values,
          x = {{ eexam_16_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 17,
      id = progress_bar_population,
      force = TRUE
    )

    # neurological assessment

    neurological_assessment_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{ eexam_20_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = neurological_assessment_values,
          x = {{ eexam_20_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 18,
      id = progress_bar_population,
      force = TRUE
    )

    # tourniquet

    tourniquet_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{ eprocedures_03_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = tourniquet_values,
          x = {{ eprocedures_03_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 19,
      id = progress_bar_population,
      force = TRUE
    )

    # trauma triage criteria steps 3 and 4

    trauma_triage_3_4_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{ einjury_04_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = trauma_triage_3_4_values,
          x = {{ einjury_04_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 20,
      id = progress_bar_population,
      force = TRUE
    )

    # fall height

    fall_height_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{ einjury_09_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        {{ einjury_09_col }} > 10
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 21,
      id = progress_bar_population,
      force = TRUE
    )

    # scene delay

    scene_delay_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{ eresponse_10_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = scene_delay_values,
          x = {{ eresponse_10_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 22,
      id = progress_bar_population,
      force = TRUE
    )

    # cause of injury

    cause_of_injury_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{ einjury_01_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        grepl(
          pattern = cause_of_injury_values,
          x = {{ einjury_01_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 23,
      id = progress_bar_population,
      force = TRUE
    )

    # respiratory rate for < 10 yrs population

    respiratory_rate_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{ evitals_14_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(
        {{ evitals_14_col }} < 10 | {{ evitals_14_col }} > 29
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 24,
      id = progress_bar_population,
      force = TRUE
    )

    # SBP check variable for ages < 10 years
    # if using calculated and system ages
    if (
      all(
        !rlang::quo_is_null(rlang::enquo(incident_date_col)),
        !rlang::quo_is_null(rlang::enquo(patient_DOB_col))
      )
    ) {
      SBP_age_10_data <- df |>
        dplyr::select(
          {{ erecord_01_col }},
          {{ epatient_15_col }},
          {{ evitals_06_col }}
        ) |>
        dplyr::distinct() |>
        dplyr::left_join(
          final_data |>
            dplyr::select({{ erecord_01_col }}, system_age_10, calc_age_10) |>
            dplyr::distinct(),

          by = rlang::as_name(rlang::enquo(erecord_01_col))
        ) |>
        dplyr::filter(system_age_10 | calc_age_10) |>
        dplyr::mutate(
          SBP_10 = ({{ evitals_06_col }} + ({{ epatient_15_col }} * 2)) >= 70
        ) |>
        dplyr::filter(SBP_10) |>
        dplyr::distinct({{ erecord_01_col }}) |>
        dplyr::pull({{ erecord_01_col }})

      # SBP check variable for ages < 10 years
      # if using system ages only
    } else if (
      all(
        is.null(incident_date_col),
        is.null(patient_DOB_col)
      )
    ) {
      SBP_age_10_data <- df |>
        dplyr::select(
          {{ erecord_01_col }},
          {{ epatient_15_col }},
          {{ evitals_06_col }}
        ) |>
        dplyr::distinct() |>
        dplyr::left_join(
          final_data |> dplyr::select({{ erecord_01_col }}, system_age_10),

          by = rlang::as_name(rlang::enquo(erecord_01_col))
        ) |>
        dplyr::filter(system_age_10) |>
        dplyr::mutate(
          SBP_10 = ({{ evitals_06_col }} + ({{ epatient_15_col }} * 2)) >= 70
        ) |>
        dplyr::filter(SBP_10) |>
        dplyr::distinct({{ erecord_01_col }}) |>
        dplyr::pull({{ erecord_01_col }})
    }

    cli::cli_progress_update(
      set = 25,
      id = progress_bar_population,
      force = TRUE
    )

    # trauma alert >= 65 years
    trauma_alert_65 <- df |>
      dplyr::select({{ erecord_01_col }}, {{ edisposition_24_col }}) |>
      dplyr::filter(
        grepl(
          pattern = trauma_alert_values_65,
          x = {{ edisposition_24_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 26,
      id = progress_bar_population,
      force = TRUE
    )

