R/safety_01.R

In nemsqar: National Emergency Medical Service Quality Alliance Measure Calculations

#' @title Safety-01 Calculation
#'
#' @description
#'
#' The `safety_01` function calculates the proportion of 911 responses where
#' "lights and sirens" were not used in an EMS dataset. It generates age-based
#' population summaries, calculating the count and proportion of "lights and
#' sirens" responses among all incidents, and within adult and pediatric groups.
#'
#' @param df A data frame or tibble containing EMS data.
#' @param patient_scene_table A data frame or tibble containing only epatient
#'   and escene fields as a fact table. Default is `NULL`.
#' @param response_table A data frame or tibble containing only the eresponse
#'   fields needed for this measure's calculations. Default is `NULL`.
#' @param erecord_01_col Column name containing the unique patient record
#'   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 Column containing age.
#' @param epatient_16_col Column for age units.
#' @param eresponse_05_col Column containing response mode codes (e.g., 911
#'   response codes).
#' @param eresponse_24_col Column detailing additional response descriptors as
#'   text.
#' @param confidence_interval `r lifecycle::badge("experimental")` Logical. If
#'   `TRUE`, the function calculates a confidence interval for the proportion
#'   estimate.
#' @param method `r lifecycle::badge("experimental")`Character. Specifies the
#'   method used to calculate confidence intervals. Options are `"wilson"`
#'   (Wilson score interval) and `"clopper-pearson"` (exact binomial interval).
#'   Partial matching is supported, so `"w"` and `"c"` can be used as shorthand.
#' @param conf.level `r lifecycle::badge("experimental")`Numeric. The confidence
#'   level for the interval, expressed as a proportion (e.g., 0.95 for a 95%
#'   confidence interval). Defaults to 0.95.
#' @param correct `r lifecycle::badge("experimental")`Logical. If `TRUE`,
#'   applies a continuity correction to the Wilson score interval when `method =
#'   "wilson"`. Defaults to `TRUE`.
#' @param ... optional additional arguments to pass onto `dplyr::summarize`.
#'
#' @return A data.frame summarizing results for two population groups (All,
#'   Adults and Peds) with the following columns:
#' - `pop`: Population type (All, Adults, and Peds).
#' - `numerator`: Count of incidents meeting the measure.
#' - `denominator`: Total count of included incidents.
#' - `prop`: Proportion of incidents meeting the measure.
#' - `prop_label`: Proportion formatted as a percentage with a specified number
#'    of decimal places.
#' - `lower_ci`: Lower bound of the confidence interval for `prop`
#'    (if `confidence_interval = TRUE`).
#' - `upper_ci`: Upper bound of the confidence interval for `prop`
#'    (if `confidence_interval = TRUE`).
#'
#' @examples
#'
#' # Synthetic test data
#'   test_data <- tibble::tibble(
#'     erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
#'     epatient_15 = c(34, 5, 45, 2, 60),  # Ages
#'     epatient_16 = c("Years", "Years", "Years", "Months", "Years"),
#'     eresponse_05 = rep(2205001, 5),
#'     eresponse_24 = rep("No Lights or Sirens", 5)
#'   )
#'
#' # Run the function
#' # Return 95% confidence intervals using the Wilson method
#'   safety_01(
#'     df = test_data,
#'     erecord_01_col = erecord_01,
#'     epatient_15_col = epatient_15,
#'     epatient_16_col = epatient_16,
#'     eresponse_05_col = eresponse_05,
#'     eresponse_24_col = eresponse_24,
#'     confidence_interval = TRUE
#'   )
#'
#' @author Nicolas Foss, Ed.D., MS
#'
#' @export
#'
safety_01 <- function(df = NULL,
                      patient_scene_table = NULL,
                      response_table = NULL,
                      erecord_01_col,
                      incident_date_col = NULL,
                      patient_DOB_col = NULL,
                      epatient_15_col,
                      epatient_16_col,
                      eresponse_05_col,
                      eresponse_24_col,
                      confidence_interval = FALSE,
                      method = c("wilson", "clopper-pearson"),
                      conf.level = 0.95,
                      correct = TRUE,
                      ...) {