    # trauma alert > 10 and < 65 years
    trauma_alert_10_64 <- df |>
      dplyr::select({{ erecord_01_col }}, {{ edisposition_24_col }}) |>
      dplyr::filter(
        grepl(
          pattern = trauma_alert_values_10_64,
          x = {{ edisposition_24_col }},
          ignore.case = TRUE
        )
      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(
      set = 27,
      id = progress_bar_population,
      force = TRUE
    )

    # assign variables to final data
    computing_population <- final_data |>
      dplyr::mutate(
        CALL_911 = {{ erecord_01_col }} %in% call_911_data,
        TRANSPORTS = {{ erecord_01_col }} %in% transport_data,
        POSSIBLE_INJURY = {{ erecord_01_col }} %in% possible_injury_data,
        GCS = {{ erecord_01_col }} %in% GCS_data,
        LUNG = {{ erecord_01_col }} %in% lung_assessment_data,
        CHEST = {{ erecord_01_col }} %in% chest_data,
        RESPIRATORY_EFFORT = {{ erecord_01_col }} %in% respiratory_effort_data,
        AIRWAY_MANAGEMENT = {{ erecord_01_col }} %in% airway_management_data,
        EXTREMITIES = {{ erecord_01_col }} %in% extremities_assessment_data,
        NEURO = {{ erecord_01_col }} %in% neurological_assessment_data,
        TOURNIQUET = {{ erecord_01_col }} %in% tourniquet_data,
        TRAUMA_TRIAGE_3_4 = {{ erecord_01_col }} %in% trauma_triage_3_4_data,
        FALL_HEIGHT = {{ erecord_01_col }} %in% fall_height_data,
        SCENE_DELAY = {{ erecord_01_col }} %in% scene_delay_data,
        INJURY_CAUSE = {{ erecord_01_col }} %in% cause_of_injury_data,
        PULSE_OXIMETRY = {{ erecord_01_col }} %in% pulse_oximetry_data,
        SBP = {{ erecord_01_col }} %in% SBP_data,
        SBP_10 = {{ erecord_01_col }} %in% SBP_age_10_data,
        HR_SBP_10_65_PLUS = {{ erecord_01_col }} %in% HR_SBP_data_10_65_plus,
        TRAUMA_TRIAGE_1_2_65 = {{ erecord_01_col }} %in%
          trauma_triage_1_2_data_65,
        TRAUMA_TRIAGE_1_2_10_64 = {{ erecord_01_col }} %in%
          trauma_triage_1_2_data_10_64,
        TRAUMA_TRIAGE_1_2_10 = {{ erecord_01_col }} %in%
          trauma_triage_1_2_data_10,
        RESPIRATORY_RATE_10 = {{ erecord_01_col }} %in% respiratory_rate_data,
        TRAUMA_ALERT_65 = {{ erecord_01_col }} %in% trauma_alert_65,
        TRAUMA_ALERT_10_64 = {{ erecord_01_col }} %in% trauma_alert_10_64
      )

    cli::cli_progress_update(
      set = 28,
      id = progress_bar_population,
      force = TRUE
    )

    # get the initial population
    initial_population <- computing_population |>
      dplyr::filter(
        POSSIBLE_INJURY,
        CALL_911,
        TRANSPORTS
      )

    # Adult and Pediatric Populations

    cli::cli_progress_update(
      set = 29,
      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))
      )
    ) {
      # filter older adult
      pop_65 <- initial_population |>
        dplyr::filter(system_age_65 | calc_age_65) |>
        dplyr::filter(
          GCS |
            LUNG |
            CHEST |
            RESPIRATORY_EFFORT |
            AIRWAY_MANAGEMENT |
            PULSE_OXIMETRY |
            HR_SBP_10_65_PLUS |
            TRAUMA_TRIAGE_1_2_65 |
            EXTREMITIES |
            NEURO |
            TOURNIQUET |
            TRAUMA_TRIAGE_3_4 |
            FALL_HEIGHT |
            SCENE_DELAY |
            INJURY_CAUSE
        )

      cli::cli_progress_update(
        set = 30,
        id = progress_bar_population,
        force = TRUE
      )

      # filter ages 10 to 65
      pop_10_64 <- initial_population |>
        dplyr::filter(system_age_10_64 | calc_age_10_64) |>
        dplyr::filter(
          GCS |
            LUNG |
            CHEST |
            RESPIRATORY_EFFORT |
            AIRWAY_MANAGEMENT |
            HR_SBP_10_65_PLUS |
            TRAUMA_TRIAGE_1_2_10_64 |
            EXTREMITIES |
            NEURO |
            TOURNIQUET |
            TRAUMA_TRIAGE_3_4 |
            FALL_HEIGHT |
            SCENE_DELAY |
            INJURY_CAUSE
        )

      cli::cli_progress_update(
        set = 31,
        id = progress_bar_population,
        force = TRUE
      )

      # filter ages < 10
      pop_10 <- initial_population |>
        dplyr::filter(system_age_10 | calc_age_10) |>
        dplyr::filter(
          GCS |
            RESPIRATORY_RATE_10 |
            LUNG |
            CHEST |
            RESPIRATORY_EFFORT |
            AIRWAY_MANAGEMENT |
            PULSE_OXIMETRY |
            SBP_10 |
            TRAUMA_TRIAGE_1_2_10 |
            EXTREMITIES |
            NEURO |
            TOURNIQUET |
            TRAUMA_TRIAGE_3_4 |
            FALL_HEIGHT |
            SCENE_DELAY |
            INJURY_CAUSE
        )
    } else if (
      # only use the system generated values

      all(
        is.null(incident_date_col),
        is.null(patient_DOB_col)
      )
    ) {
      # filter older adult
      pop_65 <- initial_population |>
        dplyr::filter(system_age_65) |>
        dplyr::filter(
          GCS |
            LUNG |
            CHEST |
            RESPIRATORY_EFFORT |
            AIRWAY_MANAGEMENT |
            PULSE_OXIMETRY |
            HR_SBP_10_65_PLUS |
            TRAUMA_TRIAGE_1_2_65 |
            EXTREMITIES |
            NEURO |
            TOURNIQUET |
            TRAUMA_TRIAGE_3_4 |
            FALL_HEIGHT |
            SCENE_DELAY |
            INJURY_CAUSE
        )

      cli::cli_progress_update(
        set = 30,
        id = progress_bar_population,
        force = TRUE
      )

      # filter ages 10 to 65
      pop_10_64 <- initial_population |>
        dplyr::filter(system_age_10_64) |>
        dplyr::filter(
          GCS |
            LUNG |
            CHEST |
            RESPIRATORY_EFFORT |
            AIRWAY_MANAGEMENT |
            HR_SBP_10_65_PLUS |
            TRAUMA_TRIAGE_1_2_10_64 |
            EXTREMITIES |
            NEURO |
            TOURNIQUET |
            TRAUMA_TRIAGE_3_4 |
            FALL_HEIGHT |
            SCENE_DELAY |
            INJURY_CAUSE
        )

      cli::cli_progress_update(
        set = 31,
        id = progress_bar_population,
        force = TRUE
      )

      # filter ages < 10
      pop_10 <- initial_population |>
        dplyr::filter(system_age_10) |>
        dplyr::filter(
          GCS |
            RESPIRATORY_RATE_10 |
            LUNG |
            CHEST |
            RESPIRATORY_EFFORT |
            AIRWAY_MANAGEMENT |
            PULSE_OXIMETRY |
            SBP_10 |
            TRAUMA_TRIAGE_1_2_10 |
            EXTREMITIES |
            NEURO |
            TOURNIQUET |
            TRAUMA_TRIAGE_3_4 |
            FALL_HEIGHT |
            SCENE_DELAY |
            INJURY_CAUSE
        )
    }

    # summarize
    # progress update, these will be repeated throughout the script
    cli::cli_progress_update(
      set = 32,
      id = progress_bar_population,
      force = TRUE
    )