  # Set default method and adjustment method
  method <- match.arg(method, choices = c("wilson", "clopper-pearson"))

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

  ) {

    # Start timing the function execution
    start_time <- Sys.time()

    # header
    cli::cli_h1("Safety-01")

    # header
    cli::cli_h2("Gathering Records for Safety-01")

    # gather the population of interest
    safety_01_populations <- safety_01_population(patient_scene_table = patient_scene_table,
                                                  response_table = response_table,
                                                  erecord_01_col = {{ erecord_01_col }},
                                                  incident_date_col = {{ incident_date_col }},
                                                  patient_DOB_col = {{ patient_DOB_col }},
                                                  epatient_15_col = {{ epatient_15_col }},
                                                  epatient_16_col = {{ epatient_16_col }},
                                                  eresponse_05_col = {{ eresponse_05_col }},
                                                  eresponse_24_col = {{ eresponse_24_col }}
                                                  )

    # create a separator
    cli::cli_text("\n")

    # header for calculations
    cli::cli_h2("Calculating Safety-01")

    # summary
    safety.01 <- results_summarize(total_population = safety_01_populations$initial_population,
                                   adult_population = safety_01_populations$adults,
                                   peds_population = safety_01_populations$peds,
                                   population_names = c("all", "adults", "peds"),
                                   measure_name = "Safety-01",
                                   numerator_col = NO_LS_CHECK,
                                   confidence_interval = confidence_interval,
                                   method = method,
                                   conf.level = conf.level,
                                   correct = correct,
                                   ...)

    # create a separator
    cli::cli_text("\n")

    # Calculate and display the runtime
    end_time <- Sys.time()
    run_time_secs <- difftime(end_time, start_time, units = "secs")
    run_time_secs <- as.numeric(run_time_secs)

    if (run_time_secs >= 60) {

      run_time <- round(run_time_secs / 60, 2)  # Convert to minutes and round
      cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")

    } else {

      run_time <- round(run_time_secs, 2)  # Keep in seconds and round
      cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")

    }

    # create a separator
    cli::cli_text("\n")

    # when confidence interval is "wilson", check for n < 10
    # to warn about incorrect Chi-squared approximation
    if (any(safety.01$denominator < 10) && method == "wilson" && confidence_interval) {

      cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")

    }

    return(safety.01)

  } else if(

    all(
      is.null(patient_scene_table),
      is.null(response_table)
    ) && !is.null(df)

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

  ) {

    # Start timing the function execution
    start_time <- Sys.time()

    # header
    cli::cli_h1("Safety-01")

    # header
    cli::cli_h2("Gathering Records for Safety-01")

    # gather the population of interest
    safety_01_populations <- safety_01_population(df = df,
                                                  erecord_01_col = {{ erecord_01_col }},
                                                  incident_date_col = {{ incident_date_col }},
                                                  patient_DOB_col = {{ patient_DOB_col }},
                                                  epatient_15_col = {{ epatient_15_col }},
                                                  epatient_16_col = {{ epatient_16_col }},
                                                  eresponse_05_col = {{ eresponse_05_col }},
                                                  eresponse_24_col = {{ eresponse_24_col }}
                                                  )

    # create a separator
    cli::cli_text("\n")

    # header for calculations
    cli::cli_h2("Calculating Safety-01")

    # summary
    safety.01 <- results_summarize(total_population = safety_01_populations$initial_population,
                                   adult_population = safety_01_populations$adults,
                                   peds_population = safety_01_populations$peds,
                                   population_names = c("all", "adults", "peds"),
                                   measure_name = "Safety-01",
                                   numerator_col = NO_LS_CHECK,
                                   confidence_interval = confidence_interval,
                                   method = method,
                                   conf.level = conf.level,
                                   correct = correct,
                                   ...)

    # create a separator
    cli::cli_text("\n")

    # Calculate and display the runtime
    end_time <- Sys.time()
    run_time_secs <- difftime(end_time, start_time, units = "secs")
    run_time_secs <- as.numeric(run_time_secs)

    if (run_time_secs >= 60) {

      run_time <- round(run_time_secs / 60, 2)  # Convert to minutes and round
      cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")

    } else {

      run_time <- round(run_time_secs, 2)  # Keep in seconds and round
      cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")

    }

    # create a separator
    cli::cli_text("\n")

    # when confidence interval is "wilson", check for n < 10
    # to warn about incorrect Chi-squared approximation
    if (any(safety.01$denominator < 10) && method == "wilson" && confidence_interval) {

      cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")

    }

    return(safety.01)

  }

}