    # summarize counts for populations filtered
    filter_counts <- tibble::tibble(
      filter = c(
        "Situation possible injury",
        "911 calls",
        "Transports",
        "GCS Motor 1-5",
        "Breath sounds absent, decreased, increased respiratory effort",
        "Flail segment, retraction, accessory muscles used in breathing",
        "Respiratory effort apneic, labored, mech. assist, rapid, shallow, weak/agonal",
        "Airway management procedures",
        "Pulse oximetry < 90",
        "SBP < 110",
        "Respiratory rate < 10 or > 29 ages < 10 yrs",
        "(SBP + (Pt. Age * 2)) >= 70 ages < 10 yrs",
        "Heart rate greater than SBP ages >= 10 yrs",
        "Met trauma triage criteria 1-2 ages 65+ yrs",
        "Met trauma triage criteria 1-2 ages 10-64 yrs",
        "Met trauma triage criteria 1-2 ages < 10 yrs",
        "Motor function abnormal/weakness or sensation absent",
        "Hemiplegia left/right, weakness left/right",
        "Tourniquet procedure", # fixed in 1.1.1
        "Trauma triage criteria 3-4",
        "Cause of Injury matches V20-V39, V80, V86",
        "Scene delay = extrication",
        "Fall > 10 ft.",
        "Pre-arrival trauma alert ages 65+",
        "Pre-arrival trauma alert ages < 65",
        "Patients 65+ yrs denominator",
        "Patients 10-64 yrs denominator",
        "Patients < 10 yrs denominator",
        "Initial population",
        "Total dataset"
      ),
      count = c(
        sum(computing_population$POSSIBLE_INJURY, na.rm = TRUE),
        sum(computing_population$CALL_911, na.rm = TRUE),
        sum(computing_population$TRANSPORTS, na.rm = TRUE),
        sum(computing_population$GCS, na.rm = TRUE),
        sum(computing_population$LUNG, na.rm = TRUE),
        sum(computing_population$CHEST, na.rm = TRUE),
        sum(computing_population$RESPIRATORY_EFFORT, na.rm = TRUE),
        sum(computing_population$AIRWAY_MANAGEMENT, na.rm = TRUE),
        sum(computing_population$PULSE_OXIMETRY, na.rm = TRUE),
        sum(computing_population$SBP, na.rm = TRUE),
        sum(computing_population$RESPIRATORY_RATE_10, na.rm = TRUE),
        sum(computing_population$SBP_10, na.rm = TRUE),
        sum(computing_population$HR_SBP_10_65_PLUS, na.rm = TRUE),
        sum(computing_population$TRAUMA_TRIAGE_1_2_65, na.rm = TRUE),
        sum(computing_population$TRAUMA_TRIAGE_1_2_10_64, na.rm = TRUE),
        sum(computing_population$TRAUMA_TRIAGE_1_2_10, na.rm = TRUE),
        sum(computing_population$EXTREMITIES, na.rm = TRUE),
        sum(computing_population$NEURO, na.rm = TRUE),
        sum(computing_population$TOURNIQUET, na.rm = TRUE),
        sum(computing_population$TRAUMA_TRIAGE_3_4, na.rm = TRUE),
        sum(computing_population$INJURY_CAUSE, na.rm = TRUE),
        sum(computing_population$SCENE_DELAY, na.rm = TRUE),
        sum(computing_population$FALL_HEIGHT, na.rm = TRUE),
        sum(computing_population$TRAUMA_ALERT_65, na.rm = TRUE),
        sum(computing_population$TRAUMA_ALERT_10_64, na.rm = TRUE),
        nrow(pop_65),
        nrow(pop_10_64),
        nrow(pop_10),
        nrow(initial_population),
        nrow(computing_population)
      )
    )

    cli::cli_progress_update(
      set = 33,
      id = progress_bar_population,
      force = TRUE
    )

    # get the population of interest
    trauma.14.population <- list(
      filter_process = filter_counts,
      population_65 = pop_65,
      population_10_64 = pop_10_64,
      population_10 = pop_10,
      initial_population = initial_population,
      computing_population = computing_population
    )

    cli::cli_progress_done(id = progress_bar_population)

    return(trauma.14.population)
  }
}